TY - JOUR AU - Wadman, Ruth AU - Walker, Lauren AU - Taylor, Olivia AU - Heron, Paul AU - Newbronner, Elizabeth AU - Spanakis, Panagiotis AU - Crosland, Suzanne AU - Peckham, Jane Emily PY - 2025/3/28 TI - Patterns of Internet Use in People Diagnosed With Severe Mental Illness: Qualitative Interview Study JO - J Med Internet Res SP - e55072 VL - 27 KW - severe mental illness KW - internet use KW - qualitative KW - typology KW - protective strategies KW - digital divide N2 - Background: People with severe mental illness (SMI) face profound health inequalities, which may be exacerbated by increased rates of digital exclusion, especially as health services move to online provision. The activities that people carry out online can affect how they feel about the internet and may determine whether a person has a positive or negative experience when using the internet. This, in turn, could affect their mental health. To support people with SMI in using digital technology and the internet safely, it is important to understand the internet and digital technology use of those with SMI and their perceived positive or negative impact on their mental health. Objective: This study aimed to explore the internet and digital technology use of those with SMI, with particular focus on any association between greater use of the internet and poorer self-reported mental health. Methods: We carried out a qualitative interview study with 16 people with SMI. The sample was drawn from a wider investigation of the impact of the pandemic and its restrictions on the health and well-being of 367 people with SMI. We purposively sampled from the wider study based on age, gender, frequency of internet use, and self-reported mental health. The data were analyzed by 2 researchers using framework analysis. Results: Participant experiences fell into 3 broad categories: those who had a positive or neutral internet-based experience, those who had negative or difficult experiences, and low users or those with poor digital literacy. Those who had positive or neutral experiences could be broken down into 2 subcategories: first, those with positive or neutral experiences of the internet who were similar in terms of the activities participated in, feelings reported, and their concerns about the internet, and second, conscious users who were mindful of their interaction with the internet world. Participants with difficult experiences fell into 2 categories: those with worries and fears related to using the internet and those who had difficulty limiting their internet use. Conclusions: People with SMI, similarly the general population, are expected to conduct more of their activities of daily living online in the postpandemic world. This research shows that most internet users with SMI have positive or neutral experiences. However, our typology reveals subgroups of the population with SMI for whom there is a relationship between internet use and difficult feelings. These subgroups can be identified by asking questions about online activities; time spent online; feelings, difficulties, or issues experienced; and use of gambling, dating, adult content, and conspiracy theory websites. Our findings point to further work in collaboration with people with lived experience to modify and test this typology. UR - https://www.jmir.org/2025/1/e55072 UR - http://dx.doi.org/10.2196/55072 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55072 ER - TY - JOUR AU - Kelly, Anthony AU - Jensen, Kjems Esben AU - Grua, Martino Eoin AU - Mathiasen, Kim AU - Van de Ven, Pepijn PY - 2025/3/26 TI - An Interpretable Model With Probabilistic Integrated Scoring for Mental Health Treatment Prediction: Design Study JO - JMIR Med Inform SP - e64617 VL - 13 KW - machine learning KW - mental health KW - Monte Carlo dropout KW - explainability KW - explainable AI KW - XAI KW - artificial intelligence KW - AI N2 - Background: Machine learning (ML) systems in health care have the potential to enhance decision-making but often fail to address critical issues such as prediction explainability, confidence, and robustness in a context-based and easily interpretable manner. Objective: This study aimed to design and evaluate an ML model for a future decision support system for clinical psychopathological treatment assessments. The novel ML model is inherently interpretable and transparent. It aims to enhance clinical explainability and trust through a transparent, hierarchical model structure that progresses from questions to scores to classification predictions. The model confidence and robustness were addressed by applying Monte Carlo dropout, a probabilistic method that reveals model uncertainty and confidence. Methods: A model for clinical psychopathological treatment assessments was developed, incorporating a novel ML model structure. The model aimed at enhancing the graphical interpretation of the model outputs and addressing issues of prediction explainability, confidence, and robustness. The proposed ML model was trained and validated using patient questionnaire answers and demographics from a web-based treatment service in Denmark (N=1088). Results: The balanced accuracy score on the test set was 0.79. The precision was ?0.71 for all 4 prediction classes (depression, panic, social phobia, and specific phobia). The area under the curve for the 4 classes was 0.93, 0.92, 0.91, and 0.98, respectively. Conclusions: We have demonstrated a mental health treatment ML model that supported a graphical interpretation of prediction class probability distributions. Their spread and overlap can inform clinicians of competing treatment possibilities for patients and uncertainty in treatment predictions. With the ML model achieving 79% balanced accuracy, we expect that the model will be clinically useful in both screening new patients and informing clinical interviews. UR - https://medinform.jmir.org/2025/1/e64617 UR - http://dx.doi.org/10.2196/64617 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64617 ER - TY - JOUR AU - Liu, Chang AU - Chamberlain, Samuel AU - Ioannidis, Konstantinos AU - Tiego, Jeggan AU - Grant, Jon AU - Yücel, Murat AU - Hellyer, Peter AU - Lochner, Christine AU - Hampshire, Adam AU - Albertella, Lucy PY - 2025/3/26 TI - Transdiagnostic Compulsivity Traits in Problematic Use of the Internet Among UK Residents: Cross-Sectional Network Analysis Study JO - J Med Internet Res SP - e66191 VL - 27 KW - compulsivity KW - problematic use of the internet KW - network analysis KW - perfectionism KW - reward drive KW - cognitive rigidity KW - transdiagnostic KW - PUI KW - mental health KW - intrapersonal factor KW - cognitive KW - internet use KW - network N2 - Background: The societal and public health costs of problematic use of the internet (PUI) are increasingly recognized as a concern across all age groups, presenting a growing challenge for mental health research. International scientific initiatives have emphasized the need to explore the potential roles of personality features in PUI. Compulsivity is a key personality trait associated with PUI and has been recognized by experts as a critical factor that should be prioritized in PUI research. Given that compulsivity is a multidimensional construct and PUI encompasses diverse symptoms, different underlying mechanisms are likely involved. However, the specific relationships between compulsivity dimensions and PUI symptoms remain unclear, limiting our understanding of compulsivity?s role in PUI. Objective: This study aimed to clarify the unique relationships among different dimensions of compulsivity, namely, perfectionism, reward drive, cognitive rigidity, and symptoms of PUI using a symptom-based network approach. Methods: A regularized partial-correlation network was fitted using a large-scale sample from the United Kingdom. Bridge centrality analysis was conducted to identify bridge nodes within the network. Node predictability analysis was performed to assess the self-determination and controllability of the nodes within the network. Results: The sample comprised 122,345 individuals from the United Kingdom (51.4% female, age: mean 43.7, SD 16.5, range 9-86 years). The analysis identified several strong mechanistic relationships. The strongest positive intracluster edge was between reward drive and PUI4 (financial consequences due to internet use; weight=0.11). Meanwhile, the strongest negative intracluster edge was between perfectionism and PUI4 (financial consequences due to internet use; weight=0.04). Cognitive rigidity showed strong relationships with PUI2 (internet use for distress relief; weight=0.06) and PUI3 (internet use for loneliness or boredom; weight=0.07). Notably, reward drive (bridge expected influence=0.32) and cognitive rigidity (bridge expected influence=0.16) were identified as key bridge nodes, positively associated with PUI symptoms. Meanwhile, perfectionism exhibited a negative association with PUI symptoms (bridge expected influence=?0.05). The network?s overall mean predictability was 0.37, with PUI6 (compulsion, predictability=0.55) showing the highest predictability. Conclusions: The findings reveal distinct relationships between different dimensions of compulsivity and individual PUI symptoms, supporting the importance of choosing targeted interventions based on individual symptom profiles. In addition, the identified bridge nodes, reward drive, and cognitive rigidity may represent promising targets for PUI prevention and intervention and warrant further investigation. UR - https://www.jmir.org/2025/1/e66191 UR - http://dx.doi.org/10.2196/66191 UR - http://www.ncbi.nlm.nih.gov/pubmed/40137076 ID - info:doi/10.2196/66191 ER - TY - JOUR AU - Brinsley, Jacinta AU - O'Connor, J. Edward AU - Singh, Ben AU - McKeon, Grace AU - Curtis, Rachel AU - Ferguson, Ty AU - Gosse, Georgia AU - Willems, Iris AU - Marent, Pieter-Jan AU - Szeto, Kimberley AU - Firth, Joseph AU - Maher, Carol PY - 2025/3/20 TI - Effectiveness of Digital Lifestyle Interventions on Depression, Anxiety, Stress, and Well-Being: Systematic Review and Meta-Analysis JO - J Med Internet Res SP - e56975 VL - 27 KW - depression KW - anxiety KW - stress KW - well-being KW - mental health KW - lifestyle intervention KW - physical activity KW - sleep KW - diet KW - digital health KW - mobile phone N2 - Background: There is a growing body of robust evidence to show that lifestyle behaviors influence mental health outcomes. Technology offers an accessible and cost-effective implementation method for interventions, yet the study of the effectiveness of interventions to date has been specific to the mode of delivery, population, or behavior. Objective: The primary aim of this review was to comprehensively evaluate the effectiveness of digital lifestyle interventions for improving symptoms of depression, anxiety, stress, and well-being as coprimary outcomes in adults. The secondary aim was to explore the technological, methodological, intervention-specific, and population-specific characteristics that were associated with major changes in mental health outcomes. Methods: A systematic search was conducted across the MEDLINE, CINAHL, Embase, Emcare, PsycINFO, and Scopus databases to identify studies published between January 2013 and January 2023. Randomized controlled trials of lifestyle interventions (physical activity, sleep, and diet) that were delivered digitally; reported changes in symptoms of depression, anxiety, stress, or well-being in adults (aged ?18 years); and were published in English were included. Multiple authors independently extracted data, which was evaluated using the 2011 Levels of Evidence from the Oxford Centre for Evidence-Based Medicine. Inverse-variance random-effects meta-analyses were used for data analysis. The primary outcome was the change in symptoms of depression, anxiety, stress, and well-being as measured by validated self-report of clinician-administered outcomes from pre- to postintervention. Subgroup analyses were conducted to determine whether results differed based on the target lifestyle behavior, delivery method, digital features, design features, or population characteristics. Results: Of the 14,356 studies identified, 61 (0.42%) were included. Digital lifestyle interventions had a significant small-to-medium effect on depression (standardized mean difference [SMD] ?0.37; P<.001), a small effect on anxiety (SMD ?0.29; P<.001) and stress (SMD ?0.17; P=.04), and no effect on well-being (SMD 0.14; P=.15). Subgroup analyses generally suggested that effects were similar regardless of the delivery method or features used, the duration and frequency of the intervention, the population, or the lifestyle behavior targeted. Conclusions: Overall, these results indicate that delivering lifestyle interventions via a range of digital methods can have significant positive effects on depression (P<.001), anxiety (P<.001), and stress (P=.04) for a broad range of populations, while effects on well-being are inconclusive. Future research should explore how these interventions can be effectively implemented and embedded within health care with a concerted focus on addressing digital health equity. Trial Registration: PROSPERO CRD42023428908; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023428908 UR - https://www.jmir.org/2025/1/e56975 UR - http://dx.doi.org/10.2196/56975 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56975 ER - TY - JOUR AU - Schnepper, Rebekka AU - Roemmel, Noa AU - Schaefert, Rainer AU - Lambrecht-Walzinger, Lena AU - Meinlschmidt, Gunther PY - 2025/3/20 TI - Exploring Biases of Large Language Models in the Field of Mental Health: Comparative Questionnaire Study of the Effect of Gender and Sexual Orientation in Anorexia Nervosa and Bulimia Nervosa Case Vignettes JO - JMIR Ment Health SP - e57986 VL - 12 KW - anorexia nervosa KW - artificial intelligence KW - bulimia nervosa KW - ChatGPT KW - eating disorders KW - LLM KW - responsible AI KW - transformer KW - bias KW - large language model KW - gender KW - vignette KW - quality of life KW - symptomatology KW - questionnaire KW - generative AI KW - mental health KW - AI N2 - Background: Large language models (LLMs) are increasingly used in mental health, showing promise in assessing disorders. However, concerns exist regarding their accuracy, reliability, and fairness. Societal biases and underrepresentation of certain populations may impact LLMs. Because LLMs are already used for clinical practice, including decision support, it is important to investigate potential biases to ensure a responsible use of LLMs. Anorexia nervosa (AN) and bulimia nervosa (BN) show a lifetime prevalence of 1%?2%, affecting more women than men. Among men, homosexual men face a higher risk of eating disorders (EDs) than heterosexual men. However, men are underrepresented in ED research, and studies on gender, sexual orientation, and their impact on AN and BN prevalence, symptoms, and treatment outcomes remain limited. Objectives: We aimed to estimate the presence and size of bias related to gender and sexual orientation produced by a common LLM as well as a smaller LLM specifically trained for mental health analyses, exemplified in the context of ED symptomatology and health-related quality of life (HRQoL) of patients with AN or BN. Methods: We extracted 30 case vignettes (22 AN and 8 BN) from scientific papers. We adapted each vignette to create 4 versions, describing a female versus male patient living with their female versus male partner (2 × 2 design), yielding 120 vignettes. We then fed each vignette into ChatGPT-4 and to ?MentaLLaMA? based on the Large Language Model Meta AI (LLaMA) architecture thrice with the instruction to evaluate them by providing responses to 2 psychometric instruments, the RAND-36 questionnaire assessing HRQoL and the eating disorder examination questionnaire. With the resulting LLM-generated scores, we calculated multilevel models with a random intercept for gender and sexual orientation (accounting for within-vignette variance), nested in vignettes (accounting for between-vignette variance). Results: In ChatGPT-4, the multilevel model with 360 observations indicated a significant association with gender for the RAND-36 mental composite summary (conditional means: 12.8 for male and 15.1 for female cases; 95% CI of the effect ?6.15 to ?0.35; P=.04) but neither with sexual orientation (P=.71) nor with an interaction effect (P=.37). We found no indications for main effects of gender (conditional means: 5.65 for male and 5.61 for female cases; 95% CI ?0.10 to 0.14; P=.88), sexual orientation (conditional means: 5.63 for heterosexual and 5.62 for homosexual cases; 95% CI ?0.14 to 0.09; P=.67), or for an interaction effect (P=.61, 95% CI ?0.11 to 0.19) for the eating disorder examination questionnaire overall score (conditional means 5.59?5.65 95% CIs 5.45 to 5.7). MentaLLaMA did not yield reliable results. Conclusions: LLM-generated mental HRQoL estimates for AN and BN case vignettes may be biased by gender, with male cases scoring lower despite no real-world evidence supporting this pattern. This highlights the risk of bias in generative artificial intelligence in the field of mental health. Understanding and mitigating biases related to gender and other factors, such as ethnicity, and socioeconomic status are crucial for responsible use in diagnostics and treatment recommendations. UR - https://mental.jmir.org/2025/1/e57986 UR - http://dx.doi.org/10.2196/57986 ID - info:doi/10.2196/57986 ER - TY - JOUR AU - Paz-Arbaizar, Leire AU - Lopez-Castroman, Jorge AU - Artés-Rodríguez, Antonio AU - Olmos, M. Pablo AU - Ramírez, David PY - 2025/3/18 TI - Emotion Forecasting: A Transformer-Based Approach JO - J Med Internet Res SP - e63962 VL - 27 KW - affect KW - emotional valence KW - machine learning KW - mental disorder KW - monitoring KW - mood KW - passive data KW - Patient Health Questionnaire-9 KW - PHQ-9 KW - psychological distress KW - time-series forecasting N2 - Background: Monitoring the emotional states of patients with psychiatric problems has always been challenging due to the noncontinuous nature of clinical assessments, the effect of the health care environment, and the inherent subjectivity of evaluation instruments. However, mental states in psychiatric disorders exhibit substantial variability over time, making real-time monitoring crucial for preventing risky situations and ensuring appropriate treatment. Objective: This study aimed to leverage new technologies and deep learning techniques to enable more objective, real-time monitoring of patients. This was achieved by passively monitoring variables such as step count, patient location, and sleep patterns using mobile devices. We aimed to predict patient self-reports and detect sudden variations in their emotional valence, identifying situations that may require clinical intervention. Methods: Data for this project were collected using the Evidence-Based Behavior (eB2) app, which records both passive and self-reported variables daily. Passive data refer to behavioral information gathered via the eB2 app through sensors embedded in mobile devices and wearables. These data were obtained from studies conducted in collaboration with hospitals and clinics that used eB2. We used hidden Markov models (HMMs) to address missing data and transformer deep neural networks for time-series forecasting. Finally, classification algorithms were applied to predict several variables, including emotional state and responses to the Patient Health Questionnaire-9. Results: Through real-time patient monitoring, we demonstrated the ability to accurately predict patients? emotional states and anticipate changes over time. Specifically, our approach achieved high accuracy (0.93) and a receiver operating characteristic (ROC) area under the curve (AUC) of 0.98 for emotional valence classification. For predicting emotional state changes 1 day in advance, we obtained an ROC AUC of 0.87. Furthermore, we demonstrated the feasibility of forecasting responses to the Patient Health Questionnaire-9, with particularly strong performance for certain questions. For example, in question 9, related to suicidal ideation, our model achieved an accuracy of 0.9 and an ROC AUC of 0.77 for predicting the next day?s response. Moreover, we illustrated the enhanced stability of multivariate time-series forecasting when HMM preprocessing was combined with a transformer model, as opposed to other time-series forecasting methods, such as recurrent neural networks or long short-term memory cells. Conclusions: The stability of multivariate time-series forecasting improved when HMM preprocessing was combined with a transformer model, as opposed to other time-series forecasting methods (eg, recurrent neural network and long short-term memory), leveraging the attention mechanisms to capture longer time dependencies and gain interpretability. We showed the potential to assess the emotional state of a patient and the scores of psychiatric questionnaires from passive variables in advance. This allows real-time monitoring of patients and hence better risk detection and treatment adjustment. UR - https://www.jmir.org/2025/1/e63962 UR - http://dx.doi.org/10.2196/63962 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63962 ER - TY - JOUR AU - Dekel, Dana AU - Marchant, Amanda AU - Del Pozo Banos, Marcos AU - Mhereeg, Mohamed AU - Lee, Chim Sze AU - John, Ann PY - 2025/3/12 TI - Exploring the Views of Young People, Including Those With a History of Self-Harm, on the Use of Their Routinely Generated Data for Mental Health Research: Web-Based Cross-Sectional Survey Study JO - JMIR Ment Health SP - e60649 VL - 12 KW - self-harm KW - mental health KW - big data KW - survey KW - youth N2 - Background: Secondary use of routinely collected health care data has great potential benefits in epidemiological studies primarily due to the large scale of preexisting data. Objective: This study aimed to engage respondents with and without a history of self-harm, gain insight into their views on the use of their data for research, and determine whether there were any differences in opinions between the 2 groups. Methods: We examined young people?s views on the use of their routinely collected data for mental health research through a web-based survey, evaluating any differences between those with and without a history of self-harm. Results: A total of 1765 respondents aged 16 to 24 years were included. Respondents? views were mostly positive toward the use and linkage of their data for research purposes for public benefit, particularly with regard to the use of health care data (mental health or otherwise), and generally echoed existing evidence on the opinions of older age groups. Individuals who reported a history of self-harm and subsequently contacted health services more often reported being ?extremely likely? or ?likely? to share mental health data (contacted: 209/609, 34.3%; 95% CI 28.0-41.2; not contacted: 169/782, 21.6%; 95% CI 15.8-28.7) and physical health data (contacted: 117/609, 19.2%; 95% CI 12.7-27.8; not contacted: 96/782, 12.3%; 95% CI 6.7-20.9) compared with those who had not contacted services. Respondents were overall less likely to want to share their social media data, which they considered to be more personal compared to their health care data. Respondents stressed the importance of anonymity and the need for an appropriate ethical framework. Conclusions: Young people are aware, and they care about how their data are being used and for what purposes, irrespective of having a history of self-harm. They are largely positive about the use of health care data (mental or physical) for research and generally echo the opinions of older age groups raising issues around data security and the use of data for the public interest. UR - https://mental.jmir.org/2025/1/e60649 UR - http://dx.doi.org/10.2196/60649 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60649 ER - TY - JOUR AU - Khundrakpam, Budhachandra AU - Segado, Melanie AU - Pazdera, Jesse AU - Gagnon Shaigetz, Vincent AU - Granek, A. Joshua AU - Choudhury, Nusrat PY - 2025/3/4 TI - An Integrated Platform Combining Immersive Virtual Reality and Physiological Sensors for Systematic and Individualized Assessment of Stress Response (bWell): Design and Implementation Study JO - JMIR Form Res SP - e64492 VL - 9 KW - virtual reality KW - stress KW - physiological response KW - NASA-Task Load Index KW - cognitive demand KW - physical demand KW - vagal tone KW - heart rate variability N2 - Background: Stress is a pervasive issue in modern society, manifesting in various forms such as emotional, physical, and work-related stress, each with distinct impacts on individuals and society. Traditional stress studies often rely on psychological, performance, or social tests; however, recently, immersive virtual reality (VR), which provides a sense of presence and natural interaction, offers the opportunity to simulate real-world tasks and stressors in controlled environments. Despite its potential, the use of VR to investigate the multifaceted manifestations of stress has not been thoroughly explored. Objective: This study aimed to explore the feasibility of using a VR-based platform, bWell, to elicit multifaceted stress responses and measure the resulting behavioral and physiological changes. Specifically, we aimed to design various VR stress exercises based on neurocardiac models to systematically test cardiac functioning within specific contexts of self-regulation (executive functioning, physical efforts, and emotional regulation). Methods: The development process adhered to guidelines for VR clinical trials and complex health interventions, encompassing 3 phases: preparation, development, and verification. The preparation phase involved a comprehensive literature review to establish links between stress, the heart, and the brain, leading to the formulation of a conceptual model based on the Neurovisceral Integration Model (NVIM) and Vagal Tank Theory (VTT). The development phase involved designing VR exercises targeting specific stressors and integrating physiological sensors such as photoplethysmography (PPG) and electromyography (EMG) to capture heart rate variability (HRV) and facial expressions. The verification phase, conducted with a small number of trials, aimed to design a study and implement a workflow for testing the feasibility, acceptability, and tolerability of the VR exercises. In addition, the potential for capturing physiological measures along with subjective ratings of stress for specific dimensions was assessed. Results: Verification trials demonstrated that the VR exercises were well tolerated, with negligible cybersickness and high user engagement. The different VR exercises successfully elicited the intended stress demands, along with the physiological responses. Conclusions: The study presents a novel VR-based experimental setup that allows a systematic and individualized assessment of stress responses, paving the way for future research to identify features that confer stress resilience and help individuals manage stress effectively. While our conceptual model highlights the role of HRV in providing valuable insights into stress responses, future research will involve multivariate and machine learning analyses to predict individual stress responses based on comprehensive sensor data, including EMG and the VR-based behavioral data, ultimately guiding personalized stress management interventions. UR - https://formative.jmir.org/2025/1/e64492 UR - http://dx.doi.org/10.2196/64492 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053709 ID - info:doi/10.2196/64492 ER - TY - JOUR AU - Jiang, AnHang AU - Li, Shuang AU - Wang, HuaBin AU - Ni, HaoSen AU - Chen, HongAn AU - Dai, JunHong AU - Xu, XueFeng AU - Li, Mei AU - Dong, Guang-Heng PY - 2025/3/4 TI - Assessing Short-Video Dependence for e-Mental Health: Development and Validation Study of the Short-Video Dependence Scale JO - J Med Internet Res SP - e66341 VL - 27 KW - short-video dependence KW - problematic short-video use KW - cutoff point KW - scale development KW - mental health KW - short video KW - internet addiction KW - latent profile analysis KW - exploratory factor analysis KW - confirmatory factor analysis N2 - Background: Short-video dependence (SVD) has become a significant mental health issue around the world. The lack of scientific tools to assess SVD hampers further advancement in this area. Objective: This study aims to develop and validate a scientific tool to measure SVD levels, ensuring a scientifically determined cutoff point. Methods: We initially interviewed 115 highly engaged short-video users aged 15 to 63 years. Based on the summary of the interview and references to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for behavioral addictions, we proposed the first version of the short-video dependence scale (SVDS). We then screened the items through item analysis (second version) and extracted common factors using exploratory factor analysis (third version) and confirmatory factor analysis (final version). Convergent validity was tested with other scales (Chinese Internet Addiction Scale [CIAS] and DSM-5). Finally, we tested the validity of the final version in 16,038 subjects and set the diagnostic cutoff point through latent profile analysis and receiver operating characteristic curve analysis. Results: The final version of the SVDS contained 20 items and 4 dimensions, which showed strong structural validity (Kaiser-Meyer-Olkin value=0.94) and internal consistency (Cronbach ?=.93), and good convergent validity (rCIAS=0.61 and rDSM-5=0.68), sensitivity (0.77, 0.83, 0.87, and 0.62 for each of the 4 dimensions), and specificity (0.75, 0.87, 0.80, and 0.79 for each of the 4 dimensions). Additionally, an SVDS score of 58 was determined as the best cutoff score, and latent profile analysis identified a 5-class model for SVD. Conclusions: We developed a tool to measure SVD levels and established a threshold to differentiate dependent users from highly engaged nondependent users. The findings provide opportunities for further research on the impacts of short-video use. UR - https://www.jmir.org/2025/1/e66341 UR - http://dx.doi.org/10.2196/66341 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053762 ID - info:doi/10.2196/66341 ER - TY - JOUR AU - Cerit, Merve AU - Lee, Y. Angela AU - Hancock, Jeffrey AU - Miner, Adam AU - Cho, Mu-Jung AU - Muise, Daniel AU - Garròn Torres, Anna-Angelina AU - Haber, Nick AU - Ram, Nilam AU - Robinson, N. Thomas AU - Reeves, Byron PY - 2025/2/26 TI - Person-Specific Analyses of Smartphone Use and Mental Health: Intensive Longitudinal Study JO - JMIR Form Res SP - e59875 VL - 9 KW - media use KW - mental health KW - mHealth KW - uHealth KW - digital health KW - precision mental health KW - idiographic analysis KW - person-specific modeling KW - p-technique KW - longitudinal study KW - precision interventions KW - smartphones KW - idiosyncrasy KW - psychological well-being KW - canonical correlation analysis KW - United States N2 - Background: Contrary to popular concerns about the harmful effects of media use on mental health, research on this relationship is ambiguous, stalling advances in theory, interventions, and policy. Scientific explorations of the relationship between media and mental health have mostly been found null or have small associations, with the results often blamed on the use of cross-sectional study designs or imprecise measures of media use and mental health. Objective: This exploratory empirical demonstration aims to answer whether mental health effects are associated with media use experiences by (1) redirecting research investments to granular and intensive longitudinal recordings of digital experiences to build models of media use and mental health for single individuals over the course of 1 year, (2) using new metrics of fragmented media use to propose explanations of mental health effects that will advance person-specific theorizing in media psychology, and (3) identifying combinations of media behaviors and mental health symptoms that may be more useful for studying media effects than single measures of dosage and affect or assessments of clinical symptoms related to specific disorders. Methods: The activity on individuals? smartphone screens was recorded every 5 seconds when devices were in use over 1 year, resulting in a dataset of 6,744,013 screenshots and 123 fortnightly surveys from 5 adult participants. Each participant contributed between 0.8 and 2.7 million screens. Six media use metrics were derived from smartphone metadata. Fortnightly surveys captured symptoms of depression, attention-deficit/hyperactivity disorder, state anxiety, and positive affect. Idiographic filter models (p-technique canonical correlation analyses) were applied to explore person-specific associations. Results: Canonical correlations revealed substantial person-specific associations between media use and mental health, ranging from r=0.82 (P=.008) to r=0.92 (P=.03). The specific combinations of media use metrics and mental health dimensions were different for each person, reflecting significant individual variability. For instance, the media use canonical variate for 1 participant was characterized by higher loadings for app-switching, which, in combination with other behaviors, correlated strongly with a mental health variate emphasizing anxiety symptoms. For another, prolonged screen time, alongside other media use behaviors, contributed to a mental health variate weighted more heavily toward depression symptoms. These within-person correlations are among the strongest reported in this literature. Conclusions: Results suggest that the relationships between media use and mental health are highly individualized, with implications for the development of personalized models and precision smartphone-informed interventions in mental health. We discuss how our approach can be extended generally, while still emphasizing the importance of idiographic approaches. This study highlights the potential for granular, longitudinal data to reveal person-specific patterns that can inform theory development, personalized screening, diagnosis, and interventions in mental health. UR - https://formative.jmir.org/2025/1/e59875 UR - http://dx.doi.org/10.2196/59875 UR - http://www.ncbi.nlm.nih.gov/pubmed/39808832 ID - info:doi/10.2196/59875 ER - TY - JOUR AU - Kotera, Yasuhiro AU - Daryanani, Riddhi AU - Skipper, Oliver AU - Simpson, Jonathan AU - Takhi, Simran AU - McPhilbin, Merly AU - Ingall, Benjamin-Rose AU - Namasaba, Mariam AU - Jepps, Jessica AU - Kellermann, Vanessa AU - Bhandari, Divya AU - Ojio, Yasutaka AU - Ronaldson, Amy AU - Guerrero, Estefania AU - Jebara, Tesnime AU - Henderson, Claire AU - Slade, Mike AU - Vilar-Lluch, Sara PY - 2025/2/21 TI - Applying Critical Discourse Analysis to Cross-Cultural Mental Health Recovery Research JO - JMIR Form Res SP - e64087 VL - 9 KW - critical discourse analysis KW - cross-cultural mental health recovery research KW - linguistic analysis KW - social inequality KW - mental health KW - recovery research KW - language KW - social inequalities KW - qualitative analytical approach KW - linguistic expressions KW - discourse KW - analysis KW - framework KW - inequalities KW - CDA UR - https://formative.jmir.org/2025/1/e64087 UR - http://dx.doi.org/10.2196/64087 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64087 ER - TY - JOUR AU - Hopkin, Gareth AU - Coole, Holly AU - Edelmann, Francesca AU - Ayiku, Lynda AU - Branson, Richard AU - Campbell, Paul AU - Cooper, Sophie AU - Salmon, Mark PY - 2025/2/19 TI - Toward a New Conceptual Framework for Digital Mental Health Technologies: Scoping Review JO - JMIR Ment Health SP - e63484 VL - 12 KW - digital mental health KW - digital health KW - mental health KW - eHealth KW - categorization KW - conceptual KW - framework KW - regulation KW - synthesis KW - review methods KW - review methodology KW - systematic N2 - Background: Digital mental health technologies (DMHTs) are becoming more widely available and are seen as having the potential to improve the quality of mental health care. However, conversations around the potential impact of DMHTs can be impacted by a lack of focus on the types of technologies that are available. Several frameworks that could apply to DMHTs are available, but they have not been developed with comprehensive methods and have limitations. Objective: To address limitations with current frameworks, we aimed to identify existing literature on the categorization of DMHTs, to explore challenges with categorizing DMHTs for specific purposes, and to develop a new conceptual framework. Methods: We used an iterative approach to develop the framework. First, we completed a rapid review of the literature to identify studies that provided domains that could be used to categorize DMHTs. Second, findings from this review and associated issues were discussed by an expert working group, including professionals from a wide range of relevant settings. Third, we synthesized findings to develop a new conceptual framework. Results: The rapid review identified 3603 unique results, and hand searching identified another 3 potentially relevant papers. Of these, 24 papers were eligible for inclusion, which provided 10 domains to categorize DMHTs. The expert working group proposed a broad framework and based on the findings of the review and group discussions, we developed a new conceptual framework with 8 domains that represent important characteristics of DMHTs. These 8 domains are population, setting, platform or system, purpose, type of approach, human interaction, human responsiveness, and functionality. Conclusions: This conceptual framework provides a structure for various stakeholders to define the key characteristics of DMHTs. It has been developed with more comprehensive methods than previous attempts with similar aims. The framework can facilitate communication within the field and could undergo further iteration to ensure it is appropriate for specific purposes. UR - https://mental.jmir.org/2025/1/e63484 UR - http://dx.doi.org/10.2196/63484 ID - info:doi/10.2196/63484 ER - TY - JOUR AU - Wang, Jianli AU - Orpana, Heather AU - Carrington, André AU - Kephart, George AU - Vasiliadis, Helen-Maria AU - Leikin, Benjamin PY - 2025/2/19 TI - Development and Validation of Prediction Models for Perceived and Unmet Mental Health Needs in the Canadian General Population: Model-Based Synthetic Estimation Study JO - JMIR Public Health Surveill SP - e66056 VL - 11 KW - population risk prediction KW - development KW - validation KW - perceived mental health need KW - unmet mental health need N2 - Background: Research has shown that perceptions of a mental health need are closely associated with service demands and are an important dimension in needs assessment. Perceived and unmet mental health needs are important factors in the decision-making process regarding mental health services planning and resources allocation. However, few prediction tools are available to be used by policy and decision makers to forecast perceived and unmet mental health needs at the population level. Objective: We aim to develop prediction models to forecast perceived and unmet mental health needs at the provincial and health regional levels in Canada. Methods: Data from 2018, 2019, and 2020 Canadian Community Health Survey and Canadian Urban Environment were used (n=65,000 each year). Perceived and unmet mental health needs were measured by the Perceived Needs for Care Questionnaire. Using the 2018 dataset, we developed the prediction models through the application of regression synthetic estimation for the Atlantic, Central, and Western regions. The models were validated in the 2019 and 2020 datasets at the provincial level and in 10 randomly selected health regions by comparing the observed and predicted proportions of the outcomes. Results: In 2018, a total of 17.82% of the participants reported perceived mental health need and 3.81% reported unmet mental health need. The proportions were similar in 2019 (18.04% and 3.91%) and in 2020 (18.1% and 3.92%). Sex, age, self-reported mental health, physician diagnosed mood and anxiety disorders, self-reported life stress and life satisfaction were the predictors in the 3 regional models. The individual based models had good discriminative power with C statistics over 0.83 and good calibration. Applying the synthetic models in 2019 and 2020 data, the models had the best performance in Ontario, Quebec, and British Columbia; the absolute differences between observed and predicted proportions were less than 1%. The absolute differences between the predicted and observed proportion of perceived mental health needs in Newfoundland and Labrador (?4.16% in 2020) and Prince Edward Island (4.58% in 2019) were larger than those in other provinces. When applying the models in the 10 selected health regions, the models calibrated well in the health regions in Ontario and in Quebec; the absolute differences in perceived mental health needs ranged from 0.23% to 2.34%. Conclusions: Predicting perceived and unmet mental health at the population level is feasible. There are common factors that contribute to perceived and unmet mental health needs across regions, at different magnitudes, due to different population characteristics. Therefore, predicting perceived and unmet mental health needs should be region specific. The performance of the models at the provincial and health regional levels may be affected by population size. UR - https://publichealth.jmir.org/2025/1/e66056 UR - http://dx.doi.org/10.2196/66056 ID - info:doi/10.2196/66056 ER - TY - JOUR AU - Peasley, Dale AU - Kuplicki, Rayus AU - Sen, Sandip AU - Paulus, Martin PY - 2025/2/13 TI - Leveraging Large Language Models and Agent-Based Systems for Scientific Data Analysis: Validation Study JO - JMIR Ment Health SP - e68135 VL - 12 KW - LLM KW - agent-based systems KW - scientific data analysis KW - data contextualization KW - AI-driven research tools KW - large language model KW - scientific data KW - analysis KW - contextualization KW - AI KW - artificial intelligence KW - research tool N2 - Background: Large language models have shown promise in transforming how complex scientific data are analyzed and communicated, yet their application to scientific domains remains challenged by issues of factual accuracy and domain-specific precision. The Laureate Institute for Brain Research?Tulsa University (LIBR-TU) Research Agent (LITURAt) leverages a sophisticated agent-based architecture to mitigate these limitations, using external data retrieval and analysis tools to ensure reliable, context-aware outputs that make scientific information accessible to both experts and nonexperts. Objective: The objective of this study was to develop and evaluate LITURAt to enable efficient analysis and contextualization of complex scientific datasets for diverse user expertise levels. Methods: An agent-based system based on large language models was designed to analyze and contextualize complex scientific datasets using a ?plan-and-solve? framework. The system dynamically retrieves local data and relevant PubMed literature, performs statistical analyses, and generates comprehensive, context-aware summaries to answer user queries with high accuracy and consistency. Results: Our experiments demonstrated that LITURAt achieved an internal consistency rate of 94.8% and an external consistency rate of 91.9% across repeated and rephrased queries. Additionally, GPT-4 evaluations rated 80.3% (171/213) of the system?s answers as accurate and comprehensive, with 23.5% (50/213) receiving the highest rating of 5 for completeness and precision. Conclusions: These findings highlight the potential of LITURAt to significantly enhance the accessibility and accuracy of scientific data analysis, achieving high consistency and strong performance in complex query resolution. Despite existing limitations, such as model stability for highly variable queries, LITURAt demonstrates promise as a robust tool for democratizing data-driven insights across diverse scientific domains. UR - https://mental.jmir.org/2025/1/e68135 UR - http://dx.doi.org/10.2196/68135 ID - info:doi/10.2196/68135 ER - TY - JOUR AU - Kearns, Amanda AU - Moorhead, Anne AU - Mulvenna, Maurice AU - Bond, Raymond PY - 2025/2/10 TI - Assessing the Uses, Benefits, and Limitations of Digital Technologies Used by Health Professionals in Supporting Obesity and Mental Health Communication: Scoping Review JO - J Med Internet Res SP - e58434 VL - 27 KW - digital communication KW - digital technology KW - digital transformation KW - health professional KW - mental health KW - obesity KW - complex needs KW - artificial intelligence KW - AI KW - PRISMA N2 - Background: Obesity and mental health issues present interconnected public health challenges that impair physical, social, and mental well-being. Digital technologies offer potential for enhancing health care communication between health professionals (HPs) and individuals living with obesity and mental health issues, but their effectiveness is not fully understood. Objective: This scoping review aims to identify and understand the different types of technologies used by HPs in supporting obesity and mental health communication. Methods: A comprehensive scoping review, which followed a validated methodology, analyzed studies published between 2013 and 2023 across 8 databases. The data extraction focused on HPs? use of communication technologies, intervention types, biopsychosocial considerations, and perceptions of technology use. The review was guided by the following research question: ?What are the uses, benefits, and limitations of digital technologies in supporting communication between HPs and persons living with obesity and mental health issues?? Results: In total, 8 studies?featuring web-based platforms, social media, synchronous video calls, telephone calls, automated SMS text messaging, and email?met the inclusion criteria. Technologies such as virtual learning collaborative dashboards and videoconferencing, supported by automated SMS text messaging and social media (Facebook and WhatsApp groups), were commonly used. Psychologists, dietitians, social workers, and health coaches used digital tools to facilitate virtual appointments, diet and mental health monitoring, and motivational and educational support through group therapy, 1-on-1 sessions, and hybrid models. Benefits included enhanced access to care and engagement, personalized digital cognitive behavioral therapy, perceived stigma reduction, privacy, and improved physical health outcomes in weight reduction. However, improvements in mental health outcomes were not statistically significant in studies reporting P values (P?.05). The limitations included engagement difficulties due to conflicting personal family and work commitments; variable communication mode preferences, with some preferring in-person sessions; and misinterpretations of SMS text messaging prompts. Conflicts arose from cultural and individual differences, weight stigma, and confusion over HP roles in obesity and mental health care. Conclusions: Digital technologies have diversified the approaches HPs can take in delivering education, counseling, and motivation to individuals with obesity and mental health issues, facilitating private, stigma-reduced environments for personalized care. While the interventions were effective in obesity management, the review revealed a shortfall in addressing mental health needs. This highlights an urgent need for digital tools to serve as media for a deeper engagement with individuals? complex biopsychosocial needs. The integration of data science and technological advancements offers promising avenues for tailored digital solutions. The findings advocate the importance of continued innovation and adaptation in digital health care communication strategies, with clearer HP roles and an interdisciplinary, empathetic approach focused on individual needs. UR - https://www.jmir.org/2025/1/e58434 UR - http://dx.doi.org/10.2196/58434 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58434 ER - TY - JOUR AU - Ang, Heng Beng AU - Gollapalli, Das Sujatha AU - Du, Mingzhe AU - Ng, See-Kiong PY - 2025/2/7 TI - Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities JO - J Med Internet Res SP - e59524 VL - 27 KW - early maladaptive schemas KW - large language models KW - online mental health communities KW - case conceptualization KW - prompt engineering KW - artificial intelligence KW - AI N2 - Background: Early maladaptive schemas (EMSs) are pervasive, self-defeating patterns of thoughts and emotions underlying most mental health problems and are central in schema therapy. However, the characteristics of EMSs vary across demographics, and despite the growing use of online mental health communities (OMHCs), how EMSs manifest in these online support-seeking environments remains unclear. Understanding these characteristics could inform the design of more effective interventions powered by artificial intelligence to address online support seekers? unique therapeutic needs. Objective: We aimed to uncover associations between EMSs and mental health problems within OMHCs and examine features of EMSs as they are reflected in OMHCs. Methods: We curated a dataset of 29,329 posts from widely accessed OMHCs, labeling each with relevant schemas and mental health problems. To identify associations, we conducted chi-square tests of independence and calculated odds ratios (ORs) with the dataset. In addition, we developed a novel group-level case conceptualization technique, leveraging GPT-4 to extract features of EMSs from OMHC texts across key schema therapy dimensions, such as schema triggers and coping responses. Results: Several associations were identified between EMSs and mental health problems, reflecting how EMSs manifest in online support-seeking contexts. Anxiety-related problems typically highlighted vulnerability to harm or illness (OR 5.64, 95% CI 5.34-5.96; P<.001), while depression-related problems emphasized unmet interpersonal needs, such as social isolation (OR 3.18, 95% CI 3.02-3.34; P<.001). Conversely, problems with eating disorders mostly exemplified negative self-perception and emotional inhibition (OR 1.89, 95% CI 1.45-2.46; P<.001). Personality disorders reflected themes of subjugation (OR 2.51, 95% CI 1.86-3.39; P<.001), while posttraumatic stress disorder problems involved distressing experiences and mistrust (OR 5.04, 95% CI 4.49-5.66; P<.001). Substance use disorder problems reflected negative self-perception of failure to achieve (OR 1.83, 95% CI 1.35-2.49; P<.001). Depression, personality disorders, and posttraumatic stress disorder were also associated with 12, 9, and 7 EMSs, respectively, emphasizing their complexities and the need for more comprehensive interventions. In contrast, anxiety, eating disorder, and substance use disorder were related to only 2 to 3 EMSs, suggesting that these problems are better addressed through targeted interventions. In addition, the EMS features extracted from our dataset averaged 13.27 (SD 3.05) negative features per schema, with 2.65 (SD 1.07) features per dimension, as supported by existing literature. Conclusions: We uncovered various associations between EMSs and mental health problems among online support seekers, highlighting the prominence of specific EMSs in each problem and the unique complexities of each problem in terms of EMSs. We also identified EMS features as expressed by support seekers in OMHCs, reinforcing the relevance of EMSs in these online support-seeking contexts. These insights are valuable for understanding how EMS are characterized in OMHCs and can inform the development of more effective artificial intelligence?powered tools to enhance support on these platforms. UR - https://www.jmir.org/2025/1/e59524 UR - http://dx.doi.org/10.2196/59524 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59524 ER - TY - JOUR AU - Chan, Tai Kai AU - Hui, LM Christy AU - Cheung, Charlton AU - Suen, Nam Yi AU - Wong, Yin Stephanie Ming AU - Wong, SM Corine AU - Kam, PH Bosco AU - Chen, Hai Eric Yu PY - 2025/1/31 TI - Exploring the Differentiation of Self-Concepts in the Physical and Virtual Worlds Using Euclidean Distance Analysis and Its Relationship With Digitalization and Mental Health Among Young People: Cross-Sectional Study JO - JMIR Ment Health SP - e60747 VL - 12 KW - digitalization KW - self KW - identity KW - psychiatric symptomatology KW - youth mental health KW - Euclidean distance analysis KW - self-differentiation KW - smartphone addiction KW - personal attributes N2 - Background: Increasing observation and evidence suggest that the process of digitalization could have profound impact to the development of human mind and self, with potential mental health consequences. Self-differentiation is important in human identity and self-concept formation, which is believed to be involved in the process of digitalization. Objective: This study aimed to investigate the relationship between digitalization and personal attributes in the actual selves in the physical and virtual worlds. Methods: A community cohort of 397 participants aged 15 to 24 years old was recruited consecutively over about 3 months. Assessment was conducted upon the indicators of digitalization (smartphone use time, leisure online time, and age of first smartphone ownership), smartphone addiction, 14 selected personal attributes in the actual selves in the physical and virtual worlds, psychiatric symptomatology and personality traits. Euclidean distance analysis between the personal attributes in the actual selves in the physical and virtual worlds for the similarities of the 2 selves was performed in the analysis. Results: The current primary findings are the negative correlations between the similarity of the personal attributes in the physical actual self and virtual actual self, and smartphone use time, smartphone addiction as well as anxiety symptomatology respectively (P<.05 to P<.01). Conclusions: The current findings provide empirical evidence for the importance of maintaining a congruent self across the physical and virtual worlds, regulating smartphone use time, preventing smartphone addiction, and safeguarding mental health. UR - https://mental.jmir.org/2025/1/e60747 UR - http://dx.doi.org/10.2196/60747 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60747 ER - TY - JOUR AU - Gautam, Dipak AU - Kellmeyer, Philipp PY - 2025/1/29 TI - Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e62865 VL - 14 KW - large language model KW - LLM KW - mental health KW - explainability KW - credibility KW - mobile phone N2 - Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored. Objective: This scoping review systematically maps the factors influencing the credibility of LLMs in mental health support, including reliability, explainability, and ethical considerations. The review is expected to offer critical insights for practitioners, researchers, and policy makers, guiding future research and policy development. These findings will contribute to the responsible integration of LLMs into mental health care, with a focus on maintaining ethical standards and user trust. Methods: This review follows PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and the Joanna Briggs Institute (JBI) methodology. Eligibility criteria include studies that apply transformer-based generative language models in mental health support, such as BERT and GPT. Sources include PsycINFO, MEDLINE via PubMed, Web of Science, IEEE Xplore, and ACM Digital Library. A systematic search of studies from 2019 onward will be conducted and updated until October 2024. Data will be synthesized qualitatively. The Population, Concept, and Context framework will guide the inclusion criteria. Two independent reviewers will screen and extract data, resolving discrepancies through discussion. Data will be synthesized and presented descriptively. Results: As of September 2024, this study is currently in progress, with the systematic search completed and the screening phase ongoing. We expect to complete data extraction by early November 2024 and synthesis by late November 2024. Conclusions: This scoping review will map the current evidence on the credibility of LLMs in mental health support. It will identify factors influencing the reliability, explainability, and ethical considerations of these models, providing insights for practitioners, researchers, policy makers, and users. These findings will fill a critical gap in the literature and inform future research, practice, and policy development, ensuring the responsible integration of LLMs in mental health services. International Registered Report Identifier (IRRID): DERR1-10.2196/62865 UR - https://www.researchprotocols.org/2025/1/e62865 UR - http://dx.doi.org/10.2196/62865 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62865 ER - TY - JOUR AU - Ennis, Edel AU - Bond, Raymond AU - Mulvenna, Maurice AU - Sweeney, Colm PY - 2025/1/29 TI - Understanding Individual Differences in Happiness Sources and Implications for Health Technology Design: Exploratory Analysis of an Open Dataset JO - JMIR Form Res SP - e65658 VL - 9 KW - happiness KW - sexes KW - age KW - marital status KW - parents KW - affections KW - achievements KW - datasets KW - digital health KW - well-being KW - mental health KW - digital mental health interventions KW - regression analyses KW - evidence based N2 - Background: Psychologists have developed frameworks to understand many constructs, which have subsequently informed the design of digital mental health interventions (DMHIs) aimed at improving mental health outcomes. The science of happiness is one such domain that holds significant applied importance due to its links to well-being and evidence that happiness can be cultivated through interventions. However, as with many constructs, the unique ways in which individuals experience happiness present major challenges for designing personalized DMHIs. Objective: This paper aims to (1) present an analysis of how sex may interact with age, marital status, and parental status to predict individual differences in sources of happiness, and (2) to present a preliminary discussion of how open datasets may contribute to the process of designing health-related technology innovations. Methods: The HappyDB is an open database of 100,535 statements of what people consider to have made them happy, with some people asking to consider the past 24 hours (49,831 statements) and some considering the last 3 months (50,704 statements). Demographic information is also provided. Binary logistic regression analyses are used to determine whether various groups differed in their likelihood of selecting or not selecting a category as a source of their happiness. Results: Sex and age interacted to influence what was selected as sources of happiness, with patterns being less consistent among female individuals in comparison with male individuals. For marital status, differences in sources of happiness were predominantly between married individuals and those who are divorced or separated, but these were the same for both sexes. Married, single, and widowed individuals were all largely similar in their likelihood of selecting each of the categories as a source of their happiness. However, there were some anomalies, and sex appeared to be important in these anomalies. Sex and parental status also interacted to influence what was selected as sources of happiness. Conclusions: Sex interacts with age, marital status, and parental status in the likelihood of reporting affection, bonding, leisure, achievement, or enjoying the moment as sources of happiness. The contribution of an open dataset to understanding individual differences in sources of happiness is discussed in terms of its potential role in addressing the challenges of designing DMHIs that are ethical, responsible, evidence based, acceptable, engaging, inclusive, and effective for users. The discussion considers how the content design of DMHIs in general may benefit from exploring new methods informed by diverse data sources. It is proposed that examining the extent to which insights from nondigital settings can inform requirements gathering for DMHIs is warranted. UR - https://formative.jmir.org/2025/1/e65658 UR - http://dx.doi.org/10.2196/65658 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65658 ER - TY - JOUR AU - Macrynikola, Natalia AU - Chen, Kelly AU - Lane, Erlend AU - Nguyen, Nic AU - Pinto, Jennifer AU - Yen, Shirley AU - Torous, John PY - 2025/1/29 TI - Testing the Feasibility, Acceptability, and Potential Efficacy of an Innovative Digital Mental Health Care Delivery Model Designed to Increase Access to Care: Open Trial of the Digital Clinic JO - JMIR Ment Health SP - e65222 VL - 12 KW - digital interventions KW - transdiagnostic treatment KW - evidence-based treatment KW - digital navigator KW - access to care KW - mobile phone N2 - Background: Mental health concerns have become increasingly prevalent; however, care remains inaccessible to many. While digital mental health interventions offer a promising solution, self-help and even coached apps have not fully addressed the challenge. There is now a growing interest in hybrid, or blended, care approaches that use apps as tools to augment, rather than to entirely guide, care. The Digital Clinic is one such model, designed to increase access to high-quality mental health services. Objective: To assess the feasibility, acceptability, and potential efficacy of the Digital Clinic model, this study aims to conduct a nonrandomized open trial with participants experiencing depression, anxiety, or both, at various levels of clinical severity. Methods: Clinicians were trained in conducting brief transdiagnostic evidence-based treatment augmented by a mental health app (mindLAMP); digital navigators were trained in supporting participants? app engagement and digital literacy while also sharing app data with both patients and clinicians. Feasibility and acceptability of this 8-week program were assessed against a range of benchmarks. Potential efficacy was assessed by calculating pre-post change in symptoms of depression (Patient Health Questionnaire-9; PHQ-9), anxiety (7-item Generalized Anxiety Disorder; GAD-7), and comorbid depression and anxiety (Patient Health Questionnaire Anxiety and Depression Scale; PHQ-ADS), as well as rates of clinically meaningful improvement and remission. Secondary outcomes included change in functional impairment, self-efficacy in managing emotions, and flourishing. Results: Of the 258 enrolled participants, 215 (83.3%) completed the 8-week program. Most were White (n=151, 70.2%) and identified as cisgender women (n=136, 63.3%), with a mean age of 41 (SD 14) years. Feasibility and acceptability were good to excellent across a range of domains. The program demonstrated potential efficacy: the average PHQ-9 score was moderate to moderately severe at baseline (mean 13.39, SD 4.53) and decreased to subclinical (mean 7.79, SD 4.61) by the end of the intervention (t126=12.50, P<.001, Cohen d=1.11). Similarly, the average GAD-7 score decreased from moderate at baseline (mean 12.93, SD 3.67) to subclinical (mean 7.35, SD 4.19) by the end of the intervention (t113=13, P<.001, Cohen d=1.22). Participation in the program was also associated with high rates of clinically significant improvement and remission. Conclusions: Results suggest that the Digital Clinic model is feasible, acceptable, and potentially efficacious, warranting a future randomized controlled trial to establish the efficacy of this innovative model of care. UR - https://mental.jmir.org/2025/1/e65222 UR - http://dx.doi.org/10.2196/65222 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65222 ER - TY - JOUR AU - Thomas, Julia AU - Lucht, Antonia AU - Segler, Jacob AU - Wundrack, Richard AU - Miché, Marcel AU - Lieb, Roselind AU - Kuchinke, Lars AU - Meinlschmidt, Gunther PY - 2025/1/29 TI - An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study JO - JMIR Public Health Surveill SP - e63809 VL - 11 KW - deep learning KW - explainable artificial intelligence (XAI) KW - large language model (LLM) KW - machine learning KW - neural network KW - prevention KW - risk monitoring KW - suicide KW - transformer model KW - suicidality KW - suicidal ideation KW - self-murder KW - self-harm KW - youth KW - adolescent KW - adolescents KW - public health KW - language model KW - language models KW - chat protocols KW - crisis helpline KW - help-seeking behaviors KW - German KW - Shapley KW - decision-making KW - mental health KW - health informatics KW - mobile phone N2 - Background: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the capability of pretrained large language models in predicting suicidal ideation and behaviors (SIB) in speech and text. Objective: This study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features. Methods: We analyzed chat protocols from adolescents and young adults (aged 14-25 years) seeking assistance from a German crisis helpline. An ML model was developed using a transformer-based language model architecture with pretrained weights and long short-term memory layers. The model predicted suicidal ideation (SI) and advanced suicidal engagement (ASE), as indicated by composite Columbia-Suicide Severity Rating Scale scores. We compared model performance against a classical word-vector-based ML model. We subsequently computed discrimination, calibration, clinical utility, and explainability information using a Shapley Additive Explanations value-based post hoc estimation model. Results: The dataset comprised 1348 help-seeking encounters (1011 for training and 337 for testing). The transformer-based classifier achieved a macroaveraged area under the curve (AUC) receiver operating characteristic (ROC) of 0.89 (95% CI 0.81-0.91) and an overall accuracy of 0.79 (95% CI 0.73-0.99). This performance surpassed the word-vector-based baseline model (AUC-ROC=0.77, 95% CI 0.64-0.90; accuracy=0.61, 95% CI 0.61-0.80). The transformer model demonstrated excellent prediction for nonsuicidal sessions (AUC-ROC=0.96, 95% CI 0.96-0.99) and good prediction for SI and ASE, with AUC-ROCs of 0.85 (95% CI 0.97-0.86) and 0.87 (95% CI 0.81-0.88), respectively. The Brier Skill Score indicated a 44% improvement in classification performance over the baseline model. The Shapley Additive Explanations model identified language features predictive of SIBs, including self-reference, negation, expressions of low self-esteem, and absolutist language. Conclusions: Neural networks using large language model?based transfer learning can accurately identify SI and ASE. The post hoc explainer model revealed language features associated with SI and ASE. Such models may potentially support clinical decision-making in suicide prevention services. Future research should explore multimodal input features and temporal aspects of suicide risk. UR - https://publichealth.jmir.org/2025/1/e63809 UR - http://dx.doi.org/10.2196/63809 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63809 ER - TY - JOUR AU - Sarai, Gurdeep AU - Jayaraman, Prakash Prem AU - Tirosh, Oren AU - Wickramasinghe, Nilmini PY - 2025/1/29 TI - Exploring Virtual Reality and Exercise Simulator Interventions in Patients With Attention Deficit Hyperactivity Disorder: Comprehensive Literature Review JO - JMIR Serious Games SP - e57297 VL - 13 KW - exercise-based simulator KW - exergame KW - virtual reality KW - physical activity KW - attention-deficit/hyperactivity disorder N2 - Background: This review explores virtual reality (VR) and exercise simulator?based interventions for individuals with attention-deficit/hyperactivity disorder (ADHD). Past research indicates that both VR and simulator-based interventions enhance cognitive functions, such as executive function and memory, though their impacts on attention vary. Objective: This study aimed to contribute to the ongoing scientific discourse on integrating technology-driven interventions into the management and evaluation of ADHD. It specifically seeks to consolidate findings on how VR and exercise simulators may support individuals with ADHD, acknowledging associated challenges and implications inherent in both technological approaches. Methods: This research looks at existing literature to examine the potential efficacy of VR and exercise simulator?based interventions for individuals with ADHD. It evaluates the capacity of these interventions to address specific challenges along with an emphasis on the adjustments for accommodating unique user behaviors. Additionally, it underscores the limited exploration of user perceptions of exercise simulator?based interventions and the undervalued role of motor function in both ADHD assessment and symptom management. Results: The findings of this scoping review reveal that, while these interventions enhance user motivation and enjoyment, certain challenges resist modification through technology. Furthermore, this study explores the intricate complexities involved in customizing these technologies to accommodate the diverse aspects of user behavior and highlights the potential limitations in the use of VR. Conclusions: This scoping review contributes to the ongoing research on enhancing interventions to support individuals with ADHD. It advocates for participant-centric approaches that aim to optimize both cognitive and motor outcomes while prioritizing the enhancement of user experiences. This study emphasizes the need for a comprehensive approach to interventions, recognizing the relationship between cognitive and motor abilities, and calls for improving technological interventions to address the varied needs of individuals with ADHD. UR - https://games.jmir.org/2025/1/e57297 UR - http://dx.doi.org/10.2196/57297 UR - http://www.ncbi.nlm.nih.gov/pubmed/39879092 ID - info:doi/10.2196/57297 ER - TY - JOUR AU - Rojas, K. Natalia AU - Martin, Sam AU - Cortina-Borja, Mario AU - Shafran, Roz AU - Fox-Smith, Lana AU - Stephenson, Terence AU - Ching, F. Brian C. AU - d'Oelsnitz, Anaïs AU - Norris, Tom AU - Xu, Yue AU - McOwat, Kelsey AU - Dalrymple, Emma AU - Heyman, Isobel AU - Ford, Tamsin AU - Chalder, Trudie AU - Simmons, Ruth AU - AU - Pinto Pereira, M. Snehal PY - 2025/1/28 TI - Health and Experiences During the COVID-19 Pandemic Among Children and Young People: Analysis of Free-Text Responses From the Children and Young People With Long COVID Study JO - J Med Internet Res SP - e63634 VL - 27 KW - children and young people KW - text mining KW - free-text responses KW - experiences KW - COVID-19 KW - long COVID KW - InfraNodus KW - sentiment analysis KW - discourse analysis KW - AI KW - artificial intelligence N2 - Background: The literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development. Objective: This study aims to analyze free-text responses from children and young people participating in the Children and Young People With Long COVID study to get a clearer understanding of how young people were feeling during the pandemic. Methods: A total of 8224 free-text responses from children and young people were analyzed using InfraNodus, an artificial intelligence?powered text network analysis tool, to determine the most prevalent topics. A random subsample of 411 (5%) of the 8224 responses underwent a manual sentiment analysis; this was reweighted to represent the general population of children and young people in England. Results: Experiences fell into 6 main overlapping topical clusters: school, examination stress, mental health, emotional impact of the pandemic, social and family support, and physical health (including COVID-19 symptoms). Sentiment analysis showed that statements were largely negative (314/411, 76.4%), with a small proportion being positive (57/411, 13.9%). Those reporting negative sentiment were mostly female (227/314, 72.3%), while those reporting positive sentiment were mostly older (170/314, 54.1%). There were significant observed associations between sentiment and COVID-19 status as well as sex (P=.001 and P<.001, respectively) such that the majority of the responses, regardless of COVID-19 status or sex, were negative; for example, 84.1% (227/270) of the responses from female individuals and 61.7% (87/141) of those from male individuals were negative. There were no observed associations between sentiment and all other examined demographics. The results were broadly similar when reweighted to the general population of children and young people in England: 78.52% (negative), 13.23% (positive), and 8.24% (neutral). Conclusions: We used InfraNodus to analyze free-text responses from a large sample of children and young people. The majority of responses (314/411, 76.4%) were negative, and many of the children and young people reported experiencing distress across a range of domains related to school, social situations, and mental health. Our findings add to the literature, highlighting the importance of specific considerations for children and young people when responding to national emergencies. UR - https://www.jmir.org/2025/1/e63634 UR - http://dx.doi.org/10.2196/63634 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63634 ER - TY - JOUR AU - Holmes, Glenn AU - Tang, Biya AU - Gupta, Sunil AU - Venkatesh, Svetha AU - Christensen, Helen AU - Whitton, Alexis PY - 2025/1/23 TI - Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review JO - J Med Internet Res SP - e63126 VL - 27 KW - suicide KW - suicide prevention KW - large language model KW - self-harm KW - artificial intelligence KW - AI KW - PRISMA N2 - Background: Prevention of suicide is a global health priority. Approximately 800,000 individuals die by suicide yearly, and for every suicide death, there are another 20 estimated suicide attempts. Large language models (LLMs) hold the potential to enhance scalable, accessible, and affordable digital services for suicide prevention and self-harm interventions. However, their use also raises clinical and ethical questions that require careful consideration. Objective: This scoping review aims to identify emergent trends in LLM applications in the field of suicide prevention and self-harm research. In addition, it summarizes key clinical and ethical considerations relevant to this nascent area of research. Methods: Searches were conducted in 4 databases (PsycINFO, Embase, PubMed, and IEEE Xplore) in February 2024. Eligible studies described the application of LLMs for suicide or self-harm prevention, detection, or management. English-language peer-reviewed articles and conference proceedings were included, without date restrictions. Narrative synthesis was used to synthesize study characteristics, objectives, models, data sources, proposed clinical applications, and ethical considerations. This review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) standards. Results: Of the 533 studies identified, 36 (6.8%) met the inclusion criteria. An additional 7 studies were identified through citation chaining, resulting in 43 studies for review. The studies showed a bifurcation of publication fields, with varying publication norms between computer science and mental health. While most of the studies (33/43, 77%) focused on identifying suicide risk, newer applications leveraging generative functions (eg, support, education, and training) are emerging. Social media was the most common source of LLM training data. Bidirectional Encoder Representations from Transformers (BERT) was the predominant model used, although generative pretrained transformers (GPTs) featured prominently in generative applications. Clinical LLM applications were reported in 60% (26/43) of the studies, often for suicide risk detection or as clinical assistance tools. Ethical considerations were reported in 33% (14/43) of the studies, with privacy, confidentiality, and consent strongly represented. Conclusions: This evolving research area, bridging computer science and mental health, demands a multidisciplinary approach. While open access models and datasets will likely shape the field of suicide prevention, documenting their limitations and potential biases is crucial. High-quality training data are essential for refining these models and mitigating unwanted biases. Policies that address ethical concerns?particularly those related to privacy and security when using social media data?are imperative. Limitations include high variability across disciplines in how LLMs and study methodology are reported. The emergence of generative artificial intelligence signals a shift in approach, particularly in applications related to care, support, and education, such as improved crisis care and gatekeeper training methods, clinician copilot models, and improved educational practices. Ongoing human oversight?through human-in-the-loop testing or expert external validation?is essential for responsible development and use. Trial Registration: OSF Registries osf.io/nckq7; https://osf.io/nckq7 UR - https://www.jmir.org/2025/1/e63126 UR - http://dx.doi.org/10.2196/63126 UR - http://www.ncbi.nlm.nih.gov/pubmed/39847414 ID - info:doi/10.2196/63126 ER - TY - JOUR AU - Cardamone, C. Nicholas AU - Olfson, Mark AU - Schmutte, Timothy AU - Ungar, Lyle AU - Liu, Tony AU - Cullen, W. Sara AU - Williams, J. Nathaniel AU - Marcus, C. Steven PY - 2025/1/21 TI - Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study JO - JMIR Med Inform SP - e65454 VL - 13 KW - artificial intelligence KW - AI KW - machine learning KW - ML KW - natural language processing KW - NLP KW - large language model KW - LLM KW - ChatGPT KW - predictive modeling KW - mental health KW - health informatics KW - electronic health record KW - EHR KW - EHR system KW - text KW - dataset KW - mental health disorder KW - emergency department KW - physical health N2 - Background: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable. Objective: This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs). Methods: Using a dataset from the EHR systems of more than 50 health care provider organizations in the United States from 2016 to 2021, we extracted all clinical terms that appeared in at least 1000 records of individuals admitted to the ED for a mental health?related problem from a source population of over 6 million ED episodes. Two experienced mental health clinicians (one medically trained psychiatrist and one clinical psychologist) reached consensus on the classification of EHR terms and diagnostic codes into categories. We evaluated an LLM?s agreement with clinical judgment across three classification tasks as follows: (1) classify terms into ?mental health? or ?physical health?, (2) classify mental health terms into 1 of 42 prespecified categories, and (3) classify physical health terms into 1 of 19 prespecified broad categories. Results: There was high agreement between the LLM and clinical experts when categorizing 4553 terms as ?mental health? or ?physical health? (?=0.77, 95% CI 0.75-0.80). However, there was still considerable variability in LLM-clinician agreement on the classification of mental health terms (?=0.62, 95% CI 0.59?0.66) and physical health terms (?=0.69, 95% CI 0.67?0.70). Conclusions: The LLM displayed high agreement with clinical experts when classifying EHR terms into certain mental health or physical health term categories. However, agreement with clinical experts varied considerably within both sets of mental and physical health term categories. Importantly, the use of LLMs presents an alternative to manual human coding, presenting great potential to create interpretable features for prediction models. UR - https://medinform.jmir.org/2025/1/e65454 UR - http://dx.doi.org/10.2196/65454 ID - info:doi/10.2196/65454 ER - TY - JOUR AU - Zhang, Ren AU - Liu, Yi AU - Zhang, Zhiwei AU - Luo, Rui AU - Lv, Bin PY - 2025/1/20 TI - Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study JO - JMIR Med Inform SP - e58649 VL - 13 KW - postpartum depression KW - machine learning KW - predictive model KW - risk factors KW - XGBoost KW - extreme gradient boosting KW - PPD N2 - Background: Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging. Objective: This study aimed to comprehensively collect variables from multiple aspects, develop and validate machine learning models to achieve precise prediction of PPD, and interpret the model to reveal clinical implications. Methods: This study recruited pregnant women who delivered at the West China Second University Hospital, Sichuan University. Various variables were collected from electronic medical record data and screened using least absolute shrinkage and selection operator penalty regression. Participants were divided into training (1358/2055, 66.1%) and validation (697/2055, 33.9%) sets by random sampling. Machine learning?based predictive models were developed in the training cohort. Models were validated in the validation cohort with receiver operating curve and decision curve analysis. Multiple model interpretation methods were implemented to explain the optimal model. Results: We recruited 2055 participants in this study. The extreme gradient boosting model was the optimal predictive model with the area under the receiver operating curve of 0.849. Shapley Additive Explanation indicated that the most influential predictors of PPD were antepartum depression, lower fetal weight, elevated thyroid-stimulating hormone, declined thyroid peroxidase antibodies, elevated serum ferritin, and older age. Conclusions: This study developed and validated a machine learning?based predictive model for PPD. Several significant risk factors and how they impact the prediction of PPD were revealed. These findings provide new insights into the early screening of individuals with high risk for PPD, emphasizing the need for comprehensive screening approaches that include both physiological and psychological factors. UR - https://medinform.jmir.org/2025/1/e58649 UR - http://dx.doi.org/10.2196/58649 ID - info:doi/10.2196/58649 ER - TY - JOUR AU - Romm, Lie Kristin AU - Skoge, Mari AU - Barrett, Ann Elizabeth AU - Berentzen, Lars-Christian AU - Bergsager, Dagfinn AU - Fugelli, Pål AU - Bjella, Thomas AU - Gardsjord, Strand Erlend AU - Kling, Kristine AU - Kruse, Hembre Sindre AU - Kværner, Jorunn Kari AU - Melle, Ingrid AU - Mork, Erlend AU - Ihler, Myhre Henrik AU - Rognli, Borger Eline AU - Simonsen, Carmen AU - Værnes, Gunnar Tor AU - Aminoff, Ragnhild Sofie PY - 2025/1/17 TI - A Mobile Health Intervention to Support Collaborative Decision-Making in Mental Health Care: Development and Usability JO - JMIR Form Res SP - e57614 VL - 9 KW - eHealth KW - shared decision-making KW - user involvement KW - user-centered design KW - mental disorder KW - mobile technology KW - illness course KW - recovery KW - mobile apps KW - mHealth N2 - Background: Shared decision-making between clinicians and service users is crucial in mental health care. One significant barrier to achieving this goal is the lack of user-centered services. Integrating digital tools into mental health services holds promise for addressing some of these challenges. However, the implementation of digital tools, such as mobile apps, remains limited, and attrition rates for mental health apps are typically high. Design thinking can support the development of tools tailored to the needs of service users and clinicians. Objective: This study aims to develop and beta test a digital tool designed for individuals with severe mental disorders or substance use disorders to facilitate shared decision-making on treatment goals and strategies within mental health services. Methods: We used a user-centered design approach to develop iTandem, an app facilitating collaborative treatment between service users and clinicians. Through qualitative interviews and workshops, we engaged 6 service users with severe mental disorders or substance use disorders, 6 clinicians, and 1 relative to identify and design relevant app modules. A beta test of iTandem was conducted to refine the app and plan for a pilot trial in a clinical setting. After 6 weeks of app use, 5 clinicians and 4 service users were interviewed to provide feedback on the concept, implementation, and technical issues. Safety and ethical considerations were thoroughly discussed and addressed. Results: To avoid overload for the service users, we applied a pragmatic take on module content and size. Thus, iTandem includes the following 8 modules, primarily based on the needs of service users and clinicians: Sleep (sleep diary), Medication (intake and side effects), Recovery (measures, including well-being and personal recovery, and exercises, including good things and personal strengths), Mood (mood diary and report of daily feelings), Psychosis (level of positive symptoms and their consequences and level of negative symptoms), Activity (goal setting and progress), Substance use (weekly use, potential triggers or strategies used to abstain), and Feedback on therapy (of individual sessions and overall rating of the past week). For the beta testing, service users and clinicians collaborated in choosing 2-3 modules in iTandem to work with during treatment sessions. The testing showed that the app was well received by service users, and that facilitation for implementation is crucial. Conclusions: iTandem and similar apps have the potential to enhance treatment outcomes by facilitating shared decision-making and tailoring treatment to the needs of service users. However, successful implementation requires thorough testing, iterative development, and evaluations of both utility and treatment effects. There is a critical need to focus on how technology integrates into clinical settings?from development to implementation?and to conduct further research on early health technology assessments to guide these processes. UR - https://formative.jmir.org/2025/1/e57614 UR - http://dx.doi.org/10.2196/57614 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57614 ER - TY - JOUR AU - Scherbakov, A. Dmitry AU - Hubig, C. Nina AU - Lenert, A. Leslie AU - Alekseyenko, V. Alexander AU - Obeid, S. Jihad PY - 2025/1/16 TI - Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review JO - JMIR Ment Health SP - e67192 VL - 12 KW - natural language processing KW - datasets KW - mental health KW - automated review KW - depression KW - suicide KW - mental health research KW - NLP KW - artificial intelligence KW - AI KW - scoping review KW - determinant KW - large language model KW - LLM KW - quantitative KW - automation N2 - Background: The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated. Objective: This review aims to summarize the use of NLP in mental health research, with a special focus on the types of text datasets and the use of social determinants of health (SDOH) in NLP projects related to mental health. Methods: The search was conducted in September 2024 using a broad search strategy in PubMed, Scopus, and CINAHL Complete. All citations were uploaded to Covidence (Veritas Health Innovation) software. The screening and extraction process took place in Covidence with the help of a custom large language model (LLM) module developed by our team. This LLM module was calibrated and tuned to automate many aspects of the review process. Results: The screening process, assisted by the custom LLM, led to the inclusion of 1768 studies in the final review. Most of the reviewed studies (n=665, 42.8%) used clinical data as their primary text dataset, followed by social media datasets (n=523, 33.7%). The United States contributed the highest number of studies (n=568, 36.6%), with depression (n=438, 28.2%) and suicide (n=240, 15.5%) being the most frequently investigated mental health issues. Traditional demographic variables, such as age (n=877, 56.5%) and gender (n=760, 49%), were commonly extracted, while SDOH factors were less frequently reported, with urban or rural status being the most used (n=19, 1.2%). Over half of the citations (n=826, 53.2%) did not provide clear information on dataset accessibility, although a sizable number of studies (n=304, 19.6%) made their datasets publicly available. Conclusions: This scoping review underscores the significant role of clinical notes and social media in NLP-based mental health research. Despite the clear relevance of SDOH to mental health, their underutilization presents a gap in current research. This review can be a starting point for researchers looking for an overview of mental health projects using text data. Shared datasets could be used to place more emphasis on SDOH in future studies. UR - https://mental.jmir.org/2025/1/e67192 UR - http://dx.doi.org/10.2196/67192 ID - info:doi/10.2196/67192 ER - TY - JOUR AU - Antolin Muñiz, Marley AU - McMahan, M. Vanessa AU - Luna Marti, Xochitl AU - Brennan, Sarah AU - Tavasieff, Sophia AU - Rodda, N. Luke AU - Knoll, James AU - Coffin, O. Phillip PY - 2025/1/13 TI - Identification of Behavioral, Clinical, and Psychological Antecedents of Acute Stimulant Poisoning: Development and Implementation of a Mixed Methods Psychological Autopsy Study JO - JMIR Form Res SP - e64873 VL - 9 KW - psychological autopsy KW - acute stimulant poisoning KW - overdose KW - cocaine KW - methamphetamine KW - fentanyl N2 - Background: Despite increasing fatal stimulant poisoning in the United States, little is understood about the mechanism of death. The psychological autopsy (PA) has long been used to distinguish the manner of death in equivocal cases, including opioid overdose, but has not been used to explicitly explore stimulant mortality. Objective: We aimed to develop and implement a large PA study to identify antecedents of fatal stimulant poisoning, seeking to maximize data gathering and ethical interactions during the collateral interviews. Methods: We ascertained death records from the California Electronic Death Reporting System (CA-EDRS) and the San Francisco Office of the County Medical Examiner (OCME) from June 2022 through December 2023. We selected deaths determined to be due to acute poisoning from cocaine or methamphetamine, which occurred 3?12 months prior and were not attributed to suicide or homicide. We identified 31 stimulant-fentanyl and 70 stimulant-no-opioid decedents. We sought 2 informants for each decedent, who were able to describe the decedent across their life course. Informants were at least 18 years of age, communicated with the decedent within the year before death, and were aware that the decedent had been using substances during that year. Upon completion of at least one informant interview conducted by staff with bachelor?s or master?s degrees, we collected OCME, medical record, and substance use disorder treatment data for the decedent. Planned analyses include least absolute shrinkage and selection operator regressions of quantitative data and thematic analyses of qualitative data. Results: We identified and interviewed at least one informant (N=141) for each decedent (N=101). Based on feedback during recruitment, we adapted language to improve rapport, including changing the term ?accidental death? to ?premature death,? offering condolences, and providing content warnings. As expected, family members were able to provide more data about the decedent?s childhood and adolescence, and nonfamily informants provided more data regarding events proximal to death. We found that the interviews were stressful for both the interviewee and interviewer, especially when participants thought the study was intrusive or experienced significant grief during the interviews. Conclusions: In developing and implementing PA research on fatal stimulant poisoning, we noted the importance of recruitment language regarding cause of death and condolences with collateral informants. Compassion and respect were critical to facilitate the interview process and maintain an ethical framework. We discuss several barriers to success and lessons learned while conducting PA interviews, as well as recommendations for future PA studies. UR - https://formative.jmir.org/2025/1/e64873 UR - http://dx.doi.org/10.2196/64873 ID - info:doi/10.2196/64873 ER - TY - JOUR AU - Tanaka, Hiroki AU - Miyamoto, Kana AU - Hamet Bagnou, Jennifer AU - Prigent, Elise AU - Clavel, Céline AU - Martin, Jean-Claude AU - Nakamura, Satoshi PY - 2025/1/10 TI - Analysis of Social Performance and Action Units During Social Skills Training: Focus Group Study of Adults With Autism Spectrum Disorder and Schizophrenia JO - JMIR Form Res SP - e59261 VL - 9 KW - social performance rating scale KW - social skills training KW - autism spectrum disorder KW - schizophrenia KW - facial expressions KW - social KW - autism KW - training KW - communication KW - trainers KW - tool KW - neurological N2 - Background: Social communication is a crucial factor influencing human social life. Quantifying the degree of difficulty faced in social communication is necessary for understanding developmental and neurological disorders and for creating systems used in automatic symptom screening and assistive methods such as social skills training (SST). SST by a human trainer is a well-established method. Previous SST used a modified roleplay test to evaluate human social communication skills. However, there are no widely accepted evaluation criteria or social behavioral markers to quantify social performance during SST. Objective: This paper has 2 objectives. First, we propose applying the Social Performance Rating Scale (SPRS) to SST data to measure social communication skills. We constructed a Japanese version of the SPRS already developed in English and French. Second, we attempt to quantify action units during SST for people with autism spectrum disorder (ASD) or schizophrenia. Methods: We used videos of interactions between trainers, adults with ASD (n=16) or schizophrenia (n=15), and control participants (n=19) during SST sessions. Two raters applied the proposed scale to annotate the collected data. We investigated the differences between roleplay tasks and participant groups (ASD, schizophrenia, and control). Furthermore, the intensity of action units on the OpenFace toolkit was measured in terms of mean and SD during SST roleplaying. Results: We found significantly greater gaze scores in adults with ASD than in adults with schizophrenia. Differences were also found between the ratings of different tasks in the adults with schizophrenia and the control participants. Action units numbered AU06 and AU12 were significantly deactivated in people with schizophrenia compared with the control group. Moreover, AU02 was significantly activated in people with ASD compared with the other groups. Conclusions: The results suggest that the SPRS can be a useful tool for assessing social communication skills in different cultures and different pathologies when used with the modified roleplay test. Furthermore, facial expressions could provide effective social and behavioral markers to characterize psychometric properties. Possible future directions include using the SPRS for assessing social behavior during interaction with a digital agent. UR - https://formative.jmir.org/2025/1/e59261 UR - http://dx.doi.org/10.2196/59261 ID - info:doi/10.2196/59261 ER - TY - JOUR AU - Zawada, J. Stephanie AU - Ganjizadeh, Ali AU - Conte, Marco Gian AU - Demaerschalk, M. Bart AU - Erickson, J. Bradley PY - 2025/1/10 TI - Exploring Remote Monitoring of Poststroke Mood With Digital Sensors by Assessment of Depression Phenotypes and Accelerometer Data in UK Biobank: Cross-Sectional Analysis JO - JMIR Neurotech SP - e56679 VL - 4 KW - depression KW - cerebrovascular disease KW - remote monitoring KW - stroke KW - accelerometers KW - mobile phone N2 - Background: Interest in using digital sensors to monitor patients with prior stroke for depression, a risk factor for poor outcomes, has grown rapidly; however, little is known about behavioral phenotypes related to future mood symptoms and if patients with and without previously diagnosed depression experience similar phenotypes. Objective: This study aimed to assess the feasibility of using digital sensors to monitor mood in patients with prior stroke with a prestroke depression diagnosis (DD) and controls. We examined relationships between physical activity behaviors and self-reported depression frequency. Methods: In the UK Biobank wearable accelerometer cohort, we retrospectively identified patients who had previously suffered a stroke (N=1603) and conducted cross-sectional analyses with those who completed a subsequent depression survey follow-up. Sensitivity analyses assessed a general population cohort excluding previous stroke participants and 2 incident cohorts: incident stroke (IS) and incident cerebrovascular disease (IC). Results: In controls, the odds of being in a higher depressed mood frequency category decreased by 23% for each minute spent in moderate?to?vigorous physical activity (odds ratio 0.77, 95% CI 0.69?0.87; P<.001). This association persisted in both general cohorts and in the IC control cohort. Conclusions: Although moderate?to?vigorous physical activity was linked with less frequent depressed mood in patients with prior stroke without DD, this finding did not persist in DDs. Thus, accelerometer-mood monitoring may provide clinically useful insights about future mood in patients with prior stroke without DDs. Considering the finding in the IC cohort and the lack of findings in the IS cohorts, accelerometer-mood monitoring may also be appropriately applied to observing broader cerebrovascular disease pathogenesis. UR - https://neuro.jmir.org/2025/1/e56679 UR - http://dx.doi.org/10.2196/56679 ID - info:doi/10.2196/56679 ER - TY - JOUR AU - Strojny, Pawe? AU - Kapela, Ksawery AU - Lipp, Natalia AU - Sikström, Sverker PY - 2024/12/31 TI - Use of 4 Open-Ended Text Responses to Help Identify People at Risk of Gaming Disorder: Preregistered Development and Usability Study Using Natural Language Processing JO - JMIR Serious Games SP - e56663 VL - 12 KW - gaming disorder KW - natural language processing KW - machine learning KW - mental health KW - NLP KW - text KW - open-ended KW - response KW - risk KW - psychological KW - Question-based Computational Language Assessment KW - QCLA KW - transformers-based KW - language model analysis KW - Polish KW - Pearson KW - correlation KW - Python N2 - Background: Words are a natural way to describe mental states in humans, while numerical values are a convenient and effective way to carry out quantitative psychological research. With the growing interest of researchers in gaming disorder, the number of screening tools is growing. However, they all require self-quantification of mental states. The rapid development of natural language processing creates an opportunity to supplement traditional rating scales with a question-based computational language assessment approach that gives a deeper understanding of the studied phenomenon without losing the rigor of quantitative data analysis. Objective: The aim of the study was to investigate whether transformer-based language model analysis of text responses from active gamers is a potential supplement to traditional rating scales. We compared a tool consisting of 4 open-ended questions formulated based on the clinician's intuition (not directly related to any existing rating scales for measuring gaming disorders) with the results of one of the commonly used rating scales. Methods: Participants recruited using an online panel were asked to answer the Word-Based Gaming Disorder Test, consisting of 4 open-ended questions about gaming. Subsequently, they completed a closed-ended Gaming Disorders Test based on a numerical scale. Of the initial 522 responses collected, we removed a total of 105 due to 1 of 3 criteria (suspiciously low survey completion time, providing nonrelevant or incomplete responses). Final analyses were conducted on the responses of 417 participants. The responses to the open-ended questions were vectorized using HerBERT, a large language model based on Google's Bidirectional Encoder Representations from Transformers (BERT). Last, a machine learning model, specifically ridge regression, was used to predict the scores of the Gaming Disorder Test based on the features of the vectorized open-ended responses. Results: The Pearson correlation between the observable scores from the Gaming Disorder test and the predictions made by the model was 0.476 when using the answers of the 4 respondents as features. When using only 1 of the 4 text responses, the correlation ranged from 0.274 to 0.406. Conclusions: Short open responses analyzed using natural language processing can contribute to a deeper understanding of gaming disorder at no additional cost in time. The obtained results confirmed 2 of 3 preregistered hypotheses. The written statements analyzed using the results of the model correlated with the rating scale. Furthermore, the inclusion in the model of data from more responses that take into account different perspectives on gaming improved the performance of the model. However, there is room for improvement, especially in terms of supplementing the questions with content that corresponds more directly to the definition of gaming disorder. Trial Registration: OSF Registries osf.io/957nz; https://osf.io/957nz UR - https://games.jmir.org/2024/1/e56663 UR - http://dx.doi.org/10.2196/56663 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56663 ER - TY - JOUR AU - Tartaglia, Julia AU - Jaghab, Brendan AU - Ismail, Mohamed AU - Hänsel, Katrin AU - Meter, Van Anna AU - Kirschenbaum, Michael AU - Sobolev, Michael AU - Kane, M. John AU - Tang, X. Sunny PY - 2024/12/30 TI - Assessing Health Technology Literacy and Attitudes of Patients in an Urban Outpatient Psychiatry Clinic: Cross-Sectional Survey Study JO - JMIR Ment Health SP - e63034 VL - 11 KW - digital literacy KW - attitudes KW - mental health KW - digital health technology KW - cluster analysis KW - psychiatry KW - mobile phone N2 - Background: Digital health technologies are increasingly being integrated into mental health care. However, the adoption of these technologies can be influenced by patients? digital literacy and attitudes, which may vary based on sociodemographic factors. This variability necessitates a better understanding of patient digital literacy and attitudes to prevent a digital divide, which can worsen existing health care disparities. Objective: This study aimed to assess digital literacy and attitudes toward digital health technologies among a diverse psychiatric outpatient population. In addition, the study sought to identify clusters of patients based on their digital literacy and attitudes, and to compare sociodemographic characteristics among these clusters. Methods: A survey was distributed to adult psychiatric patients with various diagnoses in an urban outpatient psychiatry program. The survey included a demographic questionnaire, a digital literacy questionnaire, and a digital health attitudes questionnaire. Multiple linear regression analyses were used to identify predictors of digital literacy and attitudes. Cluster analysis was performed to categorize patients based on their responses. Pairwise comparisons and one-way ANOVA were conducted to analyze differences between clusters. Results: A total of 256 patients were included in the analysis. The mean age of participants was 32 (SD 12.6, range 16-70) years. The sample was racially and ethnically diverse: White (100/256, 38.9%), Black (39/256, 15.2%), Latinx (44/256, 17.2%), Asian (59/256, 23%), and other races and ethnicities (15/256, 5.7%). Digital literacy was high for technologies such as smartphones, videoconferencing, and social media (items with >75%, 193/256 of participants reporting at least some use) but lower for health apps, mental health apps, wearables, and virtual reality (items with <42%, 108/256 reporting at least some use). Attitudes toward using technology in clinical care were generally positive (9 out of 10 items received >75% positive score), particularly for communication with providers and health data sharing. Older age (P<.001) and lower educational attainment (P<.001) negatively predicted digital literacy scores, but no demographic variables predicted attitude scores. Cluster analysis identified 3 patient groups. Relative to the other clusters, cluster 1 (n=30) had lower digital literacy and intermediate acceptance of digital technology. Cluster 2 (n=50) had higher literacy and lower acceptance. Cluster 3 (n=176) displayed both higher literacy and acceptance. Significant between-cluster differences were observed in mean age and education level between clusters (P<.001), with cluster 1 participants being older and having lower levels of formal education. Conclusions: High digital literacy and acceptance of digital technologies were observed among our patients, indicating a generally positive outlook for digital health clinics. Our results also found that patients of older age and lower formal levels of educational attainment had lower digital literacy, highlighting the need for targeted interventions to support those who may struggle with adopting digital health tools. UR - https://mental.jmir.org/2024/1/e63034 UR - http://dx.doi.org/10.2196/63034 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63034 ER - TY - JOUR AU - Loh, Yi Pei AU - Martinengo, Laura AU - Heaukulani, Creighton AU - Tan, Yang Xin AU - Hng, Moses AU - Cheah, Yin Yong AU - Morris, T. Robert J. AU - Tudor Car, Lorainne AU - Lee, Jimmy PY - 2024/12/23 TI - Characteristics and Outcomes of mHealth Interventions in Psychosis: Systematic Mapping Review JO - J Med Internet Res SP - e55924 VL - 26 KW - mHealth KW - digital health KW - mobile applications KW - psychosis KW - schizophrenia KW - schizophrenia spectrum KW - psychotic disorders KW - mapping review N2 - Background: Mobile health (mHealth) interventions have gained popularity in augmenting psychiatric care for adults with psychosis. Interest has grown in leveraging mHealth to empower individuals living with severe mental illness and extend continuity of care beyond the hospital to the community. However, reported outcomes have been mixed, likely attributed in part to the intervention and adopted outcomes, which affected between-study comparisons. Objective: This study aimed to critically review outcome measures used to evaluate mHealth interventions for adults with psychosis in relation to the characteristics of mHealth interventions. Methods: A systematic mapping review was conducted. We searched PubMed, CINAHL, Embase, PsycINFO, and Cochrane Libraries from 1973 to the present. Selection criteria included randomized controlled studies of mHealth interventions in adults diagnosed with schizophrenia spectrum disorders. Reviewers worked in pairs to screen and extract data from included studies independently using a standardized form; disagreements were resolved by consensus with an independent reviewer. We report our findings in line with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results: A total of 1703 citations were screened; 29 publications reporting on 23 studies were included in this review. mHealth interventions for psychosis span a wide range, with psychological therapy being the most-deployed intervention (12/23, 52%), followed by psychoeducation (8/23, 35%) and active self-monitoring (8/23, 35%). Several mHealth interventions for psychosis targeted multiple pillars of biopsychosocial well-being (10/23, 43%); the bulk of interventions (16/23, 70%) incorporated features promoting users? self-management. The majority of mHealth interventions were delivered through applications (14/23, 61%) as the main medium and smartphones (17/23, 74%) as the main channel of delivery. Interventions were primarily administered in the outpatient and community settings (16/23, 70%); many were also blended with in-person sessions (11/23, 48%) or guided remotely (6/23, 26%) by persons, including health care providers or trained peer supporters. The severity of psychosis-related symptoms (21/23, 91%) was the most prevalent outcome, of which positive symptoms (13/23, 57%), mood and anxiety (10/23, 43%), and overall psychopathology severity (9/23, 39%) were most commonly measured. Patient-centric outcomes, including well-being (17/23, 74%)?particularly quality of life (10/23, 43%)?and user experience (15/23, 65%), including feasibility (7/23, 30%), acceptability (7/23, 30%), and engagement (7/23, 26%). Notably, outcome choices remained diverse despite stratification by type of mHealth intervention. Conclusions: mHealth interventions for psychosis encompass a wide range of modalities and use outcome measures that probe various social and behavioral determinants of health. These should be considered complex interventions, and a holistic evaluation approach combining clinical and patient-centric outcomes is recommended. UR - https://www.jmir.org/2024/1/e55924 UR - http://dx.doi.org/10.2196/55924 UR - http://www.ncbi.nlm.nih.gov/pubmed/39714907 ID - info:doi/10.2196/55924 ER - TY - JOUR AU - Hanach, Nivine AU - Saqan, Roba AU - Radwan, Hadia AU - Baniissa, Wegdan AU - de Vries, Nanne PY - 2024/12/16 TI - Perceived Experiences and Needs of Digital Resources Among Postpartum Women in the United Arab Emirates: Qualitative Focus Group Study JO - J Med Internet Res SP - e53720 VL - 26 KW - digital health KW - social support KW - telemedicine KW - postpartum women KW - focus group KW - maternal health KW - postpartum mental health KW - postpartum depression KW - emotional support KW - health information N2 - Background: The postpartum period is a critical phase in a woman's life, marked by various physical, psychological, and social challenges. In light of the rapid proliferation and uptake of digital technologies, particularly in the United Arab Emirates (UAE), mothers increasingly seek informational and emotional support from digital resources. No previous study has thoroughly explored the usage of various digital resources beyond telehealth services in the UAE. This literature gap is particularly relevant for the postpartum period, which remains largely understudied in the UAE. Objective: This study aims to delve into the digital experiences of postpartum women in the UAE by exploring the types of resources they navigate and the purposes those resources serve. In addition, it seeks to identify their perspectives and needs regarding digital resources that support their postpartum journey. Methods: Four focus groups were conducted synchronously on the web, involving a total of 27 multicultural mothers (mean age 32.47, SD 4.56 years), between 2 and 12 months post partum and living in the UAE. Descriptive interpretive thematic analysis was used to analyze the data. Results: Sixteen out of 27 women exhibited severe depressive symptoms at the time of the discussions (Edinburgh Postnatal Depression Scale score of >12). Two main themes were generated from the analysis: (1) Mothers? Experiences with Digital Resources: Participants valued digital resources for providing immediate information, convenience, and support. They primarily used these resources to seek information on infant health, parenting advice, and emotional support through web-based communities. However, the abundance of conflicting information and the pressure to conform to health recommendations often created stress and anxiety. (2) The Perceived Need for Digital Resources: Despite their extensive use of digital resources, mothers articulated the need for a reliable UAE government digital platform tailored specifically to postpartum care, offering trusted information on infant health and postpartum mental well-being. They also emphasized the need for tailored postpartum telemedicine services and moderated web-based discussion forums to foster peer support among mothers. Conclusions: This study reveals the multifaceted role of digital resources in supporting mothers during the postpartum period, highlighting unmet needs that present opportunities for advancing postpartum care in the UAE. It demonstrates the importance of developing reliable digital solutions for postpartum women, especially regarding mental health and to enhance access to care through tailored telemedicine services. Collaborative efforts are required to ensure the implementation of user-centered digital platforms. Future research should focus on the diverse needs of postpartum women, including cultural sensitivity, the feasibility of telemedicine services, and the integration of partner support in digital interventions to improve maternal health outcomes. UR - https://www.jmir.org/2024/1/e53720 UR - http://dx.doi.org/10.2196/53720 UR - http://www.ncbi.nlm.nih.gov/pubmed/39680428 ID - info:doi/10.2196/53720 ER - TY - JOUR AU - Southwick, Lauren AU - Sharma, Meghana AU - Rai, Sunny AU - Beidas, S. Rinad AU - Mandell, S. David AU - Asch, A. David AU - Curtis, Brenda AU - Guntuku, Chandra Sharath AU - Merchant, M. Raina PY - 2024/12/16 TI - Integrating Patient-Generated Digital Data Into Mental Health Therapy: Mixed Methods Analysis of User Experience JO - JMIR Ment Health SP - e59785 VL - 11 KW - digital data KW - social media KW - psychotherapy KW - latent Dirichlet allocation KW - LDA KW - mobile phone N2 - Background: Therapists and their patients increasingly discuss digital data from social media, smartphone sensors, and other online engagements within the context of psychotherapy. Objective: We examined patients? and mental health therapists? experiences and perceptions following a randomized controlled trial in which they both received regular summaries of patients? digital data (eg, dashboard) to review and discuss in session. The dashboard included data that patients consented to share from their social media posts, phone usage, and online searches. Methods: Following the randomized controlled trial, patient (n=56) and therapist (n=44) participants completed a debriefing survey after their study completion (from December 2021 to January 2022). Participants were asked about their experience receiving a digital data dashboard in psychotherapy via closed- and open-ended questions. We calculated descriptive statistics for closed-ended questions and conducted qualitative coding via NVivo (version 10; Lumivero) and natural language processing using the machine learning tool latent Dirichlet allocation to analyze open-ended questions. Results: Of 100 participants, nearly half (n=48, 49%) described their experience with the dashboard as ?positive,? while the other half noted a ?neutral? experience. Responses to the open-ended questions resulted in three thematic areas (nine subcategories): (1) dashboard experience (positive, neutral or negative, and comfortable); (2) perception of the dashboard?s impact on enhancing therapy (accountability, increased awareness over time, and objectivity); and (3) dashboard refinements (additional sources, tailored content, and privacy). Conclusions: Patients reported that receiving their digital data helped them stay ?accountable,? while therapists indicated that the dashboard helped ?tailor treatment plans.? Patient and therapist surveys provided important feedback on their experience regularly discussing dashboards in psychotherapy. Trial Registration: ClinicalTrials.gov NCT04011540; https://clinicaltrials.gov/study/NCT04011540 UR - https://mental.jmir.org/2024/1/e59785 UR - http://dx.doi.org/10.2196/59785 ID - info:doi/10.2196/59785 ER - TY - JOUR AU - Portillo-Van Diest, Ana AU - Mortier, Philippe AU - Ballester, Laura AU - Amigo, Franco AU - Carrasco, Paula AU - Falcó, Raquel AU - Gili, Margalida AU - Kiekens, Glenn AU - H Machancoses, Francisco AU - Piqueras, A. Jose AU - Rebagliato, Marisa AU - Roca, Miquel AU - Rodríguez-Jiménez, Tíscar AU - Alonso, Jordi AU - Vilagut, Gemma PY - 2024/12/10 TI - Ecological Momentary Assessment of Mental Health Problems Among University Students: Data Quality Evaluation Study JO - J Med Internet Res SP - e55712 VL - 26 KW - experience sampling method KW - ecological momentary assessment KW - mental health KW - university students KW - participation KW - compliance KW - reliability KW - sensitivity analysis KW - mobile phone N2 - Background: The use of ecological momentary assessment (EMA) designs has been on the rise in mental health epidemiology. However, there is a lack of knowledge of the determinants of participation in and compliance with EMA studies, reliability of measures, and underreporting of methodological details and data quality indicators. Objective: This study aims to evaluate the quality of EMA data in a large sample of university students by estimating participation rate and mean compliance, identifying predictors of individual-level participation and compliance, evaluating between- and within-person reliability of measures of negative and positive affect, and identifying potential careless responding. Methods: A total of 1259 university students were invited to participate in a 15-day EMA study on mental health problems. Logistic and Poisson regressions were used to investigate the associations between sociodemographic factors, lifetime adverse experiences, stressful events in the previous 12 months, and mental disorder screens and EMA participation and compliance. Multilevel reliability and intraclass correlation coefficients were obtained for positive and negative affect measures. Careless responders were identified based on low compliance or individual reliability coefficients. Results: Of those invited, 62.1% (782/1259) participated in the EMA study, with a mean compliance of 76.9% (SD 27.7%). Participation was higher among female individuals (odds ratio [OR] 1.41, 95% CI 1.06-1.87) and lower among those aged ?30 years (OR 0.20, 95% CI 0.08-0.43 vs those aged 18-21 years) and those who had experienced the death of a friend or family member in the previous 12 months (OR 0.73, 95% CI 0.57-0.94) or had a suicide attempt in the previous 12 months (OR 0.26, 95% CI 0.10-0.64). Compliance was particularly low among those exposed to sexual abuse before the age of 18 years (exponential of ?=0.87) or to sexual assault or rape in the previous year (exponential of ?=0.80) and among those with 12-month positive alcohol use disorder screens (exponential of ?=0.89). Between-person reliability of negative and positive affect was strong (RkRn>0.97), whereas within-person reliability was fair to moderate (Rcn>0.43). Of all answered assessments, 0.86% (291/33,626) were flagged as careless responses because the response time per item was <1 second or the participants gave the same response to all items. Of the participants, 17.5% (137/782) could be considered careless responders due to low compliance (<25/56, 45%) or very low to null individual reliability (raw Cronbach ?<0.11) for either negative or positive affect. Conclusions: Data quality assessments should be carried out in EMA studies in a standardized manner to provide robust conclusions to advance the field. Future EMA research should implement strategies to mitigate nonresponse bias as well as conduct sensitivity analyses to assess possible exclusion of careless responders. UR - https://www.jmir.org/2024/1/e55712 UR - http://dx.doi.org/10.2196/55712 UR - http://www.ncbi.nlm.nih.gov/pubmed/39657180 ID - info:doi/10.2196/55712 ER - TY - JOUR AU - Perdacher, Elke AU - Kavanagh, David AU - Sheffield, Jeanie AU - Dale, Penny AU - Heffernan, Edward PY - 2024/12/6 TI - The Use of a Digital Well-Being App (Stay Strong App) With Indigenous People in Prison: Randomized Controlled Trial JO - JMIR Ment Health SP - e53280 VL - 11 KW - First Nations KW - Indigenous KW - digital mental health KW - e-mental health KW - mental health KW - social and emotional well-being KW - SEWB KW - prisoner KW - prison N2 - Background: Indigenous Australians in custody experience much greater rates of poor mental health and well-being than those of the general community, and these problems are not adequately addressed. Digital mental health strategies offer innovative opportunities to address the problems, but little is known about their feasibility in or impact on this population. Objective: This study aims to conduct a pilot trial evaluating the impact of adding the Stay Strong app to mental health and well-being services for Indigenous women and men in custody. The trial compared immediate and 3-month delayed use of the app by the health service, assessing its effects on well-being, empowerment, and psychological distress at 3 and 6 months after the baseline. Methods: Indigenous participants were recruited from 3 high-security Australian prisons from January 2017 to September 2019. The outcome measures assessed well-being (Warwick-Edinburgh Mental Wellbeing Scale), empowerment (Growth and Empowerment Measure [GEM]?giving total, 14-item Emotional Empowerment Scale, and 12 Scenarios scores), and psychological distress (Kessler Psychological Distress Scale). Intention-to-treat effects on these outcomes were analyzed using linear mixed models. Results: Substantial challenges in obtaining ethical and institutional approval for the trial were encountered, as were difficulties in timely recruitment and retention due to staff shortages and the release of participants from prison before follow-up assessments and an inability to follow up with participants after release. A total of 132 prisoners (age: mean 33, SD 8 y) were randomized into either an immediate (n=82) or a delayed treatment (n=52) group. However, only 56 (42.4%) could be assessed at 3 months and 37 (28%) at 6 months, raising questions concerning the representativeness of the results. Linear improvements over time were seen in all outcomes (GEM total: Cohen d=0.99; GEM 14-item Emotional Empowerment Scale: Cohen d=0.94; GEM 12 Scenarios: Cohen d=0.87; Warwick-Edinburgh Mental Wellbeing Scale: Cohen d=0.76; Kessler Psychological Distress Scale: Cohen d=0.49), but no differential effects for group or the addition of the Stay Strong app were found. Conclusions: We believe this to be Australia?s first evaluation of a digital mental health app in prison and the first among Indigenous people in custody. While the study demonstrated that the use of a well-being app within a prison was feasible, staff shortages led to delayed recruitment and a consequent low retention, and significant beneficial effects of the app?s use within a forensic mental health service were not seen. Additional staff resources and a longer intervention may be needed to allow a demonstration of satisfactory retention and impact in future research. Trial Registration: ANZCTR ACTRN12624001261505; https://www.anzctr.org.au/ACTRN12624001261505.aspx UR - https://mental.jmir.org/2024/1/e53280 UR - http://dx.doi.org/10.2196/53280 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53280 ER - TY - JOUR AU - Foran, M. Heather AU - Kubb, Christian AU - Mueller, Janina AU - Poff, Spencer AU - Ung, Megan AU - Li, Margaret AU - Smith, Michael Eric AU - Akinyemi, Akinniyi AU - Kambadur, Melanie AU - Waller, Franziska AU - Graf, Mario AU - Boureau, Y-Lan PY - 2024/12/6 TI - An Automated Conversational Agent Self-Help Program: Randomized Controlled Trial JO - J Med Internet Res SP - e53829 VL - 26 KW - well-being KW - chatbot KW - randomized controlled trial KW - prevention KW - flourishing N2 - Background: Health promotion and growth-based interventions can effectively improve individual well-being; however, significant gaps in access and utilization still exist. Objective: This study aims to develop and test the effectiveness and implementation of a new, widely targeted conversational agent prevention program (Zenny) designed to enhance well-being. Methods: A total of 1345 individuals in the United States were recruited online and randomly assigned to either (1) a self-help program intervention delivered via an automated conversational agent on WhatsApp or (2) an active control group that had access to evidence-based wellness resources available online. The primary outcomes were well-being (measured using the 5-item World Health Organization Well-being Scale), psychosocial flourishing (assessed with the Flourishing Scale), and positive psychological health (evaluated with the Mental Health Continuum-Short Form). Outcome measures were collected at baseline and again 1 month postassessment. All analyses were conducted using an intention-to-treat approach. Results: Both groups showed significant improvements in well-being (self-help program intervention group effect size: Cohen d=0.26, P<.001; active control group effect size: d=0.24, P<.001), psychosocial flourishing (intervention: d=0.19, P<.001; active control: d=0.18, P<.001), and positive psychological health (intervention: d=0.17, P=.001; active control: d=0.24, P<.001) at postassessment. However, there were no significant differences in effectiveness between the 2 groups (P ranged from .56 to .92). As hypothesized a priori, a greater number of days spent actively engaging with the conversational agent was associated with larger improvements in well-being at postassessment among participants in the intervention group (?=.109, P=.04). Conclusions: The findings from this study suggest that the free conversational agent wellness self-help program was as effective as evidence-based web resources. Further research should explore strategies to increase participant engagement over time, as only a portion of participants were actively involved, and higher engagement was linked to greater improvements in well-being. Long-term follow-up studies are also necessary to assess whether these effects remain stable over time. Trial Registration: ClinicalTrials.gov NCT06208566; https://clinicaltrials.gov/ct2/show/NCT06208566; OSF Registries osf.io/ahe2r; https://doi.org/10.17605/osf.io/ahe2r UR - https://www.jmir.org/2024/1/e53829 UR - http://dx.doi.org/10.2196/53829 UR - http://www.ncbi.nlm.nih.gov/pubmed/39641985 ID - info:doi/10.2196/53829 ER - TY - JOUR AU - Shojaei, Fereshtehossadat AU - Shojaei, Fatemehalsadat AU - Osorio Torres, John AU - Shih, C. Patrick PY - 2024/12/4 TI - Insights From Art Therapists on Using AI-Generated Art in Art Therapy: Mixed Methods Study JO - JMIR Form Res SP - e63038 VL - 8 KW - art therapy KW - artificial intelligence KW - AI KW - therapeutic interventions KW - assistive AI KW - engagement KW - health care KW - therapy KW - art KW - therapists' insights KW - daily life KW - practitioner KW - assistive KW - AI-generated image KW - accessibility KW - therapy sessions KW - AI-generated tool N2 - Background: With the increasing integration of artificial intelligence (AI) into various aspects of daily life, there is a growing interest among designers and practitioners in incorporating AI into their fields. In health care domains like art therapy, AI is also becoming a subject of exploration. However, the use of AI in art therapy is still undergoing investigation, with its benefits and challenges being actively explored. Objective: This study aims to investigate the integration of AI into art therapy practices to comprehend its potential impact on therapeutic processes and outcomes. Specifically, the focus is on understanding the perspectives of art therapists regarding the use of AI-assisted tools in their practice with clients, as demonstrated through the presentation of our prototype consisting of a deck of cards with words covering various categories alongside an AI-generated image. Methods: Using a co-design approach, 10 art therapists affiliated with the American Art Therapy Association participated in this study. They engaged in individual interviews where they discussed their professional perspectives on integrating AI into their therapeutic approaches and evaluating the prototype. Qualitative analysis was conducted to derive themes and insights from these sessions. Results: The study began in August 2023, with data collection involving 10 participants taking place in October 2023. Our qualitative findings provide a comprehensive evaluation of the impact of AI on facilitating therapeutic processes. The combination of a deck of cards and the use of an AI-generated tool demonstrated an enhancement in the quality and accessibility of therapy sessions. However, challenges such as credibility and privacy concerns were also identified. Conclusions: The integration of AI into art therapy presents promising avenues for innovation and progress within the field. By gaining insights into the perspectives and experiences of art therapists, this study contributes knowledge for both practical application and further research. UR - https://formative.jmir.org/2024/1/e63038 UR - http://dx.doi.org/10.2196/63038 ID - info:doi/10.2196/63038 ER - TY - JOUR AU - Gariepy, Genevieve AU - Zahan, Rifat AU - Osgood, D. Nathaniel AU - Yeoh, Benjamin AU - Graham, Eva AU - Orpana, Heather PY - 2024/12/2 TI - Dynamic Simulation Models of Suicide and Suicide-Related Behaviors: Systematic Review JO - JMIR Public Health Surveill SP - e63195 VL - 10 KW - suicide KW - agent-based modeling KW - complex system KW - complexity science KW - discrete-event simulation KW - dynamic modeling KW - microsimulation KW - system dynamics KW - systems science KW - qualitative study KW - dynamic simulation KW - database KW - depression KW - mental state KW - systematic review KW - stress N2 - Background: Suicide remains a public health priority worldwide with over 700,000 deaths annually, ranking as a leading cause of death among young adults. Traditional research methodologies have often fallen short in capturing the multifaceted nature of suicide, focusing on isolated risk factors rather than the complex interplay of individual, social, and environmental influences. Recognizing these limitations, there is a growing recognition of the value of dynamic simulation modeling to inform suicide prevention planning. Objective: This systematic review aims to provide a comprehensive overview of existing dynamic models of population-level suicide and suicide-related behaviors, and to summarize their methodologies, applications, and outcomes. Methods: Eight databases were searched, including MEDLINE, Embase, PsycINFO, Scopus, Compendex, ACM Digital Library, IEEE Xplore, and medRxiv, from inception to July 2023. We developed a search strategy in consultation with a research librarian. Two reviewers independently conducted the title and abstract and full-text screenings including studies using dynamic modeling methods (eg, System Dynamics and agent-based modeling) for suicide or suicide-related behaviors at the population level, and excluding studies on microbiology, bioinformatics, pharmacology, nondynamic modeling methods, and nonprimary modeling reports (eg, editorials and reviews). Reviewers extracted the data using a standardized form and assessed the quality of reporting using the STRESS (Strengthening the Reporting of Empirical Simulation Studies) guidelines. A narrative synthesis was conducted for the included studies. Results: The search identified 1574 studies, with 22 studies meeting the inclusion criteria, including 15 System Dynamics models, 6 agent-based models, and 1 microsimulation model. The studies primarily targeted populations in Australia and the United States, with some focusing on hypothetical scenarios. The models addressed various interventions ranging from specific clinical and health service interventions, such as mental health service capacity increases, to broader social determinants, including employment programs and reduction in access to means of suicide. The studies demonstrated the utility of dynamic models in identifying the synergistic effects of combined interventions and understanding the temporal dynamics of intervention impacts. Conclusions: Dynamic modeling of suicide and suicide-related behaviors, though still an emerging area, is expanding rapidly, adapting to a range of questions, settings, and contexts. While the quality of reporting was overall adequate, some studies lacked detailed reporting on model transparency and reproducibility. This review highlights the potential of dynamic modeling as a tool to support decision-making and to further our understanding of the complex dynamics of suicide and its related behaviors. Trial Registration: PROSPERO CRD42022346617; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346617 UR - https://publichealth.jmir.org/2024/1/e63195 UR - http://dx.doi.org/10.2196/63195 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63195 ER - TY - JOUR AU - Stefana, Alberto AU - Fusar-Poli, Paolo AU - Vieta, Eduard AU - Youngstrom, A. Eric PY - 2024/11/27 TI - Effectiveness of a Novel Web-Based Intervention to Enhance Therapeutic Relationships and Treatment Outcomes in Adult Individual Psychotherapy: Randomized Controlled Trial and Analysis of Predictors of Dropouts JO - JMIR Ment Health SP - e63234 VL - 11 KW - therapeutic relationship KW - real relationship KW - routine outcome monitoring KW - measurement-based care KW - patient-focused research KW - psychotherapy process KW - randomized controlled trial KW - RCT KW - psychotherapy KW - assessment KW - mental health KW - digital mental health KW - eHealth KW - self monitoring KW - outcomes research KW - digital health KW - health intervention KW - therapy N2 - Background: Routine process and outcome monitoring interventions added to psychotherapy are known to improve treatment outcomes, although they vary in format and effectiveness. Objective: This study aimed to evaluate whether a therapist-independent, internet-based routine process monitoring and feedback system could significantly reduce psychological distress and enhance the quality of the therapeutic relationship compared with a treatment-as-usual control group among individuals already engaged in individual psychotherapy. Methods: We randomized 475 participants into either the intervention group, which received access to an internet-based routine process monitoring and feedback system in addition to psychotherapy, or the control group, which received only psychotherapy. The trial lasted for 10 weeks. Follow-up assessments at 5 weeks and 10 weeks used the Clinical Outcomes in Routine Evaluation-Outcome Measure as the primary outcome, with the Working Alliance Inventory-Short Revised and the Real Relationship Inventory-Client form as secondary outcomes. Results: Per-protocol analyses (n=166) showed that psychological distress decreased in both groups, but there was no significant advantage for the intervention group. The intervention group experienced a decline in the genuineness dimension score of the real relationship, with an effect size of d=?0.27, compared with d=0.01 in the control group. In the intervention group (but not in the control group), dropouts showed significantly lower real relationship levels (P=.002), working alliance quality (P=.051), and emotional disclosure (P=.01) compared with those who completed the study. Additionally, logistic regression revealed distinct predictors of dropout within the control group and intervention group. Conclusions: The findings do not provide conclusive evidence for the efficacy of the new internet-based intervention in enhancing self-monitoring and prompting reflection on patients? emotional responses to their therapists. However, the intervention appears to influence patients? perceptions of the genuineness dimension in the therapeutic relationship, warranting further investigation. We hypothesize that this alteration in the genuineness dimension could be attributed to the intervention facilitating a more realistic and accurate perception of the therapeutic relationship among participants. Trial Registration: ClinicalTrials.gov NCT06038747; https://clinicaltrials.gov/study/NCT06038747 International Registered Report Identifier (IRRID): RR2-10.2196/55369 UR - https://mental.jmir.org/2024/1/e63234 UR - http://dx.doi.org/10.2196/63234 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63234 ER - TY - JOUR AU - Oh, Soyeon Sarah AU - Kang, Bada AU - Hong, Dahye AU - Kim, Ivy Jennifer AU - Jeong, Hyewon AU - Song, Jinyeop AU - Jeon, Minkyu PY - 2024/11/22 TI - A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation JO - JMIR Med Inform SP - e59396 VL - 12 KW - mild cognitive impairment KW - machine learning algorithms KW - sociodemographic factors KW - gerontology KW - geriatrics KW - older people KW - aging KW - MCI KW - dementia KW - Alzheimer KW - cognitive KW - machine learning KW - prediction KW - algorithm N2 - Background: Mild cognitive impairment (MCI) poses significant challenges in early diagnosis and timely intervention. Underdiagnosis, coupled with the economic and social burden of dementia, necessitates more precise detection methods. Machine learning (ML) algorithms show promise in managing complex data for MCI and dementia prediction. Objective: This study assessed the predictive accuracy of ML models in identifying the onset of MCI and dementia using the Korean Longitudinal Study of Aging (KLoSA) dataset. Methods: This study used data from the KLoSA, a comprehensive biennial survey that tracks the demographic, health, and socioeconomic aspects of middle-aged and older Korean adults from 2018 to 2020. Among the 6171 initial households, 4975 eligible older adult participants aged 60 years or older were selected after excluding individuals based on age and missing data. The identification of MCI and dementia relied on self-reported diagnoses, with sociodemographic and health-related variables serving as key covariates. The dataset was categorized into training and test sets to predict MCI and dementia by using multiple models, including logistic regression, light gradient-boosting machine, XGBoost (extreme gradient boosting), CatBoost, random forest, gradient boosting, AdaBoost, support vector classifier, and k-nearest neighbors, and the training and test sets were used to evaluate predictive performance. The performance was assessed using the area under the receiver operating characteristic curve (AUC). Class imbalances were addressed via weights. Shapley additive explanation values were used to determine the contribution of each feature to the prediction rate. Results: Among the 4975 participants, the best model for predicting MCI onset was random forest, with a median AUC of 0.6729 (IQR 0.3883-0.8152), followed by k-nearest neighbors with a median AUC of 0.5576 (IQR 0.4555-0.6761) and support vector classifier with a median AUC of 0.5067 (IQR 0.3755-0.6389). For dementia onset prediction, the best model was XGBoost, achieving a median AUC of 0.8185 (IQR 0.8085-0.8285), closely followed by light gradient-boosting machine with a median AUC of 0.8069 (IQR 0.7969-0.8169) and AdaBoost with a median AUC of 0.8007 (IQR 0.7907-0.8107). The Shapley values highlighted pain in everyday life, being widowed, living alone, exercising, and living with a partner as the strongest predictors of MCI. For dementia, the most predictive features were other contributing factors, education at the high school level, education at the middle school level, exercising, and monthly social engagement. Conclusions: ML algorithms, especially XGBoost, exhibited the potential for predicting MCI onset using KLoSA data. However, no model has demonstrated robust accuracy in predicting MCI and dementia. Sociodemographic and health-related factors are crucial for initiating cognitive conditions, emphasizing the need for multifaceted predictive models for early identification and intervention. These findings underscore the potential and limitations of ML in predicting cognitive impairment in community-dwelling older adults. UR - https://medinform.jmir.org/2024/1/e59396 UR - http://dx.doi.org/10.2196/59396 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59396 ER - TY - JOUR AU - Ding, Huitong AU - Gifford, Katherine AU - Shih, C. Ludy AU - Ho, Kristi AU - Rahman, Salman AU - Igwe, Akwaugo AU - Low, Spencer AU - Popp, Zachary AU - Searls, Edward AU - Li, Zexu AU - Madan, Sanskruti AU - Burk, Alexa AU - Hwang, H. Phillip AU - Anda-Duran, De Ileana AU - Kolachalama, B. Vijaya AU - Au, Rhoda AU - Lin, Honghuang PY - 2024/11/18 TI - Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study JO - JMIR Form Res SP - e60453 VL - 8 KW - digital brain health KW - older adults KW - perspectives KW - semistructured interviews KW - natural language processing KW - mobile phone N2 - Background: Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investigated. Objective: This study aims to understand the perspectives of older adults on a digital brain health platform by conducting semistructured interviews and analyzing their transcriptions by natural language processing. Methods: The study included 28 participants from the Boston University Alzheimer?s Disease Research Center, all of whom engaged with a digital brain health platform over an initial assessment period of 14 days. Semistructured interviews were conducted to collect data on participants? experiences with the digital brain health platform. The transcripts generated from these interviews were analyzed using natural language processing techniques. The frequency of positive and negative terms was evaluated through word count analysis. A sentiment analysis was used to measure the emotional tone and subjective perceptions of the participants toward the digital platform. Results: Word count analysis revealed a generally positive sentiment toward the digital platform, with ?like,? ?well,? and ?good? being the most frequently mentioned positive terms. However, terms such as ?problem? and ?hard? indicated certain challenges faced by participants. Sentiment analysis showed a slightly positive attitude with a median polarity score of 0.13 (IQR 0.08-0.15), ranging from ?1 (completely negative) to 1 (completely positive), and a median subjectivity score of 0.51 (IQR 0.47-0.53), ranging from 0 (completely objective) to 1 (completely subjective). These results suggested an overall positive attitude among the study cohort. Conclusions: The study highlights the importance of understanding older adults? attitudes toward digital health platforms amid the comprehensive evolution of the digitalization era. Future research should focus on refining digital solutions to meet the specific needs of older adults, fostering a more personalized approach to brain health. UR - https://formative.jmir.org/2024/1/e60453 UR - http://dx.doi.org/10.2196/60453 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60453 ER - TY - JOUR AU - Owen, David AU - Lynham, J. Amy AU - Smart, E. Sophie AU - Pardiñas, F. Antonio AU - Camacho Collados, Jose PY - 2024/11/15 TI - AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges JO - J Med Internet Res SP - e59225 VL - 26 KW - mental health KW - depression KW - anxiety KW - schizophrenia KW - social media KW - natural language processing KW - narrative review N2 - Background: Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care. Objective: This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence?driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness. Methods: A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review. Results: Larger datasets with precise dates of participants? diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone. Conclusions: Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user?s depression and anxiety is merited. UR - https://www.jmir.org/2024/1/e59225 UR - http://dx.doi.org/10.2196/59225 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59225 ER - TY - JOUR AU - Mao, Qian AU - Zhao, Zhen AU - Yu, Lisha AU - Zhao, Yang AU - Wang, Hailiang PY - 2024/11/12 TI - The Effects of Virtual Reality?Based Reminiscence Therapies for Older Adults With Cognitive Impairment: Systematic Review JO - J Med Internet Res SP - e53348 VL - 26 KW - virtual reality KW - reminiscence therapy KW - cognitive impairment KW - older adults KW - mobile phone N2 - Background: Reminiscence therapy (RT) is a commonly used nonpharmaceutical treatment for cognitive impairment. Artifacts or conversations are used in RT to recall individuals? memories and past experiences. Virtual reality (VR) has increasingly been used as an assistive technology during RT. However, the effects of VR-based RT (VR-RT) methods remain unclear, and insights into the related benefits and challenges are urgently needed. Objective: The study aims to systematically review the effects of VR-RTs for older adults with cognitive impairment. Methods: Seven databases (MEDLINE, Academic Search Premier, CINAHL, Web of Science, PubMed, the Cochrane Central Register of Controlled Trials, and ScienceDirect) were searched to identify relevant articles published from inception to August 10, 2023. Peer-reviewed publications that assessed the effect of VR-RTs (ie, using virtual clues to evoke participants? memories or past experiences) on cognitive-related outcomes were included. Two independent researchers conducted the literature search, review, and data extraction processes. A narrative synthesis approach was used to analyze the extracted data. Results: Of the 537 identified articles, 22 were ultimately included in the data analysis. The results revealed that VR-RTs could maintain cognitive status (4/4, 100%) and reduce anxiety (2/2, 100%) in older adults with cognitive impairment. Nevertheless, one study found a cognitive improvement after VR-RTs, whereas cognitive degradation was observed at a 3- to 6-month follow-up measure. Around 88% (7/8) of the included studies indicated that VR-RTs improved memory; however, the evidence regarding the beneficial effects of VR-RTs was limited in improving quality of life (1/4, 25%) and reducing apathy (0/2, 0%) and depression (1/3, 33%). The results indicated that VR-RTs are safe, engaging, acceptable, and satisfying for older adults with cognitive impairment. In VR scenarios, personalized stimulus materials related to the users? youth experiences were more effective for treating cognitive impairment than other stimulus materials. Conclusions: The results of this systematic review demonstrate the potential benefits of VR-RT for older adults with cognitive impairment, especially in improving emotion and memory and maintaining cognitive status. VR-RT is also safe and enjoyable for older adults. However, due to the trial heterogeneity of included studies, we can only provide qualitative results instead of performing meta-analysis to quantify the effect size of VR-RTs. Thus, more randomized controlled trials are required to examine the designs and effects of VR-RTs for groups of older adults with specific needs. UR - https://www.jmir.org/2024/1/e53348 UR - http://dx.doi.org/10.2196/53348 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53348 ER - TY - JOUR AU - Yang, Si Myung AU - Taira, Kazuya PY - 2024/11/11 TI - Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study JO - J Med Internet Res SP - e64555 VL - 26 KW - well-being KW - spatial indicator KW - infodemiology KW - search engine KW - public health KW - health policy KW - policy-making KW - Google KW - Japan N2 - Background: In recent years, the adoption of well-being indicators by national governments and international organizations has emerged as an important tool for evaluating state governance and societal progress. Traditionally, well-being has been gauged primarily through economic metrics such as gross domestic product, which fall short of capturing multifaceted well-being, including socioeconomic inequalities, life satisfaction, and health status. Current well-being indicators, including both subjective and objective measures, offer a broader evaluation but face challenges such as high survey costs and difficulties in evaluating at regional levels within countries. The emergence of web log data as an alternative source of well-being indicators offers the potential for more cost-effective, timely, and less biased assessments. Objective: This study aimed to develop a model using internet search data to predict well-being indicators at the regional level in Japan, providing policy makers with a more accessible and cost-effective tool for assessing public well-being and making informed decisions. Methods: This study used the Regional Well-Being Index (RWI) for Japan, which evaluates prefectural well-being across 47 prefectures for the years 2010, 2013, 2016, and 2019, as the outcome variable. The RWI includes a comprehensive approach integrating both subjective and objective indicators across 11 domains, including income, job, and life satisfaction. Predictor variables included z score?normalized relative search volume (RSV) data from Google Trends for words relevant to each domain. Unrelated words were excluded from the analysis to ensure relevance. The Elastic Net methodology was applied to predict RWI using RSVs, with ? balancing ridge and lasso effects and ? regulating their strengths. The model was optimized by cross-validation, determining the best mix and strength of regularization parameters to minimize prediction error. Root mean square errors (RMSE) and coefficients of determination (R2) were used to assess the model?s predictive accuracy and fit. Results: An analysis of Google Trends data yielded 275 words related to the RWI domains, and RSVs were collected for 211 words after filtering out irrelevant terms. The mean search frequencies for these words during 2010, 2013, 2016, and 2019 ranged from ?1.587 to 3.902, with SDs between 3.025 and 0.053. The best Elastic Net model (?=0.1, ?=0.906, RMSE=1.290, and R2=0.904) was built using 2010-2016 training data and 2-13 variables per domain. Applied to 2019 test data, it yielded an RMSE of 2.328 and R2 of 0.665. Conclusions: This study demonstrates the effectiveness of using internet search log data through the Elastic Net machine learning method to predict the RWI in Japanese prefectures with high accuracy, offering a rapid and cost-efficient alternative to traditional survey approaches. This study highlights the potential of this methodology to provide foundational data for evidence-based policy making aimed at enhancing well-being. UR - https://www.jmir.org/2024/1/e64555 UR - http://dx.doi.org/10.2196/64555 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64555 ER - TY - JOUR AU - Becker, Elizabeth Molly AU - Stratton Levey, Nadine AU - Yeh, Y. Gloria AU - Giacino, Joseph AU - Iverson, Grant AU - Silverberg, Noah AU - Parker, A. Robert AU - McKinnon, Ellen AU - Siravo, Caitlin AU - Shah, Priyanca AU - Vranceanu, Ana-Maria AU - Greenberg, Jonathan PY - 2024/11/8 TI - Preliminary Feasibility of a Novel Mind-Body Program to Prevent Persistent Concussion Symptoms Among Young Adults With Anxiety: Nonrandomized Open Pilot Study JO - JMIR Form Res SP - e64540 VL - 8 KW - concussions KW - mind-body KW - preventions KW - young adults KW - feasibility KW - persistence KW - open pilot KW - mind-body program KW - preliminary feasibility KW - mild traumatic brain injuries KW - United States KW - adults KW - psychological factors KW - mind-body interventions N2 - Background: Concussions are common, particularly among young adults, and often are associated with persistent, debilitating, and hard-to-treat symptoms. Anxiety and concussion symptoms often amplify each other, and growing evidence indicates that anxiety plays a key role in symptoms persistence after concussion. Targeting anxiety early after concussion may be a promising means of helping prevent persistent concussion symptoms in this population. We developed the Toolkit for Optimal Recovery after Concussion (TOR-C), the first mind-body program tailored for young adults with a recent concussion and anxiety, aiming to prevent persistent concussion symptoms. Objective: This study aims to conduct an open pilot of TOR-C to test preliminary feasibility, signal of change in measures, and treatment perceptions. Methods: Five young adults (aged 18-24 years) attended 4 weekly one-on-one live video sessions with a clinician. Participants completed questionnaires measuring treatment targets (ie, pain catastrophizing, mindfulness, fear avoidance, limiting behaviors, and all-or-nothing behaviors) and outcomes (ie, postconcussive symptoms, physical function, anxiety, depression, and pain) at baseline, immediately following the intervention, and 3 months after intervention completion. At the conclusion of the program, participants attended a qualitative interview and provided feedback about the program to help optimize study content and procedures. Results: Feasibility markers were excellent for credibility and expectancy (5/5, 100% of participants scored above the credibility and expectancy scale midpoint), client satisfaction (4/5, 80% of participants scored above the Client Satisfaction Questionnaire midpoint), therapist adherence (97% adherence), acceptability of treatment (5/5, 100% of participants attended 3 or more sessions), adherence to homework (87% home practice completion), and feasibility of assessments (no measures fully missing). The feasibility of recruitment was good (5/7, 71% of eligible participants agreed to participate). There were preliminary signals of improvements from pre-post comparisons in treatment targets (d=0.72-2.20) and outcomes (d=0.41-1.38), which were sustained after 3 months (d=0.38-2.74 and d=0.71-1.63 respectively). Exit interviews indicated overall positive perceptions of skills and highlighted barriers (eg, busyness) and facilitators (eg, accountability) to engagement. Conclusions: TOR-C shows preliminary feasibility, is associated with a signal of improvement in treatment targets and outcomes, and has the potential to support recovery from concussion. The quantitative findings along with the qualitative feedback obtained from the exit interviews will help optimize TOR-C in preparation for an upcoming randomized controlled trial of TOR-C versus an active control condition of health education for concussion recovery. International Registered Report Identifier (IRRID): RR2-10.2196/25746 UR - https://formative.jmir.org/2024/1/e64540 UR - http://dx.doi.org/10.2196/64540 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64540 ER - TY - JOUR AU - Hildebrand, Sophie Anne AU - Planert, Jari AU - Machulska, Alla AU - Margraf, Maria Lena AU - Roesmann, Kati AU - Klucken, Tim PY - 2024/11/7 TI - Exploring Psychotherapists? Attitudes on Internet- and Mobile-Based Interventions in Germany: Thematic Analysis JO - JMIR Form Res SP - e51832 VL - 8 KW - eHealth KW - psychotherapy KW - psychotherapists? perspectives KW - thematic analysis KW - internet- and mobile-based intervention N2 - Background: In recent years, internet- and mobile-based interventions (IMIs) have become increasingly relevant in mental health care and have sparked societal debates. Psychotherapists? perspectives are essential for identifying potential opportunities for improvement, facilitating conditions, and barriers to the implementation of these interventions. Objective: This study aims to explore psychotherapists? perspectives on opportunities for improvement, facilitating conditions, and barriers to using IMIs. Methods: The study used a qualitative research design, utilizing open-ended items in a cross-sectional survey. A total of 350 psychotherapists were asked to provide their written opinions on various aspects of IMIs. Thematic analysis was conducted to analyze the data and identify core themes. Results: The analysis revealed 11 core themes related to the use of IMIs, which were categorized into 4 superordinate categories: ?Applicability,? ?Treatment Resources,? ?Technology,? and ?Perceived Risks and Barriers.? While many psychotherapists viewed IMIs as a valuable support for conventional psychotherapy, they expressed skepticism about using IMIs as a substitute. Several factors were perceived as hindrances to the applicability of IMIs in clinical practice, including technological issues, subjective concerns about potential data protection risks, a lack of individualization due to the manualized nature of most IMIs, and the high time and financial costs for both psychotherapists and patients. They expressed a desire for easily accessible information on evidence and programs to reduce the time and effort required for training and advocated for this information to be integrated into the conceptualization of new IMIs. Conclusions: The findings of this study emphasize the importance of considering psychotherapists? attitudes in the development, evaluation, and implementation of IMIs. This study revealed that psychotherapists recognized both the opportunities and risks associated with the use of IMIs, with most agreeing that IMIs serve as a tool to support traditional psychotherapy rather than as a substitute for it. Furthermore, it is essential to involve psychotherapists in discussions about IMIs specifically, as well as in the development of new methodologies in psychotherapy more broadly. Overall, this study can advance the use of IMIs in mental health care and contribute to the ongoing societal debate surrounding these interventions. UR - https://formative.jmir.org/2024/1/e51832 UR - http://dx.doi.org/10.2196/51832 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51832 ER - TY - JOUR AU - Yatziv, Shai-Lee AU - Pedrelli, Paola AU - Baror, Shira AU - DeCaro, Ann Sydney AU - Shachar, Noam AU - Sofer, Bar AU - Hull, Sunday AU - Curtiss, Joshua AU - Bar, Moshe PY - 2024/11/7 TI - Facilitating Thought Progression to Reduce Depressive Symptoms: Randomized Controlled Trial JO - J Med Internet Res SP - e56201 VL - 26 KW - depression KW - cognitive neuroscience KW - facilitating thought progression KW - FTP KW - mobile phone KW - digital health KW - gamification KW - depression symptoms KW - randomized controlled trial KW - RCT KW - app KW - depressive disorder KW - web-based platforms KW - effectiveness N2 - Background: The constant rise in the prevalence of major depressive disorder calls for new, effective, and accessible interventions that can rapidly and effectively reach a wide range of audiences. Recent developments in the digital health domain suggest that dedicated online platforms may potentially address this gap. Focusing on targeting ruminative thought, a major symptomatic hallmark of depression, in this study we hypothesized that delivering a digital health?based intervention designed to systematically facilitate thought progression would substantially alleviate depression. Objective: The study aims to investigate the efficacy of a novel digital intervention on the reduction of depressive symptoms. This intervention was designed as an easy-to-use gamified app specifically aimed to facilitate thought progression through intense practicing of associative, semantically broad, fast, and creative thought patterns. Methods: A randomized clinical trial was conducted, comparing changes in depression symptoms between participants who used the app in the intervention group (n=74) and waitlist control group (n=27) over the course of 8 weeks. All participants filled out a battery of clinical questionnaires to assess the severity of depression at baseline and 4 and 8 weeks after starting the study. These primarily included the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Patient Health Questionnaire-9 as well as the Positive Affect Negative Affect Scale-Negative Affect Score, Ruminative Response Scale, and Symptoms of Depression Questionnaire. Additional questionnaires were implemented to assess anxiety, positive affect, anhedonia, and quality of life. Results: The results indicate that across multiple clinical measurements, participants in the intervention group who played the gamified app showed greater and faster improvement in depressive symptoms compared with their waitlist control counterparts. The difference between the groups in MADRS improvement was ?7.01 points (95% CI ?10.72 to ?3.29; P<.001; Cohen d=0.67). Furthermore, the difference in improvement between groups persisted up to 4 weeks posttrial (MADRS differences at week 12: F49,2=6.62; P=.003; ?p2=0.21). At the end of the trial, participants who played the app showed high interest in continuing using the app. Conclusions: The results demonstrate that a gamified app designed to facilitate thought progression is associated with improvement in depressive symptoms. Given its innovative and accessibility features, this gamified method aiming to facilitate thought progression may successfully complement traditional treatments for depression in the future, providing a safe and impactful way to enhance the lives of individuals experiencing depression and anxiety. Trial Registration: ClinicalTrials.gov NCT05685758; https://clinicaltrials.gov/study/NCT05685758 UR - https://www.jmir.org/2024/1/e56201 UR - http://dx.doi.org/10.2196/56201 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56201 ER - TY - JOUR AU - Fajnerova, Iveta AU - Hejtmánek, Luká? AU - Sedlák, Michal AU - Jablonská, Markéta AU - Francová, Anna AU - Stopková, Pavla PY - 2024/11/7 TI - The Journey From Nonimmersive to Immersive Multiuser Applications in Mental Health Care: Systematic Review JO - J Med Internet Res SP - e60441 VL - 26 KW - digital health KW - mental health care KW - clinical interventions KW - multiuser KW - immersive KW - virtual reality KW - VR KW - app KW - mental health KW - online tools KW - synthesis KW - mobile phone KW - PRISMA N2 - Background: Over the past 25 years, the development of multiuser applications has seen considerable advancements and challenges. The technological development in this field has emerged from simple chat rooms through videoconferencing tools to the creation of complex, interactive, and often multisensory virtual worlds. These multiuser technologies have gradually found their way into mental health care, where they are used in both dyadic counseling and group interventions. However, some limitations in hardware capabilities, user experience designs, and scalability may have hindered the effectiveness of these applications. Objective: This systematic review aims at summarizing the progress made and the potential future directions in this field while evaluating various factors and perspectives relevant to remote multiuser interventions. Methods: The systematic review was performed based on a Web of Science and PubMed database search covering articles in English, published from January 1999 to March 2024, related to multiuser mental health interventions. Several inclusion and exclusion criteria were determined before and during the records screening process, which was performed in several steps. Results: We identified 49 records exploring multiuser applications in mental health care, ranging from text-based interventions to interventions set in fully immersive environments. The number of publications exploring this topic has been growing since 2015, with a large increase during the COVID-19 pandemic. Most digital interventions were delivered in the form of videoconferencing, with only a few implementing immersive environments. The studies used professional or peer-supported group interventions or a combination of both approaches. The research studies targeted diverse groups and topics, from nursing mothers to psychiatric disorders or various minority groups. Most group sessions occurred weekly, or in the case of the peer-support groups, often with a flexible schedule. Conclusions: We identified many benefits to multiuser digital interventions for mental health care. These approaches provide distributed, always available, and affordable peer support that can be used to deliver necessary help to people living outside of areas where in-person interventions are easily available. While immersive virtual environments have become a common tool in many areas of psychiatric care, such as exposure therapy, our results suggest that this technology in multiuser settings is still in its early stages. Most identified studies investigated mainstream technologies, such as videoconferencing or text-based support, substituting the immersive experience for convenience and ease of use. While many studies discuss useful features of virtual environments in group interventions, such as anonymity or stronger engagement with the group, we discuss persisting issues with these technologies, which currently prevent their full adoption. UR - https://www.jmir.org/2024/1/e60441 UR - http://dx.doi.org/10.2196/60441 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60441 ER - TY - JOUR AU - Riad, Rachid AU - Denais, Martin AU - de Gennes, Marc AU - Lesage, Adrien AU - Oustric, Vincent AU - Cao, Nga Xuan AU - Mouchabac, Stéphane AU - Bourla, Alexis PY - 2024/10/31 TI - Automated Speech Analysis for Risk Detection of Depression, Anxiety, Insomnia, and Fatigue: Algorithm Development and Validation Study JO - J Med Internet Res SP - e58572 VL - 26 KW - speech analysis KW - voice detection KW - voice analysis KW - speech biomarkers KW - speech-based systems KW - computer-aided diagnosis KW - mental health symptom detection KW - machine learning KW - mental health KW - fatigue KW - anxiety KW - depression N2 - Background: While speech analysis holds promise for mental health assessment, research often focuses on single symptoms, despite symptom co-occurrences and interactions. In addition, predictive models in mental health do not properly assess the limitations of speech-based systems, such as uncertainty, or fairness for a safe clinical deployment. Objective: We investigated the predictive potential of mobile-collected speech data for detecting and estimating depression, anxiety, fatigue, and insomnia, focusing on other factors than mere accuracy, in the general population. Methods: We included 865 healthy adults and recorded their answers regarding their perceived mental and sleep states. We asked how they felt and if they had slept well lately. Clinically validated questionnaires measuring depression, anxiety, insomnia, and fatigue severity were also used. We developed a novel speech and machine learning pipeline involving voice activity detection, feature extraction, and model training. We automatically modeled speech with pretrained deep learning models that were pretrained on a large, open, and free database, and we selected the best one on the validation set. Based on the best speech modeling approach, clinical threshold detection, individual score prediction, model uncertainty estimation, and performance fairness across demographics (age, sex, and education) were evaluated. We used a train-validation-test split for all evaluations: to develop our models, select the best ones, and assess the generalizability of held-out data. Results: The best model was Whisper M with a max pooling and oversampling method. Our methods achieved good detection performance for all symptoms, depression (Patient Health Questionnaire-9: area under the curve [AUC]=0.76; F1-score=0.49 and Beck Depression Inventory: AUC=0.78; F1-score=0.65), anxiety (Generalized Anxiety Disorder 7-item scale: AUC=0.77; F1-score=0.50), insomnia (Athens Insomnia Scale: AUC=0.73; F1-score=0.62), and fatigue (Multidimensional Fatigue Inventory total score: AUC=0.68; F1-score=0.88). The system performed well when it needed to abstain from making predictions, as demonstrated by low abstention rates in depression detection with the Beck Depression Inventory and fatigue, with risk-coverage AUCs below 0.4. Individual symptom scores were accurately predicted (correlations were all significant with Pearson strengths between 0.31 and 0.49). Fairness analysis revealed that models were consistent for sex (average disparity ratio [DR] 0.86, SD 0.13), to a lesser extent for education level (average DR 0.47, SD 0.30), and worse for age groups (average DR 0.33, SD 0.30). Conclusions: This study demonstrates the potential of speech-based systems for multifaceted mental health assessment in the general population, not only for detecting clinical thresholds but also for estimating their severity. Addressing fairness and incorporating uncertainty estimation with selective classification are key contributions that can enhance the clinical utility and responsible implementation of such systems. UR - https://www.jmir.org/2024/1/e58572 UR - http://dx.doi.org/10.2196/58572 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58572 ER - TY - JOUR AU - Coppersmith, DL Daniel AU - Bentley, H. Kate AU - Kleiman, M. Evan AU - Jaroszewski, C. Adam AU - Daniel, Merryn AU - Nock, K. Matthew PY - 2024/10/31 TI - Automated Real-Time Tool for Promoting Crisis Resource Use for Suicide Risk (ResourceBot): Development and Usability Study JO - JMIR Ment Health SP - e58409 VL - 11 KW - suicidal thoughts KW - suicidal behaviors KW - ecological momentary assessment KW - crisis resources KW - real-time tool KW - self-report KW - psychoeducation KW - app N2 - Background: Real-time monitoring captures information about suicidal thoughts and behaviors (STBs) as they occur and offers great promise to learn about STBs. However, this approach also introduces questions about how to monitor and respond to real-time information about STBs. Given the increasing use of real-time monitoring, there is a need for novel, effective, and scalable tools for responding to suicide risk in real time. Objective: The goal of this study was to develop and test an automated tool (ResourceBot) that promotes the use of crisis services (eg, 988) in real time through a rule-based (ie, if-then) brief barrier reduction intervention. Methods: ResourceBot was tested in a 2-week real-time monitoring study of 74 adults with recent suicidal thoughts. Results: ResourceBot was deployed 221 times to 36 participants. There was high engagement with ResourceBot (ie, 87% of the time ResourceBot was deployed, a participant opened the tool and submitted a response to it), but zero participants reported using crisis services after engaging with ResourceBot. The most reported reasons for not using crisis services were beliefs that the resources would not help, wanting to handle things on one?s own, and the resources requiring too much time or effort. At the end of the study, participants rated ResourceBot with good usability (mean of 75.6 out of 100) and satisfaction (mean of 20.8 out of 32). Conclusions: This study highlights both the possibilities and challenges of developing effective real-time interventions for suicide risk and areas for refinement in future work. UR - https://mental.jmir.org/2024/1/e58409 UR - http://dx.doi.org/10.2196/58409 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58409 ER - TY - JOUR AU - Nordberg, S. Samuel AU - Jaso-Yim, A. Brittany AU - Sah, Pratha AU - Schuler, Keke AU - Eyllon, Mara AU - Pennine, Mariesa AU - Hoyler, H. Georgia AU - Barnes, Ben J. AU - Murillo, Hong Lily AU - O'Dea, Heather AU - Orth, Laura AU - Rogers, Elizabeth AU - Welch, George AU - Peloquin, Gabrielle AU - Youn, Jeong Soo PY - 2024/10/30 TI - Evaluating the Implementation and Clinical Effectiveness of an Innovative Digital First Care Model for Behavioral Health Using the RE-AIM Framework: Quantitative Evaluation JO - J Med Internet Res SP - e54528 VL - 26 KW - digital mental health interventions KW - implementation KW - clinical effectiveness KW - practice-oriented research KW - access to care N2 - Background: In the United States, innovation is needed to address the increasing need for mental health care services and widen the patient-to-provider ratio. Despite the benefits of digital mental health interventions (DMHIs), they have not been effective in addressing patients? behavioral health challenges as stand-alone treatments. Objective: This study evaluates the implementation and effectiveness of precision behavioral health (PBH), a digital-first behavioral health care model embedded within routine primary care that refers patients to an ecosystem of evidence-based DMHIs with strategically placed human support. Methods: Patient demographic information, triage visit outcomes, multidimensional patient-reported outcome measure, enrollment, and engagement with the DMHIs were analyzed using data from the electronic health record and vendor-reported data files. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework was used to evaluate the implementation and clinical effectiveness outcomes of PBH. Results: PBH had a 47.58% reach rate, defined as patients accepting the PBH referral from their behavioral health integrated clinician. PBH patients had high DMHI registration rates (79.62%), high activation rates (76.54%), and high retention rates at 15 days (57.69%) and 30 days (44.58%) compared to literature benchmarks. In total, 74.01% (n=168) of patients showed clinical improvement, 22.47% (n=51) showed no clinical change, and 3.52% (n=8) showed clinical deterioration in symptoms. PBH had high adoption rates, with behavioral health integrated clinicians referring on average 4.35 (SD 0.46) patients to PBH per month and 90%-100% of clinicians (n=12) consistently referring at least 1 patient to PBH each month. A third (32%, n=1114) of patients were offered PBH as a treatment option during their triage visit. Conclusions: PBH as a care model with evidence-based DMHIs, human support for patients, and integration within routine settings offers a credible service to support patients with mild to moderate mental health challenges. This type of model has the potential to address real-life access to care problems faced by health care settings. UR - https://www.jmir.org/2024/1/e54528 UR - http://dx.doi.org/10.2196/54528 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54528 ER - TY - JOUR AU - Liu, Qimin AU - Ning, Emma AU - Ross, K. Mindy AU - Cladek, Andrea AU - Kabir, Sarah AU - Barve, Amruta AU - Kennelly, Ellyn AU - Hussain, Faraz AU - Duffecy, Jennifer AU - Langenecker, A. Scott AU - Nguyen, M. Theresa AU - Tulabandhula, Theja AU - Zulueta, John AU - Demos, P. Alexander AU - Leow, Alex AU - Ajilore, Olusola PY - 2024/10/29 TI - Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder: Algorithm Development and Validation JO - J Med Internet Res SP - e51269 VL - 26 KW - keyboard typing KW - passive sensing KW - digital phenotyping KW - mood disorder KW - mixture model KW - phenotypes KW - mobile keyboard KW - smartphone KW - keyboard data KW - monitoring KW - clinical decision-making KW - depression KW - mania, mobile phone N2 - Background: Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders. Objective: This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model. Methods: Eligible study participants completed a virtual screening visit where all eligible participants were instructed to download the custom-built BiAffect smartphone keyboard (University of Illinois). The BiAffect keyboard unobtrusively captures keystroke dynamics. All eligible and consenting participants were instructed to use this keyboard exclusively for up to 4 weeks of the study in real life, and participants? compliance was checked at the 2 follow-up visits at week 2 and week 4. As part of the research protocol, every study participant underwent evaluations by a study psychiatrist during each visit. Results: We found that derived phenotypes were associated with not only the diagnoses and severity of depression and mania but also specific individual symptoms. Using a linear mixed-effects model with random intercepts accounting for the nested data structure from daily data, the backspace rates on the continuous scale did not differ between participants in the healthy control and in the mood disorders groups (P=.11). The 3-class model had mean backspace rates of 0.112, 0.180, and 0.268, respectively, with a SD of 0.048. In total, 3 classes, respectively, were estimated to comprise 37.5% (n=47), 54.4% (n=72), and 8.1% (n=9) of the sample. We grouped individuals into Low, Medium, and High backspace rate groups. Individuals with unipolar mood disorder were predominantly in the Medium group (n=54), with some in the Low group (n=27) and a few in the High group (n=6). The Medium group, compared with the Low group, had significantly higher ratings of depression (b=2.32, P=.008). The High group was not associated with ratings of depression with (P=.88) or without (P=.27) adjustment for medication and diagnoses. The High group, compared with the Low group, was associated with both nonzero ratings (b=1.91, P=.02) and higher ratings of mania (b=1.46, P<.001). The High group, compared with the Low group, showed significantly higher odds of elevated mood (P=.03), motor activity (P=.04), and irritability (P<.05). Conclusions: This study demonstrates the promise of mobile typing kinematics in mood disorder research and practice. Monitoring a single mobile typing kinematic feature, that is, backspace rates, through passive sensing imposes a low burden on the participants. Based on real-life keystroke data, our derived digital phenotypes from this single feature can be useful for researchers and practitioners to distinguish between individuals with and those without mood disorder symptoms. UR - https://www.jmir.org/2024/1/e51269 UR - http://dx.doi.org/10.2196/51269 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51269 ER - TY - JOUR AU - Branitsky, Alison AU - Bee, Penny AU - Bucci, Sandra AU - Lovell, Karina AU - Foster, Simon AU - Whelan, Pauline PY - 2024/10/24 TI - Co-Designing a Digital App to Support Young People?s Patient and Public Involvement and Engagement (VoiceIn): Development and Usability Study JO - JMIR Hum Factors SP - e53394 VL - 11 KW - patient and public involvement and engagement KW - PPIE KW - digital mental health KW - young people KW - co-design KW - mental health N2 - Background: While patient and public involvement and engagement (PPIE) is now seen as a cornerstone of mental health research, young people?s involvement in PPIE faces limitations. Work and school demands and more limited independence can make it challenging for young people to engage with PPIE. Lack of ability or desire to attend face-to-face meetings or group discussions can further compound this difficulty. The VoiceIn app and digital platform were codeveloped by a multidisciplinary team of young people, mental health researchers, and software designers, and enables young people to engage directly with PPIE opportunities via a mobile app. Objective: This paper aims to describe how VoiceIn was developed through a series of co-design workshops with relevant stakeholders, specifically (1) how the initial design of VoiceIn was informed and driven by focus groups with young people, mental health professionals, and PPIE leads; (2) how VoiceIn was refined through collaboration with the aforementioned stakeholders; (3) the priorities for an app to support PPIE; (4) the key features necessary in the PPIE app; and (5) the recommended next steps in testing and deploying the digital platform. Methods: Initial co-design workshops took place with young people, mental health professionals, and PPIE leads to identify key features of an app to support PPIE. A series of VoiceIn design prototypes were developed and iterated based on the priorities and preferences of the stakeholders. The MoSCoW (must have, should have, could have, won?t have) prioritization method was used throughout the process to identify priorities across the different stakeholder groups. Results: Co-design with young people, mental health professionals, and PPIE leads supported the successful development and improvement of the VoiceIn app. As a result of this process, key features were identified, including allowing for various modes of providing feedback (eg, polls and comments), reviewing project updates, and expressing interest in categories of research. The researcher platform was developed to support multimedia uploads for project descriptions; a jargon detector; a dedicated section for providing project updates; and a visually appealing, user-friendly design. While all stakeholder groups emphasized the importance of allowing app users to engage with the app in various ways and for there to be ongoing progress updates, group differences were also noticed. Young people expressed a desire for incentives and rewards for engaging with the app (eg, to post on their public social media profiles), and mental health professionals and PPIE leads prioritized flexibility in describing the project and its PPIE needs. Conclusions: A co-design approach was pivotal to the development of the VoiceIn app. This collaborative approach enabled the app to meet the divergent needs of young people, mental health professionals, and PPIE leads. This process mirrored the aspirations of PPIE initiatives by cocreating a digital health research tool with key stakeholders. UR - https://humanfactors.jmir.org/2024/1/e53394 UR - http://dx.doi.org/10.2196/53394 ID - info:doi/10.2196/53394 ER - TY - JOUR AU - Lee, Heather Younga AU - Zhang, Yingzhe AU - Kennedy, J. Chris AU - Mallard, T. Travis AU - Liu, Zhaowen AU - Vu, Linh Phuong AU - Feng, Anne Yen-Chen AU - Ge, Tian AU - Petukhova, V. Maria AU - Kessler, C. Ronald AU - Nock, K. Matthew AU - Smoller, W. Jordan PY - 2024/10/23 TI - Enhancing Suicide Risk Prediction With Polygenic Scores in Psychiatric Emergency Settings: Prospective Study JO - JMIR Bioinform Biotech SP - e58357 VL - 5 KW - polygenic risk score KW - suicide risk prediction KW - suicide attempt KW - predictive algorithms KW - genomics KW - genotypes KW - electronic health record KW - machine learning N2 - Background: Despite growing interest in the clinical translation of polygenic risk scores (PRSs), it remains uncertain to what extent genomic information can enhance the prediction of psychiatric outcomes beyond the data collected during clinical visits alone. Objective: This study aimed to assess the clinical utility of incorporating PRSs into a suicide risk prediction model trained on electronic health records (EHRs) and patient-reported surveys among patients admitted to the emergency department. Methods: Study participants were recruited from the psychiatric emergency department at Massachusetts General Hospital. There were 333 adult patients of European ancestry who had high-quality genotype data available through their participation in the Mass General Brigham Biobank. Multiple neuropsychiatric PRSs were added to a previously validated suicide prediction model in a prospective cohort enrolled between February 4, 2015, and March 13, 2017. Data analysis was performed from July 11, 2022, to August 31, 2023. Suicide attempt was defined using diagnostic codes from longitudinal EHRs combined with 6-month follow-up surveys. The clinical risk score for suicide attempt was calculated from an ensemble model trained using an EHR-based suicide risk score and a brief survey, and it was subsequently used to define the baseline model. We generated PRSs for depression, bipolar disorder, schizophrenia, suicide attempt, and externalizing traits using a Bayesian polygenic scoring method for European ancestry participants. Model performance was evaluated using area under the receiver operator curve (AUC), area under the precision-recall curve, and positive predictive values. Results: Of the 333 patients (n=178, 53.5% male; mean age 36.8, SD 13.6 years; n=333, 100% non-Hispanic and n=324, 97.3% self-reported White), 28 (8.4%) had a suicide attempt within 6 months. Adding either the schizophrenia PRS or all PRSs to the baseline model resulted in the numerically highest discrimination (AUC 0.86, 95% CI 0.73-0.99) compared to the baseline model (AUC 0.84, 95% Cl 0.70-0.98). However, the improvement in model performance was not statistically significant. Conclusions: In this study, incorporating genomic information into clinical prediction models for suicide attempt did not improve patient risk stratification. Larger studies that include more diverse participants are required to validate whether the inclusion of psychiatric PRSs in clinical prediction models can enhance the stratification of patients at risk of suicide attempts. UR - https://bioinform.jmir.org/2024/1/e58357 UR - http://dx.doi.org/10.2196/58357 UR - http://www.ncbi.nlm.nih.gov/pubmed/39442166 ID - info:doi/10.2196/58357 ER - TY - JOUR AU - Guo, Zhijun AU - Lai, Alvina AU - Thygesen, H. Johan AU - Farrington, Joseph AU - Keen, Thomas AU - Li, Kezhi PY - 2024/10/18 TI - Large Language Models for Mental Health Applications: Systematic Review JO - JMIR Ment Health SP - e57400 VL - 11 KW - large language models KW - mental health KW - digital health care KW - ChatGPT KW - Bidirectional Encoder Representations from Transformers KW - BERT N2 - Background: Large language models (LLMs) are advanced artificial neural networks trained on extensive datasets to accurately understand and generate natural language. While they have received much attention and demonstrated potential in digital health, their application in mental health, particularly in clinical settings, has generated considerable debate. Objective: This systematic review aims to critically assess the use of LLMs in mental health, specifically focusing on their applicability and efficacy in early screening, digital interventions, and clinical settings. By systematically collating and assessing the evidence from current studies, our work analyzes models, methodologies, data sources, and outcomes, thereby highlighting the potential of LLMs in mental health, the challenges they present, and the prospects for their clinical use. Methods: Adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, this review searched 5 open-access databases: MEDLINE (accessed by PubMed), IEEE Xplore, Scopus, JMIR, and ACM Digital Library. Keywords used were (mental health OR mental illness OR mental disorder OR psychiatry) AND (large language models). This study included articles published between January 1, 2017, and April 30, 2024, and excluded articles published in languages other than English. Results: In total, 40 articles were evaluated, including 15 (38%) articles on mental health conditions and suicidal ideation detection through text analysis, 7 (18%) on the use of LLMs as mental health conversational agents, and 18 (45%) on other applications and evaluations of LLMs in mental health. LLMs show good effectiveness in detecting mental health issues and providing accessible, destigmatized eHealth services. However, assessments also indicate that the current risks associated with clinical use might surpass their benefits. These risks include inconsistencies in generated text; the production of hallucinations; and the absence of a comprehensive, benchmarked ethical framework. Conclusions: This systematic review examines the clinical applications of LLMs in mental health, highlighting their potential and inherent risks. The study identifies several issues: the lack of multilingual datasets annotated by experts, concerns regarding the accuracy and reliability of generated content, challenges in interpretability due to the ?black box? nature of LLMs, and ongoing ethical dilemmas. These ethical concerns include the absence of a clear, benchmarked ethical framework; data privacy issues; and the potential for overreliance on LLMs by both physicians and patients, which could compromise traditional medical practices. As a result, LLMs should not be considered substitutes for professional mental health services. However, the rapid development of LLMs underscores their potential as valuable clinical aids, emphasizing the need for continued research and development in this area. Trial Registration: PROSPERO CRD42024508617; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=508617 UR - https://mental.jmir.org/2024/1/e57400 UR - http://dx.doi.org/10.2196/57400 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57400 ER - TY - JOUR AU - Rizvi, L. Shireen AU - Ruork, K. Allison AU - Yin, Qingqing AU - Yeager, April AU - Taylor, E. Madison AU - Kleiman, M. Evan PY - 2024/10/9 TI - Using Biosensor Devices and Ecological Momentary Assessment to Measure Emotion Regulation Processes: Pilot Observational Study With Dialectical Behavior Therapy JO - JMIR Ment Health SP - e60035 VL - 11 KW - wearable device KW - ecological momentary assessment KW - emotion regulation KW - psychotherapy mechanisms KW - dialectical behavior therapy KW - wearable KW - wristwatch KW - novel technology KW - psychological KW - treatment KW - pilot study KW - adult KW - personality disorder KW - mental health KW - mobile phone KW - EMA KW - observational study N2 - Background: Novel technologies, such as ecological momentary assessment (EMA) and wearable biosensor wristwatches, are increasingly being used to assess outcomes and mechanisms of change in psychological treatments. However, there is still a dearth of information on the feasibility and acceptability of these technologies and whether they can be reliably used to measure variables of interest. Objective: Our objectives were to assess the feasibility and acceptability of incorporating these technologies into dialectical behavior therapy and conduct a pilot evaluation of whether these technologies can be used to assess emotion regulation processes and associated problems over the course of treatment. Methods: A total of 20 adults with borderline personality disorder were enrolled in a 6-month course of dialectical behavior therapy. For 1 week out of every treatment month, participants were asked to complete EMA 6 times a day and to wear a biosensor watch. Each EMA assessment included measures of several negative affect and suicidal thinking, among other items. We used multilevel correlations to assess the contemporaneous association between electrodermal activity and 11 negative emotional states reported via EMA. A multilevel regression was conducted in which changes in composite ratings of suicidal thinking were regressed onto changes in negative affect. Results: On average, participants completed 54.39% (SD 33.1%) of all EMA (range 4.7%?92.4%). They also wore the device for an average of 9.52 (SD 6.47) hours per day and for 92.6% of all days. Importantly, no associations were found between emotional state and electrodermal activity, whether examining a composite of all high-arousal negative emotions or individual emotional states (within-person r ranged from ?0.026 to ?0.109). Smaller changes in negative affect composite scores were associated with greater suicidal thinking ratings at the subsequent timepoint, beyond the effect of suicidal thinking at the initial timepoint. Conclusions: Results indicated moderate overall compliance with EMA and wearing the watch; however, there was no concurrence between EMA and wristwatch data on emotions. This pilot study raises questions about the reliability and validity of these technologies incorporated into treatment studies to evaluate emotion regulation mechanisms. UR - https://mental.jmir.org/2024/1/e60035 UR - http://dx.doi.org/10.2196/60035 ID - info:doi/10.2196/60035 ER - TY - JOUR AU - Ridout, J. Samuel AU - Ridout, K. Kathryn AU - Lin, Y. Teresa AU - Campbell, I. Cynthia PY - 2024/10/2 TI - Clinical Use of Mental Health Digital Therapeutics in a Large Health Care Delivery System: Retrospective Patient Cohort Study and Provider Survey JO - JMIR Ment Health SP - e56574 VL - 11 KW - digital therapeutics KW - depression KW - anxiety KW - mental health KW - retrospective cohort KW - electronic health record KW - adults KW - survey KW - recommendation KW - mobile phone N2 - Background: While the number of digital therapeutics (DTx) has proliferated, there is little real-world research on the characteristics of providers recommending DTx, their recommendation behaviors, or the characteristics of patients receiving recommendations in the clinical setting. Objective: The aim of this study was to characterize the clinical and demographic characteristics of patients receiving DTx recommendations and describe provider characteristics and behaviors regarding DTx. Methods: This retrospective cohort study used electronic health record data from a large, integrated health care delivery system. Demographic and clinical characteristics of adult patients recommended versus not recommended DTx by a mental health provider between May 2020 and December 2021 were examined. A cross-sectional survey of mental health providers providing these recommendations was conducted in December 2022 to assess the characteristics of providers and recommendation behaviors related to DTx. Parametric and nonparametric tests were used to examine statistical significance between groups. Results: Of 335,250 patients with a mental health appointment, 53,546 (16%) received a DTx recommendation. Patients recommended to DTx were younger, were of Asian or Hispanic race or ethnicity, were female, were without medical comorbidities, and had commercial insurance compared to those without a DTx recommendation (P<.001). More patients receiving a DTx recommendation had anxiety or adjustment disorder diagnoses, but less had depression, bipolar, or psychotic disorder diagnoses (P<.001) versus matched controls not recommended to DTx. Overall, depression and anxiety symptom scores were lower in patients recommended to DTx compared to matched controls not receiving a recommendation, although female patients had a higher proportion of severe depression and anxiety scores compared to male patients. Provider survey results indicated a higher proportion of nonprescribers recommended DTx to patients compared to prescribers (P=.008). Of all providers, 29.4% (45/153) reported using the suggested internal electronic health record?based tools (eg, smart text) to recommend DTx, and of providers recommending DTx resources to patients, 64.1% (98/153) reported they follow up with patients to inquire on DTx benefits. Only 38.4% (58/151) of respondents report recommending specific DTx modules, and of those, 58.6% (34/58) report following up on the impact of these specific modules. Conclusions: DTx use in mental health was modest and varied by patient and provider characteristics. Providers do not appear to actively engage with these tools and integrate them into treatment plans. Providers, while expressing interest in potential benefits from DTx, may view DTx as a passive strategy to augment traditional treatment for select patients. UR - https://mental.jmir.org/2024/1/e56574 UR - http://dx.doi.org/10.2196/56574 ID - info:doi/10.2196/56574 ER - TY - JOUR AU - Salmi, Salim AU - Mérelle, Saskia AU - Gilissen, Renske AU - van der Mei, Rob AU - Bhulai, Sandjai PY - 2024/9/26 TI - The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach JO - JMIR Ment Health SP - e57362 VL - 11 KW - suicide KW - suicidality KW - suicide prevention KW - helpline KW - suicide helpline KW - classification KW - interpretable AI KW - explainable AI KW - conversations KW - BERT KW - bidirectional encoder representations from transformers KW - machine learning KW - artificial intelligence KW - large language models KW - LLM KW - natural language processing N2 - Background: For the provision of optimal care in a suicide prevention helpline, it is important to know what contributes to positive or negative effects on help seekers. Helplines can often be contacted through text-based chat services, which produce large amounts of text data for use in large-scale analysis. Objective: We trained a machine learning classification model to predict chat outcomes based on the content of the chat conversations in suicide helplines and identified the counsellor utterances that had the most impact on its outputs. Methods: From August 2021 until January 2023, help seekers (N=6903) scored themselves on factors known to be associated with suicidality (eg, hopelessness, feeling entrapped, will to live) before and after a chat conversation with the suicide prevention helpline in the Netherlands (113 Suicide Prevention). Machine learning text analysis was used to predict help seeker scores on these factors. Using 2 approaches for interpreting machine learning models, we identified text messages from helpers in a chat that contributed the most to the prediction of the model. Results: According to the machine learning model, helpers? positive affirmations and expressing involvement contributed to improved scores of the help seekers. Use of macros and ending the chat prematurely due to the help seeker being in an unsafe situation had negative effects on help seekers. Conclusions: This study reveals insights for improving helpline chats, emphasizing the value of an evocative style with questions, positive affirmations, and practical advice. It also underscores the potential of machine learning in helpline chat analysis. UR - https://mental.jmir.org/2024/1/e57362 UR - http://dx.doi.org/10.2196/57362 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57362 ER - TY - JOUR AU - Shen, Jocelyn AU - DiPaola, Daniella AU - Ali, Safinah AU - Sap, Maarten AU - Park, Won Hae AU - Breazeal, Cynthia PY - 2024/9/25 TI - Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study JO - JMIR Ment Health SP - e62679 VL - 11 KW - empathy KW - large language models KW - ethics KW - transparency KW - crowdsourcing KW - human-computer interaction N2 - Background: Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions. Objective: We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy. Methods: We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user?s self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers. Results: We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=?5.49, P<.001, Cohen d=0.36). Conclusions: Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots. UR - https://mental.jmir.org/2024/1/e62679 UR - http://dx.doi.org/10.2196/62679 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62679 ER - TY - JOUR AU - French, Blandine AU - Babbage, Camilla AU - Bird, Katherine AU - Marsh, Lauren AU - Pelton, Mirabel AU - Patel, Shireen AU - Cassidy, Sarah AU - Rennick-Egglestone, Stefan PY - 2024/9/16 TI - Data Integrity Issues With Web-Based Studies: An Institutional Example of a Widespread Challenge JO - JMIR Ment Health SP - e58432 VL - 11 KW - web-based research KW - web-based studies KW - qualitative studies KW - surveys KW - mental health KW - data integrity, misrepresentation UR - https://mental.jmir.org/2024/1/e58432 UR - http://dx.doi.org/10.2196/58432 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58432 ER - TY - JOUR AU - Badal, D. Varsha AU - Reinen, M. Jenna AU - Twamley, W. Elizabeth AU - Lee, E. Ellen AU - Fellows, P. Robert AU - Bilal, Erhan AU - Depp, A. Colin PY - 2024/9/16 TI - Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study JO - JMIR Aging SP - e54655 VL - 7 KW - acoustic KW - psycholinguistic KW - speech KW - speech marker KW - speech markers KW - cognitive impairment KW - CI KW - mild cognitive impairment KW - MCI KW - cognitive disability KW - cognitive restriction KW - cognitive limitation KW - machine learning KW - ML KW - artificial intelligence KW - AI KW - algorithm KW - algorithms KW - predictive model KW - predictive models KW - predictive analytics KW - predictive system KW - practical model KW - practical models KW - early warning KW - early detection KW - NLP KW - natural language processing KW - Alzheimer KW - dementia KW - neurological decline KW - neurocognition KW - neurocognitive disorder N2 - Background: About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied. Objective: This study aims to establish associations between cognitive abilities and various attributes of speech and natural language production. To date, the majority of research has been cross-sectional, relying mostly on data from structured interactions and restricted to textual versus acoustic analyses. Methods: In a sample of 71 older (mean age 83.3, SD 7.0 years) community-dwelling adults who completed qualitative interviews and cognitive testing, we investigated the performance of both acoustic and psycholinguistic features associated with cognitive deficits contemporaneously and at a 1-2 years follow up (mean follow-up time 512.3, SD 84.5 days). Results: Combined acoustic and psycholinguistic features achieved high performance (F1-scores 0.73-0.86) and sensitivity (up to 0.90) in estimating cognitive deficits across multiple domains. Performance remained high when acoustic and psycholinguistic features were used to predict follow-up cognitive performance. The psycholinguistic features that were most successful at classifying high cognitive impairment reflected vocabulary richness, the quantity of speech produced, and the fragmentation of speech, whereas the analogous top-ranked acoustic features reflected breathing and nonverbal vocalizations such as giggles or laughter. Conclusions: These results suggest that both acoustic and psycholinguistic features extracted from qualitative interviews may be reliable markers of cognitive deficits in late life. UR - https://aging.jmir.org/2024/1/e54655 UR - http://dx.doi.org/10.2196/54655 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54655 ER - TY - JOUR AU - Hall, L. Charlotte AU - Gómez Bergin, D. Aislinn AU - Rennick-Egglestone, Stefan PY - 2024/9/9 TI - Research Into Digital Health Intervention for Mental Health: 25-Year Retrospective on the Ethical and Legal Challenges JO - J Med Internet Res SP - e58939 VL - 26 KW - digital mental health intervention KW - research ethics KW - compliance KW - regulation KW - digital health KW - mobile health KW - mhealth KW - intervention KW - interventions KW - mental health KW - retrospective KW - ethical KW - legal KW - challenge KW - challenges UR - https://www.jmir.org/2024/1/e58939 UR - http://dx.doi.org/10.2196/58939 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58939 ER - TY - JOUR AU - Greene, Barry AU - Tobyne, Sean AU - Jannati, Ali AU - McManus, Killian AU - Gomes Osman, Joyce AU - Banks, Russell AU - Kher, Ranjit AU - Showalter, John AU - Bates, David AU - Pascual-Leone, Alvaro PY - 2024/8/19 TI - The Dual Task Ball Balancing Test and Its Association With Cognitive Function: Algorithm Development and Validation JO - J Med Internet Res SP - e49794 VL - 26 KW - cognitive function KW - dual task KW - inertial sensors KW - mHealth KW - tablet KW - MCI KW - Alzheimer KW - dementia KW - motor KW - older adults KW - cognitive impairment KW - balance? N2 - Background: Dual task paradigms are thought to offer a quantitative means to assess cognitive reserve and the brain?s capacity to allocate resources in the face of competing cognitive demands. The most common dual task paradigms examine the interplay between gait or balance control and cognitive function. However, gait and balance tasks can be physically challenging for older adults and may pose a risk of falls. Objective: We introduce a novel, digital dual-task assessment that combines a motor-control task (the ?ball balancing? test), which challenges an individual to maintain a virtual ball within a designated zone, with a concurrent cognitive task (the backward digit span task [BDST]). Methods: The task was administered on a touchscreen tablet, performance was measured using the inertial sensors embedded in the tablet, conducted under both single- and dual-task conditions. The clinical use of the task was evaluated on a sample of 375 older adult participants (n=210 female; aged 73.0, SD 6.5 years). Results: All older adults, including those with mild cognitive impairment (MCI) and Alzheimer disease?related dementia (ADRD), and those with poor balance and gait problems due to diabetes, osteoarthritis, peripheral neuropathy, and other causes, were able to complete the task comfortably and safely while seated. As expected, task performance significantly decreased under dual task conditions compared to single task conditions. We show that performance was significantly associated with cognitive impairment; significant differences were found among healthy participants, those with MCI, and those with ADRD. Task results were significantly associated with functional impairment, independent of diagnosis, degree of cognitive impairment (as indicated by the Mini Mental State Examination [MMSE] score), and age. Finally, we found that cognitive status could be classified with >70% accuracy using a range of classifier models trained on 3 different cognitive function outcome variables (consensus clinical judgment, Rey Auditory Verbal Learning Test [RAVLT], and MMSE). Conclusions: Our results suggest that the dual task ball balancing test could be used as a digital cognitive assessment of cognitive reserve. The portability, simplicity, and intuitiveness of the task suggest that it may be suitable for unsupervised home assessment of cognitive function. UR - https://www.jmir.org/2024/1/e49794 UR - http://dx.doi.org/10.2196/49794 UR - http://www.ncbi.nlm.nih.gov/pubmed/39158963 ID - info:doi/10.2196/49794 ER - TY - JOUR AU - Yan, Yifei AU - Li, Jun AU - Liu, Xingyun AU - Li, Qing AU - Yu, Xiaonan Nancy PY - 2024/8/8 TI - Identifying Reddit Users at a High Risk of Suicide and Their Linguistic Features During the COVID-19 Pandemic: Growth-Based Trajectory Model JO - J Med Internet Res SP - e48907 VL - 26 KW - COVID-19 pandemic KW - Reddit KW - suicide risk KW - trajectory N2 - Background: Suicide has emerged as a critical public health concern during the COVID-19 pandemic. With social distancing measures in place, social media has become a significant platform for individuals expressing suicidal thoughts and behaviors. However, existing studies on suicide using social media data often overlook the diversity among users and the temporal dynamics of suicide risk. Objective: By examining the variations in post volume trajectories among users on the r/SuicideWatch subreddit during the COVID-19 pandemic, this study aims to investigate the heterogeneous patterns of change in suicide risk to help identify social media users at high risk of suicide. We also characterized their linguistic features before and during the pandemic. Methods: We collected and analyzed post data every 6 months from March 2019 to August 2022 for users on the r/SuicideWatch subreddit (N=6163). A growth-based trajectory model was then used to investigate the trajectories of post volume to identify patterns of change in suicide risk during the pandemic. Trends in linguistic features within posts were also charted and compared, and linguistic markers were identified across the trajectory groups using regression analysis. Results: We identified 2 distinct trajectories of post volume among r/SuicideWatch subreddit users. A small proportion of users (744/6163, 12.07%) was labeled as having a high risk of suicide, showing a sharp and lasting increase in post volume during the pandemic. By contrast, most users (5419/6163, 87.93%) were categorized as being at low risk of suicide, with a consistently low and mild increase in post volume during the pandemic. In terms of the frequency of most linguistic features, both groups showed increases at the initial stage of the pandemic. Subsequently, the rising trend continued in the high-risk group before declining, while the low-risk group showed an immediate decrease. One year after the pandemic outbreak, the 2 groups exhibited differences in their use of words related to the categories of personal pronouns; affective, social, cognitive, and biological processes; drives; relativity; time orientations; and personal concerns. In particular, the high-risk group was discriminant in using words related to anger (odds ratio [OR] 3.23, P<.001), sadness (OR 3.23, P<.001), health (OR 2.56, P=.005), achievement (OR 1.67, P=.049), motion (OR 4.17, P<.001), future focus (OR 2.86, P<.001), and death (OR 4.35, P<.001) during this stage. Conclusions: Based on the 2 identified trajectories of post volume during the pandemic, this study divided users on the r/SuicideWatch subreddit into suicide high- and low-risk groups. Our findings indicated heterogeneous patterns of change in suicide risk in response to the pandemic. The high-risk group also demonstrated distinct linguistic features. We recommend conducting real-time surveillance of suicide risk using social media data during future public health crises to provide timely support to individuals at potentially high risk of suicide. UR - https://www.jmir.org/2024/1/e48907 UR - http://dx.doi.org/10.2196/48907 UR - http://www.ncbi.nlm.nih.gov/pubmed/39115925 ID - info:doi/10.2196/48907 ER - TY - JOUR AU - Lawrence, R. Hannah AU - Schneider, A. Renee AU - Rubin, B. Susan AU - Matari?, J. Maja AU - McDuff, J. Daniel AU - Jones Bell, Megan PY - 2024/7/29 TI - The Opportunities and Risks of Large Language Models in Mental Health JO - JMIR Ment Health SP - e59479 VL - 11 KW - artificial intelligence KW - AI KW - generative AI KW - large language models KW - mental health KW - mental health education KW - language model KW - mental health care KW - health equity KW - ethical KW - development KW - deployment UR - https://mental.jmir.org/2024/1/e59479 UR - http://dx.doi.org/10.2196/59479 ID - info:doi/10.2196/59479 ER - TY - JOUR AU - Omisore, Mumini Olatunji AU - Odenigbo, Ifeanyi AU - Orji, Joseph AU - Beltran, Hernandez Amelia Itzel AU - Meier, Sandra AU - Baghaei, Nilufar AU - Orji, Rita PY - 2024/7/24 TI - Extended Reality for Mental Health Evaluation: Scoping Review JO - JMIR Serious Games SP - e38413 VL - 12 KW - extended reality KW - mental disorder KW - depression KW - anxiety KW - exposure therapy N2 - Background: Mental health disorders are the leading cause of health-related problems worldwide. It is projected that mental health disorders will be the leading cause of morbidity among adults as the incidence rates of anxiety and depression grow worldwide. Recently, ?extended reality? (XR), a general term covering virtual reality (VR), augmented reality (AR), and mixed reality (MR), is paving the way for the delivery of mental health care. Objective: We aimed to investigate the adoption and implementation of XR technology used in interventions for mental disorders and to provide statistical analyses of the design, usage, and effectiveness of XR technology for mental health interventions with a worldwide demographic focus. Methods: In this paper, we conducted a scoping review of the development and application of XR in the area of mental disorders. We performed a database search to identify relevant studies indexed in Google Scholar, PubMed, and the ACM Digital Library. A search period between August 2016 and December 2023 was defined to select papers related to the usage of VR, AR, and MR in a mental health context. The database search was performed with predefined queries, and a total of 831 papers were identified. Ten papers were identified through professional recommendation. Inclusion and exclusion criteria were designed and applied to ensure that only relevant studies were included in the literature review. Results: We identified a total of 85 studies from 27 countries worldwide that used different types of VR, AR, and MR techniques for managing 14 types of mental disorders. By performing data analysis, we found that most of the studies focused on high-income countries, such as the United States (n=14, 16.47%) and Germany (n=12, 14.12%). None of the studies were for African countries. The majority of papers reported that XR techniques lead to a significant reduction in symptoms of anxiety or depression. The majority of studies were published in 2021 (n=26, 30.59%). This could indicate that mental disorder intervention received higher attention when COVID-19 emerged. Most studies (n=65, 76.47%) focused on a population in the age range of 18-65 years, while few studies (n=2, 3.35%) focused on teenagers (ie, subjects in the age range of 10-19 years). In addition, more studies were conducted experimentally (n=67, 78.82%) rather than by using analytical and modeling approaches (n=8, 9.41%). This shows that there is a rapid development of XR technology for mental health care. Furthermore, these studies showed that XR technology can effectively be used for evaluating mental disorders in a similar or better way that conventional approaches. Conclusions: In this scoping review, we studied the adoption and implementation of XR technology for mental disorder care. Our review shows that XR treatment yields high patient satisfaction, and follow-up assessments show significant improvement with large effect sizes. Moreover, the studies adopted unique designs that were set up to record and analyze the symptoms reported by their participants. This review may aid future research and development of various XR mechanisms for differentiated mental disorder procedures. UR - https://games.jmir.org/2024/1/e38413 UR - http://dx.doi.org/10.2196/38413 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/38413 ER - TY - JOUR AU - Matson, E. Theresa AU - Lee, K. Amy AU - Oliver, Malia AU - Bradley, A. Katharine AU - Hallgren, A. Kevin PY - 2024/7/22 TI - Equivalence of Alcohol Use Disorder Symptom Assessments in Routine Clinical Care When Completed Remotely via Online Patient Portals Versus In Clinic via Paper Questionnaires: Psychometric Evaluation JO - J Med Internet Res SP - e52101 VL - 26 KW - alcohol KW - alcohol use disorder KW - assessment KW - symptom checklist KW - electronic health record KW - patient portal KW - item response theory KW - differential item functioning KW - alcohol use KW - patient portals KW - in-clinic KW - psychometric evaluation KW - alcoholism KW - cross-sectional KW - United States N2 - Background: The National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommends the paper-based or computerized Alcohol Symptom Checklist to assess alcohol use disorder (AUD) symptoms in routine care when patients report high-risk drinking. However, it is unknown whether Alcohol Symptom Checklist response characteristics differ when it is administered online (eg, remotely via an online electronic health record [EHR] patient portal before an appointment) versus in clinic (eg, on paper after appointment check-in). Objective: This study evaluated the psychometric performance of the Alcohol Symptom Checklist when completed online versus in clinic during routine clinical care. Methods: This cross-sectional, psychometric study obtained EHR data from the Alcohol Symptom Checklist completed by adult patients from an integrated health system in Washington state. The sample included patients who had a primary care visit in 2021 at 1 of 32 primary care practices, were due for annual behavioral health screening, and reported high-risk drinking on the behavioral health screen (Alcohol Use Disorder Identification Test?Consumption score ?7). After screening, patients with high-risk drinking were typically asked to complete the Alcohol Symptom Checklist?an 11-item questionnaire on which patients self-report whether they had experienced each of the 11 AUD criteria listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) over a past-year timeframe. Patients could complete the Alcohol Symptom Checklist online (eg, on a computer, smartphone, or tablet from any location) or in clinic (eg, on paper as part of the rooming process at clinical appointments). We examined sample and measurement characteristics and conducted differential item functioning analyses using item response theory to examine measurement consistency across these 2 assessment modalities. Results: Among 3243 patients meeting eligibility criteria for this secondary analysis (2313/3243, 71% male; 2271/3243, 70% White; and 2014/3243, 62% non-Hispanic), 1640 (51%) completed the Alcohol Symptom Checklist online while 1603 (49%) completed it in clinic. Approximately 46% (752/1640) and 48% (764/1603) reported ?2 AUD criteria (the threshold for AUD diagnosis) online and in clinic (P=.37), respectively. A small degree of differential item functioning was observed for 4 of 11 items. This differential item functioning produced only minimal impact on total scores used clinically to assess AUD severity, affecting total criteria count by a maximum of 0.13 criteria (on a scale ranging from 0 to 11). Conclusions: Completing the Alcohol Symptom Checklist online, typically prior to patient check-in, performed similarly to an in-clinic modality typically administered on paper by a medical assistant at the time of the appointment. Findings have implications for using online AUD symptom assessments to streamline workflows, reduce staff burden, reduce stigma, and potentially assess patients who do not receive in-person care. Whether modality of DSM-5 assessment of AUD differentially impacts treatment is unknown. UR - https://www.jmir.org/2024/1/e52101 UR - http://dx.doi.org/10.2196/52101 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52101 ER - TY - JOUR AU - Bisby, A. Madelyne AU - Jones, P. Michael AU - Staples, Lauren AU - Dear, Blake AU - Titov, Nickolai PY - 2024/7/22 TI - Measurement of Daily Actions Associated With Mental Health Using the Things You Do Questionnaire?15-Item: Questionnaire Development and Validation Study JO - JMIR Form Res SP - e57804 VL - 8 KW - daily actions KW - depression KW - anxiety KW - psychometric KW - mental health KW - questionnaire KW - activities KW - goals KW - plans KW - healthy habits KW - habits KW - treatment-seeking KW - treatment KW - confirmatory factor analysis KW - survey KW - adult KW - assessment KW - digital psychology service KW - digital KW - psychology KW - depression symptoms KW - anxiety symptoms N2 - Background: A large number of modifiable and measurable daily actions are thought to impact mental health. The ?Things You Do? refers to 5 types of daily actions that have been associated with mental health: healthy thinking, meaningful activities, goals and plans, healthy habits, and social connections. Previous studies have reported the psychometric properties of the Things You Do Questionnaire (TYDQ)?21-item (TYDQ21). The 21-item version, however, has an uneven distribution of items across the 5 aforementioned factors and may be lengthy to administer on a regular basis. Objective: This study aimed to develop and evaluate a brief version of the TYDQ. To accomplish this, we identified the top 10 and 15 items on the TYDQ21 and then evaluated the performance of the 10-item and 15-item versions of the TYDQ in community and treatment-seeking samples. Methods: Using confirmatory factor analysis, the top 2 or 3 items were used to develop the 10-item and 15-item versions, respectively. Model fit, reliability, and validity were examined for both versions in 2 samples: a survey of community adults (n=6070) and adults who completed an assessment at a digital psychology service (n=14,878). Treatment responsivity was examined in a subgroup of participants (n=448). Results: Parallel analysis supported the 5-factor structure of the TYDQ. The brief (10-item and 15-item) versions were associated with better model fit than the 21-item version, as revealed by its comparative fit index, root-mean-square error of approximation, and Tucker-Lewis index. Configural, metric, and scalar invariance were supported. The 15-item version explained more variance in the 21-item scores than the 10-item version. Internal consistency was appropriate (eg, the 15-item version had a Cronbach ? of >0.90 in both samples) and there were no marked differences between how the brief versions correlated with validated measures of depression or anxiety symptoms. The measure was responsive to treatment. Conclusions: The 15-item version is appropriate for use as a brief measure of daily actions associated with mental health while balancing brevity and clinical utility. Further research is encouraged to replicate our psychometric evaluation in other settings (eg, face-to-face services). Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12613000407796; https://tinyurl.com/2s67a6ps UR - https://formative.jmir.org/2024/1/e57804 UR - http://dx.doi.org/10.2196/57804 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57804 ER - TY - JOUR AU - Hernandez, Raymond AU - Hoogendoorn, Claire AU - Gonzalez, S. Jeffrey AU - Pyatak, A. Elizabeth AU - Crespo-Ramos, Gladys AU - Schneider, Stefan PY - 2024/7/18 TI - Reliability and Validity of Ecological Momentary Assessment Response Time?Based Measures of Emotional Clarity: Secondary Data Analysis JO - JMIR Ment Health SP - e58352 VL - 11 KW - digital mental health KW - drift-diffusion model KW - ecological momentary assessment KW - emotional clarity KW - emotional health KW - emotion regulation KW - response time KW - positive affect KW - negative affect KW - type 1 diabetes KW - mobile phone N2 - Background: Emotional clarity has often been assessed with self-report measures, but efforts have also been made to measure it passively, which has advantages such as avoiding potential inaccuracy in responses stemming from social desirability bias or poor insight into emotional clarity. Response times (RTs) to emotion items administered in ecological momentary assessments (EMAs) may be an indirect indicator of emotional clarity. Another proposed indicator is the drift rate parameter, which assumes that, aside from how fast a person responds to emotion items, the measurement of emotional clarity also requires the consideration of how careful participants were in providing responses. Objective: This paper aims to examine the reliability and validity of RTs and drift rate parameters from EMA emotion items as indicators of individual differences in emotional clarity. Methods: Secondary data analysis was conducted on data from 196 adults with type 1 diabetes who completed a 2-week EMA study involving the completion of 5 to 6 surveys daily. If lower RTs and higher drift rates (from EMA emotion items) were indicators of emotional clarity, we hypothesized that greater levels (ie, higher clarity) should be associated with greater life satisfaction; lower levels of neuroticism, depression, anxiety, and diabetes distress; and fewer difficulties with emotion regulation. Because prior literature suggested emotional clarity could be valence specific, EMA items for negative affect (NA) and positive affect were examined separately. Results: Reliability of the proposed indicators of emotional clarity was acceptable with a small number of EMA prompts (ie, 4 to 7 prompts in total or 1 to 2 days of EMA surveys). Consistent with expectations, the average drift rate of NA items across multiple EMAs had expected associations with other measures, such as correlations of r=?0.27 (P<.001) with depression symptoms, r=?0.27 (P=.001) with anxiety symptoms, r=?0.15 (P=.03) with emotion regulation difficulties, and r=0.63 (P<.001) with RTs to NA items. People with a higher NA drift rate responded faster to NA emotion items, had greater subjective well-being (eg, fewer depression symptoms), and had fewer difficulties with overall emotion regulation, which are all aligned with the expectation for an emotional clarity measure. Contrary to expectations, the validities of average RTs to NA items, the drift rate of positive affect items, and RTs to positive affect items were not strongly supported by our results. Conclusions: Study findings provided initial support for the validity of NA drift rate as an indicator of emotional clarity but not for that of other RT-based clarity measures. Evidence was preliminary because the sample size was not sufficient to detect small but potentially meaningful correlations, as the sample size of the diabetes EMA study was chosen for other more primary research questions. Further research on passive emotional clarity measures is needed. UR - https://mental.jmir.org/2024/1/e58352 UR - http://dx.doi.org/10.2196/58352 UR - http://www.ncbi.nlm.nih.gov/pubmed/39024004 ID - info:doi/10.2196/58352 ER - TY - JOUR AU - Chen, Hung-Hsun AU - Lin, Chen AU - Chang, Hsiang-Chih AU - Chang, Jen-Ho AU - Chuang, Hai-Hua AU - Lin, Yu-Hsuan PY - 2024/6/17 TI - Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities JO - J Med Internet Res SP - e56144 VL - 26 KW - digital phenotyping KW - human-smartphone interaction KW - labor or leisure KW - machine learning KW - mental activity KW - physical activity N2 - Background: Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human biological rhythms. Objective: This study proposes a novel approach based on human-smartphone interaction to compute mental activity, encompassing general mental activity (GMA) and working mental activity (WMA). Methods: A total of 24 health care professionals participated, wearing wrist actigraphy devices and using the ?Staff Hours? app for more than 457 person-days, including 332 workdays and 125 nonworkdays. PA was measured using actigraphy, while GMA and WMA were assessed based on patterns of smartphone interactions. To model WMA, machine learning techniques such as extreme gradient boosting and convolutional neural networks were applied, using human-smartphone interaction patterns and GPS-defined work hours. The data were organized by date and divided into person-days, with an 80:20 split for training and testing data sets to minimize overfitting and maximize model robustness. The study also adopted the M10 metric to quantify daily activity levels by calculating the average acceleration during the 10-hour period of highest activity each day, which facilitated the assessment of the interrelations between PA, GMA, and WMA and sleep indicators. Phase differences, such as those between PA and GMA, were defined using a second-order Butterworth filter and Hilbert transform to extract and calculate circadian rhythms and instantaneous phases. This calculation involved subtracting the phase of the reference signal from that of the target signal and averaging these differences to provide a stable and clear measure of the phase relationship between the signals. Additionally, multilevel modeling explored associations between sleep indicators (total sleep time, midpoint of sleep) and next-day activity levels, accounting for the data?s nested structure. Results: Significant differences in activity levels were noted between workdays and nonworkdays, with WMA occurring approximately 1.08 hours earlier than PA during workdays (P<.001). Conversely, GMA was observed to commence about 1.22 hours later than PA (P<.001). Furthermore, a significant negative correlation was identified between the activity level of WMA and the previous night?s midpoint of sleep (?=?0.263, P<.001), indicating that later bedtimes and wake times were linked to reduced activity levels in WMA the following day. However, there was no significant correlation between WMA?s activity levels and total sleep time. Similarly, no significant correlations were found between the activity levels of PA and GMA and sleep indicators from the previous night. Conclusions: This study significantly advances the understanding of human biological rhythms by developing and highlighting GMA and WMA as key indicators, derived from human-smartphone interactions. These findings offer novel insights into how mental activities, alongside PA, are intricately linked to sleep patterns, emphasizing the potential of GMA and WMA in behavioral and health studies. UR - https://www.jmir.org/2024/1/e56144 UR - http://dx.doi.org/10.2196/56144 UR - http://www.ncbi.nlm.nih.gov/pubmed/38885499 ID - info:doi/10.2196/56144 ER - TY - JOUR AU - Kim, Hyejun AU - Son, Yejun AU - Lee, Hojae AU - Kang, Jiseung AU - Hammoodi, Ahmed AU - Choi, Yujin AU - Kim, Jin Hyeon AU - Lee, Hayeon AU - Fond, Guillaume AU - Boyer, Laurent AU - Kwon, Rosie AU - Woo, Selin AU - Yon, Keon Dong PY - 2024/5/17 TI - Machine Learning?Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study JO - J Med Internet Res SP - e55913 VL - 26 KW - adolescent KW - machine learning KW - Shapley additive explanations KW - SHAP value KW - suicidal thinking KW - XGBoost KW - mental health KW - predictive model KW - risk behavior N2 - Background: Suicide is the second-leading cause of death among adolescents and is associated with clusters of suicides. Despite numerous studies on this preventable cause of death, the focus has primarily been on single nations and traditional statistical methods. Objective: This study aims to develop a predictive model for adolescent suicidal thinking using multinational data sets and machine learning (ML). Methods: We used data from the Korea Youth Risk Behavior Web-based Survey with 566,875 adolescents aged between 13 and 18 years and conducted external validation using the Youth Risk Behavior Survey with 103,874 adolescents and Norway?s University National General Survey with 19,574 adolescents. Several tree-based ML models were developed, and feature importance and Shapley additive explanations values were analyzed to identify risk factors for adolescent suicidal thinking. Results: When trained on the Korea Youth Risk Behavior Web-based Survey data from South Korea with a 95% CI, the XGBoost model reported an area under the receiver operating characteristic (AUROC) curve of 90.06% (95% CI 89.97-90.16), displaying superior performance compared to other models. For external validation using the Youth Risk Behavior Survey data from the United States and the University National General Survey from Norway, the XGBoost model achieved AUROCs of 83.09% and 81.27%, respectively. Across all data sets, XGBoost consistently outperformed the other models with the highest AUROC score, and was selected as the optimal model. In terms of predictors of suicidal thinking, feelings of sadness and despair were the most influential, accounting for 57.4% of the impact, followed by stress status at 19.8%. This was followed by age (5.7%), household income (4%), academic achievement (3.4%), sex (2.1%), and others, which contributed less than 2% each. Conclusions: This study used ML by integrating diverse data sets from 3 countries to address adolescent suicide. The findings highlight the important role of emotional health indicators in predicting suicidal thinking among adolescents. Specifically, sadness and despair were identified as the most significant predictors, followed by stressful conditions and age. These findings emphasize the critical need for early diagnosis and prevention of mental health issues during adolescence. UR - https://www.jmir.org/2024/1/e55913 UR - http://dx.doi.org/10.2196/55913 UR - http://www.ncbi.nlm.nih.gov/pubmed/38758578 ID - info:doi/10.2196/55913 ER - TY - JOUR AU - Fernandes, Sara AU - Brousse, Yann AU - Zendjidjian, Xavier AU - Cano, Delphine AU - Riedberger, Jérémie AU - Llorca, Pierre-Michel AU - Samalin, Ludovic AU - Dassa, Daniel AU - Trichard, Christian AU - Laprevote, Vincent AU - Sauvaget, Anne AU - Abbar, Mocrane AU - Misdrahi, David AU - Berna, Fabrice AU - Lancon, Christophe AU - Coulon, Nathalie AU - El-Hage, Wissam AU - Rozier, Pierre-Emmanuel AU - Benoit, Michel AU - Giordana, Bruno AU - Caqueo-Urízar, Alejandra AU - Yon, Keon Dong AU - Tran, Bach AU - Auquier, Pascal AU - Fond, Guillaume AU - Boyer, Laurent PY - 2024/5/16 TI - Psychometric Assessment of an Item Bank for Adaptive Testing on Patient-Reported Experience of Care Environment for Severe Mental Illness: Validation Study JO - JMIR Ment Health SP - e49916 VL - 11 KW - psychiatry KW - public mental health KW - schizophrenia KW - major depressive disorders KW - bipolar disorders KW - patient-reported experience measures KW - quality of care KW - health services research KW - computerized adaptive testing KW - real-world data N2 - Background: The care environment significantly influences the experiences of patients with severe mental illness and the quality of their care. While a welcoming and stimulating environment enhances patient satisfaction and health outcomes, psychiatric facilities often prioritize staff workflow over patient needs. Addressing these challenges is crucial to improving patient experiences and outcomes in mental health care. Objective: This study is part of the Patient-Reported Experience Measure for Improving Quality of Care in Mental Health (PREMIUM) project and aims to establish an item bank (PREMIUM-CE) and to develop computerized adaptive tests (CATs) to measure the experience of the care environment of adult patients with schizophrenia, bipolar disorder, or major depressive disorder. Methods: We performed psychometric analyses including assessments of item response theory (IRT) model assumptions, IRT model fit, differential item functioning (DIF), item bank validity, and CAT simulations. Results: In this multicenter cross-sectional study, 498 patients were recruited from outpatient and inpatient settings. The final PREMIUM-CE 13-item bank was sufficiently unidimensional (root mean square error of approximation=0.082, 95% CI 0.067-0.097; comparative fit index=0.974; Tucker-Lewis index=0.968) and showed an adequate fit to the IRT model (infit mean square statistic ranging between 0.7 and 1.0). DIF analysis revealed no item biases according to gender, health care settings, diagnosis, or mode of study participation. PREMIUM-CE scores correlated strongly with satisfaction measures (r=0.69-0.78; P<.001) and weakly with quality-of-life measures (r=0.11-0.21; P<.001). CAT simulations showed a strong correlation (r=0.98) between CAT scores and those of the full item bank, and around 79.5% (396/498) of the participants obtained a reliable score with the administration of an average of 7 items. Conclusions: The PREMIUM-CE item bank and its CAT version have shown excellent psychometric properties, making them reliable measures for evaluating the patient experience of the care environment among adults with severe mental illness in both outpatient and inpatient settings. These measures are a valuable addition to the existing landscape of patient experience assessment, capturing what truly matters to patients and enhancing the understanding of their care experiences. Trial Registration: ClinicalTrials.gov NCT02491866; https://clinicaltrials.gov/study/NCT02491866 UR - https://mental.jmir.org/2024/1/e49916 UR - http://dx.doi.org/10.2196/49916 UR - http://www.ncbi.nlm.nih.gov/pubmed/38753416 ID - info:doi/10.2196/49916 ER - TY - JOUR AU - Attarha, Mouna AU - Mahncke, Henry AU - Merzenich, Michael PY - 2024/5/13 TI - The Real-World Usability, Feasibility, and Performance Distributions of Deploying a Digital Toolbox of Computerized Assessments to Remotely Evaluate Brain Health: Development and Usability Study JO - JMIR Form Res SP - e53623 VL - 8 KW - web-based cognitive assessment KW - remote data collection KW - neurocognition KW - cognitive profiles KW - normative assessment data KW - brain health KW - cognitive status KW - assessment accessibility N2 - Background: An ongoing global challenge is managing brain health and understanding how performance changes across the lifespan. Objective: We developed and deployed a set of self-administrable, computerized assessments designed to measure key indexes of brain health across the visual and auditory sensory modalities. In this pilot study, we evaluated the usability, feasibility, and performance distributions of the assessments in a home-based, real-world setting without supervision. Methods: Potential participants were untrained users who self-registered on an existing brain training app called BrainHQ. Participants were contacted via a recruitment email and registered remotely to complete a demographics questionnaire and 29 unique assessments on their personal devices. We examined participant engagement, descriptive and psychometric properties of the assessments, associations between performance and self-reported demographic variables, cognitive profiles, and factor loadings. Results: Of the 365,782 potential participants contacted via a recruitment email, 414 (0.11%) registered, of whom 367 (88.6%) completed at least one assessment and 104 (25.1%) completed all 29 assessments. Registered participants were, on average, aged 63.6 (SD 14.8; range 13-107) years, mostly female (265/414, 64%), educated (329/414, 79.5% with a degree), and White (349/414, 84.3% White and 48/414, 11.6% people of color). A total of 72% (21/29) of the assessments showed no ceiling or floor effects or had easily modifiable score bounds to eliminate these effects. When correlating performance with self-reported demographic variables, 72% (21/29) of the assessments were sensitive to age, 72% (21/29) of the assessments were insensitive to gender, 93% (27/29) of the assessments were insensitive to race and ethnicity, and 93% (27/29) of the assessments were insensitive to education-based differences. Assessments were brief, with a mean duration of 3 (SD 1.0) minutes per task. The pattern of performance across the assessments revealed distinctive cognitive profiles and loaded onto 4 independent factors. Conclusions: The assessments were both usable and feasible and warrant a full normative study. A digital toolbox of scalable and self-administrable assessments that can evaluate brain health at a glance (and longitudinally) may lead to novel future applications across clinical trials, diagnostics, and performance optimization. UR - https://formative.jmir.org/2024/1/e53623 UR - http://dx.doi.org/10.2196/53623 UR - http://www.ncbi.nlm.nih.gov/pubmed/38739916 ID - info:doi/10.2196/53623 ER - TY - JOUR AU - Hirten, P. Robert AU - Danieletto, Matteo AU - Landell, Kyle AU - Zweig, Micol AU - Golden, Eddye AU - Pyzik, Renata AU - Kaur, Sparshdeep AU - Chang, Helena AU - Helmus, Drew AU - Sands, E. Bruce AU - Charney, Dennis AU - Nadkarni, Girish AU - Bagiella, Emilia AU - Keefer, Laurie AU - Fayad, A. Zahi PY - 2024/4/25 TI - Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study JO - JMIR Ment Health SP - e55552 VL - 11 KW - biofeedback KW - digital health KW - digital technology KW - health care worker KW - HCW KW - heart rate variability KW - mHealth KW - mobile health KW - mobile phone KW - remote monitoring KW - smartphone KW - wearable devices N2 - Background: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. Objective: The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. Methods: To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. Results: In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. Conclusions: In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions. UR - https://mental.jmir.org/2024/1/e55552 UR - http://dx.doi.org/10.2196/55552 UR - http://www.ncbi.nlm.nih.gov/pubmed/38663011 ID - info:doi/10.2196/55552 ER - TY - JOUR AU - Hadar-Shoval, Dorit AU - Asraf, Kfir AU - Mizrachi, Yonathan AU - Haber, Yuval AU - Elyoseph, Zohar PY - 2024/4/9 TI - Assessing the Alignment of Large Language Models With Human Values for Mental Health Integration: Cross-Sectional Study Using Schwartz?s Theory of Basic Values JO - JMIR Ment Health SP - e55988 VL - 11 KW - large language models KW - LLMs KW - large language model KW - LLM KW - machine learning KW - ML KW - natural language processing KW - NLP KW - deep learning KW - ChatGPT KW - Chat-GPT KW - chatbot KW - chatbots KW - chat-bot KW - chat-bots KW - Claude KW - values KW - Bard KW - artificial intelligence KW - AI KW - algorithm KW - algorithms KW - predictive model KW - predictive models KW - predictive analytics KW - predictive system KW - practical model KW - practical models KW - mental health KW - mental illness KW - mental illnesses KW - mental disease KW - mental diseases KW - mental disorder KW - mental disorders KW - mobile health KW - mHealth KW - eHealth KW - mood disorder KW - mood disorders N2 - Background: Large language models (LLMs) hold potential for mental health applications. However, their opaque alignment processes may embed biases that shape problematic perspectives. Evaluating the values embedded within LLMs that guide their decision-making have ethical importance. Schwartz?s theory of basic values (STBV) provides a framework for quantifying cultural value orientations and has shown utility for examining values in mental health contexts, including cultural, diagnostic, and therapist-client dynamics. Objective: This study aimed to (1) evaluate whether the STBV can measure value-like constructs within leading LLMs and (2) determine whether LLMs exhibit distinct value-like patterns from humans and each other. Methods: In total, 4 LLMs (Bard, Claude 2, Generative Pretrained Transformer [GPT]-3.5, GPT-4) were anthropomorphized and instructed to complete the Portrait Values Questionnaire?Revised (PVQ-RR) to assess value-like constructs. Their responses over 10 trials were analyzed for reliability and validity. To benchmark the LLMs? value profiles, their results were compared to published data from a diverse sample of 53,472 individuals across 49 nations who had completed the PVQ-RR. This allowed us to assess whether the LLMs diverged from established human value patterns across cultural groups. Value profiles were also compared between models via statistical tests. Results: The PVQ-RR showed good reliability and validity for quantifying value-like infrastructure within the LLMs. However, substantial divergence emerged between the LLMs? value profiles and population data. The models lacked consensus and exhibited distinct motivational biases, reflecting opaque alignment processes. For example, all models prioritized universalism and self-direction, while de-emphasizing achievement, power, and security relative to humans. Successful discriminant analysis differentiated the 4 LLMs? distinct value profiles. Further examination found the biased value profiles strongly predicted the LLMs? responses when presented with mental health dilemmas requiring choosing between opposing values. This provided further validation for the models embedding distinct motivational value-like constructs that shape their decision-making. Conclusions: This study leveraged the STBV to map the motivational value-like infrastructure underpinning leading LLMs. Although the study demonstrated the STBV can effectively characterize value-like infrastructure within LLMs, substantial divergence from human values raises ethical concerns about aligning these models with mental health applications. The biases toward certain cultural value sets pose risks if integrated without proper safeguards. For example, prioritizing universalism could promote unconditional acceptance even when clinically unwise. Furthermore, the differences between the LLMs underscore the need to standardize alignment processes to capture true cultural diversity. Thus, any responsible integration of LLMs into mental health care must account for their embedded biases and motivation mismatches to ensure equitable delivery across diverse populations. Achieving this will require transparency and refinement of alignment techniques to instill comprehensive human values. UR - https://mental.jmir.org/2024/1/e55988 UR - http://dx.doi.org/10.2196/55988 UR - http://www.ncbi.nlm.nih.gov/pubmed/38593424 ID - info:doi/10.2196/55988 ER - TY - JOUR AU - de Azevedo Cardoso, Taiane AU - Kochhar, Shruti AU - Torous, John AU - Morton, Emma PY - 2024/4/1 TI - Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions JO - JMIR Ment Health SP - e58631 VL - 11 KW - bipolar disorder KW - digital phenotyping KW - machine learning KW - mobile health interventions KW - mobile health KW - mHealth KW - apps UR - https://mental.jmir.org/2024/1/e58631 UR - http://dx.doi.org/10.2196/58631 UR - http://www.ncbi.nlm.nih.gov/pubmed/38557724 ID - info:doi/10.2196/58631 ER - TY - JOUR AU - Bilder, A. Deborah AU - Mthembu, Mariah AU - Worsham, Whitney AU - Aguayo, Patricia AU - Knight, R. Jacob AU - Deng, W. Steven AU - Singh, P. Tejinder AU - Davis, John PY - 2024/3/29 TI - Developing and Implementing a Web-Based Branching Logic Survey to Support Psychiatric Crisis Evaluations of Individuals With Developmental Disabilities: Qualitative Study and Evaluation of Validity JO - JMIR Ment Health SP - e50907 VL - 11 KW - developmental disabilities KW - disruptive behavior KW - psychiatric comorbidity KW - web-based KW - psychiatric crisis KW - disability KW - mental health KW - behavioral crises KW - intervention KW - general population KW - screening KW - accuracy KW - mood disorder KW - sources of distress KW - autism KW - intellectual disability N2 - Background: Individuals with developmental disabilities (DD) experience increased rates of emotional and behavioral crises that necessitate assessment and intervention. Psychiatric disorders can contribute to crises; however, screening measures developed for the general population are inadequate for those with DD. Medical conditions can exacerbate crises and merit evaluation. Screening tools using checklist formats, even when designed for DD, are too limited in depth and scope for crisis assessments. The Sources of Distress survey implements a web-based branching logic format to screen for common psychiatric and medical conditions experienced by individuals with DD by querying caregiver knowledge and observations. Objective: This paper aims to (1) describe the initial survey development, (2) report on focus group and expert review processes and findings, and (3) present results from the survey?s clinical implementation and evaluation of validity. Methods: Sources of Distress was reviewed by focus groups and clinical experts; this feedback informed survey revisions. The survey was subsequently implemented in clinical settings to augment providers? psychiatric and medical history taking. Informal and formal consults followed the completion of Sources of Distress for a subset of individuals. A records review was performed to identify working diagnoses established during these consults. Results: Focus group members (n=17) expressed positive feedback overall about the survey?s content and provided specific recommendations to add categories and items. The survey was completed for 231 individuals with DD in the clinical setting (n=161, 69.7% men and boys; mean age 17.7, SD 10.3; range 2-65 years). Consults were performed for 149 individuals (n=102, 68.5% men and boys; mean age 18.9, SD 10.9 years), generating working diagnoses to compare survey screening results. Sources of Distress accuracy rates were 91% (95% CI 85%-95%) for posttraumatic stress disorder, 87% (95% CI 81%-92%) for anxiety, 87% (95% CI 81%-92%) for episodic expansive mood and bipolar disorder, 82% (95% CI 75%-87%) for psychotic disorder, 79% (95% CI 71%-85%) for unipolar depression, and 76% (95% CI 69%-82%) for attention-deficit/hyperactivity disorder. While no specific survey items or screening algorithm existed for unspecified mood disorder and disruptive mood dysregulation disorder, these conditions were caregiver-reported and working diagnoses for 11.7% (27/231) and 16.8% (25/149) of individuals, respectively. Conclusions: Caregivers described Sources of Distress as an acceptable tool for sharing their knowledge and insights about individuals with DD who present in crisis. As a screening tool, this survey demonstrates good accuracy. However, better differentiation among mood disorders is needed, including the addition of items and screening algorithm for unspecified mood disorder and disruptive mood dysregulation disorder. Additional validation efforts are necessary to include a more geographically diverse population and reevaluate mood disorder differentiation. Future study is merited to investigate the survey?s impact on the psychiatric and medical management of distress in individuals with DD. UR - https://mental.jmir.org/2024/1/e50907 UR - http://dx.doi.org/10.2196/50907 UR - http://www.ncbi.nlm.nih.gov/pubmed/38551644 ID - info:doi/10.2196/50907 ER - TY - JOUR AU - Castro Ribeiro, Thais AU - García Pagès, Esther AU - Ballester, Laura AU - Vilagut, Gemma AU - García Mieres, Helena AU - Suárez Aragonès, Víctor AU - Amigo, Franco AU - Bailón, Raquel AU - Mortier, Philippe AU - Pérez Sola, Víctor AU - Serrano-Blanco, Antoni AU - Alonso, Jordi AU - Aguiló, Jordi PY - 2024/3/29 TI - Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study JO - JMIR Res Protoc SP - e51298 VL - 13 KW - mental health KW - mental well-being KW - mobile health KW - mHealth KW - remote monitoring KW - physiological variables KW - experimental protocol KW - depression KW - anxiety N2 - Background: Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual?s well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress. Objective: This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress. Methods: This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed. Results: Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024. Conclusions: This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies. Trial Registration: OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH International Registered Report Identifier (IRRID): DERR1-10.2196/51298 UR - https://www.researchprotocols.org/2024/1/e51298 UR - http://dx.doi.org/10.2196/51298 UR - http://www.ncbi.nlm.nih.gov/pubmed/38551647 ID - info:doi/10.2196/51298 ER - TY - JOUR AU - Kilshaw, E. Robyn AU - Boggins, Abigail AU - Everett, Olivia AU - Butner, Emma AU - Leifker, R. Feea AU - Baucom, W. Brian R. PY - 2024/3/27 TI - Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study JO - JMIR Res Protoc SP - e53857 VL - 13 KW - audio data KW - computational psychiatry KW - data repository KW - digital phenotyping KW - machine learning KW - passive sensor data N2 - Background: Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population. Objective: Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity. Methods: We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home). Results: Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy. Conclusions: This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field?s move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process. International Registered Report Identifier (IRRID): DERR1-10.2196/53857 UR - https://www.researchprotocols.org/2024/1/e53857 UR - http://dx.doi.org/10.2196/53857 UR - http://www.ncbi.nlm.nih.gov/pubmed/38536220 ID - info:doi/10.2196/53857 ER - TY - JOUR AU - Mychajliw, Christian AU - Holz, Heiko AU - Minuth, Nathalie AU - Dawidowsky, Kristina AU - Eschweiler, Wilhelm Gerhard AU - Metzger, Gerhard Florian AU - Wortha, Franz PY - 2024/3/21 TI - Performance Differences of a Touch-Based Serial Reaction Time Task in Healthy Older Participants and Older Participants With Cognitive Impairment on a Tablet: Experimental Study JO - JMIR Aging SP - e48265 VL - 7 KW - serial reaction time task KW - SRTT KW - implicit learning KW - mobile digital assessments KW - cognitive impairment KW - neurodegeneration KW - tablet-based testing KW - mild cognitive impairment KW - MCI KW - dementia KW - Alzheimer KW - neuropsychology KW - aging KW - older individuals N2 - Background: Digital neuropsychological tools for diagnosing neurodegenerative diseases in the older population are becoming more relevant and widely adopted because of their diagnostic capabilities. In this context, explicit memory is mainly examined. The assessment of implicit memory occurs to a lesser extent. A common measure for this assessment is the serial reaction time task (SRTT). Objective: This study aims to develop and empirically test a digital tablet?based SRTT in older participants with cognitive impairment (CoI) and healthy control (HC) participants. On the basis of the parameters of response accuracy, reaction time, and learning curve, we measure implicit learning and compare the HC and CoI groups. Methods: A total of 45 individuals (n=27, 60% HCs and n=18, 40% participants with CoI?diagnosed by an interdisciplinary team) completed a tablet-based SRTT. They were presented with 4 blocks of stimuli in sequence and a fifth block that consisted of stimuli appearing in random order. Statistical and machine learning modeling approaches were used to investigate how healthy individuals and individuals with CoI differed in their task performance and implicit learning. Results: Linear mixed-effects models showed that individuals with CoI had significantly higher error rates (b=?3.64, SE 0.86; z=?4.25; P<.001); higher reaction times (F1,41=22.32; P<.001); and lower implicit learning, measured via the response increase between sequence blocks and the random block (?=?0.34; SE 0.12; t=?2.81; P=.007). Furthermore, machine learning models based on these findings were able to reliably and accurately predict whether an individual was in the HC or CoI group, with an average prediction accuracy of 77.13% (95% CI 74.67%-81.33%). Conclusions: Our results showed that the HC and CoI groups differed substantially in their performance in the SRTT. This highlights the promising potential of implicit learning paradigms in the detection of CoI. The short testing paradigm based on these results is easy to use in clinical practice. UR - https://aging.jmir.org/2024/1/e48265 UR - http://dx.doi.org/10.2196/48265 UR - http://www.ncbi.nlm.nih.gov/pubmed/38512340 ID - info:doi/10.2196/48265 ER - TY - JOUR AU - Jacobucci, Ross AU - Ammerman, Brooke AU - Ram, Nilam PY - 2024/3/20 TI - Examining Passively Collected Smartphone-Based Data in the Days Prior to Psychiatric Hospitalization for a Suicidal Crisis: Comparative Case Analysis JO - JMIR Form Res SP - e55999 VL - 8 KW - screenomics KW - digital phenotyping KW - passive assessment KW - intensive time sampling KW - suicide risk KW - suicidal behaviors KW - risk detection KW - Comparative Analysis KW - suicide KW - suicidal KW - risk KW - risks KW - behavior KW - behaviors KW - detection KW - prediction KW - Smartphone-Based KW - screenomic KW - case review KW - participant KW - participants KW - smartphone KW - smartphones KW - suicidal ideation N2 - Background: Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots. Objective: This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research. Methods: To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots?text?captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one?s phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots). Results: Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness. Conclusions: Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual?s psychological processes in the time leading up to a suicidal crisis. UR - https://formative.jmir.org/2024/1/e55999 UR - http://dx.doi.org/10.2196/55999 UR - http://www.ncbi.nlm.nih.gov/pubmed/38506916 ID - info:doi/10.2196/55999 ER - TY - JOUR AU - Yeo, GeckHong AU - Reich, M. Stephanie AU - Liaw, A. Nicole AU - Chia, Min Elizabeth Yee PY - 2024/2/29 TI - The Effect of Digital Mental Health Literacy Interventions on Mental Health: Systematic Review and Meta-Analysis JO - J Med Internet Res SP - e51268 VL - 26 KW - review and meta-analysis KW - digital mental health literacy KW - digital mental health interventions KW - mental health functioning N2 - Background: Accelerated by technological advancements and the recent global pandemic, there is burgeoning interest in digital mental health literacy (DMHL) interventions that can positively affect mental health. However, existing work remains inconclusive regarding the effectiveness of DMHL interventions. Objective: This systematic review and meta-analysis investigated the components and modes of DMHL interventions, their moderating factors, and their long-term impacts on mental health literacy and mental health. Methods: We used a random-effects model to conduct meta-analyses and meta-regressions on moderating effects of DMHL interventions on mental health. Results: Using 144 interventions with 206 effect sizes, we found a moderate effect of DMHL interventions in enhancing distal mental health outcomes (standardized mean difference=0.42, 95% CI ?0.10 to 0.73; P<.001) and a large effect in increasing proximal mental health literacy outcomes (standardized mean difference=0.65, 95% CI 0.59-0.74; P<.001). Uptake of DMHL interventions was comparable with that of control conditions, and uptake of DMHL interventions did not moderate the effects on both proximal mental health literacy outcomes and distal mental health outcomes. DMHL interventions were as effective as face-to-face interventions and did not differ by platform type or dosage. DMHL plus interventions (DMHL psychoeducation coupled with other active treatment) produced large effects in bolstering mental health, were more effective than DMHL only interventions (self-help DMHL psychoeducation), and were comparable with non-DMHL interventions (treatment as usual). DMHL interventions demonstrated positive effects on mental health that were sustained over follow-up assessments and were most effective in enhancing the mental health of emerging and older adults. Conclusions: For theory building, our review and meta-analysis found that DMHL interventions are as effective as face-to-face interventions. DMHL interventions confer optimal effects on mental health when DMHL psychoeducation is combined with informal, nonprofessional active treatment components such as skills training and peer support, which demonstrate comparable effectiveness with that of treatment as usual (client-professional interactions and therapies). These effects, which did not differ by platform type or dosage, were sustained over time. Additionally, most DMHL interventions are found in Western cultural contexts, especially in high-income countries (Global North) such as Australia, the United States, and the United Kingdom, and limited research is conducted in low-income countries in Asia and in South American and African countries. Most of the DMHL studies did not report information on the racial or ethnic makeup of the samples. Future work on DMHL interventions that target racial or ethnic minority groups, particularly the design, adoption, and evaluation of the effects of culturally adaptive DMHL interventions on uptake and mental health functioning, is needed. Such evidence can drive the adoption and implementation of DMHL interventions at scale, which represents a key foundation for practice-changing impact in the provision of mental health resources for individuals and the community. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42023363995; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023363995 UR - https://www.jmir.org/2024/1/e51268 UR - http://dx.doi.org/10.2196/51268 UR - http://www.ncbi.nlm.nih.gov/pubmed/38421687 ID - info:doi/10.2196/51268 ER - TY - JOUR AU - Funnell, L. Erin AU - Spadaro, Benedetta AU - Martin-Key, A. Nayra AU - Benacek, Jiri AU - Bahn, Sabine PY - 2024/2/23 TI - Perception of Apps for Mental Health Assessment With Recommendations for Future Design: United Kingdom Semistructured Interview Study JO - JMIR Form Res SP - e48881 VL - 8 KW - app design KW - digital health KW - eHealth KW - interviews KW - mental health KW - mHealth KW - mobile phone N2 - Background: Mental health care provision in the United Kingdom is overwhelmed by a high demand for services. There are high rates of under-, over-, and misdiagnosis of common mental health disorders in primary care and delays in accessing secondary care. This negatively affects patient functioning and outcomes. Digital tools may offer a time-efficient avenue for the remote assessment and triage of mental health disorders that can be integrated directly into existing care pathways to support clinicians. However, despite the potential of digital tools in the field of mental health, there remain gaps in our understanding of how the intended user base, people with lived experiences of mental health concerns, perceive these technologies. Objective: This study explores the perspectives and attitudes of individuals with lived experiences of mental health concerns on mental health apps that are designed to support self-assessment and triage. Methods: A semistructured interview approach was used to explore the perspectives of the interviewees using 5 open-ended questions. Interviews were transcribed verbatim from audio data recordings. The average interview lasted 46 minutes (rounded to the nearest min; SD 12.93 min). A thematic analysis was conducted. Results: Overall, 16 individuals were interviewed in this study. The average age was 42.25 (SD 15.18) years, half of the interviewees identified as women (8/16, 50%), and all were White (16/16, 100%). The thematic analysis revealed six major themes: (1) availability and accessibility, (2) quality, (3) attitudes, (4) safety, (5) impact, and (6) functionality. Conclusions: Engaging in clear communication regarding data security and privacy policies, adopting a consent-driven approach to data sharing, and identifying gaps in the app marketplace to foster the inclusion of a range of mental health conditions and avoid oversaturation of apps for common mental health disorders (eg, depression and anxiety) were identified as priorities from interviewees? comments. Furthermore, reputation was identified as a driver of uptake and engagement, with endorsement from a respected source (ie, health care provider, academic institution) or direct recommendation from a trusted health care professional associated with increased interest and trust. Furthermore, there was an interest in the role that co-designed digital self-assessments could play in existing care pathways, particularly in terms of facilitating informed discussions with health care professionals during appointments and by signposting individuals to the most appropriate services. In addition, interviewees discussed the potential of mental health apps to provide waiting list support to individuals awaiting treatment by providing personalized psychoeducation, self-help tips, and sources of help. However, concerns regarding the quality of care being affected because of digital delivery have been reported; therefore, frequent monitoring of patient acceptability and care outcomes is warranted. In addition, communicating the rationale and benefits of digitizing services will likely be important for securing interest and uptake from health care service users. UR - https://formative.jmir.org/2024/1/e48881 UR - http://dx.doi.org/10.2196/48881 UR - http://www.ncbi.nlm.nih.gov/pubmed/38393760 ID - info:doi/10.2196/48881 ER - TY - JOUR AU - Park, Y. Susanna AU - Do, Bridgette AU - Yourell, Jacqlyn AU - Hermer, Janice AU - Huberty, Jennifer PY - 2024/2/6 TI - Digital Methods for the Spiritual and Mental Health of Generation Z: Scoping Review JO - Interact J Med Res SP - e48929 VL - 13 KW - Generation Z KW - Gen Z KW - spiritual health KW - digital mental health KW - spirituality N2 - Background: Generation Z (Gen Z) includes individuals born between 1995 and 2012. These individuals experience high rates of anxiety and depression. Most Gen Z individuals identify with being spiritual, and aspects from religion and spirituality can be integrated into mental health treatment and care as both are related to lower levels of depression. However, research on the spiritual and mental health of Gen Z is sparse. To date, there are no systematic or scoping reviews on digital methods to address the spiritual and mental health of Gen Z. Objective: This scoping review aimed to describe the current state of digital methods to address spiritual and mental health among Gen Z, identify the knowledge gaps, and make suggestions for how to leverage digital spiritual and mental health interventions for Gen Z. Methods: A comprehensive literature search was conducted in PubMed, Scopus, PsycInfo, CINAHL, Education Full Text, Google Scholar, SocIndex, and Sociological Abstracts. The inclusion criteria were as follows: (1) study population born between 1995 and 2012 (ie, Gen Z); (2) reporting on spiritual health or well-being, spirituality or religion, and mental health or well-being; (3) reporting on using digital methods; (4) publication in 1996 or beyond; (5) human subject research; (6) full text availability in English; (7) primary research study design; and (8) peer-reviewed article. Two authors screened articles and subsequently extracted data from the included articles to describe the available evidence. Results: A total of 413 articles were screened at the title and abstract levels, of which 27 were further assessed with full text for eligibility. Five studies met the inclusion criteria, and data were extracted to summarize study characteristics and findings. The studies were performed across 4 different countries. There were 2 mixed-methods studies (South Africa and Canada), 2 cross-sectional studies (China and United States), and 1 randomized controlled trial (United States). Of these studies, only 2 discussed digital interventions (a text messaging?based intervention to improve spiritual and mental health, and a feasibility study for a mental health app). Other studies had a digital component with minor or unclear spiritual and mental health measures. Overall, there was a lack of consistency in how spiritual and mental health were measured. Conclusions: Few studies have focused on assessing the spiritual and mental health of Gen Z in the digital context, and no research to date has examined a digital spiritual and mental health application among Gen Z. Research is needed to inform the development and evaluation of approaches to address the spiritual and mental health of Gen Z via digital means (eg, mobile apps). UR - https://www.i-jmr.org/2024/1/e48929 UR - http://dx.doi.org/10.2196/48929 UR - http://www.ncbi.nlm.nih.gov/pubmed/38261532 ID - info:doi/10.2196/48929 ER - TY - JOUR AU - Schwarz, Julian AU - Meier-Diedrich, Eva AU - Neumann, Katharina AU - Heinze, Martin AU - Eisenmann, Yvonne AU - Thoma, Samuel PY - 2024/2/5 TI - Reasons for Acceptance or Rejection of Online Record Access Among Patients Affected by a Severe Mental Illness: Mixed Methods Study JO - JMIR Ment Health SP - e51126 VL - 11 KW - open notes KW - patient-clinician relations KW - electronic health record KW - clinical notes KW - visit notes KW - patient participation KW - online record access KW - mental illness KW - patient portal KW - mental health KW - qualitative interview KW - patient education N2 - Background: Over the past few years, online record access (ORA) has been established through secure patient portals in various countries, allowing patients to access their health data, including clinical notes (?open notes?). Previous research indicates that ORA in mental health, particularly among patients with severe mental illness (SMI), has been rarely offered. Little is known about the expectations and motivations of patients with SMI when reading what their clinicians share via ORA. Objective: The aim of this study is to explore the reasons why patients with SMI consider or reject ORA and whether sociodemographic characteristics may influence patient decisions. Methods: ORA was offered to randomly selected patients at 3 university outpatient clinics in Brandenburg, Germany, which exclusively treat patients with SMI. Within the framework of a mixed methods evaluation, qualitative interviews were conducted with patients who chose to participate in ORA and those who declined, aiming to explore the underlying reasons for their decisions. The interviews were transcribed and analyzed using thematic analysis. Sociodemographic characteristics of patients were examined using descriptive statistics to identify predictors of acceptance or rejection of ORA. Results: Out of 103 included patients, 58% (n=60) wished to read their clinical notes. The reasons varied, ranging from a desire to engage more actively in their treatment to critically monitoring it and using the accessible data for third-party purposes. Conversely, 42% (n=43) chose not to use ORA, voicing concerns about possibly harming the trustful relationship with their clinicians as well as potential personal distress or uncertainty arising from reading the notes. Practical barriers such as a lack of digital literacy or suspected difficult-to-understand medical language were also named as contributing factors. Correlation analysis revealed that the majority of patients with depressive disorder desired to read the clinical notes (P<.001), while individuals with psychotic disorders showed a higher tendency to decline ORA (P<.05). No significant group differences were observed for other patient groups or characteristics. Conclusions: The adoption of ORA is influenced by a wide range of motivational factors, while patients also present a similar variety of reasons for declining its use. The results emphasize the urgent need for knowledge and patient education regarding factors that may hinder the decision to use ORA, including its practical usage, its application possibilities, and concerns related to data privacy. Further research is needed to explore approaches for adequately preparing individuals with SMI to transition from their inherent interest to active engagement with ORA. Trial Registration: German Clinical Trial Register DRKS00030188; https://drks.de/search/en/trial/DRKS00030188 UR - https://mental.jmir.org/2024/1/e51126 UR - http://dx.doi.org/10.2196/51126 UR - http://www.ncbi.nlm.nih.gov/pubmed/38315523 ID - info:doi/10.2196/51126 ER - TY - JOUR AU - Smith, Helm Ashley AU - Touchett, Hilary AU - Chen, Patricia AU - Fletcher, Terri AU - Arney, Jennifer AU - Hogan, Julianna AU - Wassef, Miryam AU - Cloitre, Marylene AU - Lindsay, A. Jan PY - 2024/2/2 TI - Patient Satisfaction With a Coach-Guided, Technology-Based Mental Health Treatment: Qualitative Interview Study and Theme Analysis JO - JMIR Ment Health SP - e50977 VL - 11 KW - coaching KW - digital treatment KW - interview KW - mental health KW - patient satisfaction KW - PTSD KW - qualitative assessment KW - qualitative methods KW - sentiment analysis KW - technology-based KW - telehealth KW - trauma KW - veterans KW - video telehealth KW - web-based treatment N2 - Background: Technology-based mental health interventions address barriers rural veterans face in accessing care, including provider scarcity and distance from the hospital or clinic. webSTAIR is a 10-module, web-based treatment based on Skills Training in Affective and Interpersonal Regulation, designed to treat posttraumatic stress disorder and depression in individuals exposed to trauma. Previous work has demonstrated that webSTAIR is acceptable to participants and effective at reducing symptoms of posttraumatic stress disorder and depression when delivered synchronously or asynchronously (over 5 or 10 sessions). Objective: This study explored factors that lead to greater patient satisfaction with webSTAIR, a web-based, coach-guided intervention. Methods: We analyzed qualitative interview data to identify themes related to patient satisfaction with webSTAIR delivered with synchronous video-based coaching. Results: Four themes emerged from the data: (1) coaching provides accountability and support, (2) self-pacing offers value that meets individual needs, (3) participants like the comfort and convenience of the web-based format, and (4) technical issues were common but not insurmountable. Conclusions: We conclude that participants valued the accountability, flexibility, and convenience of tech-based interventions with video-delivered coaching. UR - https://mental.jmir.org/2024/1/e50977 UR - http://dx.doi.org/10.2196/50977 UR - http://www.ncbi.nlm.nih.gov/pubmed/38306167 ID - info:doi/10.2196/50977 ER - TY - JOUR AU - Le Barbenchon, Emmanuelle AU - Trousselard, Marion AU - Pellissier, Sonia AU - Moisseron-Baudé, Mathilde AU - Chachignon, Philippine AU - Bouny, Pierre AU - Touré Cuq, Emma AU - Jacob, Sandrine AU - Vigier, Cécile AU - Hidalgo, Maud AU - Claverie, Damien AU - Duffaud, M. Anais PY - 2024/1/26 TI - Implementation of a Primary Prevention Program for Posttraumatic Stress Disorder in a Cohort of Professional Soldiers (PREPAR): Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e47175 VL - 13 KW - posttraumatic stress disorder, military, primary prevention, biopsychosocial, resilience, coping, stigma, biophysiology KW - PTSD KW - implementation KW - soldier KW - veterans KW - prevention program N2 - Background: Posttraumatic stress disorder (PTSD) is a psychiatric disorder that can manifest after a traumatic event where the individual perceives a threat to his or her life or that of others. Its estimated prevalence in the European population is 0.7% to 1.9%. According to the ?dose-response? model, individuals who are most exposed to traumatic events are most at risk of developing PTSD. Hence, it is unsurprising that studies have observed a higher prevalence among the military population, ranging from 10% to 18%, or even up to 45%. This project?s overall goal is to evaluate the primary prevention actions that can strengthen the resilience of at-risk professionals, notably military personnel, in the short term, with the medium- to long-term aim of preventing the occurrence of PTSD and improving the patient?s prognosis. Objective: This study?s objectives are (1) to design a primary prevention program for PTSD, tailored to the studied military population and compatible with operational constraints; and (2) to implement and validate the Primary Prevention of Posttraumatic Stress Disorder in Military Professionals (PREPARE) program in the short term with operational personnel belonging to the French Mountain Infantry Brigade. Methods: This is a single-center, prospective, randomized, parallel-group controlled cohort study. The cohort is divided into 2 groups: the nonintervention group receives no training, and the intervention group follows a dedicated prevention program (structured into 8 workshops and 2 debriefing and practice reinforcement workshops). Each participant is evaluated 4 times (at inclusion, +4 months, +6 months, and +12 months). During each visit, participants complete several psychosocial questionnaires (which take 15-80 minutes to complete). Samples (a 30-mL blood sample and three 5-mL saliva samples) are collected on 3 occasions: at inclusion, +4 months, and +12 months. Emotional reactivity (electrocardiogram and electrodermal activity) is measured before, during, and after the classic and the emotional Stroop task. Results: The project is currently ongoing, and results are expected to be published by the end of 2024. Conclusions: The study adopts an integrative approach to the processes that play a role in the risk of developing PTSD. Our biopsychosocial perspective makes it possible to target levers related to factors specific to the individual and socio-professional factors. The following dimensions are addressed: (1) biophysiology (by studying markers of the neurobiological stress response, wear and tear, and vulnerability phenomena and reinforcing the flexibility of the autonomic nervous system), (2) psychology (by facilitating and measuring the development of flexible coping strategies to deal with stress and evaluating the moderating role of the individual?s sense of duty in the development of PTSD), and (3) social (by facilitating community strategies aimed at reducing stigmatization and supporting the use of care by professionals in difficulty, in the institutional context). Trial Registration: ClinicalTrials.gov NCT05094531; https://clinicaltrials.gov/study/NCT05094531 International Registered Report Identifier (IRRID): DERR1-10.2196/47175 UR - https://www.researchprotocols.org/2024/1/e47175 UR - http://dx.doi.org/10.2196/47175 UR - http://www.ncbi.nlm.nih.gov/pubmed/38277204 ID - info:doi/10.2196/47175 ER - TY - JOUR AU - Lossio-Ventura, Antonio Juan AU - Weger, Rachel AU - Lee, Y. Angela AU - Guinee, P. Emily AU - Chung, Joyce AU - Atlas, Lauren AU - Linos, Eleni AU - Pereira, Francisco PY - 2024/1/25 TI - A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data JO - JMIR Ment Health SP - e50150 VL - 11 KW - sentiment analysis KW - COVID-19 survey KW - large language model KW - few-shot learning KW - zero-shot learning KW - ChatGPT KW - COVID-19 N2 - Background: Health care providers and health-related researchers face significant challenges when applying sentiment analysis tools to health-related free-text survey data. Most state-of-the-art applications were developed in domains such as social media, and their performance in the health care context remains relatively unknown. Moreover, existing studies indicate that these tools often lack accuracy and produce inconsistent results. Objective: This study aims to address the lack of comparative analysis on sentiment analysis tools applied to health-related free-text survey data in the context of COVID-19. The objective was to automatically predict sentence sentiment for 2 independent COVID-19 survey data sets from the National Institutes of Health and Stanford University. Methods: Gold standard labels were created for a subset of each data set using a panel of human raters. We compared 8 state-of-the-art sentiment analysis tools on both data sets to evaluate variability and disagreement across tools. In addition, few-shot learning was explored by fine-tuning Open Pre-Trained Transformers (OPT; a large language model [LLM] with publicly available weights) using a small annotated subset and zero-shot learning using ChatGPT (an LLM without available weights). Results: The comparison of sentiment analysis tools revealed high variability and disagreement across the evaluated tools when applied to health-related survey data. OPT and ChatGPT demonstrated superior performance, outperforming all other sentiment analysis tools. Moreover, ChatGPT outperformed OPT, exhibited higher accuracy by 6% and higher F-measure by 4% to 7%. Conclusions: This study demonstrates the effectiveness of LLMs, particularly the few-shot learning and zero-shot learning approaches, in the sentiment analysis of health-related survey data. These results have implications for saving human labor and improving efficiency in sentiment analysis tasks, contributing to advancements in the field of automated sentiment analysis. UR - https://mental.jmir.org/2024/1/e50150 UR - http://dx.doi.org/10.2196/50150 UR - http://www.ncbi.nlm.nih.gov/pubmed/38271138 ID - info:doi/10.2196/50150 ER - TY - JOUR AU - Loewen, Álvaro AU - Blasco-Fontecilla, Hilario AU - Li, Chao AU - Bella-Fernández, Marcos AU - Ruiz-Antorán, Belén PY - 2024/1/4 TI - Prevalence of Body Dysmorphic Disorder in the Spanish Population: Cross-Sectional Web-Based Questionnaire Study JO - JMIR Form Res SP - e46515 VL - 8 KW - body dysmorphic disorder KW - prevalence KW - adults KW - Spain KW - comorbidities KW - mental health KW - depression KW - anxiety KW - OCD KW - obsessive-compulsive disorder N2 - Background: Body dysmorphic disorder (BDD) is defined as excessive concern with mild or nonexistent defects in personal physical appearance, which are not perceived by others. The worldwide prevalence of BDD ranges between 0.5% and 3.2%, with no differences across genders. The mean age of onset of BDD is 16.9 years. BDD is typically associated with young age, psychiatric disorders, and dermatological procedures. Patients with BDD typically display poorer mental health status than patients diagnosed with other mental disorders. Objective: The aim of this study was to estimate the prevalence of BDD in Spain and to identify the variables associated with BDD. Methods: We performed a cross-sectional descriptive study by collecting data through an anonymous web-based survey targeting the Spanish population aged 18 years or older. The measures in this study were (1) sociodemographic variables, (2) variables associated with dermatological and psychiatric disorders and cosmetic procedures, (3) scales measuring quality of life (12-item Short Form health survey, version 2) and (4) BDD (BDD Questionnaire). Statistical analysis was performed with SPSS software version 21. P values less than .05 were considered significant. Results: Of the 2091 participants who took the survey, 322 (15.2%) met the criteria of having BDD. The mean age of the participants with BDD was 23.5 (SD 9.6) years. In terms of BDD prevalence, women accounted for 19.9% (284/1421), men accounted for 5.2% (34/653), and students accounted for 25.2% (263/1043). Approximately 46.6% (150/322) of the participants with BDD reported a history of psychiatric comorbidities, including anxiety disorders, depressive disorders, and eating disorders. BDD was significantly associated with female gender, younger age (18-24 years), students, monthly income of less than ?500 (?1=US $1.11), and the presence of dermatological and some psychiatric disorders such as depression, anxiety, and eating disorders (P<.05). The number of body parts of concern in participants with BDD was significantly higher than that in those without BDD (4.6 vs 2.2, respectively; P<.001). Regarding the body parts of concern, body fat was the most common concern for both groups with BDD and without BDD, followed by thighs, face, hips, and skin in the BDD group and thighs, teeth, and hair in the non-BDD group. Participants with BDD showed a significantly poorer self-perception of their mental health, irrespective of the presence of any mental disorder (P<.001). Conclusions: Our findings showed that the prevalence of BDD in Spain was higher than expected. Further, BDD is frequently associated with other psychiatric disorders, particularly depressive disorder, anxiety disorder, and eating disorder. Participants with BDD had a poorer perception of quality of life associated with mental but not physical health problems. Finally, the perception of quality of mental health life in participants with BDD was independent of diagnosis of any mental disorder. UR - https://formative.jmir.org/2024/1/e46515 UR - http://dx.doi.org/10.2196/46515 UR - http://www.ncbi.nlm.nih.gov/pubmed/38175692 ID - info:doi/10.2196/46515 ER - TY - JOUR AU - Tumaliuan, Beatriz Faye AU - Grepo, Lorelie AU - Jalao, Rex Eugene PY - 2024/9/4 TI - Development of Depression Data Sets and a Language Model for Depression Detection: Mixed Methods Study JO - JMIR Data SP - e53365 VL - 5 KW - depression data set KW - depression detection KW - social media KW - natural language processing KW - Filipino N2 - Background: Depression detection in social media has gained attention in recent years with the help of natural language processing (NLP) techniques. Because of the low-resource standing of Filipino depression data, valid data sets need to be created to aid various machine learning techniques in depression detection classification tasks. Objective: The primary objective is to build a depression corpus of Philippine Twitter users who were clinically diagnosed with depression by mental health professionals and develop from this a corpus of depression symptoms that can later serve as a baseline for predicting depression symptoms in the Filipino and English languages. Methods: The proposed process included the implementation of clinical screening methods with the help of clinical psychologists in the recruitment of study participants who were young adults aged 18 to 30 years. A total of 72 participants were assessed by clinical psychologists and provided their Twitter data: 60 with depression and 12 with no depression. Six participants provided 2 Twitter accounts each, making 78 Twitter accounts. A data set was developed consisting of depression symptom?annotated tweets with 13 depression categories. These were created through manual annotation in a process constructed, guided, and validated by clinical psychologists. Results: Three annotators completed the process for approximately 79,614 tweets, resulting in a substantial interannotator agreement score of 0.735 using Fleiss ? and a 95.59% psychologist validation score. A word2vec language model was developed using Filipino and English data sets to create a 300-feature word embedding that can be used in various machine learning techniques for NLP. Conclusions: This study contributes to depression research by constructing depression data sets from social media to aid NLP in the Philippine setting. These 2 validated data sets can be significant in user detection or tweet-level detection of depression in young adults in further studies. UR - https://data.jmir.org/2024/1/e53365 UR - http://dx.doi.org/10.2196/53365 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53365 ER - TY - JOUR AU - De Veirman, M. Ann E. AU - Thewissen, Viviane AU - Spruijt, G. Matthijs AU - Bolman, W. Catherine A. PY - 2022/12/20 TI - Factors Associated With Intention and Use of e?Mental Health by Mental Health Counselors in General Practices: Web-Based Survey JO - JMIR Form Res SP - e34754 VL - 6 IS - 12 KW - mental health counselors KW - general practices KW - e?mental health KW - adoption readiness KW - eligibility for e?mental health KW - e?mental health use KW - mental health KW - eHealth N2 - Background: Mental health care counselors have a high intention to use e?mental health (EMH), whereas actual use is limited. Facilitating future use requires insight into underlying factors as well as eligibility criteria that mental health care counselors use in their decision to apply EMH. Objective: The aim of this study was to unfold the intention and underlying reasons for mental health counselors to use EMH and to unveil the criteria they use to estimate patient eligibility for EMH. The theoretical framework was based on the reasoned action approach model, the Unified Theory of Acceptance and Use of Technology, and the Measurement Instrument for Determinants of Innovation model. Methods: To empirically validate our theoretical model, a web-based survey was conducted among mental health care counselors (n=132). To unveil the eligibility criteria, participants were asked to rank their reasons for considering EMH suitable or unsuitable for a patient. Results: The mean intention to use EMH was positive (mean 4.04, SD 0.64). The mean use of EMH before the COVID-19 pandemic was 38% (mean 0.38, SD 0.22), and it was 49% (mean 0.49, SD 0.25) during the pandemic. In total, 57% of the patient population was considered eligible for EMH. Usefulness and benefits (?=.440; P<.001), Task perception (?=.306; P=.001), and Accessibility (?=.140; P=.02) explained the intention to use EMH (F3,131=54.151; P<.001; R2=0.559). In turn, intention explained patient eligibility (F1,130=34.716; P<.001; R2=0.211), whereas intention and patient eligibility explained EMH use (F2,129=41.047; P<.001; R2=0.389). Patient eligibility partially mediated the relationship between intention to use EMH and EMH use, with a larger direct effect (c?=0.116; P<.001) than indirect effect (c=0.065, 95% CI 0.035-0.099; P<.001). Mental health counselors assessed patients? eligibility for EMH mainly through the availability of computers and the internet and patient motivation. Conclusions: To stimulate the use of EMH, intention and patient eligibility need to be influenced. Intention, in turn, can be enhanced by addressing the perceived usefulness and benefits of EMH, perceived accessibility, and task perception. Access to a computer and patients? motivation to use EMH are important in facilitating patient eligibility. To cause an impact with EMH in general practice, mental health counselors need to be convinced of the benefits of EMH and transfer this enthusiasm to the patient. It is recommended to involve mental health counselors in the development of EMH to increase the (perceived) added value and use. UR - https://formative.jmir.org/2022/12/e34754 UR - http://dx.doi.org/10.2196/34754 UR - http://www.ncbi.nlm.nih.gov/pubmed/36538357 ID - info:doi/10.2196/34754 ER - TY - JOUR AU - Borghouts, Judith AU - Eikey, V. Elizabeth AU - De Leon, Cinthia AU - Schueller, M. Stephen AU - Schneider, Margaret AU - Stadnick, A. Nicole AU - Zheng, Kai AU - Wilson, Lorraine AU - Caro, Damaris AU - Mukamel, B. Dana AU - Sorkin, H. Dara PY - 2022/12/13 TI - Understanding the Role of Support in Digital Mental Health Programs With Older Adults: Users? Perspective and Mixed Methods Study JO - JMIR Form Res SP - e43192 VL - 6 IS - 12 KW - older adults KW - mental health KW - digital mental health intervention KW - human support N2 - Background: Digital mental health interventions have the potential to increase mental health support among isolated older adults. However, the older adult population can experience several barriers to accessing and using digital health resources and may need extra support to experience its benefits. Objective: This paper aimed to understand what older adults experience as an important aspect of support during engagement in a digital mental health program. The program entailed 3 months of staff support to participate in digital literacy training and engage with the digital mental health platform myStrength, which offers support for a range of mental health challenges, including depression and anxiety. Methods: A total of 30 older adults participated in surveys and interviews to assess their experience of participating in a digital mental health program provided by county mental health services. As part of the program, participants attended 4 classes of digital literacy training, had access to the digital mental health platform myStrength for 2 months with staff support (and 10 months after the program without support), and received support from program staff during the entire 3-month program. Survey data were analyzed using descriptive statistics, and interview data were analyzed using thematic analysis. Results: A thematic analysis of the interview data revealed that participants valued ongoing support in 3 main areas: technical support to assist them in using technology, guided support to remind them to use myStrength and practice skills they had learned, and social support to enable them to connect with others through the program. Furthermore, participants reported that social connections was the most important aspect of the program and that they were mainly motivated to participate in the program because it was recommended to them by trusted others such as a community partner or because they believed it could potentially help others. Conclusions: Our findings can be used to inform the design of future digital mental health programs for older adults who may have unique support needs in terms of dedicated technical support and ongoing guided support to use technology and social support to increase social connectedness. UR - https://formative.jmir.org/2022/12/e43192 UR - http://dx.doi.org/10.2196/43192 UR - http://www.ncbi.nlm.nih.gov/pubmed/36512387 ID - info:doi/10.2196/43192 ER - TY - JOUR AU - Braitman, L. Abby AU - Strowger, Megan AU - Shipley, L. Jennifer AU - Ortman, Jordan AU - MacIntyre, I. Rachel AU - Bauer, A. Elizabeth PY - 2022/12/9 TI - Data Quality and Study Compliance Among College Students Across 2 Recruitment Sources: Two Study Investigation JO - JMIR Form Res SP - e39488 VL - 6 IS - 12 KW - data quality KW - attention checks KW - recruitment KW - retention KW - college students KW - mobile phone N2 - Background: Models of satisficing suggest that study participants may not fully process survey items and provide accurate responses when survey burden is higher and when participant motivation is lower. Participants who do not fully process survey instructions can reduce a study?s power and hinder generalizability. Common concerns among researchers using self-report measures are data quality and participant compliance. Similarly, attrition can hurt the power and generalizability of a study. Objective: Given that college students comprise most samples in psychological studies, especially examinations of student issues and psychological health, it is critical to understand how college student recruitment sources impact data quality (operationalized as attention check items with directive instructions and correct answers) and retention (operationalized as the completion of follow-up surveys over time). This examination aimed to examine the following: whether data quality varies across recruitment sources, whether study retention varies across recruitment sources, the impact of data quality on study variable associations, the impact of data quality on measures of internal consistency, and whether the demographic qualities of participants significantly vary across those who failed attention checks versus those who did not. Methods: This examination was a follow-up analysis of 2 previously published studies to explore data quality and study compliance. Study 1 was a cross-sectional, web-based survey examining college stressors and psychological health (282/407, 69.3% female; 230/407, 56.5% White, 113/407, 27.8% Black; mean age 22.65, SD 6.73 years). Study 2 was a longitudinal college drinking intervention trial with an in-person baseline session and 2 web-based follow-up surveys (378/528, 71.6% female; 213/528, 40.3% White, 277/528, 52.5% Black; mean age 19.85, SD 1.65 years). Attention checks were included in both studies to assess data quality. Participants for both studies were recruited from a psychology participation pool (a pull-in method; for course credit) and the general student body (a push-out method; for monetary payment or raffle entry). Results: A greater proportion of participants recruited through the psychology pool failed attention checks in both studies, suggesting poorer data quality. The psychology pool was also associated with lower retention rates over time. After screening out those who failed attention checks, some correlations among the study variables were stronger, some were weaker, and some were fairly similar, potentially suggesting bias introduced by including these participants. Differences among the indicators of internal consistency for the study measures were negligible. Finally, attention check failure was not significantly associated with most demographic characteristics but varied across some racial identities. This suggests that filtering out data from participants who failed attention checks may not limit sample diversity. Conclusions: Investigators conducting college student research should carefully consider recruitment and include attention checks or other means of detecting poor quality data. Recommendations for researchers are discussed. UR - https://formative.jmir.org/2022/12/e39488 UR - http://dx.doi.org/10.2196/39488 UR - http://www.ncbi.nlm.nih.gov/pubmed/36485020 ID - info:doi/10.2196/39488 ER - TY - JOUR AU - Langener, Simon AU - Klaassen, Randy AU - VanDerNagel, Joanne AU - Heylen, Dirk PY - 2022/12/7 TI - Immersive Virtual Reality Avatars for Embodiment Illusions in People With Mild to Borderline Intellectual Disability: User-Centered Development and Feasibility Study JO - JMIR Serious Games SP - e39966 VL - 10 IS - 4 KW - virtual reality KW - VR KW - embodiment KW - avatar KW - embodied learning KW - body-centered KW - intellectual disability KW - addiction KW - user-centered design N2 - Background: Immersive virtual reality (IVR) has been investigated as a tool for treating psychiatric conditions. In particular, the practical nature of IVR, by offering a doing instead of talking approach, could support people who do not benefit from existing treatments. Hence, people with mild to borderline intellectual disability (MBID; IQ=50-85) might profit particularly from IVR therapies, for instance, to circumvent issues in understanding relevant concepts and interrelations. In this context, immersing the user into a virtual body (ie, avatar) appears promising for enhancing learning (eg, by changing perspectives) and usability (eg, natural interactions). However, design requirements, immersion procedures, and proof of concept of such embodiment illusion (ie, substituting the real body with a virtual one) have not been explored in this group. Objective: Our study aimed to establish design guidelines for IVR embodiment illusions in people with MBID. We explored 3 factors to induce the illusion, by testing the avatar?s appearance, locomotion using IVR controllers, and virtual object manipulation. Furthermore, we report on the feasibility to induce the embodiment illusion and provide procedural guidance. Methods: We conducted a user-centered study with 29 end users in care facilities, to investigate the avatar?s appearance, controller-based locomotion (ie, teleport, joystick, or hybrid), and object manipulation. Overall, 3 iterations were conducted using semistructured interviews to explore design factors to induce embodiment illusions in our group. To further understand the influence of interactions on the illusion, we measured the sense of embodiment (SoE) during 5 interaction tasks. Results: IVR embodiment illusions can be induced in adults with MBID. To induce the illusion, having a high degree of control over the body outweighed avatar customization, despite the participants? desire to replicate their own body image. Similarly, the highest SoE was measured during object manipulation tasks, which required a combination of (virtual) locomotion and object manipulation behavior. Notably, interactions that are implausible (eg, teleport and occlusions when grabbing) showed a negative influence on SoE. In contrast, implementing artificial interaction aids into the IVR avatar?s hands (ie, for user interfaces) did not diminish the illusion, presuming that the control was unimpaired. Nonetheless, embodiment illusions showed a tedious and complex need for (control) habituation (eg, motion sickness), possibly hindering uptake in practice. Conclusions: Balancing the embodiment immersion by focusing on interaction habituation (eg, controller-based locomotion) and lowering customization effort seems crucial to achieve both high SoE and usability for people with MBID. Hence, future studies should investigate the requirements for natural IVR avatar interactions by using multisensory integrations for the virtual body (eg, animations, physics-based collision, and touch) and other interaction techniques (eg, hand tracking and redirected walking). In addition, procedures and use for learning should be explored for tailored mental health therapies in people with MBID. UR - https://games.jmir.org/2022/4/e39966 UR - http://dx.doi.org/10.2196/39966 UR - http://www.ncbi.nlm.nih.gov/pubmed/36476721 ID - info:doi/10.2196/39966 ER - TY - JOUR AU - Fortuna, Karen AU - Hill, Julia AU - Chalker, Samantha AU - Ferron, Joelle PY - 2022/12/7 TI - Certified Peer Support Specialists Training in Technology and Delivery of Digital Peer Support Services: Cross-sectional Study JO - JMIR Form Res SP - e40065 VL - 6 IS - 12 KW - digital peer support KW - mHealth KW - COVID-19 KW - mental health KW - remote service KW - remote mental health KW - telehealth KW - peer support KW - psychological health N2 - Background: When the COVID-19 pandemic lockdown measures were instituted, the wide-scale necessity for remote mental health care increased among professional clinicians, such as psychiatrists, psychologists, social workers, and certified peer support (CPS) specialists. Factors contributing to increased demand include concern for the safety of loved ones, the safety of oneself, overall well-being, unemployment, and loneliness for older individuals. While demand continues to increase and a shortage of mental health professionals persists, understanding the training, technology, media, and delivery of digital peer support services can facilitate community-based support services to assist patients in coping with mental health symptoms between clinical encounters with licensed professionals. Digital peer support consists of asynchronous and synchronous, live or automated, peer support services such as applications, social media, and phone calls. Objective: The purpose of this cross-sectional study is to determine how digital peer support is delivered, by which technologies it is delivered, and how certified digital peer supporters are trained within the United States to inform future delivery of digital peer support. Methods: We used an online cross-sectional self-report survey developed alongside certified peer specialists. The study included questions regarding the types of peer support training and the delivery methods used within their practices. We advertised the survey through a certified peer support specialist listserve, Facebook, and Twitter. Results: Certified peer specialists provide mutual social emotional support to those with a similar mental health condition. Of certified peer specialists trained in CPS, the majority of CPS specialists were trained in peer support (418/426, 98.1%). Peer support specialists deliver services via telephone calls (182/293, 62.1%), via videoconference-based services (160/293, 54.6%), via SMS text messages (123/293, 42%), via smartphone apps (68/293, 23.2%), and via social media (65/293, 22.2%). Certified peer specialists deliver services through virtual reality (11/293, 3.8%) and through video games (6/293, 2%). Virtual reality and video games may represent emerging technologies to develop and deliver community-based support. Conclusions: This study examined the modes of digital peer support intervention as well as the training and demographic background of peer supporters. Given the demand for mental health care, digital peer support emerges as one option to increase access. These results suggest that CPS specialists commonly use SMS text messaging, phone calls, and videoconferences to engage in peer support. Less frequently, they may use diverse modes such as apps, social media, and video games. It is important to consider the backgrounds of peer supporters and the mediums of communication to best accommodate areas where access to peer support is emerging. Larger longitudinal studies and a variety of experimental designs may be considered to understand the efficacy of digital interventions and digital peer support training to direct optimal care. UR - https://formative.jmir.org/2022/12/e40065 UR - http://dx.doi.org/10.2196/40065 UR - http://www.ncbi.nlm.nih.gov/pubmed/36476983 ID - info:doi/10.2196/40065 ER - TY - JOUR AU - Mayer, Gwendolyn AU - Gronewold, Nadine AU - Polte, Kirsten AU - Hummel, Svenja AU - Barniske, Joshua AU - Korbel, J. Jakob AU - Zarnekow, Rüdiger AU - Schultz, Jobst-Hendrik PY - 2022/12/5 TI - Experiences of Patients and Therapists Testing a Virtual Reality Exposure App for Symptoms of Claustrophobia: Mixed Methods Study JO - JMIR Ment Health SP - e40056 VL - 9 IS - 12 KW - virtual reality KW - exposure therapy KW - anxiety disorders KW - claustrophobia KW - think-aloud KW - mixed methods KW - virtual reality exposure therapy KW - VR KW - anxiety KW - therapy KW - mental health KW - user experience KW - perspective N2 - Background: The effectiveness of virtual reality exposure (VRE) in the treatment of anxiety disorders is well established. Several psychological mechanisms of VRE have been identified, whereby both emotional processing and the sense of presence play a key role. However, there are only few studies that contribute to our knowledge of examples of implementation in the case of VRE for claustrophobia based on patients' experiences and the perspective of therapists. Objective: This study asks for key elements of a VRE app that are necessary for effective exposure for people with claustrophobic symptoms. Methods: A mixed methods design was applied in which patients (n=15) and therapeutic experts (n=15) tested a VRE intervention of an elevator ride at 5 intensity levels. Intensity was varied by elevator size, duration of the elevator ride, and presence of virtual humans. Quantitative measures examined self-reported presence with the Igroup Presence Questionnaire (IPQ) ranging from 0 to 6 and 15 Likert-scaled evaluation items that had been developed for the purpose of this study, ranging from 1 to 5. In both measures, higher scores indicate higher levels of presence or agreement. Think-aloud protocols of the patients and semistructured interviews posttreatment of all participants were conducted to gain in-depth perspectives on emotional processes. Results: The intervention induced a feeling of presence in patients and experts, posttreatment scores showed a high IPQ presence score (mean 3.84, SD 0.88), with its subscores IPQ spatial presence (mean 4.53, SD 1.06), IPQ involvement (mean 3.83, SD 1.22), and IPQ experienced realism (mean 2.75, SD 1.02). Patients preferred a setting in the presence of a therapist (mean 4.13, SD 0.83) more than the experts did (mean 3.33, SD 1.54). Think-aloud protocols of the patients revealed that presence and anxiety both were achieved. Qualitative interviews of patients and experts uncovered 8 topics: feelings and emotions, personal story, telepresence, potential therapeutic effects, barriers, conditions and requirements, future prospects, and realization. The intensity levels were felt to appropriately increase in challenge, with ambivalent results regarding the final level. Virtual humans contributed to feelings of fear. Conclusions: Key elements of a VRE app for claustrophobic symptoms should include variation of intensity by adding challenging cues in order to evoke presence and anxiety. Virtual humans are a suitable possibility to make the intervention realistic and to provide a sense of closeness; however, some of the fears might then be related to symptoms of social phobia or agoraphobia. Patients may need the physical presence of a therapist, though not all of them share this view. A higher degree of sophistication in the intensity levels is needed to deliver targeted help for specific symptoms of anxiety. UR - https://mental.jmir.org/2022/12/e40056 UR - http://dx.doi.org/10.2196/40056 UR - http://www.ncbi.nlm.nih.gov/pubmed/36469413 ID - info:doi/10.2196/40056 ER - TY - JOUR AU - Winkler, Tanita AU - Büscher, Rebekka AU - Larsen, Erik Mark AU - Kwon, Sam AU - Torous, John AU - Firth, Joseph AU - Sander, B. Lasse PY - 2022/11/29 TI - Passive Sensing in the Prediction of Suicidal Thoughts and Behaviors: Protocol for a Systematic Review JO - JMIR Res Protoc SP - e42146 VL - 11 IS - 11 KW - suicide prediction KW - passive sensing KW - review KW - systematic review KW - sensors KW - suicidal thoughts and behaviors KW - digital markers KW - behavioral markers N2 - Background: Suicide is a severe public health problem, resulting in a high number of attempts and deaths each year. Early detection of suicidal thoughts and behaviors (STBs) is key to preventing attempts. We discuss passive sensing of digital and behavioral markers to enhance the detection and prediction of STBs. Objective: The paper presents the protocol for a systematic review that aims to summarize existing research on passive sensing of STBs and evaluate whether the STB prediction can be improved using passive sensing compared to prior prediction models. Methods: A systematic search will be conducted in the scientific databases MEDLINE, PubMed, Embase, PsycINFO, and Web of Science. Eligible studies need to investigate any passive sensor data from smartphones or wearables to predict STBs. The predictive value of passive sensing will be the primary outcome. The practical implications and feasibility of the studies will be considered as secondary outcomes. Study quality will be assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). If studies are sufficiently homogenous, we will conduct a meta-analysis of the predictive value of passive sensing on STBs. Results: The review process started in July 2022 with data extraction in September 2022. Results are expected in December 2022. Conclusions: Despite intensive research efforts, the ability to predict STBs is little better than chance. This systematic review will contribute to our understanding of the potential of passive sensing to improve STB prediction. Future research will be stimulated since gaps in the current literature will be identified and promising next steps toward clinical implementation will be outlined. Trial Registration: OSF Registries osf-registrations-hzxua-v1; https://osf.io/hzxua International Registered Report Identifier (IRRID): DERR1-10.2196/42146 UR - https://www.researchprotocols.org/2022/11/e42146 UR - http://dx.doi.org/10.2196/42146 UR - http://www.ncbi.nlm.nih.gov/pubmed/36445737 ID - info:doi/10.2196/42146 ER - TY - JOUR AU - Ader, Leonie AU - Schick, Anita AU - Simons, Claudia AU - Delespaul, Philippe AU - Myin-Germeys, Inez AU - Vaessen, Thomas AU - Reininghaus, Ulrich PY - 2022/11/23 TI - Positive Affective Recovery in Daily Life as a Momentary Mechanism Across Subclinical and Clinical Stages of Mental Disorder: Experience Sampling Study JO - JMIR Ment Health SP - e37394 VL - 9 IS - 11 KW - experience sampling methodology KW - ecological momentary assessment KW - trajectory KW - transdiagnostic KW - resilience KW - stress reactivity KW - psychosis KW - depression N2 - Background: Identifying momentary risk and protective mechanisms may enhance our understanding and treatment of mental disorders. Affective stress reactivity is one mechanism that has been reported to be altered in individuals with early and later stages of mental disorder. Additionally, initial evidence suggests individuals with early and enduring psychosis may have an extended recovery period of negative affect in response to daily stressors (ie, a longer duration until affect reaches baseline levels after stress), but evidence on positive affective recovery as a putative protective mechanism remains limited. Objective: This study aimed to investigate trajectories of positive affect in response to stress across the continuum of mental disorder in a transdiagnostic sample. Methods: Using the Experience Sampling Method, minor activity-, event-, and overall stress and positive affect were assessed 10 times a day, with time points approximately 90 minutes apart on six consecutive days in a pooled data set including 367 individuals with a mental disorder, 217 individuals at risk for a severe mental disorder, and 227 controls. Multilevel analysis and linear contrasts were used to investigate trajectories of positive affect within and between groups. Results: Baseline positive affect differed across groups, and we observed stress reactivity in positive affect within each group. We found evidence for positive affective recovery after reporting activity- or overall stress within each group. While controls recovered to baseline positive affect about 90 minutes after stress, patients and at-risk individuals required about 180 minutes to recover. However, between-group differences in the affective recovery period fell short of significance (all P>.05). Conclusions: The results provide first evidence that positive affective recovery may be relevant within transdiagnostic subclinical and clinical stages of mental disorder, suggesting that it may be a potential target for mobile health interventions fostering resilience in daily life. UR - https://mental.jmir.org/2022/11/e37394 UR - http://dx.doi.org/10.2196/37394 UR - http://www.ncbi.nlm.nih.gov/pubmed/36416883 ID - info:doi/10.2196/37394 ER - TY - JOUR AU - Acien, Alejandro AU - Morales, Aythami AU - Vera-Rodriguez, Ruben AU - Fierrez, Julian AU - Mondesire-Crump, Ijah AU - Arroyo-Gallego, Teresa PY - 2022/11/21 TI - Detection of Mental Fatigue in the General Population: Feasibility Study of Keystroke Dynamics as a Real-world Biomarker JO - JMIR Biomed Eng SP - e41003 VL - 7 IS - 2 KW - fatigue KW - keystroke KW - biometrics KW - digital biomarker KW - TypeNet KW - domain adaptation KW - fatigue detection KW - typing patterns KW - circadian cycles KW - mental fatigue KW - psychomotor patterns KW - monitoring KW - mental health KW - keystroke dynamics N2 - Background: Mental fatigue is a common and potentially debilitating state that can affect individuals? health and quality of life. In some cases, its manifestation can precede or mask early signs of other serious mental or physiological conditions. Detecting and assessing mental fatigue can be challenging nowadays as it relies on self-evaluation and rating questionnaires, which are highly influenced by subjective bias. Introducing more objective, quantitative, and sensitive methods to characterize mental fatigue could be critical to improve its management and the understanding of its connection to other clinical conditions. Objective: This paper aimed to study the feasibility of using keystroke biometrics for mental fatigue detection during natural typing. As typing involves multiple motor and cognitive processes that are affected by mental fatigue, our hypothesis was that the information captured in keystroke dynamics can offer an interesting mean to characterize users? mental fatigue in a real-world setting. Methods: We apply domain transformation techniques to adapt and transform TypeNet, a state-of-the-art deep neural network, originally intended for user authentication, to generate a network optimized for the fatigue detection task. All experiments were conducted using 3 keystroke databases that comprise different contexts and data collection protocols. Results: Our preliminary results showed area under the curve performances ranging between 72.2% and 80% for fatigue versus rested sample classification, which is aligned with previously published models on daily alertness and circadian cycles. This demonstrates the potential of our proposed system to characterize mental fatigue fluctuations via natural typing patterns. Finally, we studied the performance of an active detection approach that leverages the continuous nature of keystroke biometric patterns for the assessment of users? fatigue in real time. Conclusions: Our results suggest that the psychomotor patterns that characterize mental fatigue manifest during natural typing, which can be quantified via automated analysis of users? daily interaction with their device. These findings represent a step towards the development of a more objective, accessible, and transparent solution to monitor mental fatigue in a real-world environment. UR - https://biomedeng.jmir.org/2022/2/e41003 UR - http://dx.doi.org/10.2196/41003 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875698 ID - info:doi/10.2196/41003 ER - TY - JOUR AU - Cantù, Filippo AU - Biagianti, Bruno AU - Lisi, Ilaria AU - R Zanier, Elisa AU - Bottino, Nicola AU - Fornoni, Chiara AU - Gallo, Francesca AU - Ginex, Valeria AU - Tombola, Valentina AU - Zito, Silvana AU - Colombo, Elisa AU - Stocchetti, Nino AU - Brambilla, Paolo PY - 2022/11/16 TI - Psychotherapeutic and Psychiatric Intervention in Patients With COVID-19 and Their Relatives: Protocol for the DigiCOVID Trial JO - JMIR Res Protoc SP - e39080 VL - 11 IS - 11 KW - telepsychiatry KW - telemedicine KW - COVID-19 KW - mental health KW - digital mental health KW - digital support KW - clinical outcome KW - telehealth KW - psychiatric health KW - health intervention N2 - Background: The COVID-19 pandemic is negatively impacting the mental health of both patients with COVID-19 and the general population. As current guidelines are limiting in-person contacts to reduce the spread of the virus, the development of a digital approach to implement in psychiatric and psychological consultations is needed. In this paper, we present the DigiCOVID protocol, a digital approach to offer remote, personalized psychological and psychiatric support to former or current patients with COVID-19 and their relatives. Objective: The main goal of this project is to evaluate the feasibility, acceptability, and usability of the DigiCOVID protocol. Furthermore, we also aim to assess the impact of the abovementioned protocol by means of pre-post changes in psychological clinical variables. Methods: Participants undergo an initial telephonic screening to ensure inclusion criteria are met. Secondly, participants complete a video-assisted neuropsychological IQ test as well as web-based self-reports of health and general well-being. Participants are then assigned to a psychotherapist who offers 8 teletherapy sessions. At the end of the therapy cycle, the web-based questionnaires are administered for a posttreatment evaluation. Results: As of April 2022, we enrolled a total of 122 participants, of which 94 have completed neuropsychological tests and web-based questionnaires. Conclusions: Our study aims at testing the feasibility and preliminary efficacy of DigiCOVID, a remote telemedicine protocol for the improvement of psychological and psychiatric health in patients with COVID-19 and their relatives. To date, the approach used seems to be feasible and highly customizable to patients? needs, and therefore, the DigiCOVID protocol might pave the way for future telepsychiatry-based interventions. Trial Registration: ClinicalTrials.gov NCT05231018; https://clinicaltrials.gov/ct2/show/NCT05231018?term=NCT05231018 &draw=2&rank=1 International Registered Report Identifier (IRRID): DERR1-10.2196/39080 UR - https://www.researchprotocols.org/2022/11/e39080 UR - http://dx.doi.org/10.2196/39080 UR - http://www.ncbi.nlm.nih.gov/pubmed/36228130 ID - info:doi/10.2196/39080 ER - TY - JOUR AU - Shubina, Ivanna PY - 2022/11/11 TI - Scientific Publication Patterns of Systematic Reviews on Psychosocial Interventions Improving Well-being: Bibliometric Analysis JO - Interact J Med Res SP - e41456 VL - 11 IS - 2 KW - psychosocial intervention KW - well-being KW - systematic review KW - bibliometric analysis KW - bibliometrics KW - scientific research KW - medical research KW - publication KW - publish KW - citation KW - scientometrics KW - mental health N2 - Background: Despite numerous empirical studies and systematic reviews conducted on the effectiveness of interventions improving psychological well-being, there is no holistic overview of published systematic reviews in this field. Objective: This bibliometric study explored the scientific patterns of the effectiveness of different psychosocial interventions improving well-being among various categories of individuals with mental and physical diseases, to synthesize well-being intervention studies, and to suggest gaps and further studies in this emerging field. Methods: The bibliometric analysis included identifying the most productive authors, institutions, and countries; most explored fields and subjects of study; most active journals and publishers; and performing citation analysis and analyzing publication trends between 2014 and 2022. We focused on data retrieved from known databases, and the study was conducted with a proven bibliometric approach. Results: In total, 156 studies were found concerning the research domains and retrieved using LENS software from high-ranking databases (Crossref, Microsoft Academic, PubMed, and Core). These papers were written in English by 100 authors from 24 countries, among which, the leading country was the United Kingdom. Descriptive characteristics of the publications involved an increased number of publications in 2017 (n=35) and 2019 (n=34) and a decreased number in 2021 (n=4). The top 2 leading authors by citation score are James Thomas (3 papers and 260 citations) and Chris Dickens (3 papers and 182 citations). However, the most cited study had 592 citations. BMJ Open (n=6 articles) is the leading journal in the field of medicine; Clinical Psychology Review (n=5), in psychology; and Frontiers in Psychology, in psychological intervention (n=5) and psychology (n=5). The top 2 publishers were Wiley (n=28) and Elsevier (n=25). Conclusions: This study indicates an overall interest in the declared domains within the last decade. Our findings primarily indicate that psychosocial interventions (PIs) were evaluated as being effective in managing mental and physical problems and enhancing well-being. Cognitive behavioral therapy was assessed as being effective in treating anxiety, psychoeducation in relapse prevention, and gratitude interventions in improving overall health, and the mindfulness approach had a positive impact on decreasing distress and depression. Moreover, all these intervention types resulted in an overall increase in an individuals? well-being and resilience. Integrating social and cultural factors while considering individual differences increases the efficiency of PIs. Furthermore, PIs were evaluated as being effective in managing symptoms of eating disorders, dementia, and cancer. Our findings could help provide researchers an overview of the publication trends on research domains of focus for further studies, since it shows current findings and potential research needs in these fields, and would also benefit practitioners working on increasing their own and their patients' well-being. UR - https://www.i-jmr.org/2022/2/e41456 UR - http://dx.doi.org/10.2196/41456 UR - http://www.ncbi.nlm.nih.gov/pubmed/36367767 ID - info:doi/10.2196/41456 ER - TY - JOUR AU - Sterling, Andrew William AU - Sobolev, Michael AU - Van Meter, Anna AU - Guinart, Daniel AU - Birnbaum, L. Michael AU - Rubio, M. Jose AU - Kane, M. John PY - 2022/11/10 TI - Digital Technology in Psychiatry: Survey Study of Clinicians JO - JMIR Form Res SP - e33676 VL - 6 IS - 11 KW - digital psychiatry KW - passive monitoring technology KW - digital phenotype KW - psychiatry KW - mental health KW - clinicians KW - clinician perspectives KW - digital health KW - physicians KW - psychiatrists N2 - Background: Digital technology has the potential to transform psychiatry, but its adoption has been limited. The proliferation of telepsychiatry during the COVID-19 pandemic has increased the urgency of optimizing technology for clinical practice. Understanding clinician attitudes and preferences is crucial to effective implementation and patient benefit. Objective: Our objective was to elicit clinician perspectives on emerging digital technology. Methods: Clinicians in a large psychiatry department (inpatient and outpatient) were invited to complete a web-based survey about their attitudes toward digital technology in practice, focusing on implementation, clinical benefits, and expectations about patients? attitudes. The survey consisted of 23 questions that could be answered on either a 3-point or 5-point Likert scale. We report the frequencies and percentages of responses. Results: In total, 139 clinicians completed the survey?they represent a variety of years of experience, credentials, and diagnostic subspecialties (response rate 69.5%). Overall, 83.4% (n=116) of them stated that digital data could improve their practice, and 23.0% (n=32) of responders reported that they had viewed patients? profiles on social media. Among anticipated benefits, clinicians rated symptom self-tracking (n=101, 72.7%) as well as clinical intervention support (n=90, 64.7%) as most promising. Among anticipated challenges, clinicians mostly expressed concerns over greater time demand (n=123, 88.5%) and whether digital data would be actionable (n=107, 77%). Furthermore, 95.0% (n=132) of clinicians expected their patients to share digital data. Conclusions: Overall, clinicians reported a positive attitude toward the use of digital data to not only improve patient outcomes but also highlight significant barriers that implementation would need to overcome. Although clinicians? self-reported attitudes about digital technology may not necessarily translate into behavior, our results suggest that technologies that reduce clinician burden and are easily interpretable have the greatest likelihood of uptake. UR - https://formative.jmir.org/2022/11/e33676 UR - http://dx.doi.org/10.2196/33676 UR - http://www.ncbi.nlm.nih.gov/pubmed/36355414 ID - info:doi/10.2196/33676 ER - TY - JOUR AU - Ludin, Nicola AU - Holt-Quick, Chester AU - Hopkins, Sarah AU - Stasiak, Karolina AU - Hetrick, Sarah AU - Warren, Jim AU - Cargo, Tania PY - 2022/11/4 TI - A Chatbot to Support Young People During the COVID-19 Pandemic in New Zealand: Evaluation of the Real-World Rollout of an Open Trial JO - J Med Internet Res SP - e38743 VL - 24 IS - 11 KW - COVID-19 KW - youth KW - chatbots KW - adolescent mental health KW - dialog-based intervention KW - digital mental health N2 - Background: The number of young people in New Zealand (Aotearoa) who experience mental health challenges is increasing. As those in Aotearoa went into the initial COVID-19 lockdown, an ongoing digital mental health project was adapted and underwent rapid content authoring to create the Aroha chatbot. This dynamic digital support was designed with and for young people to help manage pandemic-related worry. Objective: Aroha was developed to provide practical evidence-based tools for anxiety management using cognitive behavioral therapy and positive psychology. The chatbot included practical ideas to maintain social and cultural connection, and to stay active and well. Methods: Stay-at-home orders under Aotearoa?s lockdown commenced on March 20, 2020. By leveraging previously developed chatbot technology and broader existing online trial infrastructure, the Aroha chatbot was launched promptly on April 7, 2020. Dissemination of the chatbot for an open trial was via a URL, and feedback on the experience of the lockdown and the experience of Aroha was gathered via online questionnaires and a focus group, and from community members. Results: In the 2 weeks following the launch of the chatbot, there were 393 registrations, and 238 users logged into the chatbot, of whom 127 were in the target age range (13-24 years). Feedback guided iterative and responsive content authoring to suit the dynamic situation and motivated engineering to dynamically detect and react to a range of conversational intents. Conclusions: The experience of the implementation of the Aroha chatbot highlights the feasibility of providing timely event-specific digital mental health support and the technology requirements for a flexible and enabling chatbot architectural framework. UR - https://www.jmir.org/2022/11/e38743 UR - http://dx.doi.org/10.2196/38743 UR - http://www.ncbi.nlm.nih.gov/pubmed/36219754 ID - info:doi/10.2196/38743 ER - TY - JOUR AU - Schick, Anita AU - Feine, Jasper AU - Morana, Stefan AU - Maedche, Alexander AU - Reininghaus, Ulrich PY - 2022/10/31 TI - Validity of Chatbot Use for Mental Health Assessment: Experimental Study JO - JMIR Mhealth Uhealth SP - e28082 VL - 10 IS - 10 KW - chatbot KW - distress KW - monitoring KW - mobile health KW - social desirability KW - social presence N2 - Background: Mental disorders in adolescence and young adulthood are major public health concerns. Digital tools such as text-based conversational agents (ie, chatbots) are a promising technology for facilitating mental health assessment. However, the human-like interaction style of chatbots may induce potential biases, such as socially desirable responding (SDR), and may require further effort to complete assessments. Objective: This study aimed to investigate the convergent and discriminant validity of chatbots for mental health assessments, the effect of assessment mode on SDR, and the effort required by participants for assessments using chatbots compared with established modes. Methods: In a counterbalanced within-subject design, we assessed 2 different constructs?psychological distress (Kessler Psychological Distress Scale and Brief Symptom Inventory-18) and problematic alcohol use (Alcohol Use Disorders Identification Test-3)?in 3 modes (chatbot, paper-and-pencil, and web-based), and examined convergent and discriminant validity. In addition, we investigated the effect of mode on SDR, controlling for perceived sensitivity of items and individuals? tendency to respond in a socially desirable way, and we also assessed the perceived social presence of modes. Including a between-subject condition, we further investigated whether SDR is increased in chatbot assessments when applied in a self-report setting versus when human interaction may be expected. Finally, the effort (ie, complexity, difficulty, burden, and time) required to complete the assessments was investigated. Results: A total of 146 young adults (mean age 24, SD 6.42 years; n=67, 45.9% female) were recruited from a research panel for laboratory experiments. The results revealed high positive correlations (all P<.001) of measures of the same construct across different modes, indicating the convergent validity of chatbot assessments. Furthermore, there were no correlations between the distinct constructs, indicating discriminant validity. Moreover, there were no differences in SDR between modes and whether human interaction was expected, although the perceived social presence of the chatbot mode was higher than that of the established modes (P<.001). Finally, greater effort (all P<.05) and more time were needed to complete chatbot assessments than for completing the established modes (P<.001). Conclusions: Our findings suggest that chatbots may yield valid results. Furthermore, an understanding of chatbot design trade-offs in terms of potential strengths (ie, increased social presence) and limitations (ie, increased effort) when assessing mental health were established. UR - https://mhealth.jmir.org/2022/10/e28082 UR - http://dx.doi.org/10.2196/28082 UR - http://www.ncbi.nlm.nih.gov/pubmed/36315228 ID - info:doi/10.2196/28082 ER - TY - JOUR AU - Benrimoh, David AU - Chheda, D. Forum AU - Margolese, C. Howard PY - 2022/10/28 TI - The Best Predictor of the Future?the Metaverse, Mental Health, and Lessons Learned From Current Technologies JO - JMIR Ment Health SP - e40410 VL - 9 IS - 10 KW - metaverse KW - mental health KW - social media KW - virtual reality KW - VR KW - digital experience KW - human interaction KW - mental health risk KW - teleworking KW - assisted therapy KW - teletherapy KW - benefits KW - safety KW - mental health problems KW - data security KW - privacy KW - protection KW - user safety KW - safety regulations KW - mobile phone UR - https://mental.jmir.org/2022/10/e40410 UR - http://dx.doi.org/10.2196/40410 UR - http://www.ncbi.nlm.nih.gov/pubmed/36306155 ID - info:doi/10.2196/40410 ER - TY - JOUR AU - Mehta, Meherwan Urvakhsh AU - Basavaraju, Rakshathi AU - Ramesh, Abhishek AU - Kesavan, Muralidharan AU - Thirthalli, Jagadisha PY - 2022/10/21 TI - Motor Resonance During Action Observation and Its Relevance to Virtual Clinical Consultations: Observational Study Using Transcranial Magnetic Stimulation JO - JMIR Ment Health SP - e40652 VL - 9 IS - 10 KW - mirror neuron activity KW - virtual interactions KW - digital psychiatry KW - telepsychiatry KW - virtual mental health interventions KW - motor resonance N2 - Background: Virtual clinical interactions have increased tremendously since the onset of the COVID-19 pandemic. While they certainly have their advantages, there also exist potential limitations, for example, in establishing a therapeutic alliance, discussing complex clinical scenarios, etc. This may be due to possible disruptions in the accurate activation of the human mirror neuron system (MNS), a posited physiological template for effective social communication. Objective: This study aimed to compare motor resonance, a putative marker of MNS activity, estimated using transcranial magnetic stimulation (TMS) elicited while viewing virtual (video-based) and actual or real (enacted by a person) actions in healthy individuals. We hypothesized that motor resonance will be greater during real compared to virtual action observation. Methods: We compared motor resonance or motor-evoked potential (MEP) facilitation during the observation of virtual (presented via videos) and real (enacted in person) actions, relative to static image observation in healthy individuals using TMS. The MEP recordings were obtained by 2 single-pulse (neuronal membrane excitability?driven) TMS paradigms of different intensities and 2 paired-pulse (cortical gamma-aminobutyric acid-interneuron?driven) TMS paradigms. Results: This study comprised 64 participants. Using the repeated measures ANOVA, we observed a significant time effect for MEP facilitation from static to virtual and real observation states when recorded using 3 of the 4 TMS paradigms. Post hoc pairwise comparisons with Benjamini-Hochberg false discovery rate correction revealed significant MEP facilitation in both virtual and real observation states relative to static image observation; however, we also observed a significant time effect between the 2 action observation states (real > virtual) with 2 of the 4 TMS paradigms. Conclusions: Our results indicate that visual cues expressed via both virtual (video) or real (in person) modes elicit physiological responses within the putative MNS, but this effect is more pronounced for actions presented in person. This has relevance to the appropriate implementation of digital health solutions, especially those pertaining to mental health. UR - https://mental.jmir.org/2022/10/e40652 UR - http://dx.doi.org/10.2196/40652 UR - http://www.ncbi.nlm.nih.gov/pubmed/36269658 ID - info:doi/10.2196/40652 ER - TY - JOUR AU - Lai, Byron AU - Davis, Drew AU - Young, Raven AU - Swanson-Kimani, Erin AU - Wozow, Cynthia AU - Chaviano, Kelli AU - Rimmer, H. James PY - 2022/10/13 TI - Group Telegaming Through Immersive Virtual Reality to Improve Mental Health Among Adolescents With Physical Disabilities: Pre- and Posttrial Protocol JO - JMIR Res Protoc SP - e42651 VL - 11 IS - 10 KW - disability KW - physical activity KW - active video gaming KW - mindfulness N2 - Background: Adolescents with physical disabilities have higher rates of mental health conditions and issues than adolescents without disabilities, and this disparity was exacerbated by the onset of the COVID-19 pandemic. They also have limited access to on-site programs and nearby peers. Objective: This pilot aims to investigate the potential effects of a low-dose multiplayer virtual reality telegaming program on depression, socialization, and loneliness among a cohort of children with physical disabilities. A secondary aim is to describe feasibility metrics, namely, recruitment and adherence rates and perceived program enjoyment and satisfaction. The tertiary aim is to describe behavioral mechanisms that affect participant adherence and social participation in the classes. Methods: This study is a single-group pre- and posttest?designed trial. A single cohort of 12 children with physical disabilities will pilot a 1-month program that includes 2 supervised 1-hour sessions per week of group-based exergaming. Participants will complete questionnaires before and after the program. The primary aim measures will include the Children?s Depression Inventory 2 Short Form, a measure of feelings of depression, and the UCLA Loneliness Scale, a measure of both loneliness and social isolation. Secondary aim measures will include three posttest Likert scale questionnaires: perceived program enjoyment, program satisfaction, and satisfaction with multiplayer experiences. At postintervention or dropout, participants will undergo semistructured interviews to identify behavioral mechanisms that underlie participation. Data will be reported descriptively and be supported by t tests as appropriate. Results: Recruitment procedures started in July 2022. All data are expected to be collected by January 2023. Full trial results are expected to be published by March 2023. Secondary analyses of data will be subsequently published. Conclusions: This trial tests a peer-to-peer virtual reality telegaming program that includes a completely remote enrollment, assessment, and intervention protocol. This program is accessible and short in duration and frequency, allowing it to be integrated into other interventions. Knowledge obtained from this study will inform the development of a larger trial for improving the mental health and well-being of adolescents with physical disabilities. Trial Registration: ClinicalTrials.gov NCT05259462; https://clinicaltrials.gov/ct2/show/NCT05259462 International Registered Report Identifier (IRRID): PRR1-10.2196/42651 UR - https://www.researchprotocols.org/2022/10/e42651 UR - http://dx.doi.org/10.2196/42651 UR - http://www.ncbi.nlm.nih.gov/pubmed/36194864 ID - info:doi/10.2196/42651 ER - TY - JOUR AU - Guinart, Daniel AU - Sobolev, Michael AU - Patil, Bhagyashree AU - Walsh, Megan AU - Kane, M. John PY - 2022/10/12 TI - A Digital Intervention Using Daily Financial Incentives to Increase Medication Adherence in Severe Mental Illness: Single-Arm Longitudinal Pilot Study JO - JMIR Ment Health SP - e37184 VL - 9 IS - 10 KW - antipsychotic KW - adherence KW - digital KW - mobile health KW - mHealth KW - financial incentives N2 - Background: Medication nonadherence is prevalent in severe mental illness and is associated with multiple negative outcomes. Mobile technology and financial incentives show promise to improve medication adherence; however, studies in mental health, especially with oral medications, are lacking. Objective: The aim of this paper is to assess the feasibility and effectiveness of offering financial incentives through a mobile app based on behavioral economics principles to improve medication adherence in severe mental illness. Methods: A 10-week, single-arm longitudinal pilot study was conducted. Patients earned rewards in the context of app-based adherence incentives. The reward was split into biweekly payments made in increments of US $15, minus any US $2 per day penalties for missed check-ins. Time-varying effect modeling was used to summarize the patients? response during the study. Results: A total of 25 patients were enrolled in this pilot study, of which 72% (n=18) were female, and 48% (n=12) were of a White racial background. Median age was 24 (Q1-Q3: 20.5-30) years. Participants were more frequently diagnosed with schizophrenia and related disorders (n=9, 36%), followed by major depressive disorder (n=8, 32%). App engagement and medication adherence in the first 2 weeks were higher than in the last 8 weeks of the study. At study endpoint, app engagement remained high (n=24, Z=?3.17; P<.001), but medication adherence was not different from baseline (n=24, Z=?0.59; P=.28). Conclusions: Financial incentives were effectively delivered using an app and led to high engagement throughout the study and a significantly increased medication adherence for 2 weeks. Leveraging behavioral economics and mobile health technology can increase medication adherence in the short term. Trial Registration: ClinicalTrials.gov NCT04191876; https://clinicaltrials.gov/ct2/show/NCT04191876 UR - https://mental.jmir.org/2022/10/e37184 UR - http://dx.doi.org/10.2196/37184 UR - http://www.ncbi.nlm.nih.gov/pubmed/36222818 ID - info:doi/10.2196/37184 ER - TY - JOUR AU - Rice, Kevin AU - Seidman, Joshua AU - Mahoney, Oneil PY - 2022/9/30 TI - A Health Equity?Oriented Research Agenda Requires Comprehensive Community Engagement JO - J Particip Med SP - e37657 VL - 14 IS - 1 KW - mental health KW - community-based participatory action research KW - health equity KW - serious mental illness KW - health policy KW - research KW - community KW - engagement KW - disparity KW - participatory care KW - consumers UR - https://jopm.jmir.org/2022/1/e37657 UR - http://dx.doi.org/10.2196/37657 UR - http://www.ncbi.nlm.nih.gov/pubmed/36178726 ID - info:doi/10.2196/37657 ER - TY - JOUR AU - Kabir, Khubayeeb Muhammad AU - Islam, Maisha AU - Kabir, Binte Anika Nahian AU - Haque, Adiba AU - Rhaman, Khalilur Md PY - 2022/9/28 TI - Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques JO - JMIR Form Res SP - e36118 VL - 6 IS - 9 KW - mental health forums KW - natural language processing KW - severity KW - major depressive disorder KW - deep learning KW - machine learning KW - multiclass text classification N2 - Background: There are a myriad of language cues that indicate depression in written texts, and natural language processing (NLP) researchers have proven the ability of machine learning and deep learning approaches to detect these cues. However, to date, these approaches bridging NLP and the domain of mental health for Bengali literature are not comprehensive. The Bengali-speaking population can express emotions in their native language in greater detail. Objective: Our goal is to detect the severity of depression using Bengali texts by generating a novel Bengali corpus of depressive posts. We collaborated with mental health experts to generate a clinically sound labeling scheme and an annotated corpus to train machine learning and deep learning models. Methods: We conducted a study using Bengali text-based data from blogs and open source platforms. We constructed a procedure for annotated corpus generation and extraction of textual information from Bengali literature for predictive analysis. We developed our own structured data set and designed a clinically sound labeling scheme with the help of mental health professionals, adhering to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) during the process. We used 5 machine learning models for detecting the severity of depression: kernel support vector machine (SVM), random forest, logistic regression K-nearest neighbor (KNN), and complement naive Bayes (NB). For the deep learning approach, we used long short-term memory (LSTM) units and gated recurrent units (GRUs) coupled with convolutional blocks or self-attention layers. Finally, we aimed for enhanced outcomes by using state-of-the-art pretrained language models. Results: The independent recurrent neural network (RNN) models yielded the highest accuracies and weighted F1 scores. GRUs, in particular, produced 81% accuracy. The hybrid architectures could not surpass the RNNs in terms of performance. Kernel SVM with term frequency?inverse document frequency (TF-IDF) embeddings generated 78% accuracy on test data. We used validation and training loss curves to observe and report the performance of our architectures. Overall, the number of available data remained the limitation of our experiment. Conclusions: The findings from our experimental setup indicate that machine learning and deep learning models are fairly capable of assessing the severity of mental health issues from texts. For the future, we suggest more research endeavors to increase the volume of Bengali text data, in particular, so that modern architectures reach improved generalization capability. UR - https://formative.jmir.org/2022/9/e36118 UR - http://dx.doi.org/10.2196/36118 UR - http://www.ncbi.nlm.nih.gov/pubmed/36169989 ID - info:doi/10.2196/36118 ER - TY - JOUR AU - Garrote-Cámara, Elena Maria AU - Juárez-Vela, Raúl AU - Sufrate-Sorzano, Teresa AU - Durante, Angela AU - Ferrara, Paolo AU - Terzoni, Stefano AU - Pérez, Jesús AU - Santolalla-Arnedo, Iván PY - 2022/9/21 TI - Transcultural Adaptation of and Theoretical Validation Models for the Spanish Version of the Nurses? Global Assessment of Suicide Risk Scale: Protocol for a Multicenter Cross-sectional Study JO - JMIR Res Protoc SP - e39482 VL - 11 IS - 9 KW - mental health KW - suicide KW - psychiatric nursing KW - Spanish KW - translate KW - translation KW - scale KW - measurement KW - assessment KW - adapt KW - adaptation KW - cultural KW - transcultural KW - suicidal KW - nurse KW - nursing KW - psychiatric KW - public health KW - prevention KW - treatment KW - risk KW - development KW - lethal KW - patient KW - scientific literature KW - variables KW - reliability KW - validate KW - validity KW - tool KW - Nurse´s Global Assessment of Suicide Risk KW - psychometric N2 - Background: The use of validated instruments means providing health professionals with reliable and valid tools. The Nurses? Global Assessment of Suicide Risk (NGASR) scale has proven to be valid and reliable in supporting the nursing evaluation of suicide risk in different languages and cultural environments. Objective: The aims of our study are to translate and adapt the NGASR scale for the Spanish population and evaluate its psychometric properties in patients with suicide risk factors. Methods: The translation, adaptation, and modeling of the tool will be performed. The sample will include 165 participants. The psychometric analysis will include reliability and validity tests of the tool?s internal structure. The tool?s reliability will be assessed by exploring internal consistency and calculating the Cronbach ? coefficient; significance values of .70 or higher will be accepted as indicators of good internal consistency. The underlying factor structure of the Spanish version of the NGASR scale will be assessed by performing an exploratory factor analysis. The Kaiser-Meyer-Olkin measure of sample adequacy and the Bartlett sphericity statistic will be calculated beforehand. For the latter, if P is <.05 for the null hypothesis of sphericity, the null hypothesis will be rejected. Results: Participants will be recruited between April 2022 and December 2022. Our study is expected to conclude in the first quarter of 2023. Conclusions: We hope to find the same firmness that colleagues have found in other countries in order to consolidate and promote the use of the NGASR tool in the Spanish population. The prevention and treatment of suicidal behavior require holistic, multidisciplinary, and comprehensive management. International Registered Report Identifier (IRRID): PRR1-10.2196/39482 UR - https://www.researchprotocols.org/2022/9/e39482 UR - http://dx.doi.org/10.2196/39482 UR - http://www.ncbi.nlm.nih.gov/pubmed/3612975 ID - info:doi/10.2196/39482 ER - TY - JOUR AU - O'Daffer, Alison AU - Colt, F. Susannah AU - Wasil, R. Akash AU - Lau, Nancy PY - 2022/9/20 TI - Efficacy and Conflicts of Interest in Randomized Controlled Trials Evaluating Headspace and Calm Apps: Systematic Review JO - JMIR Ment Health SP - e40924 VL - 9 IS - 9 KW - mHealth KW - psychological interventions KW - mobile health KW - mental health KW - health applications KW - health apps KW - mindfulness KW - meditation app KW - digital health application KW - digital health intervention N2 - Background: Although there are thousands of mental health apps, 2 apps, Headspace and Calm, claim a large percentage of the marketplace. These two mindfulness and meditation apps have reached tens of millions of active users. To guide consumers, clinicians, and researchers, we performed a systematic review of randomized controlled trials (RCTs) of Headspace and Calm. Objective: Our study aimed to evaluate intervention efficacy, risk of bias, and conflicts of interest (COIs) in the evidence base for Headspace and Calm, the two most popular mental health apps at the time of our search. Methods: To identify studies, we searched academic databases (Google Scholar, MEDLINE, and PsycINFO) and the websites of Headspace and Calm in May 2021 for RCTs of Headspace and Calm testing efficacy via original data collection, published in English in peer-reviewed journals. For each study, we coded (1) study characteristics (eg, participants, sample size, and outcome measures), (2) intervention characteristics (eg, free vs paid version of the app and intended frequency of app usage), (3) all study outcomes, (4) Cochrane risk of bias variables, and (5) COI variables (eg, presence or absence of a preregistration and the presence or absence of a COI statement involving the company). Results: We identified 14 RCTs of Headspace and 1 RCT of Calm. Overall, 93% (13/14) of RCTs of Headspace and 100% (1/1) of RCTs of Calm recruited participants from a nonclinical population. Studies commonly measured mindfulness, well-being, stress, depressive symptoms, and anxiety symptoms. Headspace use improved depression in 75% of studies that evaluated it as an outcome. Findings were mixed for mindfulness, well-being, stress, and anxiety, but at least 40% of studies showed improvement for each of these outcomes. Studies were generally underpowered to detect ?small? or ?medium? effect sizes. Furthermore, 50% (7/14) of RCTs of Headspace and 0% (0/1) of RCTs of Calm reported a COI that involved Headspace or Calm (the companies). The most common COI was the app company providing premium app access for free for participants, and notably, 14% (2/14) of RCTs of Headspace reported Headspace employee involvement in study design, execution, and data analysis. Only 36% (5/14) of RCTs of Headspace were preregistered, and the 1 RCT of Calm was not preregistered. Conclusions: The empirical research on Headspace appears promising, whereas there is an absence of randomized trials on Calm. Limitations of this study include an inability to compare Headspace and Calm owing to the dearth of RCTs studying Calm and the reliance on author reports to evaluate COIs. When determining whether or not mental health apps are of high quality, identification of high-quality apps and evaluation of their effectiveness and investigators? COIs should be ensured. UR - https://mental.jmir.org/2022/9/e40924 UR - http://dx.doi.org/10.2196/40924 UR - http://www.ncbi.nlm.nih.gov/pubmed/36125880 ID - info:doi/10.2196/40924 ER - TY - JOUR AU - Aemissegger, Vera AU - Lopez-Alcalde, Jesus AU - Witt, M. Claudia AU - Barth, Jürgen PY - 2022/9/14 TI - Comparability of Patients in Trials of eHealth and Face-to-Face Psychotherapeutic Interventions for Depression: Meta-synthesis JO - J Med Internet Res SP - e36978 VL - 24 IS - 9 KW - depression KW - mental health KW - digital intervention KW - eHealth KW - web-based KW - randomized controlled trial KW - RCT KW - meta-analysis KW - epidemiology KW - epidemiological KW - depressive disorder KW - mental illness KW - mental condition KW - mental disorder KW - psychotherapy KW - psychotherapeutic intervention KW - CBT KW - iCBT KW - cognitive behavioral therapy KW - face-to-face KW - cognitive therapy KW - interpersonal therapy N2 - Background: Depressive disorders (DDs) are a public health problem. Face-to-face psychotherapeutic interventions are a first-line option for their treatment in adults. There is a growing interest in eHealth interventions to maximize accessibility for effective treatments. Thus, the number of randomized controlled trials (RCTs) of eHealth psychotherapeutic interventions has increased, and these interventions are being offered to patients. However, it is unknown whether patients with DDs differ in internet-based and face-to-face intervention trials. This information is essential to gain knowledge about eHealth trials? external validity. Objective: We aimed to compare the baseline characteristics of patients with DDs included in the RCTs of eHealth and face-to-face psychotherapeutic interventions with a cognitive component. Methods: In this meta-epidemiological study, we searched 5 databases between 1990 and November 2017 (MEDLINE, Embase, PsycINFO, Google Scholar, and the database of Cuijpers et al). We included RCTs of psychotherapeutic interventions with a cognitive component (eg, cognitive therapy, cognitive behavioral therapy [CBT], or interpersonal therapy) delivered face-to-face or via the internet to adults with DDs. Each included study had a matching study for predefined criteria to allow a valid comparison of characteristics and was classified as a face-to-face (CBT) or eHealth (internet CBT) intervention trial. Two authors selected the studies, extracted data, and resolved disagreements by discussion. We tested whether predefined baseline characteristics differed in face-to-face and internet-based trials using a mixed-effects model and testing for differences with z tests (statistical significance set at .05). For continuous outcomes, we also estimated the difference in means between subgroups with 95% CI. Results: We included 58 RCTs (29 matching pairs) with 3846 participants (female: n=2803, 72.9%) and mean ages ranging from 20-74 years. White participants were the most frequent (from 63.6% to 100%). Other socioeconomic characteristics were poorly described. The participants presented DDs of different severity measured with heterogeneous instruments. Internet CBT trials had a longer depression duration at baseline (7.19 years higher, CI 95% 2.53-11.84; 10.0 vs 2.8 years; P=.002), but the proportion of patients with previous depression treatment was lower (24.8% vs 42%; P=.04). Subgroup analyses found no evidence of differences for the remaining baseline characteristics: age, gender, education, living area, depression severity, history of depression, actual antidepressant medication, actual physical comorbidity, actual mental comorbidity, study dropout, quality of life, having children, family status, and employment. We could not compare proficiency with computers due to the insufficient number of studies. Conclusions: The baseline characteristics of patients with DDs included in the RCTs of eHealth and face-to-face psychotherapeutic interventions are generally similar. However, patients in eHealth trials had a longer duration of depression, and a lower proportion had received previous depression treatment, which might indicate that eHealth trials attract patients who postpone earlier treatment attempts. Trial Registration: PROSPERO CRD42019085880; https://tinyurl.com/4xufwcyr UR - https://www.jmir.org/2022/9/e36978 UR - http://dx.doi.org/10.2196/36978 UR - http://www.ncbi.nlm.nih.gov/pubmed/36103217 ID - info:doi/10.2196/36978 ER - TY - JOUR AU - Marin, Anna AU - DeCaro, Renée AU - Schiloski, Kylie AU - Elshaar, Ala?a AU - Dwyer, Brigid AU - Vives-Rodriguez, Ana AU - Palumbo, Rocco AU - Turk, Katherine AU - Budson, Andrew PY - 2022/9/12 TI - Home-Based Electronic Cognitive Therapy in Patients With Alzheimer Disease: Feasibility Randomized Controlled Trial JO - JMIR Form Res SP - e34450 VL - 6 IS - 9 KW - cognitive training KW - Alzheimer disease dementia KW - technology N2 - Background: Can home-based computerized cognitive training programs be a useful tool to sustain cognition and quality of life in patients with Alzheimer disease (AD)? To date, the progressive nature of the disease has made this question difficult to answer. Computerized platforms provide more accessibility to cognitive trainings; however, the feasibility of long-term, home-based computerized programs for patients with AD dementia remains unclear. Objective: We aimed to investigate the feasibility of a 24-week home-based intervention program using the Constant Therapy app and its preliminary efficacy on cognition in patients with AD. Constant Therapy is a program developed for patients with speech and cognitive deficits. We hypothesized that patients with AD would use Constant Therapy daily over the course of the 24-week period. Methods: Data were collected over a 48-week period. We recruited participants aged between 50 and 90 years with a diagnosis of mild cognitive impairment due to AD or mild AD dementia. Participants were randomly assigned to either the Constant Therapy (n=10) or active control (n=9) group. The Constant Therapy group completed a tablet-based training during the first 24 weeks; the second 24 weeks of computerized training were optional. The active control group completed paper-and-pencil games during the first 24 weeks and were invited to complete an optional Constant Therapy training during the second 24 weeks. Every 6 weeks, the participants completed the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The participants independently accessed Constant Therapy using an Apple iPad. Our primary feasibility outcomes were the rate of adherence and daily use of Constant Therapy over 24 weeks. Our secondary outcomes were Constant Therapy performance over 24 weeks and change in RBANS scores between the 2 experimental groups. Results: Feasibility analyses were computed for participants who completed 24 weeks of Constant Therapy. We found that long-term use of the Constant Therapy program was feasible in patients with AD over 24 weeks (adherence 80%; program use 121/168 days, for 32 minutes daily). These participants showed an overall improvement in accuracy and latency (P=.005) in the Constant Therapy scores, as well as specific improvements in visual and auditory memory, attention, and arithmetic tasks. The Constant Therapy group showed improvement in the RBANS coding subtest. No unexpected problems or adverse events were observed. Conclusions: Long-term (eg, 24 weeks) computerized cognitive training using Constant Therapy is feasible in patients with AD in the mild cognitive impairment and mild dementia stages. Patients adhered more to Constant Therapy than to the paper-and-pencil training over 24 weeks and improved their performance over time. These findings support the development of future randomized controlled trials that will investigate the efficacy of Constant Therapy to sustain cognitive function in patients with AD. Trial Registration: ClinicalTrials.gov NCT02521558; https://clinicaltrials.gov/ct2/show/NCT02521558 UR - https://formative.jmir.org/2022/9/e34450 UR - http://dx.doi.org/10.2196/34450 UR - http://www.ncbi.nlm.nih.gov/pubmed/36094804 ID - info:doi/10.2196/34450 ER - TY - JOUR AU - Banwell, Emily AU - Hanley, Terry AU - De Ossorno Garcia, Santiago AU - Mindel, Charlotte AU - Kayll, Thomas AU - Sefi, Aaron PY - 2022/9/9 TI - The Helpfulness of Web-Based Mental Health and Well-being Forums for Providing Peer Support for Young People: Cross-sectional Exploration JO - JMIR Form Res SP - e36432 VL - 6 IS - 9 KW - adolescent mental health KW - peer support KW - web-based forums KW - web-based mental health N2 - Background: Young people are increasingly seeking out web-based support for their mental health and well-being. Peer support forums are popular with this age group, with young individuals valuing the fact that the forums are available 24/7, providing a safe and anonymous space for exploration. Currently, little systematic evaluation of the helpfulness of such forums in providing support has been conducted. Objective: This study examined the helpfulness of the support offered within web-based mental health and well-being peer support forums for young people. It specifically investigated the self-reported user ratings of helpfulness reported through the completion of a developing experience measure. The ratings will be used to consider further development of the measure and reflect upon the overall helpfulness of the forums as indicated by the reported scores. Methods: The study used routinely collected practice-based outcome data from web-based mental health forums for young people. These forums are hosted by the UK-based web-based therapy and support service, Kooth. A cross-sectional design was used to explore?using a range of inferential statistical measures?the outcomes reported by those accessing the forums using a Peer Online Community Experience Measure (POCEM). To consider the helpfulness in general, 23,443 POCEMs completed in 2020 were used. A second data set of 17,137 completed POCEMs from the same year was used to consider whether various engagement indicators had an impact upon the helpfulness rating. Results: Female users aged between 11 and 16 years predominantly completed the POCEM. This is in keeping with the majority of those using the service. In total, 74.6% (8240/11,045) of the scores on the POCEM indicated that the individuals found the posts helpful. An ANOVA indicated that male users were more likely to report obtaining intrapersonal support, whereas female users obtained interpersonal support. Furthermore, the POCEM scores reflected the internal consistency of the measure and provided an insight into the way that young people made use of the peer support resource; for instance, posts that were rated more helpful were correlated with spending longer time reading them, and the topics discussed varied throughout the day with more mental health issues being discussed later at night. Conclusions: The results seem to demonstrate that, overall, the young people involved in this study found web-based peer support helpful. They indicate that peer support can provide an important strand of care within a supportive mental health ecosystem, particularly during time periods when in-person support is typically closed. However, limitations were noted, suggesting that caution is needed when interpreting the results of this study. Although such services are incredibly well used, they have received little research attention to date. As such, further investigation into what constitutes helpful and unhelpful peer support is needed. UR - https://formative.jmir.org/2022/9/e36432 UR - http://dx.doi.org/10.2196/36432 UR - http://www.ncbi.nlm.nih.gov/pubmed/36083629 ID - info:doi/10.2196/36432 ER - TY - JOUR AU - Preston, M. A'mie AU - Brown, Lana AU - Padala, P. Kalpana AU - Padala, R. Prasad PY - 2022/9/2 TI - Veterans Affairs Health Care Provider Perceptions of Virtual Reality: Brief Exploratory Survey JO - Interact J Med Res SP - e38490 VL - 11 IS - 2 KW - virtual reality KW - older adults KW - provider perception N2 - Background: Virtual reality (VR), a simulated experience that can be similar to or completely different from the real world, has become increasingly useful within the psychiatric and medical fields. This VR technology has been applied in medical school trainings, exposure therapy for individuals with posttraumatic stress disorder (PTSD), and reminiscence therapy associated with mood disorders for older adults. Perceptions of VR through the lens of the health care provider require further exploration. VR has grown in popularity; however, this modality continues to be underused in most Veterans Affairs (VA) hospitals. Objective: A web-based survey was used to explore health care provider perceptions of immersive VR availability and use for older adults and identify potential barriers for immersive VR use in older adults with cognitive impairment. Methods: An 8-item web-based survey was developed to obtain health care provider feedback. This survey was disseminated throughout a single Veterans Integrated Services Network (VISN). The VR survey was developed via the Survey Monkey platform and distributed through the secure VA email network. Providers were asked to voluntarily participate in the brief, anonymous survey and offer their perceptions of immersive VR use within their patient population. Survey data were reviewed and interpreted using descriptive statistics. Results: A total of 49 respondents completed the survey over a 15-day period. Of them, 36 respondents (73%) had heard of a VR device, though the majority (n=44, 90%) had never used or prescribed a VR device. Respondents identified several potential barriers to immersive VR use in older adults with cognitive impairment (eg, hearing difficulties, perceptions of technology, cognitive concerns, access to resources, and visual impairment). Despite the barriers identified, providers (n=48, 98%) still reported that they would feel comfortable prescribing immersive VR as an intervention for their patient population. Conclusions: Survey findings revealed that health care providers within this VISN for VAs have heard of VR, although they may not have actively engaged in its use. Most of the providers reported that they would prescribe the use of an immersive VR intervention for their older adult patients. This key point highlights the desire to implement VR strategies for patient use by their providers. If underlying barriers can be addressed and relatively resolved, this technological intervention has the potential to create substantial breakthroughs in clinical care. UR - https://www.i-jmr.org/2022/2/e38490 UR - http://dx.doi.org/10.2196/38490 UR - http://www.ncbi.nlm.nih.gov/pubmed/36053568 ID - info:doi/10.2196/38490 ER - TY - JOUR AU - Bai, Jinbing AU - Zhang, Wenhui AU - Choi, Daesung AU - Kim, Sangmi PY - 2022/8/26 TI - Methodology Considerations in Studying Mental Health, Sleep Quality, and Biopsychosocial Determinants Among Chinese and Korean Americans During the COVID-19 Pandemic JO - Asian Pac Isl Nurs J SP - e39760 VL - 6 IS - 1 KW - Asian American KW - gut microbiome KW - mental health KW - methodology KW - sleep disturbance KW - COVID-19 N2 - International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2020-047281 UR - https://apinj.jmir.org/2022/1/e39760 UR - http://dx.doi.org/10.2196/39760 UR - http://www.ncbi.nlm.nih.gov/pubmed/36186662 ID - info:doi/10.2196/39760 ER - TY - JOUR AU - Chikersal, Prerna AU - Venkatesh, Shruthi AU - Masown, Karman AU - Walker, Elizabeth AU - Quraishi, Danyal AU - Dey, Anind AU - Goel, Mayank AU - Xia, Zongqi PY - 2022/8/24 TI - Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping JO - JMIR Ment Health SP - e38495 VL - 9 IS - 8 KW - mobile sensing KW - sensor KW - sensing KW - mobile health KW - mHealth KW - algorithm KW - multiple sclerosis KW - disability KW - mental health KW - depression KW - sleep KW - fatigue KW - tiredness KW - predict KW - machine learning KW - feature selection KW - neurological disorder KW - COVID-19 KW - isolation KW - behavior change KW - health outcome KW - fitness KW - movement KW - physical activity KW - exercise KW - tracker KW - digital phenotyping N2 - Background: The COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS). Objective: We presented a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated stay-at-home period due to a global pandemic. Methods: First, we extracted features that capture behavior changes due to the stay-at-home order. Then, we adapted and applied an existing algorithm to these behavior-change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the stay-at-home period. Results: Using data collected between November 2019 and May 2020, the algorithm detected depression with an accuracy of 82.5% (65% improvement over baseline; F1-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; F1-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; F1-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; F1-score: 0.84). Conclusions: Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics, which would cause drastic behavior changes. UR - https://mental.jmir.org/2022/8/e38495 UR - http://dx.doi.org/10.2196/38495 UR - http://www.ncbi.nlm.nih.gov/pubmed/35849686 ID - info:doi/10.2196/38495 ER - TY - JOUR AU - Sillice, Aline Marie AU - Stein, Michael AU - Battle, L. Cynthia AU - Meshesha, Z. Lidia AU - Lindsay, Clifford AU - Agu, Emmanuel AU - Abrantes, M. Ana PY - 2022/8/15 TI - Exploring Factors Associated With Mobile Phone Behaviors and Attitudes Toward Technology Among Adults With Alcohol Use Disorder and Implications for mHealth Interventions: Exploratory Study JO - JMIR Form Res SP - e32768 VL - 6 IS - 8 KW - mobile phone use patterns KW - substance use KW - alcohol KW - technological attitude KW - alcohol use disorder KW - demographic differences KW - anxiety KW - depression KW - mobile phone KW - patient attitude N2 - Background: Alcohol use disorder (AUD) is associated with severe chronic medical conditions and premature mortality. Expanding the reach or access to effective evidence-based treatments to help persons with AUD is a public health objective. Mobile phone or smartphone technology has the potential to increase the dissemination of clinical and behavioral interventions (mobile health interventions) that increase the initiation and maintenance of sobriety among individuals with AUD. Studies about how this group uses their mobile phone and their attitudes toward technology may have meaningful implications for participant engagement with these interventions. Objective: This exploratory study examined the potential relationships among demographic characteristics (race, gender, age, marital status, and income), substance use characteristics (frequency of alcohol and cannabis use), and clinical variables (anxiety and depression symptoms) with indicators of mobile phone use behaviors and attitudes toward technology. Methods: A sample of 71 adults with AUD (mean age 42.9, SD 10.9 years) engaged in an alcohol partial hospitalization program completed 4 subscales from the Media Technology Usage and Attitudes assessment: Smartphone Usage measures various mobile phone behaviors and activities, Positive Attitudes and Negative Attitudes measure attitudes toward technology, and the Technological Anxiety/Dependence measure assesses level of anxiety when individuals are separated from their phone and dependence on this device. Participants also provided demographic information and completed the Epidemiologic Studies Depression Scale (CES-D) and the Generalized Anxiety Disorder (GAD-7) scale. Lastly, participants reported their frequency of alcohol use over the past 3 months using the Drug Use Frequency Scale. Results: Results for the demographic factors showed a significant main effect for age, Smartphone Usage (P=.003; ?p2=0.14), and Positive Attitudes (P=.01; ?p2=0.07). Marital status (P=.03; ?p2=0.13) and income (P=.03; ?p2=0.14) were associated only with the Technological Anxiety and Dependence subscale. Moreover, a significant trend was found for alcohol use and the Technological Anxiety/Dependence subscale (P=.06; R2=0.02). Lastly, CES-D scores (P=.03; R2=0.08) and GAD symptoms (P=.004; R2=0.13) were significant predictors only of the Technological Anxiety/Dependence subscale. Conclusions: Findings indicate differences in mobile phone use patterns and attitudes toward technology across demographic, substance use, and clinical measures among patients with AUD. These results may help inform the development of future mHealth interventions among this population. UR - https://formative.jmir.org/2022/8/e32768 UR - http://dx.doi.org/10.2196/32768 UR - http://www.ncbi.nlm.nih.gov/pubmed/35969449 ID - info:doi/10.2196/32768 ER - TY - JOUR AU - Curcic, Jelena AU - Vallejo, Vanessa AU - Sorinas, Jennifer AU - Sverdlov, Oleksandr AU - Praestgaard, Jens AU - Piksa, Mateusz AU - Deurinck, Mark AU - Erdemli, Gul AU - Bügler, Maximilian AU - Tarnanas, Ioannis AU - Taptiklis, Nick AU - Cormack, Francesca AU - Anker, Rebekka AU - Massé, Fabien AU - Souillard-Mandar, William AU - Intrator, Nathan AU - Molcho, Lior AU - Madero, Erica AU - Bott, Nicholas AU - Chambers, Mieko AU - Tamory, Josef AU - Shulz, Matias AU - Fernandez, Gerardo AU - Simpson, William AU - Robin, Jessica AU - Snædal, G. Jón AU - Cha, Jang-Ho AU - Hannesdottir, Kristin PY - 2022/8/10 TI - Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study JO - JMIR Res Protoc SP - e35442 VL - 11 IS - 8 KW - digital endpoints KW - cognition KW - Alzheimer disease KW - brain amyloid KW - methodology study KW - clinical trial design KW - mobile phone N2 - Background: More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. Objective: This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. Methods: The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies? ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. Results: Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. Conclusions: This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. International Registered Report Identifier (IRRID): DERR1-10.2196/35442 UR - https://www.researchprotocols.org/2022/8/e35442 UR - http://dx.doi.org/10.2196/35442 UR - http://www.ncbi.nlm.nih.gov/pubmed/35947423 ID - info:doi/10.2196/35442 ER - TY - JOUR AU - Bos, M. Fionneke AU - von Klipstein, Lino AU - Emerencia, C. Ando AU - Veermans, Erwin AU - Verhage, Tom AU - Snippe, Evelien AU - Doornbos, Bennard AU - Hadders-Prins, Grietje AU - Wichers, Marieke AU - Riese, Harriëtte PY - 2022/8/9 TI - A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application JO - JMIR Ment Health SP - e36430 VL - 9 IS - 8 KW - eHealth KW - clinical implementation KW - ecological momentary assessment KW - experience sampling method KW - smartphone KW - mobile health KW - mHealth KW - personalized diaries KW - personalized psychiatry KW - client-tailored KW - cocreation KW - shared decision-making KW - mobile phone N2 - Background: Smartphone self-monitoring of mood, symptoms, and contextual factors through ecological momentary assessment (EMA) provides insights into the daily lives of people undergoing psychiatric treatment. Therefore, EMA has the potential to improve their care. To integrate EMA into treatment, a clinical tool that helps clients and clinicians create personalized EMA diaries and interpret the gathered data is needed. Objective: This study aimed to develop a web-based application for personalized EMA in specialized psychiatric care in close collaboration with all stakeholders (ie, clients, clinicians, researchers, and software developers). Methods: The participants were 52 clients with mood, anxiety, and psychotic disorders and 45 clinicians (psychiatrists, psychologists, and psychiatric nurses). We engaged them in interviews, focus groups, and usability sessions to determine the requirements for an EMA web application and repeatedly obtained feedback on iteratively improved high-fidelity EMA web application prototypes. We used human-centered design principles to determine important requirements for the web application and designed high-fidelity prototypes that were continuously re-evaluated and adapted. Results: The iterative development process resulted in Personalized Treatment by Real-time Assessment (PETRA), which is a scientifically grounded web application for the integration of personalized EMA in Dutch clinical care. PETRA includes a decision aid to support clients and clinicians with constructing personalized EMA diaries, an EMA diary item repository, an SMS text message?based diary delivery system, and a feedback module for visualizing the gathered EMA data. PETRA is integrated into electronic health record systems to ensure ease of use and sustainable integration in clinical care and adheres to privacy regulations. Conclusions: PETRA was built to fulfill the needs of clients and clinicians for a user-friendly and personalized EMA tool embedded in routine psychiatric care. PETRA is unique in this codevelopment process, its extensive but user-friendly personalization options, its integration into electronic health record systems, its transdiagnostic focus, and its strong scientific foundation in the design of EMA diaries and feedback. The clinical effectiveness of integrating personalized diaries via PETRA into care requires further research. As such, PETRA paves the way for a systematic investigation of the utility of personalized EMA for routine mental health care. UR - https://mental.jmir.org/2022/8/e36430 UR - http://dx.doi.org/10.2196/36430 UR - http://www.ncbi.nlm.nih.gov/pubmed/35943762 ID - info:doi/10.2196/36430 ER - TY - JOUR AU - Netter, Anna-Lena AU - Beintner, Ina AU - Brakemeier, Eva-Lotta PY - 2022/8/9 TI - Adding an App-Based Intervention to the Cognitive Behavioral Analysis System of Psychotherapy in Routine Outpatient Psychotherapy Treatment: Proof-of-Concept Study JO - JMIR Form Res SP - e35482 VL - 6 IS - 8 KW - Cognitive Behavioral Analysis System of Psychotherapy KW - persistent depressive disorder KW - blended therapy KW - internet and mobile-based Interventions KW - routine clinical care KW - eHealth KW - mobile phone N2 - Background: The Cognitive Behavioral Analysis System of Psychotherapy (CBASP) is an empirically supported psychotherapeutic treatment developed specifically for persistent depressive disorder. However, given the high rates of nonresponse and relapse, there is a need for optimization. Studies suggest that outcomes can be improved by increasing the treatment dose via, for example, the continuous web-based application of therapy strategies between sessions. The strong emphasis in CBASP on the therapeutic relationship, combined with limited therapeutic availabilities, encourages the addition of web-based interventions to face-to-face therapy in terms of blended therapy. Objective: The aim of this study was to test an app-based intervention called CBASPath, which was designed to be used as a blended therapy tool. CBASPath offers 8 sequential modules with app-based exercises to facilitate additional engagement with the therapy content and a separate exercise to conduct situational analyses within the app at any time. Methods: CBASPath was tested in an open pilot study as part of routine outpatient CBASP treatment. Participating patients were asked to report their use patterns and blended use (integrated use of the app as part of therapy sessions) at 3 assessment points over the 6-month test period and rate the usability and quality of and their satisfaction with CBASPath. Results: The results of the pilot trial showed that 93% (12/13) of participants used CBASPath as a blended tool during their therapy and maintained this throughout the study period. Overall, they reported good usability and quality ratings along with high user satisfaction. All participants showed favorable engagement with CBASPath; however, the frequency of use differed widely among the participants and assessment points. Situational analysis was used by all participants, and the number of completed modules ranged from 1 to 7. All participants reported blended use, although the frequency of integration in the face-to-face sessions varied widely. Conclusions: Our findings suggest that the digital augmentation of complex and highly interactive CBASP therapy in the form of blended therapy with CBASPath is feasible in routine outpatient care. Therapeutic guidance might contribute to high adherence and increase patient self-management. A few adjustments, such as saving entries directly in the app, could facilitate higher user engagement. A randomized controlled trial is now needed to investigate the efficacy and added value of this blended approach. In the long term, CBASPath could help optimize persistent depressive disorder treatment and reduce relapse by intensifying therapy and providing long-term patient support through the app. UR - https://formative.jmir.org/2022/8/e35482 UR - http://dx.doi.org/10.2196/35482 UR - http://www.ncbi.nlm.nih.gov/pubmed/35943764 ID - info:doi/10.2196/35482 ER - TY - JOUR AU - Ma, S. Jennifer AU - O?Riordan, Megan AU - Mazzer, Kelly AU - Batterham, J. Philip AU - Bradford, Sally AU - Kõlves, Kairi AU - Titov, Nickolai AU - Klein, Britt AU - Rickwood, J. Debra PY - 2022/8/5 TI - Consumer Perspectives on the Use of Artificial Intelligence Technology and Automation in Crisis Support Services: Mixed Methods Study JO - JMIR Hum Factors SP - e34514 VL - 9 IS - 3 KW - consumer KW - community KW - help-seeker KW - perspective KW - technology KW - artificial intelligence KW - crisis KW - support KW - acceptability N2 - Background: Emerging technologies, such as artificial intelligence (AI), have the potential to enhance service responsiveness and quality, improve reach to underserved groups, and help address the lack of workforce capacity in health and mental health care. However, little research has been conducted on the acceptability of AI, particularly in mental health and crisis support, and how this may inform the development of responsible and responsive innovation in the area. Objective: This study aims to explore the level of support for the use of technology and automation, such as AI, in Lifeline?s crisis support services in Australia; the likelihood of service use if technology and automation were implemented; the impact of demographic characteristics on the level of support and likelihood of service use; and reasons for not using Lifeline?s crisis support services if technology and automation were implemented in the future. Methods: A mixed methods study involving a computer-assisted telephone interview and a web-based survey was undertaken from 2019 to 2020 to explore expectations and anticipated outcomes of Lifeline?s crisis support services in a nationally representative community sample (n=1300) and a Lifeline help-seeker sample (n=553). Participants were aged between 18 and 93 years. Quantitative descriptive analysis, binary logistic regression models, and qualitative thematic analysis were conducted to address the research objectives. Results: One-third of the community and help-seeker participants did not support the collection of information about service users through technology and automation (ie, via AI), and approximately half of the participants reported that they would be less likely to use the service if automation was introduced. Significant demographic differences were observed between the community and help-seeker samples. Of the demographics, only older age predicted being less likely to endorse technology and automation to tailor Lifeline?s crisis support service and use such services (odds ratio 1.48-1.66, 99% CI 1.03-2.38; P<.001 to P=.005). The most common reason for reluctance, reported by both samples, was that respondents wanted to speak to a real person, assuming that human counselors would be replaced by automated robots or machine services. Conclusions: Although Lifeline plans to always have a real person providing crisis support, help-seekers automatically fear this will not be the case if new technology and automation such as AI are introduced. Consequently, incorporating innovative use of technology to improve help-seeker outcomes in such services will require careful messaging and assurance that the human connection will continue. UR - https://humanfactors.jmir.org/2022/3/e34514 UR - http://dx.doi.org/10.2196/34514 UR - http://www.ncbi.nlm.nih.gov/pubmed/35930334 ID - info:doi/10.2196/34514 ER - TY - JOUR AU - Young, S. Alexander AU - Choi, Abigail AU - Cannedy, Shay AU - Hoffmann, Lauren AU - Levine, Lionel AU - Liang, Li-Jung AU - Medich, Melissa AU - Oberman, Rebecca AU - Olmos-Ochoa, T. Tanya PY - 2022/8/5 TI - Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study JO - JMIR Res Protoc SP - e39010 VL - 11 IS - 8 KW - serious mental illness KW - mobile health KW - mental health KW - passive sensing KW - health informatics KW - behavior KW - sensor KW - self-tracking KW - predict KW - assessment N2 - Background: Serious mental illnesses (SMI) are common, disabling, and challenging to treat, requiring years of monitoring and treatment adjustments. Stress or reduced medication adherence can lead to rapid worsening of symptoms and behaviors. Illness exacerbations and relapses generally occur with little or no clinician awareness in real time, leaving limited opportunity to modify treatments. Previous research suggests that passive mobile sensing may be beneficial for individuals with SMI by helping them monitor mental health status and behaviors, and quickly detect worsening mental health for prompt assessment and intervention. However, there is too little research on its feasibility and acceptability and the extent to which passive data can predict changes in behaviors or symptoms. Objective: The aim of this research is to study the feasibility, acceptability, and safety of passive mobile sensing for tracking behaviors and symptoms of patients in treatment for SMI, as well as developing analytics that use passive data to predict changes in behaviors and symptoms. Methods: A mobile app monitors and transmits passive mobile sensor and phone utilization data, which is used to track activity, sociability, and sleep in patients with SMI. The study consists of a user-centered design phase and a mobile sensing phase. In the design phase, focus groups, interviews, and usability testing inform further app development. In the mobile sensing phase, passive mobile sensing occurs with participants engaging in weekly assessments for 9 months. Three- and nine-month interviews study the perceptions of passive mobile sensing and ease of app use. Clinician interviews before and after the mobile sensing phase study the usefulness and feasibility of app utilization in clinical care. Predictive analytic models are built, trained, and selected, and make use of machine learning methods. Models use sensor and phone utilization data to predict behavioral changes and symptoms. Results: The study started in October 2020. It has received institutional review board approval. The user-centered design phase, consisting of focus groups, usability testing, and preintervention clinician interviews, was completed in June 2021. Recruitment and enrollment for the mobile sensing phase began in October 2021. Conclusions: Findings may inform the development of passive sensing apps and self-tracking in patients with SMI, and integration into care to improve assessment, treatment, and patient outcomes. Trial Registration: ClinicalTrials.gov NCT05023252; https://clinicaltrials.gov/ct2/show/NCT05023252 International Registered Report Identifier (IRRID): DERR1-10.2196/39010 UR - https://www.researchprotocols.org/2022/8/e39010 UR - http://dx.doi.org/10.2196/39010 UR - http://www.ncbi.nlm.nih.gov/pubmed/35930336 ID - info:doi/10.2196/39010 ER - TY - JOUR AU - Michalak, E. Erin AU - Barnes, J. Steven AU - Morton, Emma AU - O'Brien, L. Heather AU - Murray, Greg AU - Hole, Rachelle AU - Meyer, Denny PY - 2022/8/4 TI - Supporting Self-management and Quality of Life in Bipolar Disorder With the PolarUs App (Alpha): Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e36213 VL - 11 IS - 8 KW - eHealth KW - mobile health KW - mHealth KW - bipolar disorder KW - self-management KW - engagement KW - mobile phone N2 - Background: Quality of life (QoL) is increasingly being recognized as a key outcome of interventions for bipolar disorder (BD). Mobile phone apps can increase access to evidence-based self-management strategies and provide real-time support. However, although individuals with lived experiences desire support with monitoring and improving broader health domains, existing BD apps largely target mood symptoms only. Further, evidence from the broader mobile health (mHealth) literature has shown that the desires and goals of end users are not adequately considered during app development, and as a result, engagement with mental health apps is suboptimal. To capitalize on the potential of apps to optimize wellness in BD, there is a need for interventions developed in consultation with real-world users designed to support QoL self-monitoring and self-management. Objective: This mixed methods pilot study was designed to evaluate the alpha version of the newly developed PolarUs app, developed to support QoL self-monitoring and self-management in people with BD. Co-designed using a community-based participatory research framework, the PolarUs app builds on the web-based adaptation of a BD-specific QoL self-assessment measure and integrates material from a web-based portal providing information on evidence-informed self-management strategies in BD. The primary objectives of this project were to evaluate PolarUs app feasibility (via behavioral use metrics), the impact of PolarUs (via the Brief Quality of Life in Bipolar Disorder scale, our primary outcome measure), and explore engagement with the PolarUs app (via quantitative and qualitative methods). Methods: Participants will be residents of North America (N=150), aged >18 years, with a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision diagnosis of BD type 1, BD type 2, or BD not otherwise specified as assessed by structured diagnostic interview. An embedded mixed methods research design will be adopted; qualitative interviews with a purposefully selected subsample (approximately, n=30) of participants will be conducted to explore in more depth feasibility, impact, and engagement with the PolarUs app over the 12-week study period. Results: At the time of publication of this protocol, the development of the alpha version of the PolarUs app was complete. Participant enrollment has begun in June 2022. Data collection is expected to be completed by December 2022. Conclusions: Beyond contributing knowledge on the feasibility and impact of a novel app to support QoL and self-management in BD, this study will also provide new insights related to engagement with mHealth apps. Furthermore, it will function as a case study of successful co-design between people with BD, health care providers, and BD researchers, providing a template for the future use of community-based participatory research frameworks in mHealth intervention development. The results will be used to further refine the PolarUs app and inform the design of a larger clinical trial. International Registered Report Identifier (IRRID): PRR1-10.2196/36213 UR - https://www.researchprotocols.org/2022/8/e36213 UR - http://dx.doi.org/10.2196/36213 UR - http://www.ncbi.nlm.nih.gov/pubmed/35925666 ID - info:doi/10.2196/36213 ER - TY - JOUR AU - Thorarinsdottir, Kristjana AU - Holmes, A. Emily AU - Hardarson, Johann AU - Stephenssen, S. Elin AU - Jonasdottir, H. Marianna AU - Kanstrup, Marie AU - Singh, Laura AU - Hauksdottir, Arna AU - Halldorsdottir, Thorhildur AU - Gudmundsdottir, Berglind AU - Thordardottir, Edda AU - Valdimarsdottir, Unnur AU - Bjornsson, Andri PY - 2022/7/20 TI - Using a Brief Mental Imagery Competing Task to Reduce the Number of Intrusive Memories: Exploratory Case Series With Trauma-Exposed Women JO - JMIR Form Res SP - e37382 VL - 6 IS - 7 KW - trauma KW - intrusive memories KW - visuospatial task KW - Tetris gameplay KW - mental imagery KW - imagery competing task KW - case series KW - mobile phone KW - posttraumatic stress N2 - Background: Novel interventions should be developed for people who have undergone psychological trauma. In a previous case study, we found that the number of intrusive memories of trauma could be reduced with a novel intervention. The intervention included a brief memory reminder, a visuospatial task and mental rotation, and targeted trauma memory hotspots one at a time in separate sessions. Objective: This case series (N=3) extended the first case study with 3 new cases to determine whether a similar pattern of beneficial results is observed. We explored whether the brief intervention would result in reduced numbers of intrusive memories and whether it would impact symptoms of posttraumatic stress, depression and anxiety, and general functioning. Acceptability of the intervention was also explored. Methods: A total of 3 women completed the study: 2 with posttraumatic stress disorder and other comorbidities and 1 with subthreshold posttraumatic stress disorder. The primary outcome was the change in the number of intrusive memories from the baseline phase to the intervention phase and at the 1-month follow-up, with an assessment of the intrusion frequency at 3 months. Participants monitored the number of intrusive memories in a daily diary for 1 week at baseline, for maximum of 6 weeks during the intervention phase and for 1 week at the 1-month and 3-month follow-ups. The intervention was delivered in person or digitally, with guidance from a clinical psychologist. A repeated AB design was used (A was a preintervention baseline phase and B intervention phase). Intrusions were targeted individually, creating repetitions of an AB design. Results: The total number of intrusive memories was reduced from the baseline to the intervention phase for all participants. The total number for participant 3 (P3) reduced from 38.8 per week during the baseline phase to 18.0 per week in the intervention phase. It was 13 at the 3-month follow-up. The total number for P4 reduced from 10.8 per week at baseline to 4.7 per week in the intervention phase. It was 0 at the 3-month follow-up. The total number for P5 was reduced from 33.7 at baseline to 20.7 per week in the intervention phase. It was 8 at the 3-month follow-up. All participants reported reduction in posttraumatic stress symptoms in the postintervention phase. Depression and anxiety symptoms reduced in 2 of the 3 participants in the postintervention phase. Acceptability was favorable. Conclusions: We observed good compliance with the intervention and intrusive memory diary in all 3 cases. The number of intrusive memories was reduced for all participants during the intervention phase and at the 1-month follow-up, with some improvement in other symptoms and functioning. Further research should explore the remote delivery of the intervention and whether nonspecialists can deliver the intervention effectively. UR - https://formative.jmir.org/2022/7/e37382 UR - http://dx.doi.org/10.2196/37382 UR - http://www.ncbi.nlm.nih.gov/pubmed/35857368 ID - info:doi/10.2196/37382 ER - TY - JOUR AU - Cotes, O. Robert AU - Boazak, Mina AU - Griner, Emily AU - Jiang, Zifan AU - Kim, Bona AU - Bremer, Whitney AU - Seyedi, Salman AU - Bahrami Rad, Ali AU - Clifford, D. Gari PY - 2022/7/13 TI - Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study JO - JMIR Res Protoc SP - e36417 VL - 11 IS - 7 KW - digital biomarker KW - machine learning KW - computer vision KW - schizophrenia KW - depression KW - multimodal KW - technology KW - acoustic KW - heart rate KW - biomarker N2 - Background: Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient?s outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician?s interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders. Objective: We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects. Methods: We plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system?s capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system?s sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes. Results: Data collection began in July 2020 and is expected to continue through December 2022. Conclusions: If successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity. International Registered Report Identifier (IRRID): DERR1-10.2196/36417 UR - https://www.researchprotocols.org/2022/7/e36417 UR - http://dx.doi.org/10.2196/36417 UR - http://www.ncbi.nlm.nih.gov/pubmed/35830230 ID - info:doi/10.2196/36417 ER - TY - JOUR AU - Teferra, Gashaw Bazen AU - Borwein, Sophie AU - DeSouza, D. Danielle AU - Simpson, William AU - Rheault, Ludovic AU - Rose, Jonathan PY - 2022/7/8 TI - Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study JO - JMIR Ment Health SP - e36828 VL - 9 IS - 7 KW - mental health KW - generalized anxiety disorder KW - impromptu speech KW - acoustic features KW - linguistic features KW - mobile phone N2 - Background: The measurement and monitoring of generalized anxiety disorder requires frequent interaction with psychiatrists or psychologists. Access to mental health professionals is often difficult because of high costs or insufficient availability. The ability to assess generalized anxiety disorder passively and at frequent intervals could be a useful complement to conventional treatment and help with relapse monitoring. Prior work suggests that higher anxiety levels are associated with features of human speech. As such, monitoring speech using personal smartphones or other wearable devices may be a means to achieve passive anxiety monitoring. Objective: This study aims to validate the association of previously suggested acoustic and linguistic features of speech with anxiety severity. Methods: A large number of participants (n=2000) were recruited and participated in a single web-based study session. Participants completed the Generalized Anxiety Disorder 7-item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. Acoustic and linguistic speech features were a priori selected based on the existing speech and anxiety literature, along with related features. Associations between speech features and anxiety levels were assessed using age and personal income as covariates. Results: Word count and speaking duration were negatively correlated with anxiety scores (r=?0.12; P<.001), indicating that participants with higher anxiety scores spoke less. Several acoustic features were also significantly (P<.05) associated with anxiety, including the mel-frequency cepstral coefficients, linear prediction cepstral coefficients, shimmer, fundamental frequency, and first formant. In contrast to previous literature, second and third formant, jitter, and zero crossing rate for the z score of the power spectral density acoustic features were not significantly associated with anxiety. Linguistic features, including negative-emotion words, were also associated with anxiety (r=0.10; P<.001). In addition, some linguistic relationships were sex dependent. For example, the count of words related to power was positively associated with anxiety in women (r=0.07; P=.03), whereas it was negatively associated with anxiety in men (r=?0.09; P=.01). Conclusions: Both acoustic and linguistic speech measures are associated with anxiety scores. The amount of speech, acoustic quality of speech, and gender-specific linguistic characteristics of speech may be useful as part of a system to screen for anxiety, detect relapse, or monitor treatment. UR - https://mental.jmir.org/2022/7/e36828 UR - http://dx.doi.org/10.2196/36828 UR - http://www.ncbi.nlm.nih.gov/pubmed/35802401 ID - info:doi/10.2196/36828 ER - TY - JOUR AU - Whitehead, C. Jocelyne AU - Neeman, Ron AU - Doniger, M. Glen PY - 2022/7/8 TI - Preliminary Real-World Evidence Supporting the Efficacy of a Remote Neurofeedback System in Improving Mental Health: Retrospective Single-Group Pretest-Posttest Study JO - JMIR Form Res SP - e35636 VL - 6 IS - 7 KW - EEG biofeedback KW - remote care KW - neurofeedback KW - attention-deficit/hyperactivity disorder KW - delta/alpha ratio N2 - Background: Neurofeedback training (NFT) has been shown to be effective in treating several disorders (eg, attention-deficit/hyperactivity disorder [ADHD], anxiety, and depression); however, little is currently known regarding the effectiveness of remote NFT systems. Objective: This retrospective study provides real-world data (N=593) to assess the efficacy of app-based remote NFT in improving brain health and cognitive performance. Methods: Improvement was measured from pre- to postintervention of in-app assessments that included validated symptom questionnaires (the 12-item General Health Questionnaire, the ADHD Rating Scale IV, the Adult ADHD Self-Report Scale, the 7-item Generalized Anxiety Disorder scale, and the 9-item Patient Health Questionnaire), a cognitive test of attention and executive functioning (ie, continuous performance task), and resting electroencephalography (EEG) markers. Clinically significant improvement was evaluated using standard approaches. Results: The greatest improvement was reported for the anxiety questionnaire, for which 69% (68/99) of participants moved from abnormal to healthy score ranges. Overall, adult and child participants who engaged in neurofeedback to improve attention and executive functions demonstrated improved ADHD scores and enhanced performance on a cognitive (ie, response inhibition) task. Adults with ADHD additionally demonstrated elevated delta/alpha and theta/alpha ratios at baseline and a reduction in the delta/alpha ratio indicator following neurofeedback. Conclusions: Preliminary findings suggest the efficacy of app-based remote neurofeedback in improving mental health, given the reduced symptom severity from pre- to postassessment for general psychological health, ADHD, anxiety, and depression, as well as adjusted resting EEG neural markers for individuals with symptoms of ADHD. Collectively, this supports the utility of the in-app assessment in monitoring behavioral and neural indices of mental health. UR - https://formative.jmir.org/2022/7/e35636 UR - http://dx.doi.org/10.2196/35636 UR - http://www.ncbi.nlm.nih.gov/pubmed/35802411 ID - info:doi/10.2196/35636 ER - TY - JOUR AU - Venkatesan, Aarathi AU - Forster, Benjamin AU - Rao, Prasanna AU - Miller, Melissa AU - Scahill, Michael PY - 2022/7/5 TI - Improvements in Depression Outcomes Following a Digital Cognitive Behavioral Therapy Intervention in a Polychronic Population: Retrospective Study JO - JMIR Form Res SP - e38005 VL - 6 IS - 7 KW - depression KW - anxiety KW - CBT KW - digital mental health intervention KW - cognitive behavioral therapy KW - digital health KW - obesity KW - diabetes KW - mental health N2 - Background: Digital mental health interventions have shown promise in reducing barriers to effective care for depression. Depression and related mental disorders are known to be highly comorbid with common chronic physical conditions, such as obesity and type 2 diabetes. While some research has explored the interaction dynamics of treating populations living with both mental and physical disorders, very little is known about such dynamics in digital care. Objective: We aimed to examine the effectiveness of a 12-week, therapist-supported, app-based cognitive behavioral therapy program in improving symptoms of depression and anxiety. The studied population included adults with a heavy burden of chronic physical disease, including obesity and type 2 diabetes. Methods: A total of 1512 participants with at least moderate depression were enrolled. The treatment cohort consisted of 831 (54.96%) participants who completed a follow-up assessment. The program included structured lessons and tools (ie, exercises and practices) and offered one-on-one weekly video counseling sessions with a licensed therapist for 12 weeks and monthly sessions thereafter. The clinically validated 8-item Patient Health Questionnaire (PHQ-8) and the 7-item Generalized Anxiety Disorder scale (GAD-7) were used to assess depression and anxiety, respectively. Linear mixed-effects modeling was employed to examine changes in depression and anxiety over time. Given correlation among various measures of program usage, a composite variable for depth of usage was used to analyze the correlation between usage and changes in depressive symptoms. Body weight changes from baseline were assessed primarily with digitally connected scales. Results: Out of 831 participants in the treatment cohort, 74.5% (n=619) showed a clinically significant reduction in depressive symptom severity after 12 weeks, where follow-up PHQ-8 scores had shifted downward by at least one diagnostic category. In total, 67.5% (n=561) of the participants showed a reliable improvement in PHQ-8 scores as measured by the reliable change index. There was an average reduction of 5.9 (SD 5.2) points (P<.001) between baseline and follow-up. Greater program usage was correlated with greater likelihood of reliable improvement in depressive symptoms (odds ratio 1.3, 95% CI 1.1-1.5; P=.002). An exploratory analysis of body weight changes with a multilevel, mixed-effect model suggested that reliable improvement in depressive symptoms at follow-up was associated with significantly greater weight loss at 9 months (?=?1.11, P=.002). Conclusions: The results provide further support that digital interventions can support clinically meaningful improvements in depression. Some form of synergy in treatment of comorbid depression and obesity or diabetes could be studied in future research. The study was limited by postintervention participant attrition as well as the retrospective observational study design. UR - https://formative.jmir.org/2022/7/e38005 UR - http://dx.doi.org/10.2196/38005 UR - http://www.ncbi.nlm.nih.gov/pubmed/35788442 ID - info:doi/10.2196/38005 ER - TY - JOUR AU - Leung, I. Tiffany AU - Kuhn, Tobias AU - Dumontier, Michel PY - 2022/7/1 TI - Representing Physician Suicide Claims as Nanopublications: Proof-of-Concept Study Creating Claim Networks JO - JMIRx Med SP - e34979 VL - 3 IS - 3 KW - physician suicide KW - suicide KW - suicide prevention KW - physician well-being KW - physician mental health KW - nanopublication KW - physician KW - doctor KW - mental health KW - semantic publishing KW - bibliometrics KW - claim network KW - information distortion KW - misinformation N2 - Background: In the poorly studied field of physician suicide, various factors can contribute to misinformation or information distortion, which in turn can influence evidence-based policies and prevention of suicide in this unique population. Objective: The aim of this paper is to use nanopublications as a scientific publishing approach to establish a citation network of claims in peer-reviewed publications about the rate of suicide among US physicians. Methods: A list of articles from a previously published scoping literature review on physician suicide was used to identify those articles that commented on or investigated suicidal behaviors of physician populations, including students, postgraduate trainees, and practicing physicians. The included articles were from peer-reviewed publications and asserted a claim about the annual rate of physician suicide. Manual data extraction was performed to collect article (or resource) type, title, authors, digital object identifier or URI, publication year, claim (about annual physician suicide rate), data of last access of the article (eg, for a webpage), and citations supporting the claim. Additional articles, websites, or other links were only added to the set of claims if they were cited by a peer-reviewed article already included in the data set. A nanopublication was created for each article or resource using Nanobench with an investigator-developed literature-based claim nanopublication template. Results: A set of 49 claims concerning the rate of US physician suicide was represented as nanopublications. Analysis of the claim network revealed that (1) the network is not fully connected, (2) no single primary source of the claim could be identified, and (3) all end-point citations had a claim with no further citation, had no apparent claim, or could not be accessed to verify the claim. The nanopublication strategy also enabled the capture of variant claims published on a website. Conclusions: Nanopublications remain to be adopted in broader scientific publishing in medicine, especially in publishing about physician mental health and suicide. This proof-of-concept study highlights an opportunity for more coordinated research efforts in the subject of physician suicide. Our work integrates these various claims and enables the verification of nonauthoritative assertions, thereby better equipping researchers to advance evidence-based knowledge and to make informed statements in the advocacy of physician suicide prevention. Representing physician suicide rate claims as nanopublications can be extended and improved in future work. UR - https://med.jmirx.org/2022/3/e34979 UR - http://dx.doi.org/10.2196/34979 UR - http://www.ncbi.nlm.nih.gov/pubmed/27725715 ID - info:doi/10.2196/34979 ER - TY - JOUR AU - Myers, L. Amanda AU - Mbao, Mbita AU - Kadakia, Arya AU - Collings, Shira AU - Fortuna, L. Karen PY - 2022/6/30 TI - Experiences of Community Members Engaged in eCPR (Emotional Connecting, Empowering, Revitalizing) Training: Qualitative Focus Group Study JO - JMIR Form Res SP - e32219 VL - 6 IS - 6 KW - mental health KW - trauma KW - peer support KW - community mental health education N2 - Background: The United Nations has called for wide-scale community mental health psychoeducation; however, few programs currently exist. Emotional Connecting, Empowering, Revitalizing (eCPR) is a community education and training program developed by individuals with a lived experience of mental health challenges or trauma. It is designed to provide community members with skills and confidence to support someone experiencing mental health challenges. Objective: This qualitative study aimed to examine the user experiences of diverse community members engaged in eCPR training. This study reviewed their attitudes toward training and opportunities for improvement in future implementations of training. Methods: eCPR training participants (N=31) were invited to participate in virtual focus groups between June 2020 and July 2020. Data were analyzed using the rigorous and accelerated data reduction method, which converts raw textual data into concise data tables to develop a codebook, and thematic analysis was performed to identify common themes. Results: The themes identified when analyzing the data included emotional holding and containment, training feedback, principles and practices of eCPR, implementation, connection in a digital environment, skills practice, and shared experiences. Conclusions: eCPR may benefit individuals from multiple, diverse demographics. It can enhance their ability to connect with others to understand what it means to be with someone who is experiencing a mental health challenge or crisis, to accept their own emotions, and to be confident in being their most authentic self in both their work and personal lives. eCPR may answer the call of the United Nations by bringing opportunities for authenticity and healing to community settings. Exploring the effects of delivering eCPR in communities on individuals experiencing distress is an important next step. This study found that eCPR may be beneficial to many groups of trainees with varying backgrounds and experiences. These findings are important, as they speak to the potential for eCPR to be implemented in a variety of community settings with the intention of working to improve mental health in everyday settings. UR - https://formative.jmir.org/2022/6/e32219 UR - http://dx.doi.org/10.2196/32219 UR - http://www.ncbi.nlm.nih.gov/pubmed/35771610 ID - info:doi/10.2196/32219 ER - TY - JOUR AU - Victor, E. Sarah AU - Christensen, Kirsten AU - Johnson, L. Sheri AU - Van Allen, Jason AU - Brick, A. Leslie PY - 2022/6/30 TI - Dynamic Regulatory Processes in the Transition From Suicidal Ideation to Action in Adults Leaving Inpatient Psychiatric Care: Protocol for an Intensive Longitudinal Study JO - JMIR Res Protoc SP - e38582 VL - 11 IS - 6 KW - ecological momentary assessment KW - suicidal ideation KW - suicidal behavior KW - actigraphy KW - sleep KW - cognitive control KW - longitudinal KW - affect KW - impulsivity N2 - Background: US suicide rates have risen steadily in the past decade, and suicide risk is especially high in the months after discharge from inpatient psychiatric treatment. However, suicide research has lagged in examining dynamic within-person processes that contribute to risk over time among individuals known to be at high risk of suicide. Almost no research has examined how affective, cognitive, and physiological processes change over minutes, hours, or days to confer risk of suicidal behavior in daily life. Objective: This protocol describes a longitudinal study designed to examine real-world changes in risk of suicide across multiple assessment domains. Specifically, the study involves following adults known to be at high risk of suicide after discharge from inpatient psychiatric care using self-report, interview, actigraphy, and behavioral methods to identify proximal contributors to suicidal thoughts and behaviors. First, we hypothesize that negative affective experiences, which are featured in most major suicide theories, will comprise a latent factor indicative of psychache (emotional pain), which will predict increases in suicidal thinking over time. Second, we hypothesize that poor inhibitory control in the context of negative affective stimuli, as well as emotion-related impulsivity, will predict the transition from suicidal thinking to suicidal behavior over time. Third, we hypothesize that short sleep duration will precede within-person increases in suicidal ideation as well as increased odds of suicidal behavior among those reporting suicidal thoughts. Methods: The desired sample size is 130 adults with past-week suicidal thoughts or behaviors who are receiving inpatient psychiatric treatment. Participants will complete a battery of measures while on the inpatient unit to assess negative affective experiences, emotion-related impulsivity, inhibitory control, typical sleep patterns, and relevant covariates. After discharge from inpatient care, participants will complete 4 weeks of signal-contingent ecological momentary assessment surveys, as well as mobile behavioral measures of inhibitory control, while wearing an actigraphy device that will gather objective data on sleep. Participants will complete interviews regarding suicidal thoughts and behaviors at 4 and 8 weeks after discharge. Results: The study was funded by the National Institutes of Health in November 2020. Recruitment began in April 2021. Data analysis will begin after completion of data collection. Conclusions: This study will elucidate how affective, cognitive, and physiological risk factors contribute (or do not contribute) to within-person fluctuations in suicide risk in daily life, with important implications for extant theories of suicide. Of import, the examined risk factors are all modifiable; thus, the results will inform identification of key targets for just-in-time, flexible, personalized, digital interventions that can be used to decrease emotional distress and prevent suicide among those at highest risk. International Registered Report Identifier (IRRID): DERR1-10.2196/38582 UR - https://www.researchprotocols.org/2022/6/e38582 UR - http://dx.doi.org/10.2196/38582 UR - http://www.ncbi.nlm.nih.gov/pubmed/35771618 ID - info:doi/10.2196/38582 ER - TY - JOUR AU - Siddhpuria, Shailee AU - Breau, Genevieve AU - Lackie, E. Madison AU - Lavery, M. Brynn AU - Ryan, Deirdre AU - Shulman, Barbara AU - Kennedy, L. Andrea AU - Brotto, A. Lori PY - 2022/6/23 TI - Women?s Preferences and Design Recommendations for a Postpartum Depression Psychoeducation Intervention: User Involvement Study JO - JMIR Form Res SP - e33411 VL - 6 IS - 6 KW - postpartum KW - depression KW - perinatal mental health KW - patient engagement KW - women?s health KW - qualitative KW - psychoeducation KW - digital tools N2 - Background: Postpartum depression (PPD) is one of the leading causes of maternal morbidity, affecting up to 18% of Canadian new mothers. Yet, PPD often remains untreated due to numerous barriers in access to care, including location and cost. Development of eHealth interventions in collaboration with patient partners offers an exciting opportunity to fill this care gap and provide effective and affordable care to new parents across British Columbia. Objective: Our aim was to determine the content and design preferences of women previously diagnosed with PPD to inform changes to the development of a web-enabled intervention for education and management of PPD. Methods: Webpage prototypes were created to mimic the web-enabled resource using findings from completed focus group research that assessed what women want in a web-enabled support resource for PPD. A convenience sample of women aged >18 years and previously diagnosed with PPD was recruited. Feedback was collected on the content and design of the prototypes via semistructured interviews and online surveys. Qualitative, inductive analytic, and quantitative methods were used. Results: A total of 9 women (mean age 37.2 years, SD 4.8 years) completed the interview and a majority of the survey. The following 6 themes were identified: (1) inefficacy of text-heavy layouts, (2) highlighting key information, (3) clarity/understandability of the language, (4) finding support groups, (5) validation and immediate help for feelings of isolation, and (6) helpfulness and accessibility of the resource. Each theme identified elements of content or design that were either effective or may be improved upon. Most women (8/9, 89%) favored content relating to foundational knowledge of PPD, such as symptoms and management options. The layout, language, and content were found to be generally easy to understand, clear, trustworthy, and helpful. Conclusions: Six key areas were identified by women previously diagnosed with PPD, as requiring focus in a web-enabled psychoeducation program. Consistent with past research, this study also found that support and enthusiasm for web-enabled programs support PPD management as an adjunct to other evidence-based treatments. UR - https://formative.jmir.org/2022/6/e33411 UR - http://dx.doi.org/10.2196/33411 UR - http://www.ncbi.nlm.nih.gov/pubmed/35737435 ID - info:doi/10.2196/33411 ER - TY - JOUR AU - Debrot, Anik AU - Kheyar, Maya AU - Efinger, Liliane AU - Berthoud, Laurent AU - Pomini, Valentino PY - 2022/6/23 TI - Supporting People Who Have Lost a Close Person by Bereavement or Separation: Protocol of a Randomized Controlled Trial Comparing Two French-Language Internet-Based Interventions JO - JMIR Res Protoc SP - e39026 VL - 11 IS - 6 KW - internet-based interventions KW - grief KW - bereavement KW - separation KW - divorce KW - identity KW - digital health KW - mental health KW - psychotherapy N2 - Background: Internet-based interventions (IBIs) are as efficient as face-to-face psychotherapy for a variety of mental health disorders, including complicated grief. Most evidence stems from guided IBIs. However, recent research indicates that the benefit of guidance is lower in more interactive IBIs. As such, providing guidance only to people requiring it (guidance on demand) appears a cost-effective solution. This is particularly important to develop given the recent rise in grief symptoms in the context of the COVID-19 pandemic. This paper presents the protocol of a randomized controlled trial comparing the efficacy and adherence rate of 2 IBIs for grief-related symptoms after the loss a close one following death or romantic separation, using a guidance on demand framework. LIVIA 2.0 was developed based on theoretical and empirical findings on grief processes and IBIs, and it will be compared to LIVIA 1 that has already demonstrated its efficacy. Objective: Our main hypotheses are that LIVIA 1 (control condition) and LIVIA 2.0 (experimental condition) increase participants? well-being and decrease their distress at posttest and at follow-up, that LIVIA 2.0 is more efficient than LIVIA 1 for all outcomes, and that LIVIA 2.0 has less dropouts than LIVIA 1. Methods: Outcomes will be assessed at pretest, posttest (12 weeks later), and follow-up (24 weeks later). We will recruit 234 participants through a variety of means, including social media and contacts with the press. Primary outcomes are grief symptoms, depressive symptoms, and eudemonic well-being. Secondary outcomes are anxiety symptoms, grief coping strategies, aspects related to self-identity reorganization, and program satisfaction. LIVIA 2.0 participants will additionally undergo a weekly mood and grief symptom monitoring, allowing us to explore the short-term efficacy of the sessions. Results: The creation and development of the content of LIVIA 2.0 was completed during the first phase of the project. Participant recruitment will begin in May 2022 and will last until January 2023. Conclusions: This study will emphasize the relevance of the innovations included in LIVIA 2.0 regarding the efficacy and dropout rate of IBIs for grief symptoms and will allow investigations on how these changes impact the demand for guidance. In the current postpandemic times, developing and assessing IBIs targeting grief symptoms are particularly critical given the rise in grief-related symptoms. Trial Registration: clinicaltrials.gov NCT05219760; https://tinyurl.com/3dzztjts International Registered Report Identifier (IRRID): PRR1-10.2196/39026 UR - https://www.researchprotocols.org/2022/6/e39026 UR - http://dx.doi.org/10.2196/39026 UR - http://www.ncbi.nlm.nih.gov/pubmed/35737454 ID - info:doi/10.2196/39026 ER - TY - JOUR AU - Darnell, Doyanne AU - Pullmann, D. Michael AU - Hull, D. Thomas AU - Chen, Shiyu AU - Areán, Patricia PY - 2022/6/22 TI - Predictors of Disengagement and Symptom Improvement Among Adults With Depression Enrolled in Talkspace, a Technology-Mediated Psychotherapy Platform: Naturalistic Observational Study JO - JMIR Form Res SP - e36521 VL - 6 IS - 6 KW - depression KW - psychotherapy KW - disengagement KW - internet KW - web-based KW - technology-mediated psychotherapy N2 - Background: Depression is a common psychiatric condition with an estimated lifetime prevalence for major depression of 16.6% in the US adult population and is effectively treated through psychotherapy. The widespread availability of the internet and personal devices such as smartphones are changing the landscape of delivery of psychotherapy; however, little is known about whether and for whom this type of therapy is beneficial, and whether having synchronous video-based sessions provides additional benefits to clients above and beyond messaging-based therapy. Objective: This study examined the outcomes associated with the use of a digital platform (Talkspace) for technology-mediated psychotherapy. We examined the duration of client engagement in therapy and client depression score trajectories over 16 weeks. We explored the association of client characteristics, therapist characteristics, and service plan type with time-to-disengagement and trajectories of change in depression scores. Methods: This naturalistic observational study assessed data collected routinely by the platform between January 2016 and January 2018 and examined psychotherapy outcomes among a large representative sample of adult clients with clinically significant depression. Treatment disengagement was defined as a lack of client-initiated communication for more than 4 weeks. Clients completed the Patient Health Questionnaire-8 item (PHQ-8) at intake and every 3 weeks via an in-app survey. Cox regression analysis was used to examine the time until and predictors of disengagement. Changes in depression scores and predictors of change over time were examined using mixed-effects regression. Results: The study included 5890 clients and 1271 therapists. Client scores on the PHQ-8 declined over time, with the average client improving from a score of 15 to below the clinical cutoff of 10 by week 6. At the same time point, 37% of clients had disengaged from the therapy. When combined into a final Cox regression model, those who were more likely to disengage were clients aged 18 to 25 years versus those aged ?50 years (odds ratio [OR] 0.82, 95% CI 0.74-0.9; P<.001), had higher education (OR 1.14, 95% CI 1.06-1.22; P<.001), had been in therapy before (OR 1.09, 95% CI 1.02-1.17; P=.01), and were living with a partner but unmarried versus single (OR 1.14, 95% CI 1.02-1.27; P=.02). Having a therapist with >10 years of experience was related to lower odds of disengagement (OR 0.87, 95% CI 0.8-0.94; P=.01). When combined into a final regression model predicting improvement in depression scores over time, clients showing more improvement were those with an associate?s degree or higher (linear estimate=?0.07, P=.002) and higher intake PHQ-8 scores (estimate=3.73, P<.001). There were no differences based on the plan type. Conclusions: Our findings add to the growing literature showing the benefits of technology-mediated psychotherapy over a relatively brief period (16 weeks). UR - https://formative.jmir.org/2022/6/e36521 UR - http://dx.doi.org/10.2196/36521 UR - http://www.ncbi.nlm.nih.gov/pubmed/35731563 ID - info:doi/10.2196/36521 ER - TY - JOUR AU - Luo, Xiaochen AU - Bugatti, Matteo AU - Molina, Lucero AU - Tilley, L. Jacqueline AU - Mahaffey, Brittain AU - Gonzalez, Adam PY - 2022/6/21 TI - Conceptual Invariance, Trajectories, and Outcome Associations of Working Alliance in Unguided and Guided Internet-Based Psychological Interventions: Secondary Analysis of a Randomized Controlled Trial JO - JMIR Ment Health SP - e35496 VL - 9 IS - 6 KW - working alliance KW - internet-based psychological interventions KW - video support KW - text support KW - trajectory KW - MyCompass N2 - Background: The role of working alliance remains unclear for many forms of internet-based interventions (IBIs), a set of effective psychotherapy alternatives that do not require synchronous interactions between patients and therapists. Objective: This study examined the conceptual invariance, trajectories, and outcome associations of working alliance across an unguided IBI and guided IBIs that incorporated clinician support through asynchronous text messaging or video messaging. Methods: Adults with high education attainment (n=145) with subclinical levels of anxiety, stress, or depressive symptoms were randomized to 1 of 3 treatment conditions for 7 weeks. All participants received treatments from MyCompass, an unguided IBI using cognitive behavior therapy. Participants in condition 2 and 3 received supplemental, asynchronous clinician support through text and video, respectively. Working alliance with the IBIs was measured weekly using select items from the 12-item version of the Agnew Relationship Measure. Symptom and functional outcomes were assessed at baseline, at the end of treatment, and 1-month follow-up. Results: Working alliance with the IBIs was conceptually invariant across the 3 conditions. Working alliance followed a quadratic pattern of change over time for all conditions and declined significantly only in the text-support condition. After controlling for baseline symptoms, higher baseline levels of working alliance predicted less depression and less functional impairment at follow-up, whereas faster increases in working alliance predicted less worry at the end of treatment and at follow-up, all of which only occurred in the video-support condition. Conclusions: Working alliance with the IBIs was generally established in the initial sessions. Although working alliance is conceptually invariant across IBIs with or without clinician support, the associations between working alliance and treatment outcomes among IBIs may differ depending on clinician involvement and the modalities of support. Trial Registration: ClinicalTrials.gov NCT05122429; https://clinicaltrials.gov/ct2/show/NCT05122429 UR - https://mental.jmir.org/2022/6/e35496 UR - http://dx.doi.org/10.2196/35496 UR - http://www.ncbi.nlm.nih.gov/pubmed/35727626 ID - info:doi/10.2196/35496 ER - TY - JOUR AU - Antoniou, Mark AU - Estival, Dominique AU - Lam-Cassettari, Christa AU - Li, Weicong AU - Dwyer, Anne AU - Neto, Almeida Abìlio de PY - 2022/6/21 TI - Predicting Mental Health Status in Remote and Rural Farming Communities: Computational Analysis of Text-Based Counseling JO - JMIR Form Res SP - e33036 VL - 6 IS - 6 KW - e-mental health KW - text-based KW - counseling KW - Linguistic Inquiry and Word Count KW - LIWC KW - depression KW - anxiety KW - stress N2 - Background: Australians living in rural and remote areas are at elevated risk of mental health problems and must overcome barriers to help seeking, such as poor access, stigma, and entrenched stoicism. e-Mental health services circumvent such barriers using technology, and text-based services are particularly well suited to clients concerned with privacy and self-presentation. They allow the client to reflect on the therapy session after it has ended as the chat log is stored on their device. The text also offers researchers an opportunity to analyze language use patterns and explore how these relate to mental health status. Objective: In this project, we investigated whether computational linguistic techniques can be applied to text-based communications with the goal of identifying a client?s mental health status. Methods: Client-therapist text messages were analyzed using the Linguistic Inquiry and Word Count tool. We examined whether the resulting word counts related to the participants? presenting problems or their self-ratings of mental health at the completion of counseling. Results: The results confirmed that word use patterns could be used to differentiate whether a client had one of the top 3 presenting problems (depression, anxiety, or stress) and, prospectively, to predict their self-rated mental health after counseling had been completed. Conclusions: These findings suggest that language use patterns are useful for both researchers and clinicians trying to identify individuals at risk of mental health problems, with potential applications in screening and targeted intervention. UR - https://formative.jmir.org/2022/6/e33036 UR - http://dx.doi.org/10.2196/33036 UR - http://www.ncbi.nlm.nih.gov/pubmed/35727623 ID - info:doi/10.2196/33036 ER - TY - JOUR AU - Park, Jinkyung AU - Arunachalam, Ramanathan AU - Silenzio, Vincent AU - Singh, K. Vivek PY - 2022/6/14 TI - Fairness in Mobile Phone?Based Mental Health Assessment Algorithms: Exploratory Study JO - JMIR Form Res SP - e34366 VL - 6 IS - 6 KW - algorithmic bias KW - mental health KW - health equity KW - medical informatics KW - health information systems KW - gender bias KW - mobile phone N2 - Background: Approximately 1 in 5 American adults experience mental illness every year. Thus, mobile phone?based mental health prediction apps that use phone data and artificial intelligence techniques for mental health assessment have become increasingly important and are being rapidly developed. At the same time, multiple artificial intelligence?related technologies (eg, face recognition and search results) have recently been reported to be biased regarding age, gender, and race. This study moves this discussion to a new domain: phone-based mental health assessment algorithms. It is important to ensure that such algorithms do not contribute to gender disparities through biased predictions across gender groups. Objective: This research aimed to analyze the susceptibility of multiple commonly used machine learning approaches for gender bias in mobile mental health assessment and explore the use of an algorithmic disparate impact remover (DIR) approach to reduce bias levels while maintaining high accuracy. Methods: First, we performed preprocessing and model training using the data set (N=55) obtained from a previous study. Accuracy levels and differences in accuracy across genders were computed using 5 different machine learning models. We selected the random forest model, which yielded the highest accuracy, for a more detailed audit and computed multiple metrics that are commonly used for fairness in the machine learning literature. Finally, we applied the DIR approach to reduce bias in the mental health assessment algorithm. Results: The highest observed accuracy for the mental health assessment was 78.57%. Although this accuracy level raises optimism, the audit based on gender revealed that the performance of the algorithm was statistically significantly different between the male and female groups (eg, difference in accuracy across genders was 15.85%; P<.001). Similar trends were obtained for other fairness metrics. This disparity in performance was found to reduce significantly after the application of the DIR approach by adapting the data used for modeling (eg, the difference in accuracy across genders was 1.66%, and the reduction is statistically significant with P<.001). Conclusions: This study grounds the need for algorithmic auditing in phone-based mental health assessment algorithms and the use of gender as a protected attribute to study fairness in such settings. Such audits and remedial steps are the building blocks for the widespread adoption of fair and accurate mental health assessment algorithms in the future. UR - https://formative.jmir.org/2022/6/e34366 UR - http://dx.doi.org/10.2196/34366 UR - http://www.ncbi.nlm.nih.gov/pubmed/35699997 ID - info:doi/10.2196/34366 ER - TY - JOUR AU - Mehrabi, Samira AU - Muñoz, E. John AU - Basharat, Aysha AU - Boger, Jennifer AU - Cao, Shi AU - Barnett-Cowan, Michael AU - Middleton, E. Laura PY - 2022/6/13 TI - Immersive Virtual Reality Exergames to Promote the Well-being of Community-Dwelling Older Adults: Protocol for a Mixed Methods Pilot Study JO - JMIR Res Protoc SP - e32955 VL - 11 IS - 6 KW - virtual reality KW - exergames KW - community-dwelling older adults KW - pilot protocol KW - feasibility KW - well-being KW - physical activity KW - cognition KW - perception KW - mood KW - COVID-19 N2 - Background: Despite the proven benefits of exercise in older adults, challenges such as access and motivation can deter their engagement. Interactive virtual reality (VR) games combined with exercise (exergames) are a plausible strategy to encourage physical activity among this population. However, there has been little research on the feasibility, acceptability, and potential benefits of deploying at-home VR exergames among community-dwelling older adults. Objective: The objectives of this study are to estimate the feasibility, usability, and acceptability of a co-designed VR exergame in community-dwelling older adults; examine intervention feasibility and assessment protocols for a future large-scale trial; and provide pilot data on outcomes of interest (physical activity, exercise self-efficacy, mood, cognition, perception, and gameplay metrics). Methods: The study will be a remote, 6-week intervention comprising an experimental and a control group. A sample of at least 12 community-dwelling older adults (with no or mild cognitive impairment) will be recruited for each group. Both groups will follow the same study procedures and assessment methods. However, the experimental group will engage with a co-designed VR exergame (Seas The Day) thrice weekly for approximately 20 minutes using the Oculus Quest 2 (Facebook Reality Labs) VR headset. The control group will read (instead of playing Seas The Day) thrice weekly for approximately 20 minutes over the 6-week period. A mixed methods evaluation will be used. Changes in physical activity, exercise self-efficacy, mood, cognition, and perception will be compared before and after acute data as well as before and after the 6 weeks between the experimental (exergaming) and control (reading) groups. Qualitative data from postintervention focus groups or interviews and informal notes and reports from all participants will be analyzed to assess the feasibility of the study protocol. Qualitative data from the experimental group will also be analyzed to assess the feasibility, usability, and acceptability of at-home VR exergames and explore perceived facilitators of and barriers to uptaking VR systems among community-dwelling older adults. Results: The screening and recruitment process for the experimental group started in May 2021, and the data collection process will be completed by September 2021. The timeline of the recruitment process for the control group is September 2021 to December 2021. We anticipate an estimated adherence rate of ?80%. Challenges associated with VR technology and the complexity of remote assessments are expected. Conclusions: This pilot study will provide important information on the feasibility, acceptability, and usability of a custom-made VR exergaming intervention to promote older adults? well-being. Findings from this study will be useful to inform the methodology, design, study procedures, and assessment protocol for future large-scale trials of VR exergames with older adults as well as deepen the understanding of remote deployment and at-home use of VR for exercise in older adults. International Registered Report Identifier (IRRID): DERR1-10.2196/32955 UR - https://www.researchprotocols.org/2022/6/e32955 UR - http://dx.doi.org/10.2196/32955 UR - http://www.ncbi.nlm.nih.gov/pubmed/35700014 ID - info:doi/10.2196/32955 ER - TY - JOUR AU - Vial, Stéphane AU - Boudhraâ, Sana AU - Dumont, Mathieu PY - 2022/6/7 TI - Human-Centered Design Approaches in Digital Mental Health Interventions: Exploratory Mapping Review JO - JMIR Ment Health SP - e35591 VL - 9 IS - 6 KW - design KW - human-centered design KW - user experience KW - mental health KW - digital mental health N2 - Background: Digital mental health interventions have a great potential to alleviate mental illness and increase access to care. However, these technologies face significant challenges, especially in terms of user engagement and adoption. It has been suggested that this issue stems from a lack of user perspective in the development process; accordingly, several human-centered design approaches have been developed over the years to consider this important aspect. Yet, few human-centered design approaches to digital solutions exist in the field of mental health, and rarely are end users involved in their development. Objective: The main objective of this literature review is to understand how human-centered design is considered in e-mental health intervention research. Methods: An exploratory mapping review was conducted of mental health journals with the explicit scope of covering e-mental health technology. The human-centered design approaches reported and the core elements of design activity (ie, object, context, design process, and actors involved) were examined among the eligible studies. Results: A total of 30 studies met the inclusion criteria, of which 22 mentioned using human-centered design approaches or specific design methods in the development of an e-mental health solution. Reported approaches were classified as participatory design (11/27, 41%), codesign (6/27, 22%), user-centered design (5/27, 19%), or a specific design method (5/27, 19%). Just over half (15/27, 56%) of the approaches mentioned were supported by references. End users were involved in each study to some extent but not necessarily in designing. About 27% (8/30) of all the included studies explicitly mentioned the presence of designers on their team. Conclusions: Our results show that some attempts have indeed been made to integrate human-centered design approaches into digital mental health technology development. However, these attempts rely very little on designers and design research. Researchers from other domains and technology developers would be wise to learn the underpinnings of human-centered design methods before selecting one over another. Inviting designers for assistance when implementing a particular approach would also be beneficial. To further motivate interest in and use of human-centered design principles in the world of e-mental health, we make nine suggestions for better reporting of human-centered design approaches in future research. UR - https://mental.jmir.org/2022/6/e35591 UR - http://dx.doi.org/10.2196/35591 UR - http://www.ncbi.nlm.nih.gov/pubmed/35671081 ID - info:doi/10.2196/35591 ER - TY - JOUR AU - Fletcher, Kathryn AU - Lindblom, Katrina AU - Seabrook, Elizabeth AU - Foley, Fiona AU - Murray, Greg PY - 2022/5/31 TI - Pilot Testing in the Wild: Feasibility, Acceptability, Usage Patterns, and Efficacy of an Integrated Web and Smartphone Platform for Bipolar II Disorder JO - JMIR Form Res SP - e32740 VL - 6 IS - 5 KW - bipolar disorder KW - smartphone KW - app KW - web-based intervention KW - ecological momentary assessment KW - mobile phone N2 - Background: Bipolar II disorder (BD-II) is associated with significant burden, disability, and mortality; however, there continues to be a dearth of evidence-based psychological interventions for this condition. Technology-mediated interventions incorporating self-management have untapped potential to help meet this need as an adjunct to usual clinical care. Objective: The objective of this pilot study is to assess the feasibility, acceptability, and clinical utility of a novel intervention for BD-II (Tailored Recovery-oriented Intervention for Bipolar II Experiences; TRIBE), in which mindfulness-based psychological content is delivered via an integrated web and smartphone platform. The focus of the study is evaluation of the dynamic use patterns emerging from ecological momentary assessment and intervention to assist the real-world application of mindfulness skills learned from web-delivered modules. Methods: An open trial design using pretest and posttest assessments with nested qualitative evaluation was used. Individuals (aged 18-65 years) with a diagnosis of BD-II were recruited worldwide and invited to use a prototype of the TRIBE intervention over a 3-week period. Data were collected via web-based questionnaires and phone interviews at baseline and 3-week follow-up. Results: A total of 25 participants completed baseline and follow-up assessments. Adherence rates (daily app use) were 65.6% across the 3-week study, with up to 88% (22/25) of participants using the app synergistically alongside the web-based program. Despite technical challenges with the prototype intervention (from user, hardware, and software standpoints), acceptability was adequate, and most participants rated the intervention positively in terms of concept (companion app with website: 19/25, 76%), content (19/25, 76%), and credibility and utility in supporting their management of bipolar disorder (17/25, 68%). Evaluation using behavioral archetypes identified important use pathways and a provisional model to inform platform refinement. As hypothesized, depression scores significantly decreased after the intervention (Montgomery-Asberg Depression Rating Scale baseline mean 8.60, SD 6.86, vs follow-up mean 6.16, SD 5.11; t24=2.63; P=.01; Cohen d=0.53, 95% CI 0.52-4.36). Conclusions: Our findings suggest that TRIBE is feasible and represents an appropriate and acceptable self-management program for patients with BD-II. Preliminary efficacy results are promising and support full development of TRIBE informed by the present behavioral archetype analysis. Modifications suggested by the pilot study include increasing the duration of the intervention and increasing technical support. UR - https://formative.jmir.org/2022/5/e32740/ UR - http://dx.doi.org/10.2196/32740 UR - http://www.ncbi.nlm.nih.gov/pubmed/35639462 ID - info:doi/10.2196/32740 ER - TY - JOUR AU - Rodríguez-Rivas, E. Matías AU - Cangas, J. Adolfo AU - Cariola, A. Laura AU - Varela, J. Jorge AU - Valdebenito, Sara PY - 2022/5/30 TI - Innovative Technology?Based Interventions to Reduce Stigma Toward People With Mental Illness: Systematic Review and Meta-analysis JO - JMIR Serious Games SP - e35099 VL - 10 IS - 2 KW - stigma KW - mental illness KW - technology-based KW - serious games KW - virtual reality KW - e-contact KW - simulation intervention KW - internet intervention KW - meta-analysis N2 - Background: Stigma toward people with mental illness presents serious consequences for the impacted individuals, such as social exclusion and increased difficulties in the recovery process. Recently, several interventions have been developed to mitigate public stigma, based on the use of innovative technologies, such as virtual reality and video games. Objective: This review aims to systematically review, synthesize, measure, and critically discuss experimental studies that measure the effect of technological interventions on stigmatization levels. Methods: This systematic review and meta-analysis was based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines and included studies in English and Spanish published between 2016 and 2021. Searches were run in 5 different databases (ie, PubMed, PsycInfo, Scopus, Cochrane Library, and ScienceDirect). Only randomized controlled trials were included. Two independent reviewers determined the eligibility, extracted data, and rated methodological quality of the studies. Meta-analyses were performed using the Comprehensive Meta-Analysis software. Results: Based on the 1158 articles screened, 72 articles were evaluated as full text, of which 9 were included in the qualitative and quantitative syntheses. A diversity of interventions was observed, including video games, audiovisual simulation of hallucinations, virtual reality, and electronic contact with mental health services users. The meta-analysis (n=1832 participants) demonstrated that these interventions had a consistent medium effect on reducing the level of public stigma (d=?0.64; 95% CI 0.31-0.96; P<.001). Conclusions: Innovative interventions involving the use of technologies are an effective tool in stigma reduction, therefore new challenges are proposed and discussed for the demonstration of their adaptability to different contexts and countries, thus leading to their massification. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021261935; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021261935 UR - https://games.jmir.org/2022/2/e35099 UR - http://dx.doi.org/10.2196/35099 UR - http://www.ncbi.nlm.nih.gov/pubmed/35635744 ID - info:doi/10.2196/35099 ER - TY - JOUR AU - Poulton, Antoinette AU - Chen, Evelyn Li Peng AU - Dali, Gezelle AU - Fox, Michael AU - Hester, Robert PY - 2022/5/30 TI - Web-Based Independent Versus Laboratory-Based Stop-Signal Task Performance: Within-Subjects Counterbalanced Comparison Study JO - J Med Internet Res SP - e32922 VL - 24 IS - 5 KW - Stop-Signal Task KW - response inhibition KW - inhibitory control KW - online assessment KW - web-based assessment KW - cognition N2 - Background: Considered a facet of behavioral impulsivity, response inhibition facilitates adaptive and goal-directed behavior. It is often assessed using the Stop-Signal Task (SST), which is presented on stand-alone computers under controlled laboratory conditions. Sample size may consequently be a function of cost or time and sample diversity constrained to those willing or able to attend the laboratory. Statistical power and generalizability of results might, in turn, be impacted. Such limitations may potentially be overcome via the implementation of web-based testing. Objective: The aim of this study was to investigate if there were differences between variables derived from a web-based SST when it was undertaken independently?that is, outside the laboratory, on any computer, and in the absence of researchers?versus when it was performed under laboratory conditions. Methods: We programmed a web-based SST in HTML and JavaScript and employed a counterbalanced design. A total of 166 individuals (mean age 19.72, SD 1.85, range 18-36 years; 146/166, 88% female) were recruited. Of them, 79 undertook the independent task prior to visiting the laboratory and 78 completed the independent task following their laboratory visit. The average time between SST testing was 3.72 (SD 2.86) days. Dependent samples and Bayesian paired samples t tests were used to examine differences between laboratory-based and independent SST variables. Correlational analyses were conducted on stop-signal reaction times (SSRT). Results: After exclusions, 123 participants (mean age 19.73, SD 1.97 years) completed the SST both in the laboratory and independently. While participants were less accurate on go trials and exhibited reduced inhibitory control when undertaking the independent?compared to the laboratory-based?SST, there was a positive association between the SSRT of each condition (r=.48; P<.001; 95% CI 0.33-0.61). Conclusions: Findings suggest a web-based SST, which participants undertake on any computer, at any location, and in the absence of the researcher, is a suitable measure of response inhibition. UR - https://www.jmir.org/2022/5/e32922 UR - http://dx.doi.org/10.2196/32922 UR - http://www.ncbi.nlm.nih.gov/pubmed/35635745 ID - info:doi/10.2196/32922 ER - TY - JOUR AU - Birrell, Louise AU - Furneaux-Bate, Ainsley AU - Debenham, Jennifer AU - Spallek, Sophia AU - Newton, Nicola AU - Chapman, Catherine PY - 2022/5/27 TI - Development of a Peer Support Mobile App and Web-Based Lesson for Adolescent Mental Health (Mind Your Mate): User-Centered Design Approach JO - JMIR Form Res SP - e36068 VL - 6 IS - 5 KW - mobile health KW - depression KW - anxiety KW - psychosocial support system KW - alcohol drinking KW - adolescent KW - digital technology KW - mobile intervention KW - intervention KW - social KW - economic KW - development KW - mind your mate KW - app KW - application KW - mHealth KW - mobile phone N2 - Background: Digital technologies and mobile interventions are possible tools for prevention initiatives to target the substantial social and economic impacts that anxiety, mood, and substance use disorders have on young people. Objective: This paper described the design and development of the Mind your Mate program, a smartphone app and introductory classroom lesson enhancing peer support around the topics of anxiety, depression, and substance use for adolescents. Methods: The development of Mind your Mate was an iterative process conducted in collaboration with adolescents (n=23), experts, school staff, and software developers. The development process consisted of 3 stages: scoping; end-user consultations, including a web-based survey and 2 focus groups with 23 adolescents (mean age 15.9, SD 0.6 years); and app development and beta-testing. Results: This process resulted in a smartphone peer support app and introductory classroom lesson aimed at empowering adolescents to access evidence-based information and tools to better support peers regarding mental health and substance use?related issues. The program contains links to external support services and encourages adolescents to reach out for help if they are concerned about themselves or a friend. Conclusions: The Mind your Mate program was developed in collaboration with a number of key stakeholders in youth mental health, including adolescents. The resulting program has the potential to be taken to scale to aid prevention efforts for youth mental health and substance use. The next step is to conduct a randomized controlled trial testing the feasibility, acceptability, and efficacy of the program. UR - https://formative.jmir.org/2022/5/e36068 UR - http://dx.doi.org/10.2196/36068 UR - http://www.ncbi.nlm.nih.gov/pubmed/35622401 ID - info:doi/10.2196/36068 ER - TY - JOUR AU - Dewa, H. Lindsay AU - Pappa, Sofia AU - Greene, Talya AU - Cooke, James AU - Mitchell, Lizzie AU - Hadley, Molly AU - Di Simplicio, Martina AU - Woodcock, Thomas AU - Aylin, Paul PY - 2022/5/17 TI - The Association Between Sleep Disturbance and Suicidality in Psychiatric Inpatients Transitioning to the Community: Protocol for an Ecological Momentary Assessment Study JO - JMIR Res Protoc SP - e33817 VL - 11 IS - 5 KW - sleep KW - suicide KW - psychiatric inpatient KW - ecological momentary assessment KW - EMA KW - experience sampling KW - coproduction KW - sleep disturbance KW - discharge N2 - Background: Patients are at high risk of suicidal behavior and death by suicide immediately following discharge from inpatient psychiatric hospitals. Furthermore, there is a high prevalence of sleep problems in inpatient settings, which is associated with worse outcomes following hospitalization. However, it is unknown whether poor sleep is associated with suicidality following initial hospital discharge. Objective: Our study objective is to describe a protocol for an ecological momentary assessment (EMA) study that aims to examine the relationship between sleep and suicidality in discharged patients. Methods: Our study will use an EMA design based on a wearable device to examine the sleep-suicide relationship during the transition from acute inpatient care to the community. Prospectively discharged inpatients 18 to 35 years old with mental disorders (N=50) will be assessed for eligibility and recruited across 2 sites. Data on suicidal ideation, behavior, and imagery; nonsuicidal self-harm and imagery; defeat, entrapment, and hopelessness; affect; and sleep will be collected on the Pro-Diary V wrist-worn electronic watch for up to 14 days. Objective sleep and daytime activity will be measured using the inbuilt MotionWare software. Questionnaires will be administered face-to-face at baseline and follow up, and data will also be collected on the acceptability and feasibility of using the Pro-Diary V watch to monitor the transition following discharge. The study has been, and will continue to be, coproduced with young people with experience of being in an inpatient setting and suicidality. Results: South Birmingham Research Ethics Committee (21/WM/0128) approved the study on June 28, 2021. We expect to see a relationship between poor sleep and postdischarge suicidality. Results will be available in 2022. Conclusions: This protocol describes the first coproduced EMA study to examine the relationship between sleep and suicidality and to apply the integrated motivational volitional model in young patients transitioning from a psychiatric hospital to the community. We expect our findings will inform coproduction in suicidology research and clarify the role of digital monitoring of suicidality and sleep before and after initial hospital discharge. International Registered Report Identifier (IRRID): PRR1-10.2196/33817 UR - https://www.researchprotocols.org/2022/5/e33817 UR - http://dx.doi.org/10.2196/33817 UR - http://www.ncbi.nlm.nih.gov/pubmed/35579920 ID - info:doi/10.2196/33817 ER - TY - JOUR AU - Choudhary, Soumya AU - Thomas, Nikita AU - Ellenberger, Janine AU - Srinivasan, Girish AU - Cohen, Roy PY - 2022/5/16 TI - A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study JO - JMIR Form Res SP - e37736 VL - 6 IS - 5 KW - mobile phone KW - depression KW - digital phenotyping KW - digital mental health N2 - Background: Depression is a major global cause of morbidity, an economic burden, and the greatest health challenge leading to chronic disability. Mobile monitoring of mental conditions has long been a sought-after metric to overcome the problems associated with the screening, diagnosis, and monitoring of depression and its heterogeneous presentation. The widespread availability of smartphones has made it possible to use their data to generate digital behavioral models that can be used for both clinical and remote screening and monitoring purposes. This study is novel as it adds to the field by conducting a trial using private and nonintrusive sensors that can help detect and monitor depression in a continuous, passive manner. Objective: This study demonstrates a novel mental behavioral profiling metric (the Mental Health Similarity Score), derived from analyzing passively monitored, private, and nonintrusive smartphone use data, to identify and track depressive behavior and its progression. Methods: Smartphone data sets and self-reported Patient Health Questionnaire-9 (PHQ-9) depression assessments were collected from 558 smartphone users on the Android operating system in an observational study over an average of 10.7 (SD 23.7) days. We quantified 37 digital behavioral markers from the passive smartphone data set and explored the relationship between the digital behavioral markers and depression using correlation coefficients and random forest models. We leveraged 4 supervised machine learning classification algorithms to predict depression and its severity using PHQ-9 scores as the ground truth. We also quantified an additional 3 digital markers from gyroscope sensors and explored their feasibility in improving the model?s accuracy in detecting depression. Results: The PHQ-9 2-class model (none vs severe) achieved the following metrics: precision of 85% to 89%, recall of 85% to 89%, F1 of 87%, and accuracy of 87%. The PHQ-9 3-class model (none vs mild vs severe) achieved the following metrics: precision of 74% to 86%, recall of 76% to 83%, F1 of 75% to 84%, and accuracy of 78%. A significant positive Pearson correlation was found between PHQ-9 questions 2, 6, and 9 within the severely depressed users and the mental behavioral profiling metric (r=0.73). The PHQ-9 question-specific model achieved the following metrics: precision of 76% to 80%, recall of 75% to 81%, F1 of 78% to 89%, and accuracy of 78%. When a gyroscope sensor was added as a feature, the Pearson correlation among questions 2, 6, and 9 decreased from 0.73 to 0.46. The PHQ-9 2-class model+gyro features achieved the following metrics: precision of 74% to 78%, recall of 67% to 83%, F1 of 72% to 78%, and accuracy of 76%. Conclusions: Our results demonstrate that the Mental Health Similarity Score can be used to identify and track depressive behavior and its progression with high accuracy. UR - https://formative.jmir.org/2022/5/e37736 UR - http://dx.doi.org/10.2196/37736 UR - http://www.ncbi.nlm.nih.gov/pubmed/35420993 ID - info:doi/10.2196/37736 ER - TY - JOUR AU - Ho, Ying Ka AU - Cheung, Mang Po AU - Cheng, Wing Tap AU - Suen, Yin Wing AU - Ho, Ying Hiu AU - Cheung, Ki Daphne Sze PY - 2022/5/11 TI - Virtual Reality Intervention for Managing Apathy in People With Cognitive Impairment: Systematic Review JO - JMIR Aging SP - e35224 VL - 5 IS - 2 KW - virtual reality KW - apathy KW - cognitive impairment KW - dementia KW - systematic review N2 - Background: Apathy is common in people with cognitive impairment. It leads to different consequences, such as more severe cognitive deficits, rapid functional decline, and decreased quality of life. Virtual reality (VR) interventions are increasingly being used to manage apathy in individuals with cognitive impairment. However, reports of VR interventions are scattered across studies, which has hindered the development and use of the interventions. Objective: This study aimed to systematically review existing evidence on the use of VR interventions for managing apathy in people with cognitive impairment with regard to the effectiveness, contents, and implementation of the interventions. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. The PubMed, Embase, CINAHL, and PsycINFO databases were systematically searched for experimental studies published up to March 13, 2022, that reported the effects of VR interventions on apathy in older adults with cognitive impairment. Hand searching and citation chasing were conducted. The results of the included studies were synthesized by using a narrative synthesis. Their quality was appraised by using the Effective Public Health Practice Project quality assessment tool. However, because the VR interventions varied in duration, content, and implementation across studies, a meta-analysis was not conducted. Results: A total of 22 studies were identified from the databases, of which 6 (27%) met the inclusion criteria. Of these 6 studies, 2 (33%) were randomized controlled trials, 1 (17%) was a controlled clinical trial, and 3 (50%) were quasi-experimental studies. Individual studies showed significant improvement in apathy and yielded within-group medium to large effect sizes. The level of immersion ranged from low to high. Minor adverse effects were reported. The VR content mostly included natural scenes, followed by city views and game-based activities. A background soundtrack was often used with natural scenes. Most (5/6, 83%) of the studies were conducted in a residential care setting and were implemented by health care professionals or researchers. Safety precautions were taken in most (5/6, 83%) of the studies. Conclusions: Although preliminary evidence shows that VR interventions may be effective and feasible for alleviating apathy in people with cognitive impairment, the methodological limitations in the included studies make it difficult to reach a firm conclusion on these points. The implementation of the interventions was highlighted and discussed. More rigorous studies are encouraged. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021268289; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021268289 UR - https://aging.jmir.org/2022/2/e35224 UR - http://dx.doi.org/10.2196/35224 UR - http://www.ncbi.nlm.nih.gov/pubmed/35544317 ID - info:doi/10.2196/35224 ER - TY - JOUR AU - Rodriguez-Ferrer, M. Jose AU - Manzano-León, Ana AU - Cangas, J. Adolfo AU - Aguilar-Parra, M. Jose PY - 2022/5/5 TI - A Web-Based Escape Room to Raise Awareness About Severe Mental Illness Among University Students: Randomized Controlled Trial JO - JMIR Serious Games SP - e34222 VL - 10 IS - 2 KW - escape room KW - severe mental disorder KW - higher education KW - nursing education KW - mental health KW - mental disorder KW - serious games N2 - Background: People with severe mental illness (SMI) face discriminatory situations because of prejudice toward them, even among health care personnel. Escape rooms can be a novel educational strategy for learning about and empathizing with SMI, thus reducing stigma among health care students. Objective: This study aimed to examine the effect of the Without Memories escape room on nursing students? stigma against SMI. Methods: A pre- and postintervention study was conducted with a control group and an experimental group. A total of 306 students from 2 Andalusian universities participated in the study. Data were collected through a pre-post study questionnaire, consisting of an adapted version of the Attributional Style Questionnaire and a questionnaire on motivation for cooperative playful learning strategies. The control group carried out an escape room scenario without sensitizing content, whereas the experimental group carried out an escape room scenario on SMI, with both escape rooms being carried out in a 1-hour session of subjects related to mental health. To answer the research questions, a 2-way analysis of variance with repeated measures, a linear regression, and a 2-way analysis of variance were performed. Results: After the intervention, a significant reduction (P<.001) was observed in the experimental group in stigmatizing attitudes compared with the control group, in which no statistically significant changes (P>.05) were observed. In contrast, the linear regression (t195=?22.15; P<.001) showed that there was an inverse relationship between flow and the level of reduced stigma. When controlling for having or not having a close relative, the intervention was also shown to be effective (P<.001) in reducing the stigma displayed, both for people with affected and unaffected relatives. Conclusions: Our findings suggest that the Without Memories escape room can be used as an effective tool to educate and raise awareness about stigmatizing attitudes toward SMI in university students studying health care. Future testing of the effectiveness of educational escape rooms should be designed with new programs through playful strategies of longer duration to evaluate whether they can achieve a greater impact on motivation, acquisition of knowledge, and awareness. In addition, the feasibility of implementing the Without Memories escape room in other careers related to health and community should be investigated. UR - https://games.jmir.org/2022/2/e34222 UR - http://dx.doi.org/10.2196/34222 UR - http://www.ncbi.nlm.nih.gov/pubmed/35511232 ID - info:doi/10.2196/34222 ER - TY - JOUR AU - Weir, Charlene PY - 2022/5/4 TI - Through the Narrative Looking Glass: Commentary on ?Impact of Electronic Health Records on Information Practices in Mental Health Contexts: Scoping Review? JO - J Med Internet Res SP - e38513 VL - 24 IS - 5 KW - electronic health records KW - psychiatry KW - mental health KW - electronic medical records KW - health informatics KW - mental illness KW - scoping review KW - clinical decision support UR - https://www.jmir.org/2022/5/e38513 UR - http://dx.doi.org/10.2196/38513 UR - http://www.ncbi.nlm.nih.gov/pubmed/35507399 ID - info:doi/10.2196/38513 ER - TY - JOUR AU - Kariotis, Charles Timothy AU - Prictor, Megan AU - Chang, Shanton AU - Gray, Kathleen PY - 2022/5/4 TI - Impact of Electronic Health Records on Information Practices in Mental Health Contexts: Scoping Review JO - J Med Internet Res SP - e30405 VL - 24 IS - 5 KW - electronic health records KW - psychiatry KW - mental health KW - electronic medical records KW - health informatics KW - mental illness KW - scoping review KW - clinical decision support N2 - Background: The adoption of electronic health records (EHRs) and electronic medical records (EMRs) has been slow in the mental health context, partly because of concerns regarding the collection of sensitive information, the standardization of mental health data, and the risk of negatively affecting therapeutic relationships. However, EHRs and EMRs are increasingly viewed as critical to improving information practices such as the documentation, use, and sharing of information and, more broadly, the quality of care provided. Objective: This paper aims to undertake a scoping review to explore the impact of EHRs on information practices in mental health contexts and also explore how sensitive information, data standardization, and therapeutic relationships are managed when using EHRs in mental health contexts. Methods: We considered a scoping review to be the most appropriate method for this review because of the relatively recent uptake of EHRs in mental health contexts. A comprehensive search of electronic databases was conducted with no date restrictions for articles that described the use of EHRs, EMRs, or associated systems in the mental health context. One of the authors reviewed all full texts, with 2 other authors each screening half of the full-text articles. The fourth author mediated the disagreements. Data regarding study characteristics were charted. A narrative and thematic synthesis approach was taken to analyze the included studies? results and address the research questions. Results: The final review included 40 articles. The included studies were highly heterogeneous with a variety of study designs, objectives, and settings. Several themes and subthemes were identified that explored the impact of EHRs on information practices in the mental health context. EHRs improved the amount of information documented compared with paper. However, mental health?related information was regularly missing from EHRs, especially sensitive information. EHRs introduced more standardized and formalized documentation practices that raised issues because of the focus on narrative information in the mental health context. EHRs were found to disrupt information workflows in the mental health context, especially when they did not include appropriate templates or care plans. Usability issues also contributed to workflow concerns. Managing the documentation of sensitive information in EHRs was problematic; clinicians sometimes watered down sensitive information or chose to keep it in separate records. Concerningly, the included studies rarely involved service user perspectives. Furthermore, many studies provided limited information on the functionality or technical specifications of the EHR being used. Conclusions: We identified several areas in which work is needed to ensure that EHRs benefit clinicians and service users in the mental health context. As EHRs are increasingly considered critical for modern health systems, health care decision-makers should consider how EHRs can better reflect the complexity and sensitivity of information practices and workflows in the mental health context. UR - https://www.jmir.org/2022/5/e30405 UR - http://dx.doi.org/10.2196/30405 UR - http://www.ncbi.nlm.nih.gov/pubmed/35507393 ID - info:doi/10.2196/30405 ER - TY - JOUR AU - Mercadal Rotger, Josep AU - Cabré, Victor PY - 2022/5/2 TI - Therapeutic Alliance in Online and Face-to-face Psychological Treatment: Comparative Study JO - JMIR Ment Health SP - e36775 VL - 9 IS - 5 KW - online psychological intervention KW - therapeutic alliance KW - digital health KW - mental health KW - mental health education KW - mental health treatment KW - health interventions KW - health professional KW - online health KW - web-based health KW - intervention modality N2 - Background: Since the COVID-19 pandemic, the number of online mental health treatments have grown exponentially. Additionally, it seems inevitable that this technical resource is here to stay at health centers. However, there is still very little scholarly literature published on this topic, and therefore, the impact of the changes that have had to be dealt with in this regard has not been studied. Objective: This study aims to evaluate the differences in the establishment of the therapeutic alliance (TA) based on the intervention modality (online or face-to-face), the type of attachment, and diagnosis. Methods: A total of 291 subjects participated in the study, 149 (51.2%) of whom were men and 142 were (48.8%) women between the ages of 18 and 30 years. The instruments used were sociodemographic data, SOFTA-o (System for Observing Family Therapeutic Alliances?observational), and Relationship Questionnaire. Results: The results show that the treatments conducted face-to-face obtain significantly better scores in the creation of the TA than those conducted online (t=?42.045, df=289, P<.001). The same holds true with attachment, in that users with secure attachment show a better TA than those with insecure attachment (t=6.068, P<.001,), although there were no significant differences with the diagnosis (F=4.566, P=.44), age (r=0.02, P=.70), and sex (t=0.217, P=.33). Conclusions: We believe that professionals are not yet prepared to conduct remote treatment with a degree of efficacy similar to that of face-to-face. It is essential for professionals to receive training in this new technical resource and to understand and incorporate the variants it entails into their daily practice. UR - https://mental.jmir.org/2022/5/e36775 UR - http://dx.doi.org/10.2196/36775 UR - http://www.ncbi.nlm.nih.gov/pubmed/35499910 ID - info:doi/10.2196/36775 ER - TY - JOUR AU - Yeager, M. Carolyn AU - Benight, C. Charles PY - 2022/5/2 TI - Engagement, Predictors, and Outcomes of a Trauma Recovery Digital Mental Health Intervention: Longitudinal Study JO - JMIR Ment Health SP - e35048 VL - 9 IS - 5 KW - engagement KW - digital health KW - digital mental health intervention KW - social cognitive theory KW - SCT KW - self-efficacy KW - outcome expectations KW - trauma KW - posttraumatic stress disorder KW - PTSD N2 - Background: Worldwide, exposure to potentially traumatic events is extremely common, and many individuals develop posttraumatic stress disorder (PTSD) along with other disorders. Unfortunately, considerable barriers to treatment exist. A promising approach to overcoming treatment barriers is a digital mental health intervention (DMHI). However, engagement with DMHIs is a concern, and theoretically based research in this area is sparse and often inconclusive. Objective: The focus of this study is on the complex issue of DMHI engagement. On the basis of the social cognitive theory framework, the conceptualization of engagement and a theoretically based model of predictors and outcomes were investigated using a DMHI for trauma recovery. Methods: A 6-week longitudinal study with a national sample of survivors of trauma was conducted to measure engagement, predictors of engagement, and mediational pathways to symptom reduction while using a trauma recovery DMHI (time 1: N=915; time 2: N=350; time 3: N=168; and time 4: N=101). Results: Confirmatory factor analysis of the engagement latent constructs of duration, frequency, interest, attention, and affect produced an acceptable model fit (?22=8.3; P=.02; comparative fit index 0.973; root mean square error of approximation 0.059; 90% CI 0.022-0.103). Using the latent construct, the longitudinal theoretical model demonstrated adequate model fit (comparative fit index 0.929; root mean square error of approximation 0.052; 90% CI 0.040-0.064), indicating that engagement self-efficacy (?=.35; P<.001) and outcome expectations (?=.37; P<.001) were significant predictors of engagement (R2=39%). The overall indirect effect between engagement and PTSD symptom reduction was significant (?=?.065; P<.001; 90% CI ?0.071 to ?0.058). This relationship was serially mediated by both skill activation self-efficacy (?=.80; P<.001) and trauma coping self-efficacy (?=.40; P<.001), which predicted a reduction in PTSD symptoms (?=?.20; P=.02). Conclusions: The results of this study may provide a solid foundation for formalizing the nascent science of engagement. Engagement conceptualization comprised general measures of attention, interest, affect, and use that could be applied to other applications. The longitudinal research model supported 2 theoretically based predictors of engagement: engagement self-efficacy and outcome expectancies. A total of 2 task-specific self-efficacies?skill activation and trauma coping?proved to be significant mediators between engagement and symptom reduction. Taken together, this model can be applied to other DMHIs to understand engagement, as well as predictors and mechanisms of action. Ultimately, this could help improve the design and development of engaging and effective trauma recovery DMHIs. UR - https://mental.jmir.org/2022/5/e35048 UR - http://dx.doi.org/10.2196/35048 UR - http://www.ncbi.nlm.nih.gov/pubmed/35499857 ID - info:doi/10.2196/35048 ER - TY - JOUR AU - Polhemus, Ashley AU - Novak, Jan AU - Majid, Shazmin AU - Simblett, Sara AU - Morris, Daniel AU - Bruce, Stuart AU - Burke, Patrick AU - Dockendorf, F. Marissa AU - Temesi, Gergely AU - Wykes, Til PY - 2022/4/28 TI - Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives JO - JMIR Ment Health SP - e25249 VL - 9 IS - 4 KW - digital health KW - remote measurement technology KW - neurology KW - mental health KW - data visualization KW - user-centered design N2 - Background: Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions. Objective: The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. Methods: In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis. Results: We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users? experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions. Conclusions: When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not ?one-size-fits-all,? and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health. UR - https://mental.jmir.org/2022/4/e25249 UR - http://dx.doi.org/10.2196/25249 UR - http://www.ncbi.nlm.nih.gov/pubmed/35482368 ID - info:doi/10.2196/25249 ER - TY - JOUR AU - Meheli, Saha AU - Sinha, Chaitali AU - Kadaba, Madhura PY - 2022/4/27 TI - Understanding People With Chronic Pain Who Use a Cognitive Behavioral Therapy?Based Artificial Intelligence Mental Health App (Wysa): Mixed Methods Retrospective Observational Study JO - JMIR Hum Factors SP - e35671 VL - 9 IS - 2 KW - chronic pain KW - digital mental health KW - mobile health KW - mHealth KW - pain management KW - artificial intelligence KW - cognitive behavioral therapy KW - conversational agent KW - software agent KW - pain conditions KW - depression KW - anxiety N2 - Background: Digital health interventions can bridge barriers in access to treatment among individuals with chronic pain. Objective: This study aimed to evaluate the perceived needs, engagement, and effectiveness of the mental health app Wysa with regard to mental health outcomes among real-world users who reported chronic pain and engaged with the app for support. Methods: Real-world data from users (N=2194) who reported chronic pain and associated health conditions in their conversations with the mental health app were examined using a mixed methods retrospective observational study. An inductive thematic analysis was used to analyze the conversational data of users with chronic pain to assess perceived needs, along with comparative macro-analyses of conversational flows to capture engagement within the app. Additionally, the scores from a subset of users who completed a set of pre-post assessment questionnaires, namely Patient Health Questionnaire-9 (PHQ-9) (n=69) and Generalized Anxiety Disorder Assessment-7 (GAD-7) (n=57), were examined to evaluate the effectiveness of Wysa in providing support for mental health concerns among those managing chronic pain. Results: The themes emerging from the conversations of users with chronic pain included health concerns, socioeconomic concerns, and pain management concerns. Findings from the quantitative analysis indicated that users with chronic pain showed significantly greater app engagement (P<.001) than users without chronic pain, with a large effect size (Vargha and Delaney A=0.76-0.80). Furthermore, users with pre-post assessments during the study period were found to have significant improvements in group means for both PHQ-9 and GAD-7 symptom scores, with a medium effect size (Cohen d=0.60-0.61). Conclusions: The findings indicate that users look for tools that can help them address their concerns related to mental health, pain management, and sleep issues. The study findings also indicate the breadth of the needs of users with chronic pain and the lack of support structures, and suggest that Wysa can provide effective support to bridge the gap. UR - https://humanfactors.jmir.org/2022/2/e35671 UR - http://dx.doi.org/10.2196/35671 UR - http://www.ncbi.nlm.nih.gov/pubmed/35314422 ID - info:doi/10.2196/35671 ER - TY - JOUR AU - Jacobson, Natasha AU - Lithgow, Brian AU - Jafari Jozani, Mohammad AU - Moussavi, Zahra PY - 2022/4/27 TI - The Effect of Transcranial Alternating Current Stimulation With Cognitive Training on Executive Brain Function in Individuals With Dementia: Protocol for a Crossover Randomized Controlled Trial JO - JMIR Res Protoc SP - e37282 VL - 11 IS - 4 KW - transcranial alternating current stimulation KW - Alzheimer disease KW - cognitive impairment KW - double blind KW - treatment KW - placebo-controlled KW - randomized KW - crossover KW - dementia KW - cognitive N2 - Background: Although memory and cognitive declines are associated with normal brain aging, they may also be precursors to dementia. Objective: We aim to offer a novel approach to prevent or slow the progress of neurodegenerative dementia, or plausibly, improve the cognitive functions of individuals with dementia. Methods: We will recruit and enroll 75 participants (older than 50 years old with either mild cognitive impairment or probable early or moderate dementia) for this double-blind randomized controlled study to estimate the efficacy of active transcranial alternating current stimulation with cognitive treatment (in comparison with sham transcranial alternating current stimulation). This will be a crossover study; a cycle consists of sham or active treatment for a period of 4 weeks (5 days per week, in two 30-minute sessions with a half-hour break in between), and participants are randomized into 2 groups, with stratification by age, sex, and cognitive level (measured with the Montreal Cognitive Assessment). Outcomes will be assessed before and after each treatment cycle. The primary outcomes are changes in Wechsler Memory Scale Older Adult Battery and Alzheimer Disease Assessment Scale scores. Secondary outcomes are changes in performance on tests of frontal lobe functioning (verbal fluency), neuropsychiatric symptoms (Neuropsychiatric Inventory Questionnaire), mood changes (Montgomery-Åsberg Depression Rating Scale), and short-term recall (visual 1-back task). Exploratory outcome measures will also be assessed: static and dynamic vestibular response using electrovestibulography, neuronal changes using functional near-infrared spectroscopy, and change in spatial orientation using virtual reality navigation. Results: As of February 10, 2022, the study is ongoing: 7 patients have been screened, and all were deemed eligible for and enrolled in the study; 4 participants have completed baseline assessments. Conclusions: We anticipate that transcranial alternating current stimulation will be a well-tolerated treatment, with no serious side effects and with considerable short- and long-term cognitive improvements. Trial Registration: Clinicaltrials.gov NCT05203523; https://clinicaltrials.gov/show/NCT05203523 International Registered Report Identifier (IRRID): DERR1-10.2196/37282 UR - https://www.researchprotocols.org/2022/4/e37282 UR - http://dx.doi.org/10.2196/37282 UR - http://www.ncbi.nlm.nih.gov/pubmed/35475789 ID - info:doi/10.2196/37282 ER - TY - JOUR AU - Vega, Julio AU - Bell, T. Beth AU - Taylor, Caitlin AU - Xie, Jue AU - Ng, Heidi AU - Honary, Mahsa AU - McNaney, Roisin PY - 2022/4/25 TI - Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating JO - JMIR Ment Health SP - e32146 VL - 9 IS - 4 KW - eating disorder KW - binge eating KW - mental health KW - mobile sensing KW - context-aware computing KW - NAP KW - EMA KW - mobile phone N2 - Background: Binge eating is a subjective loss of control while eating, which leads to the consumption of large amounts of food. It can cause significant emotional distress and is often accompanied by purging behaviors (eg, meal skipping, overexercising, or vomiting). Objective: The aim of this study was to explore the potential of mobile sensing to detect indicators of binge-eating episodes, with a view toward informing the design of future context-aware mobile interventions. Methods: This study was conducted in 2 stages. The first involved the development of the DeMMI (Detecting Mental health behaviors using Mobile Interactions) app. As part of this, we conducted a consultation session to explore whether the types of sensor data we were proposing to capture were useful and appropriate, as well as to gather feedback on some specific app features relating to self-reporting. The second stage involved conducting a 6-week period of data collection with 10 participants experiencing binge eating (logging both their mood and episodes of binge eating) and 10 comparison participants (logging only mood). An optional interview was conducted after the study, which discussed their experience using the app, and 8 participants (n=3, 38% binge eating and n=5, 63% comparisons) consented. Results: The findings showed unique differences in the types of sensor data that were triangulated with the individuals? episodes (with nearby Bluetooth devices, screen and app use features, mobility features, and mood scores showing relevance). Participants had a largely positive opinion about the app, its unobtrusive role, and its ease of use. Interacting with the app increased participants? awareness of and reflection on their mood and phone usage patterns. Moreover, they expressed no privacy concerns as these were alleviated by the study information sheet. Conclusions: This study contributes a series of recommendations for future studies wishing to scale our approach and for the design of bespoke mobile interventions to support this population. UR - https://mental.jmir.org/2022/4/e32146 UR - http://dx.doi.org/10.2196/32146 UR - http://www.ncbi.nlm.nih.gov/pubmed/35086064 ID - info:doi/10.2196/32146 ER - TY - JOUR AU - Harvey, Daisy AU - Lobban, Fiona AU - Rayson, Paul AU - Warner, Aaron AU - Jones, Steven PY - 2022/4/22 TI - Natural Language Processing Methods and Bipolar Disorder: Scoping Review JO - JMIR Ment Health SP - e35928 VL - 9 IS - 4 KW - bipolar disorder KW - mental health KW - mental illness KW - natural language processing KW - computational linguistics N2 - Background: Health researchers are increasingly using natural language processing (NLP) to study various mental health conditions using both social media and electronic health records (EHRs). There is currently no published synthesis that relates specifically to the use of NLP methods for bipolar disorder, and this scoping review was conducted to synthesize valuable insights that have been presented in the literature. Objective: This scoping review explored how NLP methods have been used in research to better understand bipolar disorder and identify opportunities for further use of these methods. Methods: A systematic, computerized search of index and free-text terms related to bipolar disorder and NLP was conducted using 5 databases and 1 anthology: MEDLINE, PsycINFO, Academic Search Ultimate, Scopus, Web of Science Core Collection, and the ACL Anthology. Results: Of 507 identified studies, a total of 35 (6.9%) studies met the inclusion criteria. A narrative synthesis was used to describe the data, and the studies were grouped into four objectives: prediction and classification (n=25), characterization of the language of bipolar disorder (n=13), use of EHRs to measure health outcomes (n=3), and use of EHRs for phenotyping (n=2). Ethical considerations were reported in 60% (21/35) of the studies. Conclusions: The current literature demonstrates how language analysis can be used to assist in and improve the provision of care for people living with bipolar disorder. Individuals with bipolar disorder and the medical community could benefit from research that uses NLP to investigate risk-taking, web-based services, social and occupational functioning, and the representation of gender in bipolar disorder populations on the web. Future research that implements NLP methods to study bipolar disorder should be governed by ethical principles, and any decisions regarding the collection and sharing of data sets should ultimately be made on a case-by-case basis, considering the risk to the data participants and whether their privacy can be ensured. UR - https://mental.jmir.org/2022/4/e35928 UR - http://dx.doi.org/10.2196/35928 UR - http://www.ncbi.nlm.nih.gov/pubmed/35451984 ID - info:doi/10.2196/35928 ER - TY - JOUR AU - Newson, Jane Jennifer AU - Pastukh, Vladyslav AU - Thiagarajan, C. Tara PY - 2022/4/20 TI - Assessment of Population Well-being With the Mental Health Quotient: Validation Study JO - JMIR Ment Health SP - e34105 VL - 9 IS - 4 KW - psychiatry KW - public health KW - methods KW - mental health KW - population health KW - social determinants of health KW - global health KW - behavioral symptoms KW - diagnosis KW - symptom assessment KW - psychopathology KW - mental disorders KW - mHealth KW - depression KW - anxiety KW - attention deficit disorder with hyperactivity KW - autistic disorder KW - internet N2 - Background: The Mental Health Quotient (MHQ) is an anonymous web-based assessment of mental health and well-being that comprehensively covers symptoms across 10 major psychiatric disorders, as well as positive elements of mental function. It uses a novel life impact scale and provides a score to the individual that places them on a spectrum from Distressed to Thriving along with a personal report that offers self-care recommendations. Since April 2020, the MHQ has been freely deployed as part of the Mental Health Million Project. Objective: This paper demonstrates the reliability and validity of the MHQ, including the construct validity of the life impact scale, sample and test-retest reliability of the assessment, and criterion validation of the MHQ with respect to clinical burden and productivity loss. Methods: Data were taken from the Mental Health Million open-access database (N=179,238) and included responses from English-speaking adults (aged?18 years) from the United States, Canada, the United Kingdom, Ireland, Australia, New Zealand, South Africa, Singapore, India, and Nigeria collected during 2021. To assess sample reliability, random demographically matched samples (each 11,033/179,238, 6.16%) were compared within the same 6-month period. Test-retest reliability was determined using the subset of individuals who had taken the assessment twice ?3 days apart (1907/179,238, 1.06%). To assess the construct validity of the life impact scale, additional questions were asked about the frequency and severity of an example symptom (feelings of sadness, distress, or hopelessness; 4247/179,238, 2.37%). To assess criterion validity, elements rated as having a highly negative life impact by a respondent (equivalent to experiencing the symptom ?5 days a week) were mapped to clinical diagnostic criteria to calculate the clinical burden (174,618/179,238, 97.42%). In addition, MHQ scores were compared with the number of workdays missed or with reduced productivity in the past month (7625/179,238, 4.25%). Results: Distinct samples collected during the same period had indistinguishable MHQ distributions and MHQ scores were correlated with r=0.84 between retakes within an 8- to 120-day period. Life impact ratings were correlated with frequency and severity of symptoms, with a clear linear relationship (R2>0.99). Furthermore, the aggregate MHQ scores were systematically related to both clinical burden and productivity. At one end of the scale, 89.08% (8986/10,087) of those in the Distressed category mapped to one or more disorders and had an average productivity loss of 15.2 (SD 11.2; SEM [standard error of measurement] 0.5) days per month. In contrast, at the other end of the scale, 0% (1/24,365) of those in the Thriving category mapped to any of the 10 disorders and had an average productivity loss of 1.3 (SD 3.6; SEM 0.1) days per month. Conclusions: The MHQ is a valid and reliable assessment of mental health and well-being when delivered anonymously on the web. UR - https://mental.jmir.org/2022/4/e34105 UR - http://dx.doi.org/10.2196/34105 UR - http://www.ncbi.nlm.nih.gov/pubmed/35442210 ID - info:doi/10.2196/34105 ER - TY - JOUR AU - Guemghar, Imane AU - Pires de Oliveira Padilha, Paula AU - Abdel-Baki, Amal AU - Jutras-Aswad, Didier AU - Paquette, Jesseca AU - Pomey, Marie-Pascale PY - 2022/4/19 TI - Social Robot Interventions in Mental Health Care and Their Outcomes, Barriers, and Facilitators: Scoping Review JO - JMIR Ment Health SP - e36094 VL - 9 IS - 4 KW - social robots KW - socially assistive robots KW - SARs KW - mental health KW - mental health services KW - dementia KW - autism spectrum disorder KW - schizophrenia KW - depression KW - scoping review N2 - Background: The use of social robots as innovative therapeutic tools has been increasingly explored in recent years in an effort to address the growing need for alternative intervention modalities in mental health care. Objective: The aim of this scoping review was to identify and describe social robot interventions in mental health facilities and to highlight their outcomes as well as the barriers and facilitators to their implementation. Methods: A scoping review of the literature published since 2015 was conducted using the Arksey and O?Malley?s framework. The MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO databases were searched, and 2239 papers were retrieved. The papers included were primary empirical studies published in peer-reviewed literature. Eligible studies were set in mental health facilities and they included participants with a known mental health disorder. The methodological quality of the included papers was also assessed using the Mixed Methods Appraisal Tool. Results: A total of 30 papers met the eligibility criteria for this review. Studies involved participants with dementia, cognitive impairment, schizophrenia, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, and an intellectual disability. The outcomes studied included engagement, social interaction, emotional state, agitation, behavior, and quality of life. Conclusions: The methodological weaknesses of the studies conducted this far and the lack of diversity in the conditions studied limit the generalizability of the results. However, despite the presence of certain barriers to their implementation (eg, technical problems, unsuitable environment, staff resistance), social robot interventions generally show positive effects on patients with mental health disorders. Studies of stronger methodological quality are needed to further understand the benefits and the place of social robots in mental health care. UR - https://mental.jmir.org/2022/4/e36094 UR - http://dx.doi.org/10.2196/36094 UR - http://www.ncbi.nlm.nih.gov/pubmed/35438639 ID - info:doi/10.2196/36094 ER - TY - JOUR AU - Yuan, Jing AU - Au, Rhoda AU - Karjadi, Cody AU - Ang, Fang Ting AU - Devine, Sherral AU - Auerbach, Sanford AU - DeCarli, Charles AU - Libon, J. David AU - Mez, Jesse AU - Lin, Honghuang PY - 2022/4/15 TI - Associations Between the Digital Clock Drawing Test and Brain Volume: Large Community-Based Prospective Cohort (Framingham Heart Study) JO - J Med Internet Res SP - e34513 VL - 24 IS - 4 KW - Clock Drawing Test KW - digital KW - neuropsychological test KW - cognitive KW - technology KW - Boston Process Approach KW - neurology KW - Framingham Heart Study KW - dementia KW - Alzheimer N2 - Background: The digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies. Objective: We aimed to investigate the association between dCDT features and brain volume. Methods: This study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact. Results: A total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value <.05/18). Eleven dCDT composite scores were associated with cerebral white matter volume, but only 1 dCDT composite score was associated with cerebral gray matter volume. None of the dCDT composite scores was associated with hippocampal volume or WMH volume. The classification model for differentiating MCI and normal cognition participants, which incorporated age, sex, education, MRI measures, and dCDT composite scores, showed an area under the curve of 0.897. Conclusions: dCDT composite scores were significantly associated with multiple brain MRI measures in a large community-based cohort. The dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of MCI. UR - https://www.jmir.org/2022/4/e34513 UR - http://dx.doi.org/10.2196/34513 UR - http://www.ncbi.nlm.nih.gov/pubmed/35436225 ID - info:doi/10.2196/34513 ER - TY - JOUR AU - Brown, Poppy AU - Waite, Felicity AU - Lambe, Sinead AU - Jones, Julia AU - Jenner, Lucy AU - Diamond, Rowan AU - Freeman, Daniel PY - 2022/4/12 TI - Automated Virtual Reality Cognitive Therapy (gameChange) in Inpatient Psychiatric Wards: Qualitative Study of Staff and Patient Views Using an Implementation Framework JO - JMIR Form Res SP - e34225 VL - 6 IS - 4 KW - virtual reality KW - automated KW - therapy KW - inpatient psychiatric care KW - implementation N2 - Background: Automated virtual reality (VR) therapy could allow a greater number of patients to receive evidence-based psychological therapy. The aim of the gameChange VR therapy is to help patients overcome anxious avoidance of everyday social situations. gameChange has been evaluated with outpatients, but it may also help inpatients prepare for discharge from psychiatric hospital. Objective: The aim of this study is to explore the views of patients and staff on the provision of VR therapy on psychiatric wards. Methods: Focus groups or individual interviews were conducted with patients (n=19) and National Health Service staff (n=22) in acute psychiatric wards. Questions were derived from the nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability framework. Expectations of VR therapy were discussed, and participants were then given the opportunity to try out the gameChange VR therapy before they were asked questions that focused on opinions about the therapy and feasibility of adoption. Results: There was great enthusiasm for the use of gameChange VR therapy on psychiatric wards. It was considered that gameChange could help build confidence, reduce anxiety, and ?bridge that gap? between the differences of being in hospital and being discharged to the community. However, it was reflected that the VR therapy may not suit everyone, especially if they are acutely unwell. VR on hospital wards for entertainment and relaxation was also viewed positively. Participants were particularly impressed by the immersive quality of gameChange and the virtual coach. It was considered that a range of staff groups could support VR therapy delivery. The staff thought that implementation would be facilitated by having a lead staff member, having ongoing training accessible, and involving the multidisciplinary team in decision-making for VR therapy use. The most significant barrier to implementation identified by patients and staff was a practical one: access to sufficient, private space to provide the therapy. Conclusions: Patients and staff were keen for VR to be used on psychiatric wards. In general, patients and staff viewed automated VR therapy as possible to implement within current care provision, with few significant barriers other than constraints of space. Patients and staff thought of many further uses of VR on psychiatric wards. The value of VR therapy on psychiatric wards now requires systematic evaluation. International Registered Report Identifier (IRRID): RR2-10.2196/20300 UR - https://formative.jmir.org/2022/4/e34225 UR - http://dx.doi.org/10.2196/34225 UR - http://www.ncbi.nlm.nih.gov/pubmed/35412462 ID - info:doi/10.2196/34225 ER - TY - JOUR AU - Stupinski, Marie Anne AU - Alshaabi, Thayer AU - Arnold, V. Michael AU - Adams, Lydia Jane AU - Minot, R. Joshua AU - Price, Matthew AU - Dodds, Sheridan Peter AU - Danforth, M. Christopher PY - 2022/3/30 TI - Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study JO - JMIR Ment Health SP - e33685 VL - 9 IS - 3 KW - mental health KW - stigma KW - natural language processing N2 - Background: Mental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group. Objective: By measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health. Methods: We explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification. Results: We found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time. Conclusions: These results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years. UR - https://mental.jmir.org/2022/3/e33685 UR - http://dx.doi.org/10.2196/33685 UR - http://www.ncbi.nlm.nih.gov/pubmed/35353049 ID - info:doi/10.2196/33685 ER - TY - JOUR AU - Martin-Key, A. Nayra AU - Spadaro, Benedetta AU - Funnell, Erin AU - Barker, Jane Eleanor AU - Schei, Sofie Thea AU - Tomasik, Jakub AU - Bahn, Sabine PY - 2022/3/30 TI - The Current State and Validity of Digital Assessment Tools for Psychiatry: Systematic Review JO - JMIR Ment Health SP - e32824 VL - 9 IS - 3 KW - diagnostic accuracy KW - digital mental health KW - digital questionnaire KW - psychiatry KW - systematic review N2 - Background: Given the role digital technologies are likely to play in the future of mental health care, there is a need for a comprehensive appraisal of the current state and validity (ie, screening or diagnostic accuracy) of digital mental health assessments. Objective: The aim of this review is to explore the current state and validity of question-and-answer?based digital tools for diagnosing and screening psychiatric conditions in adults. Methods: This systematic review was based on the Population, Intervention, Comparison, and Outcome framework and was carried out in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. MEDLINE, Embase, Cochrane Library, ASSIA, Web of Science Core Collection, CINAHL, and PsycINFO were systematically searched for articles published between 2005 and 2021. A descriptive evaluation of the study characteristics and digital solutions and a quantitative appraisal of the screening or diagnostic accuracy of the included tools were conducted. Risk of bias and applicability were assessed using the revised tool for the Quality Assessment of Diagnostic Accuracy Studies 2. Results: A total of 28 studies met the inclusion criteria, with the most frequently evaluated conditions encompassing generalized anxiety disorder, major depressive disorder, and any depressive disorder. Most of the studies used digitized versions of existing pen-and-paper questionnaires, with findings revealing poor to excellent screening or diagnostic accuracy (sensitivity=0.32-1.00, specificity=0.37-1.00, area under the receiver operating characteristic curve=0.57-0.98) and a high risk of bias for most of the included studies. Conclusions: The field of digital mental health tools is in its early stages, and high-quality evidence is lacking. International Registered Report Identifier (IRRID): RR2-10.2196/25382 UR - https://mental.jmir.org/2022/3/e32824 UR - http://dx.doi.org/10.2196/32824 UR - http://www.ncbi.nlm.nih.gov/pubmed/35353053 ID - info:doi/10.2196/32824 ER - TY - JOUR AU - Villarreal-Zegarra, David AU - Alarcon-Ruiz, A. Christoper AU - Melendez-Torres, GJ AU - Torres-Puente, Roberto AU - Navarro-Flores, Alba AU - Cavero, Victoria AU - Ambrosio-Melgarejo, Juan AU - Rojas-Vargas, Jefferson AU - Almeida, Guillermo AU - Albitres-Flores, Leonardo AU - Romero-Cabrera, B. Alejandra AU - Huarcaya-Victoria, Jeff PY - 2022/3/29 TI - Development of a Framework for the Implementation of Synchronous Digital Mental Health: Realist Synthesis of Systematic Reviews JO - JMIR Ment Health SP - e34760 VL - 9 IS - 3 KW - telemedicine KW - digital health KW - internet-based intervention KW - mental health KW - mental disorders KW - systematic reviews KW - qualitative research KW - realist review KW - mHealth KW - eHealth KW - telehealth N2 - Background: The use of technologies has served to reduce gaps in access to treatment, and digital health interventions show promise in the care of mental health problems. However, to understand what and how these interventions work, it is imperative to document the aspects related to their challenging implementation. Objective: The aim of this study was to determine what evidence is available for synchronous digital mental health implementation and to develop a framework, informed by a realist review, to explain what makes digital mental health interventions work for people with mental health problems. Methods: The SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, and Research type) framework was used to develop the following review question: What makes digital mental health interventions with a synchronous component work on people with mental health problems, including depression, anxiety, or stress, based on implementation, economic, quantitative, qualitative, and mixed methods studies? The MEDLINE, EBM Reviews, PsycINFO, EMBASE, SCOPUS, CINAHL Complete, and Web of Science databases were searched from January 1, 2015, to September 2020 with no language restriction. A Measurement Tool to Assess Systematic Reviews-2 (AMSTAR-2) was used to assess the risk of bias and Confidence in Evidence from Reviews of Qualitative Research (CERQual) was used to assess the confidence in cumulative evidence. Realist synthesis analysis allowed for developing a framework on the implementation of synchronous digital mental health using a grounded-theory approach with an emergent approach. Results: A total of 21 systematic reviews were included in the study. Among these, 90% (n=19) presented a critically low confidence level as assessed with AMSTAR-2. The realist synthesis allowed for the development of three hypotheses to identify the context and mechanisms in which these interventions achieve these outcomes: (1) these interventions reach populations otherwise unable to have access because they do not require the physical presence of the therapist nor the patient, thereby tackling geographic barriers posed by in-person therapy; (2) these interventions reach populations otherwise unable to have access because they can be successfully delivered by nonspecialists, which makes them more cost-effective to implement in health services; and (3) these interventions are acceptable and show good results in satisfaction because they require less need of disclosure and provide more privacy, comfortability, and participation, enabling the establishment of rapport with the therapist. Conclusions: We developed a framework with three hypotheses that explain what makes digital mental health interventions with a synchronous component work on people with mental health problems. Each hypothesis represents essential outcomes in the implementation process. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020203811; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020203811 International Registered Report Identifier (IRRID): RR2-10.12688/f1000research.27150.2 UR - https://mental.jmir.org/2022/3/e34760 UR - http://dx.doi.org/10.2196/34760 UR - http://www.ncbi.nlm.nih.gov/pubmed/35348469 ID - info:doi/10.2196/34760 ER - TY - JOUR AU - Hallenbeck, Wu Haijing AU - Jaworski, K. Beth AU - Wielgosz, Joseph AU - Kuhn, Eric AU - Ramsey, M. Kelly AU - Taylor, Katherine AU - Juhasz, Katherine AU - McGee-Vincent, Pearl AU - Mackintosh, Margaret-Anne AU - Owen, E. Jason PY - 2022/3/29 TI - PTSD Coach Version 3.1: A Closer Look at the Reach, Use, and Potential Impact of This Updated Mobile Health App in the General Public JO - JMIR Ment Health SP - e34744 VL - 9 IS - 3 KW - posttraumatic stress disorder KW - trauma KW - mental health KW - mHealth KW - mobile app KW - public health KW - self-management KW - mobile phone N2 - Background: With widespread smartphone ownership, mobile health apps (mHealth) can expand access to evidence-based interventions for mental health conditions, including posttraumatic stress disorder (PTSD). Research to evaluate new features and capabilities in these apps is critical but lags behind app development. The initial release of PTSD Coach, a free self-management app developed by the US Departments of Veterans Affairs and Defense, was found to have a positive public health impact. However, major stakeholder-driven updates to the app have yet to be evaluated. Objective: We aimed to characterize the reach, use, and potential impact of PTSD Coach Version 3.1 in the general public. As part of characterizing use, we investigated the use of specific app features, which extended previous work on PTSD Coach. Methods: We examined the naturalistic use of PTSD Coach during a 1-year observation period between April 20, 2020, and April 19, 2021, using anonymous in-app event data to generate summary metrics for users. Results: During the observation period, PTSD Coach was broadly disseminated to the public, reaching approximately 150,000 total users and 20,000 users per month. On average, users used the app 3 times across 3 separate days for 18 minutes in total, with steep drop-offs in use over time; a subset of users, however, demonstrated high or sustained engagement. More than half of users (79,099/128,691, 61.46%) accessed one or more main content areas of the app (ie, Manage Symptoms, Track Progress, Learn, or Get Support). Among content areas, features under Manage Symptoms (including coping tools) were accessed most frequently, by over 40% of users (53,314/128,691, 41.43% to 56,971/128,691, 44.27%, depending on the feature). Users who provided initial distress ratings (56,971/128,691, 44.27%) reported relatively high momentary distress (mean 6.03, SD 2.52, on a scale of 0-10), and the use of a coping tool modestly improved momentary distress (mean ?1.38, SD 1.70). Among users who completed at least one PTSD Checklist for DSM-5 (PCL-5) assessment (17,589/128,691, 13.67%), PTSD symptoms were largely above the clinical threshold (mean 49.80, SD 16.36). Among users who completed at least two PCL-5 assessments (4989/128,691, 3.88%), PTSD symptoms decreased from the first to last assessment (mean ?4.35, SD 15.29), with approximately one-third (1585/4989, 31.77%) of these users experiencing clinically significant improvements. Conclusions: PTSD Coach continues to fulfill its mission as a public health resource. Version 3.1 compares favorably with version 1 on most metrics related to reach, use, and potential impact. Although benefits appear modest on an individual basis, the app provides these benefits to a large population. For mHealth apps to reach their full potential in supporting trauma recovery, future research should aim to understand the utility of individual app features and identify strategies to maximize overall effectiveness and engagement. UR - https://mental.jmir.org/2022/3/e34744 UR - http://dx.doi.org/10.2196/34744 UR - http://www.ncbi.nlm.nih.gov/pubmed/35348458 ID - info:doi/10.2196/34744 ER - TY - JOUR AU - Hanano, Maria AU - Rith-Najarian, Leslie AU - Boyd, Meredith AU - Chavira, Denise PY - 2022/3/28 TI - Measuring Adherence Within a Self-Guided Online Intervention for Depression and Anxiety: Secondary Analyses of a Randomized Controlled Trial JO - JMIR Ment Health SP - e30754 VL - 9 IS - 3 KW - self-guided KW - adherence KW - depression KW - anxiety KW - online intervention N2 - Background: Self-guided online interventions offer users the ability to participate in an intervention at their own pace and address some traditional service barriers (eg, attending in-person appointments, cost). However, these interventions suffer from high dropout rates, and current literature provides little guidance for defining and measuring online intervention adherence as it relates to clinical outcomes. Objective: This study aims to develop and test multiple measures of adherence to a specific self-guided online intervention, as guided by best practices from the literature. Methods: We conducted secondary analyses on data from a randomized controlled trial of an 8-week online cognitive behavioral program that targets depression and anxiety in college students. We defined multiple behavioral and attitudinal adherence measures at varying levels of effort (ie, low, moderate, and high). Linear regressions were run with adherence terms predicting improvement in the primary outcome measure, the 21-item Depression, Anxiety, and Stress Scale (DASS-21). Results: Of the 947 participants, 747 initiated any activity and 449 provided posttest data. Results from the intent-to-treat sample indicated that high level of effort for behavioral adherence significantly predicted symptom change (F4,746=17.18, P<.001; and ?=?.26, P=.04). Moderate level of effort for attitudinal adherence also significantly predicted symptom change (F4,746=17.25, P<.001; and ?=?.36, P=.03). Results differed in the initiators-only sample, such that none of the adherence measures significantly predicted symptom change (P=.09-.27). Conclusions: Our findings highlight the differential results of dose-response models testing adherence measures in predicting clinical outcomes. We summarize recommendations that might provide helpful guidance to future researchers and intervention developers aiming to investigate online intervention adherence. Trial Registration: ClinicalTrials.gov NCT04361045; https://clinicaltrials.gov/ct2/show/NCT04361045 UR - https://mental.jmir.org/2022/3/e30754 UR - http://dx.doi.org/10.2196/30754 UR - http://www.ncbi.nlm.nih.gov/pubmed/35343901 ID - info:doi/10.2196/30754 ER - TY - JOUR AU - Niemeijer, Koen AU - Mestdagh, Merijn AU - Kuppens, Peter PY - 2022/3/18 TI - Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study JO - J Med Internet Res SP - e25643 VL - 24 IS - 3 KW - mobile sensing KW - sleep KW - subjective sleep quality KW - negative affect KW - depression KW - multiverse KW - multilevel modeling KW - machine learning KW - mood KW - mood disorder KW - mobile sensors KW - sleep quality KW - clinical applications N2 - Background: Sleep influences moods and mood disorders. Existing methods for tracking the quality of people?s sleep are laborious and obtrusive. If a method were available that would allow effortless and unobtrusive tracking of sleep quality, it would mark a significant step toward obtaining sleep data for research and clinical applications. Objective: Our goal was to evaluate the potential of mobile sensing data to obtain information about a person?s sleep quality. For this purpose, we investigated to what extent various automatically gathered mobile sensing features are capable of predicting (1) subjective sleep quality (SSQ), (2) negative affect (NA), and (3) depression; these variables are associated with objective sleep quality. Through a multiverse analysis, we examined how the predictive quality varied as a function of the selected sensor, the extracted feature, various preprocessing options, and the statistical prediction model. Methods: We used data from a 2-week trial where we collected mobile sensing and experience sampling data from an initial sample of 60 participants. After data cleaning and removing participants with poor compliance, we retained 50 participants. Mobile sensing data involved the accelerometer, charging status, light sensor, physical activity, screen activity, and Wi-Fi status. Instructions were given to participants to keep their smartphone charged and connected to Wi-Fi at night. We constructed 1 model for every combination of multiverse parameters to evaluate their effects on each of the outcome variables. We evaluated the statistical models by applying them to training, validation, and test sets to prevent overfitting. Results: Most models (on either of the outcome variables) were not informative on the validation set (ie, predicted R2?0). However, our best models achieved R2 values of 0.658, 0.779, and 0.074 for SSQ, NA, and depression, respectively on the training set and R2 values of 0.348, 0.103, and 0.025, respectively on the test set. Conclusions: The approach demonstrated in this paper has shown that different choices (eg, preprocessing choices, various statistical models, different features) lead to vastly different results that are bad and relatively good as well. Nevertheless, there were some promising results, particularly for SSQ, which warrant further research on this topic. UR - https://www.jmir.org/2022/3/e25643 UR - http://dx.doi.org/10.2196/25643 UR - http://www.ncbi.nlm.nih.gov/pubmed/35302502 ID - info:doi/10.2196/25643 ER - TY - JOUR AU - Zirikly, Ayah AU - Desmet, Bart AU - Newman-Griffis, Denis AU - Marfeo, E. Elizabeth AU - McDonough, Christine AU - Goldman, Howard AU - Chan, Leighton PY - 2022/3/18 TI - Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case JO - JMIR Med Inform SP - e32245 VL - 10 IS - 3 KW - natural language processing KW - text mining KW - bioinformatics KW - health informatics KW - machine learning KW - disability KW - mental health KW - functioning KW - NLP KW - electronic health record KW - framework KW - EHR KW - automation KW - eHealth KW - decision support KW - functional status KW - whole-person function UR - https://medinform.jmir.org/2022/3/e32245 UR - http://dx.doi.org/10.2196/32245 UR - http://www.ncbi.nlm.nih.gov/pubmed/35302510 ID - info:doi/10.2196/32245 ER - TY - JOUR AU - Haucke, Matthias AU - Heinz, Andreas AU - Liu, Shuyan AU - Heinzel, Stephan PY - 2022/3/17 TI - The Impact of COVID-19 Lockdown on Daily Activities, Cognitions, and Stress in a Lonely and Distressed Population: Temporal Dynamic Network Analysis JO - J Med Internet Res SP - e32598 VL - 24 IS - 3 KW - COVID-19 KW - mental health KW - outbreak KW - epidemic KW - pandemic KW - psychological response KW - emotional well-being KW - ecological momentary assessment KW - risk KW - protective factors KW - lockdown measures KW - loneliness KW - mood inertia KW - stressors KW - mobile apps KW - mHealth KW - digital health KW - EMA KW - smartphone apps KW - network model KW - cognition KW - stress KW - temporal dynamic network KW - permutation testing KW - network comparison KW - network characteristics KW - multilevel vector autoregressive model KW - mlVAR N2 - Background: The COVID-19 pandemic and its associated lockdown measures impacted mental health worldwide. However, the temporal dynamics of causal factors that modulate mental health during lockdown are not well understood. Objective: We aimed to understand how a COVID-19 lockdown changes the temporal dynamics of loneliness and other factors affecting mental health. This is the first study that compares network characteristics between lockdown stages to prioritize mental health intervention targets. Methods: We combined ecological momentary assessments with wrist-worn motion tracking to investigate the mechanism and changes in network centrality of symptoms and behaviors before and during lockdown. A total of 258 participants who reported at least mild loneliness and distress were assessed 8 times a day for 7 consecutive days over a 213-day period from August 8, 2020, through March 9, 2021, in Germany, covering a ?no-lockdown? and a ?lockdown? stage. COVID-19?related worry, information-seeking, perceived restriction, and loneliness were assessed by digital visual analog scales ranging from 0 to 100. Social activity was assessed on a 7-point Likert scale, while physical activity was recorded from wrist-worn actigraphy devices. Results: We built a multilevel vector autoregressive model to estimate dynamic networks. To compare network characteristics between a no-lockdown stage and a lockdown stage, we performed permutation tests. During lockdown, loneliness had the highest impact within the network, as indicated by its centrality index (ie, an index to identify variables that have a strong influence on the other variables). Moreover, during lockdown, the centrality of loneliness significantly increased. Physical activity contributed to a decrease in loneliness amid the lockdown stage. Conclusions: The COVID-19 lockdown increased the central role of loneliness in triggering stress-related behaviors and cognition. Our study indicates that loneliness should be prioritized in mental health interventions during lockdown. Moreover, physical activity can serve as a buffer for loneliness amid social restrictions. UR - https://www.jmir.org/2022/3/e32598 UR - http://dx.doi.org/10.2196/32598 UR - http://www.ncbi.nlm.nih.gov/pubmed/35191843 ID - info:doi/10.2196/32598 ER - TY - JOUR AU - Gliske, Kate AU - Welsh, W. Justine AU - Braughton, E. Jacqueline AU - Waller, A. Lance AU - Ngo, M. Quyen PY - 2022/3/14 TI - Telehealth Services for Substance Use Disorders During the COVID-19 Pandemic: Longitudinal Assessment of Intensive Outpatient Programming and Data Collection Practices JO - JMIR Ment Health SP - e36263 VL - 9 IS - 3 KW - telehealth KW - substance use disorder KW - COVID-19 KW - substance use treatment KW - feasibility study KW - routine outcome monitoring data KW - mental health KW - addiction KW - digital health KW - telemedicine KW - outpatient program KW - virtual health KW - addiction treatment KW - virtual care KW - patient outcomes N2 - Background: The onset of the COVID-19 pandemic necessitated the rapid transition of many types of substance use disorder (SUD) treatments to telehealth formats, despite limited information about what makes treatment effective in this novel format. Objective: This study aims to examine the feasibility and effectiveness of virtual intensive outpatient programming (IOP) treatment for SUD in the context of a global pandemic, while considering the unique challenges posed to data collection during an unprecedented public health crisis. Methods: The study is based on a longitudinal study with a baseline sample of 3642 patients who enrolled in intensive outpatient addiction treatment (in-person, hybrid, or virtual care) from January 2020 to March 2021 at a large substance use treatment center in the United States. The analytical sample consisted of patients who completed the 3-month postdischarge outcome survey as part of routine outcome monitoring (n=1060, 29.1% response rate). Results: No significant differences were detected by delivery format in continuous abstinence (?22=0.4, P=.81), overall quality of life (F2,826=2.06, P=.13), financial well-being (F2,767=2.30, P=.10), psychological well-being (F2,918=0.72, P=.49), and confidence in one?s ability to stay sober (F2,941=0.21, P=.81). Individuals in hybrid programming were more likely to report a higher level of general health than those in virtual IOP (F2,917=4.19, P=.01). Conclusions: Virtual outpatient care for the treatment of SUD is a feasible alternative to in-person-only programming, leading to similar self-reported outcomes at 3 months postdischarge. Given the many obstacles presented throughout data collection during a pandemic, further research is needed to better understand under what conditions telehealth is an acceptable alternative to in-person care. UR - https://mental.jmir.org/2022/3/e36263 UR - http://dx.doi.org/10.2196/36263 UR - http://www.ncbi.nlm.nih.gov/pubmed/35285807 ID - info:doi/10.2196/36263 ER - TY - JOUR AU - Zhang, Yuezhou AU - Folarin, A. Amos AU - Sun, Shaoxiong AU - Cummins, Nicholas AU - Vairavan, Srinivasan AU - Bendayan, Rebecca AU - Ranjan, Yatharth AU - Rashid, Zulqarnain AU - Conde, Pauline AU - Stewart, Callum AU - Laiou, Petroula AU - Sankesara, Heet AU - Matcham, Faith AU - White, M. Katie AU - Oetzmann, Carolin AU - Ivan, Alina AU - Lamers, Femke AU - Siddi, Sara AU - Vilella, Elisabet AU - Simblett, Sara AU - Rintala, Aki AU - Bruce, Stuart AU - Mohr, C. David AU - Myin-Germeys, Inez AU - Wykes, Til AU - Haro, Maria Josep AU - Penninx, WJH Brenda AU - Narayan, A. Vaibhav AU - Annas, Peter AU - Hotopf, Matthew AU - Dobson, JB Richard AU - PY - 2022/3/11 TI - Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study JO - JMIR Ment Health SP - e34898 VL - 9 IS - 3 KW - depression KW - mobile health KW - location data KW - mobility KW - dynamic structural equation modeling KW - mHealth KW - mental health KW - medical informatics KW - modeling N2 - Background: The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. Objective: We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. Methods: Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse?Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants? location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. Results: This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (?=0.09, P=.01), Location Entropy (time distribution on different locations) (?=?0.04, P=.02), and Residential Location Count (reflecting traveling) (?=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (?=?0.07, P<.001) the subsequent periodicity of mobility. Conclusions: Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings. UR - https://mental.jmir.org/2022/3/e34898 UR - http://dx.doi.org/10.2196/34898 UR - http://www.ncbi.nlm.nih.gov/pubmed/35275087 ID - info:doi/10.2196/34898 ER - TY - JOUR AU - Bentley, H. Kate AU - Zuromski, L. Kelly AU - Fortgang, G. Rebecca AU - Madsen, M. Emily AU - Kessler, Daniel AU - Lee, Hyunjoon AU - Nock, K. Matthew AU - Reis, Y. Ben AU - Castro, M. Victor AU - Smoller, W. Jordan PY - 2022/3/11 TI - Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers JO - JMIR Form Res SP - e30946 VL - 6 IS - 3 KW - suicide KW - machine learning KW - implementation KW - mobile phone N2 - Background: Interest in developing machine learning models that use electronic health record data to predict patients? risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk?prediction models useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders, including the frontline providers who will be using such tools, at each stage of the implementation process. Objective: The aim of this focus group study is to inform ongoing and future efforts to deploy suicide risk?prediction models in clinical practice. The specific goals are to better understand hospital providers? current practices for assessing and managing suicide risk; determine providers? perspectives on using automated suicide risk?prediction models in practice; and identify barriers, facilitators, recommendations, and factors to consider. Methods: We conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by 2 independent study staff members. All coded text was reviewed and discrepancies were resolved in consensus meetings with doctoral-level staff. Results: Although most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers? general attitudes toward the practical use of automated suicide risk?prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the health care system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider training. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings. Conclusions: Providers were dissatisfied with current suicide risk assessment methods and were open to the use of a machine learning?based risk-prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of these new approaches in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk. UR - https://formative.jmir.org/2022/3/e30946 UR - http://dx.doi.org/10.2196/30946 UR - http://www.ncbi.nlm.nih.gov/pubmed/35275075 ID - info:doi/10.2196/30946 ER - TY - JOUR AU - Schriger, H. Simone AU - Klein, R. Melanie AU - Last, S. Briana AU - Fernandez-Marcote, Sara AU - Dallard, Natalie AU - Jones, Bryanna AU - Beidas, S. Rinad PY - 2022/3/3 TI - Community Mental Health Clinicians? Perspectives on Telehealth During the COVID-19 Pandemic: Mixed Methods Study JO - JMIR Pediatr Parent SP - e29250 VL - 5 IS - 1 KW - telehealth KW - COVID-19 KW - evidence-based practice KW - community mental health KW - trauma-focused cognitive behavioral therapy KW - implementation science KW - youth mental health N2 - Background: In March 2020, a rapid shift to telehealth occurred in community mental health settings in response to the need for physical distancing to decrease transmission of the virus causing COVID-19. Whereas treatment delivered over telehealth was previously utilized sparingly in community settings, it quickly became the primary mode of treatment delivery for the vast majority of clinicians, many of whom had little time to prepare for this shift and limited to no experience using telehealth. Little is known about community mental health clinicians? experiences using telehealth. Although telehealth may make mental health treatment more accessible for some clients, it may create additional barriers for others given the high rates of poverty among individuals seeking treatment from community mental health centers. Objective: We examined community mental health clinicians? perspectives on using telehealth to deliver trauma-focused cognitive behavioral therapy to youth. We sought to better understand the acceptability of using telehealth, as well as barriers and facilitators to usage. Methods: We surveyed 45 clinicians across 15 community clinics in Philadelphia. Clinicians rated their satisfaction with telehealth using a quantitative scale and shared their perspectives on telehealth in response to open-ended questions. Therapists? responses were coded using an open-coding approach wherein coders generated domains, themes, and subthemes. Results: Clinicians rated telehealth relatively positively on the quantitative survey, expressing overall satisfaction with their current use of telehealth during the pandemic, and endorsing telehealth as a helpful mode of connecting with clients. Responses to open-ended questions fell into five domains. Clinicians noted that (1) telehealth affects the content (ie, what is discussed) and process (ie, how it is discussed) of therapy; (2) telehealth alters engagement, retention, and attendance; (3) technology is a crucial component of utilizing telehealth; (4) training, resources, and support are needed to facilitate telehealth usage; and (5) the barriers, facilitators, and level of acceptability of telehealth differ across individual clinicians and clients. Conclusions: First, telehealth is likely a better fit for some clients and clinicians than others, and attention should be given to better understanding who is most likely to succeed using this modality. Second, although telehealth increased convenience and accessibility of treatment, clinicians noted that across the board, it was difficult to engage clients (eg, young clients were easily distracted), and further work is needed to identify better telehealth engagement strategies. Third, for many clients, the telehealth modality may actually create an additional barrier to care, as children from families living in poverty may not have the requisite devices or quality broadband connection to make telehealth workable. Better strategies to address disparities in access to and quality of digital technologies are needed to render telehealth an equitable option for all youth seeking mental health services. UR - https://pediatrics.jmir.org/2022/1/e29250 UR - http://dx.doi.org/10.2196/29250 UR - http://www.ncbi.nlm.nih.gov/pubmed/35023839 ID - info:doi/10.2196/29250 ER - TY - JOUR AU - Teague, J. Samantha AU - Shatte, R. Adrian B. AU - Weller, Emmelyn AU - Fuller-Tyszkiewicz, Matthew AU - Hutchinson, M. Delyse PY - 2022/2/28 TI - Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review JO - JMIR Ment Health SP - e33058 VL - 9 IS - 2 KW - social media KW - SNS KW - mental health KW - disaster KW - big data KW - digital psychiatry N2 - Background: With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. Objective: This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. Methods: A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. Results: Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. Conclusions: The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media?based disaster mental health monitoring, such as combining surveillance with social media?based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters. UR - https://mental.jmir.org/2022/2/e33058 UR - http://dx.doi.org/10.2196/33058 UR - http://www.ncbi.nlm.nih.gov/pubmed/35225815 ID - info:doi/10.2196/33058 ER - TY - JOUR AU - Schroeder, H. Alexandra AU - Bogie, M. Bryce J. AU - Rahman, T. Tabassum AU - Thérond, Alexandra AU - Matheson, Hannah AU - Guimond, Synthia PY - 2022/2/18 TI - Feasibility and Efficacy of Virtual Reality Interventions to Improve Psychosocial Functioning in Psychosis: Systematic Review JO - JMIR Ment Health SP - e28502 VL - 9 IS - 2 KW - auditory verbal hallucinations KW - cognitive remediation KW - functional outcomes KW - neurocognition KW - paranoia KW - psychosis KW - schizophrenia KW - social skills KW - virtual reality (VR) KW - vocational skills N2 - Background: Functional recovery in psychosis remains a challenge despite current evidence-based treatment approaches. To address this problem, innovative interventions using virtual reality (VR) have recently been developed. VR technologies have enabled the development of realistic environments in which individuals with psychosis can receive psychosocial treatment interventions in more ecological settings than traditional clinics. These interventions may therefore increase the transfer of learned psychosocial skills to real-world environments, thereby promoting long-term functional recovery. However, the overall feasibility and efficacy of such interventions within the psychosis population remain unclear. Objective: This systematic review aims to investigate whether VR-based psychosocial interventions are feasible and enjoyable for individuals with psychosis, synthesize current evidence on the efficacy of VR-based psychosocial interventions for psychosis, and identify the limitations in the current literature to guide future research. Methods: This research followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Literature searches were conducted in PubMed and PsycINFO in May 2021. We searched for peer-reviewed English articles that used a psychosocial intervention with a VR component. Participants in the included studies were diagnosed with schizophrenia, schizoaffective disorder, or another psychotic disorder. The included studies were divided into four categories as follows: cognitive remediation interventions, social skills interventions, vocational skills interventions, and auditory verbal hallucinations and paranoia interventions. The risk of bias assessment was performed for each study. Results: A total of 18 studies were included in this systematic review. Of these 18 studies, 4 (22%) studies used a cognitive remediation intervention, 4 (22%) studies used a social skills intervention, 3 (17%) studies used a vocational skills intervention, and 7 (39%) studies implemented an intervention aimed at improving auditory verbal hallucinations or paranoia. A total of 745 individuals with psychosis were included in the study. All the studies that evaluated feasibility showed that VR-based psychosocial interventions were feasible and enjoyable for individuals with psychosis. The preliminary evidence on efficacy included in this review suggests that VR-based psychosocial interventions can improve cognitive, social, and vocational skills in individuals with psychosis. VR-based interventions may also improve the symptoms of auditory verbal hallucinations and paranoia. The skills that participants learned through these interventions were durable, transferred into real-world environments, and led to improved functional outcomes, such as autonomy, managing housework, and work performance. Conclusions: VR-based interventions may represent a novel and efficacious approach for improving psychosocial functioning in psychosis. Therefore, VR-based psychosocial interventions represent a promising adjunctive therapy for the treatment of psychosis, which may be used to improve psychosocial skills, community functioning, and quality of life. UR - https://mental.jmir.org/2022/2/e28502 UR - http://dx.doi.org/10.2196/28502 UR - http://www.ncbi.nlm.nih.gov/pubmed/35179501 ID - info:doi/10.2196/28502 ER - TY - JOUR AU - Myers, Rae Jennifer AU - Glenn, M. Jordan AU - Madero, N. Erica AU - Anderson, John AU - Mak-McCully, Rachel AU - Gray, Michelle AU - Gills, L. Joshua AU - Harrison, E. John PY - 2022/2/18 TI - Asynchronous Remote Assessment for Cognitive Impairment: Reliability Verification of the Neurotrack Cognitive Battery JO - JMIR Form Res SP - e34237 VL - 6 IS - 2 KW - cognition KW - screening KW - remote testing KW - psychometric KW - challenge KW - validation KW - assessment KW - impairment KW - access KW - reliability KW - stability KW - testing KW - utility N2 - Background: As evidenced by the further reduction in access to testing during the COVID-19 pandemic, there is an urgent, growing need for remote cognitive assessment for individuals with cognitive impairment. The Neurotrack Cognitive Battery (NCB), our response to this need, was evaluated for its temporal reliability and stability as part of ongoing validation testing. Objective: The aim of this study is to assess the temporal reliability of the NCB tests (5 total) across a 1-week period and to determine the temporal stability of these measures across 3 consecutive administrations in a single day. Methods: For test-retest reliability, a range of 29-66 cognitively healthy participants (ages 18-68 years) completed each cognitive assessment twice, 1 week apart. In a separate study, temporal stability was assessed using data collected from 31 different cognitively healthy participants at 3 consecutive timepoints in a single day. Results: Correlations for the assessments were between 0.72 and 0.83, exceeding the standard acceptable threshold of 0.70 for temporal reliability. Intraclass correlations ranged from 0.60 to 0.84, indicating moderate to good temporal stability. Conclusions: These results highlight the NCB as a brief, easy-to-administer, and reliable assessment for remote cognitive testing. Additional validation research is underway to determine the full magnitude of the clinical utility of the NCB. UR - https://formative.jmir.org/2022/2/e34237 UR - http://dx.doi.org/10.2196/34237 UR - http://www.ncbi.nlm.nih.gov/pubmed/35179511 ID - info:doi/10.2196/34237 ER - TY - JOUR AU - Mendes, M. Jean P. AU - Moura, R. Ivan AU - Van de Ven, Pepijn AU - Viana, Davi AU - Silva, S. Francisco J. AU - Coutinho, R. Luciano AU - Teixeira, Silmar AU - Rodrigues, C. Joel J. P. AU - Teles, Soares Ariel PY - 2022/2/17 TI - Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review JO - J Med Internet Res SP - e28735 VL - 24 IS - 2 KW - mental health KW - digital phenotyping KW - sensing apps KW - data sets KW - sensor data N2 - Background: Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients? interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies. Objective: This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective. Methods: We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends. Results: A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal. Conclusions: This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings. UR - https://www.jmir.org/2022/2/e28735 UR - http://dx.doi.org/10.2196/28735 UR - http://www.ncbi.nlm.nih.gov/pubmed/35175202 ID - info:doi/10.2196/28735 ER - TY - JOUR AU - Wang, Hsin-Yi AU - Cheng, Cecilia PY - 2022/2/17 TI - The Associations Between Gaming Motivation and Internet Gaming Disorder: Systematic Review and Meta-analysis JO - JMIR Ment Health SP - e23700 VL - 9 IS - 2 KW - gaming motivation KW - problematic gaming KW - gaming disorder KW - video gaming KW - online gaming KW - compulsive gaming KW - escapism KW - culture KW - cross-cultural comparison, cultural individualism N2 - Background: There has been a surge in interest in examining internet gaming disorder (IGD) and its associations with gaming motivation. Three broad components of gaming motivation have been proposed: achievement, immersion, and social. Achievement-oriented players are motivated by gaining in-game rewards, immersion-oriented players are motivated by the experience of immersion in the virtual world, and social-oriented players are motivated by the need to socialize with other players through gaming. Objective: This study aimed to (1) quantitatively synthesize the growing body of literature to systematically examine the discrepancies in the magnitude of associations between various components of gaming motivation and IGD and (2) examine the moderating role of cultural dimension on the association between escapism gaming motivation and IGD. Methods: We conducted a systematic search of multiple databases between 2002 and 2020. Studies were included if they (1) included quantitative data, (2) used measures assessing both gaming motivation and IGD, and (3) contained sufficient information for effect size calculation. Results: The findings revealed IGD to have a stronger association with achievement motivation (r=0.32) than with immersion (r=0.22) or social motivation (r=0.20), but the strongest such association was found to be with escapism motivation (r=0.40), a subcomponent of immersion motivation. Our cross-cultural comparison further showed a stronger association between escapism motivation and IGD in studies conducted in individualistic (vs collectivistic) regions. Conclusions: This meta-analysis highlights the importance of acknowledging the discrepancies among different components of gaming motivation with respect to their role in the development of IGD, as well as the potential cultural variations in the strength of such associations. UR - https://mental.jmir.org/2022/2/e23700 UR - http://dx.doi.org/10.2196/23700 UR - http://www.ncbi.nlm.nih.gov/pubmed/35175204 ID - info:doi/10.2196/23700 ER - TY - JOUR AU - Carlier, Chiara AU - Niemeijer, Koen AU - Mestdagh, Merijn AU - Bauwens, Michael AU - Vanbrabant, Peter AU - Geurts, Luc AU - van Waterschoot, Toon AU - Kuppens, Peter PY - 2022/2/11 TI - In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study JO - JMIR Ment Health SP - e31724 VL - 9 IS - 2 KW - depression KW - emotions KW - mobile sensing KW - language KW - LIWC KW - openSMILE KW - speech KW - writing KW - mobile phone N2 - Background: Emotions and mood are important for overall well-being. Therefore, the search for continuous, effortless emotion prediction methods is an important field of study. Mobile sensing provides a promising tool and can capture one of the most telling signs of emotion: language. Objective: The aim of this study is to examine the separate and combined predictive value of mobile-sensed language data sources for detecting both momentary emotional experience as well as global individual differences in emotional traits and depression. Methods: In a 2-week experience sampling method study, we collected self-reported emotion ratings and voice recordings 10 times a day, continuous keyboard activity, and trait depression severity. We correlated state and trait emotions and depression and language, distinguishing between speech content (spoken words), speech form (voice acoustics), writing content (written words), and writing form (typing dynamics). We also investigated how well these features predicted state and trait emotions using cross-validation to select features and a hold-out set for validation. Results: Overall, the reported emotions and mobile-sensed language demonstrated weak correlations. The most significant correlations were found between speech content and state emotions and between speech form and state emotions, ranging up to 0.25. Speech content provided the best predictions for state emotions. None of the trait emotion?language correlations remained significant after correction. Among the emotions studied, valence and happiness displayed the most significant correlations and the highest predictive performance. Conclusions: Although using mobile-sensed language as an emotion marker shows some promise, correlations and predictive R2 values are low. UR - https://mental.jmir.org/2022/2/e31724 UR - http://dx.doi.org/10.2196/31724 UR - http://www.ncbi.nlm.nih.gov/pubmed/35147507 ID - info:doi/10.2196/31724 ER - TY - JOUR AU - Lynham, Joanne Amy AU - Jones, R. Ian AU - Walters, R. James T. PY - 2022/2/10 TI - Web-Based Cognitive Testing in Psychiatric Research: Validation and Usability Study JO - J Med Internet Res SP - e28233 VL - 24 IS - 2 KW - cognition KW - mental health KW - online KW - digital KW - assessment KW - validation KW - memory KW - attention KW - mobile phone N2 - Background: Cognitive impairments are features of many psychiatric disorders and affect functioning. A barrier to cognitive research on psychiatric disorders is the lack of large cross-disorder data sets. However, the collection of cognitive data can be logistically challenging and expensive. Web-based collection may be an alternative; however, little is known about who does and does not complete web-based cognitive assessments for psychiatric research. Objective: The aims of this study are to develop a web-based cognitive battery for use in psychiatric research, validate the battery against the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery, and compare the characteristics of the participants who chose to take part with those of the individuals who did not participate. Methods: Tasks were developed by The Many Brains Project and selected to measure the domains specified by the MATRICS initiative. We undertook a cross-validation study of 65 participants with schizophrenia, bipolar disorder, depression, or no history of psychiatric disorders to compare the web-based tasks with the MATRICS Consensus Cognitive Battery. Following validation, we invited participants from 2 large ongoing genetic studies, which recruited participants with psychiatric disorders to complete the battery and evaluated the demographic and clinical characteristics of those who took part. Results: Correlations between web-based and MATRICS tasks ranged between 0.26 and 0.73. Of the 961 participants, 887 (92.3%) completed at least one web-based task, and 644 (67%) completed all tasks, indicating adequate completion rates. Predictors of web-based participation included being female (odds ratio [OR] 1.3, 95% CI 1.07-1.58), ethnicity other than White European (OR 0.66, 95% CI 0.46-0.96), higher levels of education (OR 1.19, 95% CI 1.11-1.29), diagnosis of an eating disorder (OR 2.17, 95% CI 1.17-4) or depression and anxiety (OR 5.12, 95% CI 3.38-7.83), and absence of a diagnosis of schizophrenia (OR 0.59, 95% CI 0.35-0.94). Lower performance on the battery was associated with poorer functioning (B=?1.76, SE 0.26; P<.001). Conclusions: Our findings offer valuable insights into the advantages and disadvantages of testing cognitive function remotely for mental health research. UR - https://www.jmir.org/2022/2/e28233 UR - http://dx.doi.org/10.2196/28233 UR - http://www.ncbi.nlm.nih.gov/pubmed/35142640 ID - info:doi/10.2196/28233 ER - TY - JOUR AU - van Loenen, Inge AU - Scholten, Willemijn AU - Muntingh, Anna AU - Smit, Johannes AU - Batelaan, Neeltje PY - 2022/2/10 TI - The Effectiveness of Virtual Reality Exposure?Based Cognitive Behavioral Therapy for Severe Anxiety Disorders, Obsessive-Compulsive Disorder, and Posttraumatic Stress Disorder: Meta-analysis JO - J Med Internet Res SP - e26736 VL - 24 IS - 2 KW - anxiety disorders KW - virtual reality KW - virtual reality exposure therapy KW - cognitive behavioral therapy KW - meta-analysis KW - mobile phone N2 - Background: In recent years, virtual reality exposure?based cognitive behavioral therapy (VRE-CBT) has shown good treatment results in (subclinical) anxiety disorders and seems to be a good alternative to exposure in vivo in regular cognitive behavioral therapy (CBT). However, previous meta-analyses on the efficacy of VRE-CBT on anxiety disorders have included studies on specific phobias and subthreshold anxiety; therefore, these results may not be generalizable to patients with more severe and disabling anxiety disorders. Objective: The objective of our study is to determine the efficacy of VRE-CBT on more severe anxiety disorders, excluding specific phobias and subthreshold anxiety disorders. Meta-analyses will be conducted to examine the efficacy of VRE-CBT versus waitlist and regular CBT. Our secondary objectives are to examine whether the efficacy differs according to the type of anxiety disorder, type of recruitment, and type of VRE-CBT (virtual reality exposure either with or without regular CBT). Furthermore, attrition in VRE-CBT and CBT will be compared. Methods: Studies published until August 20, 2020, were retrieved through systematic literature searches in PubMed, PsycINFO, and Embase. We calculated the effect sizes (Hedges g) for the difference between the conditions and their 95% CIs for posttest and follow-up measurements in a random effects model. A separate meta-analysis was performed to compare attrition between the VRE-CBT and CBT conditions. Results: A total of 16 trials with 817 participants were included. We identified 10 comparisons between VRE-CBT and a waitlist condition and 13 comparisons between VRE-CBT and a CBT condition. With regard to risk of bias, information on random sequence generation, allocation concealment, and risk of bias for selective outcome reporting was often absent or unclear. The mean effect size of VRE-CBT compared with waitlist (nco=10) was medium and significant, favoring VRE-CBT (Hedges g=?0.490, 95% CI ?0.82 to ?0.16; P=.003). The mean effect size of VRE-CBT compared with CBT (nco=13) was small and nonsignificant, favoring CBT (Hedges g=0.083, 95% CI ?0.13 to 0.30; P=.45). The dropout rates between VRE-CBT and CBT (nco=10) showed no significant difference (odds ratio 0.79, 95% CI 0.49-1.27; P=.32). There were no indications of small study effects or publication bias. Conclusions: The results of our study show that VRE-CBT is more effective than waitlist and as effective as CBT in the treatment of more severe anxiety disorders. Therefore, VRE-CBT may be considered a promising alternative to CBT for patients with more severe anxiety disorders. Higher-quality randomized controlled trials are needed to verify the robustness of these findings. UR - https://www.jmir.org/2022/2/e26736 UR - http://dx.doi.org/10.2196/26736 UR - http://www.ncbi.nlm.nih.gov/pubmed/35142632 ID - info:doi/10.2196/26736 ER - TY - JOUR AU - Shen, X. Francis AU - Silverman, C. Benjamin AU - Monette, Patrick AU - Kimble, Sara AU - Rauch, L. Scott AU - Baker, T. Justin PY - 2022/2/9 TI - An Ethics Checklist for Digital Health Research in Psychiatry: Viewpoint JO - J Med Internet Res SP - e31146 VL - 24 IS - 2 KW - digital phenotyping KW - computataional psychiatry KW - ethics KW - law KW - privacy KW - informed consent N2 - Background: Psychiatry has long needed a better and more scalable way to capture the dynamics of behavior and its disturbances, quantitatively across multiple data channels, at high temporal resolution in real time. By combining 24/7 data?on location, movement, email and text communications, and social media?with brain scans, genetics, genomics, neuropsychological batteries, and clinical interviews, researchers will have an unprecedented amount of objective, individual-level data. Analyzing these data with ever-evolving artificial intelligence could one day include bringing interventions to patients where they are in the real world in a convenient, efficient, effective, and timely way. Yet, the road to this innovative future is fraught with ethical dilemmas as well as ethical, legal, and social implications (ELSI). Objective: The goal of the Ethics Checklist is to promote careful design and execution of research. It is not meant to mandate particular research designs; indeed, at this early stage and without consensus guidance, there are a range of reasonable choices researchers may make. However, the checklist is meant to make those ethical choices explicit, and to require researchers to give reasons for their decisions related to ELSI issues. The Ethics Checklist is primarily focused on procedural safeguards, such as consulting with experts outside the research group and documenting standard operating procedures for clearly actionable data (eg, expressed suicidality) within written research protocols. Methods: We explored the ELSI of digital health research in psychiatry, with a particular focus on what we label ?deep phenotyping? psychiatric research, which combines the potential for virtually boundless data collection and increasingly sophisticated techniques to analyze those data. We convened an interdisciplinary expert stakeholder workshop in May 2020, and this checklist emerges out of that dialogue. Results: Consistent with recent ELSI analyses, we find that existing ethical guidance and legal regulations are not sufficient for deep phenotyping research in psychiatry. At present, there are regulatory gaps, inconsistencies across research teams in ethics protocols, and a lack of consensus among institutional review boards on when and how deep phenotyping research should proceed. We thus developed a new instrument, an Ethics Checklist for Digital Health Research in Psychiatry (?the Ethics Checklist?). The Ethics Checklist is composed of 20 key questions, subdivided into 6 interrelated domains: (1) informed consent; (2) equity, diversity, and access; (3) privacy and partnerships; (4) regulation and law; (5) return of results; and (6) duty to warn and duty to report. Conclusions: Deep phenotyping research offers a vision for vastly more effective care for people with, or at risk for, psychiatric disease. The potential perils en route to realizing this vision are significant; however, and researchers must be willing to address the questions in the Ethics Checklist before embarking on each leg of the journey. UR - https://www.jmir.org/2022/2/e31146 UR - http://dx.doi.org/10.2196/31146 UR - http://www.ncbi.nlm.nih.gov/pubmed/35138261 ID - info:doi/10.2196/31146 ER - TY - JOUR AU - Drissi, Nidal AU - Ouhbi, Sofia AU - Amiri, Leena AU - Al Mugaddam, Fadwa AU - Jan, K. Reem AU - Isomursu, Minna PY - 2022/2/7 TI - A Conceptual Framework to Design Connected Mental Health Solutions in the United Arab Emirates: Questionnaire Study JO - JMIR Form Res SP - e27675 VL - 6 IS - 2 KW - mental health KW - digital health KW - eHealth KW - connected health KW - mHealth KW - perceptions KW - attitudes KW - framework KW - design KW - UAE KW - mental health care professionals KW - Arab culture N2 - Background: Connected mental health (CMH) is a field presenting information and communications technology?based mental care interventions that could help overcome many mental care delivery barriers. Culture and background influence people?s attitudes, preferences, and acceptance of such solutions. Therefore, the suitability of CMH solutions to the targeted population is an important factor in their successful adoption. Objective: The aim of this study is to develop a framework for the design and creation of CMH solutions suitable for the UAE context. The framework is based on investigating enablers and barriers of CMH adoption in the United Arab Emirates, from the mental health professional's (MHP) perspective and from related literature. Methods: A survey of literature on relevant studies addressing the use of technology for mental care in Arab countries, and a web-based questionnaire-based survey with 17 MHPs practicing in the United Arab Emirates investigating their attitudes and views toward CMH was conducted. Results from the questionnaire and from related studies were analyzed to develop the design framework. Results: On the basis of findings from the literature survey and analyzing MHP answers to the web-based survey, a framework for the design of CMH solutions for the UAE population was developed. The framework presents four types of recommendation categories: favorable criteria, which included blended care, anonymity, and ease of use; cultural factors including availability in multiple languages, mainly Arabic and English, in addition to religious and cultural considerations; technical considerations, including good-quality communication, availability in formats compatible with mobile phones, and providing technical support; and users? health and data safety considerations, including users? suitability testing, confidentiality, and ensuring MHP integrity. Conclusions: CMH has the potential to help overcome many mental care barriers in the United Arab Emirates in particular and in the Arab world in general. CMH adoption in the United Arab Emirates has a potential for success. However, many factors should be taken into account, mainly cultural, religious, and linguistic aspects. UR - https://formative.jmir.org/2022/2/e27675 UR - http://dx.doi.org/10.2196/27675 UR - http://www.ncbi.nlm.nih.gov/pubmed/35129458 ID - info:doi/10.2196/27675 ER - TY - JOUR AU - Vlake, H. Johan AU - van Bommel, Jasper AU - Wils, Evert-Jan AU - Bienvenu, Joe AU - Hellemons, E. Merel AU - Korevaar, IM Tim AU - Schut, FC Anna AU - Labout, AM Joost AU - Schreuder, LH Lois AU - van Bavel, P. Marten AU - Gommers, Diederik AU - van Genderen, E. Michel PY - 2022/1/31 TI - Intensive Care Unit?Specific Virtual Reality for Critically Ill Patients With COVID-19: Multicenter Randomized Controlled Trial JO - J Med Internet Res SP - e32368 VL - 24 IS - 1 KW - SARS-CoV-2 KW - intensive care KW - post-intensive care syndrome KW - virtual reality KW - quality of life KW - satisfaction KW - COVID-19 N2 - Background: Although psychological sequelae after intensive care unit (ICU) treatment are considered quite intrusive, robustly effective interventions to treat or prevent these long-term sequelae are lacking. Recently, it was demonstrated that ICU-specific virtual reality (ICU-VR) is a feasible and acceptable intervention with potential mental health benefits. However, its effect on mental health and ICU aftercare in COVID-19 ICU survivors is unknown. Objective: This study aimed to explore the effects of ICU-VR on mental health and on patients? perceived quality of, satisfaction with, and rating of ICU aftercare among COVID-19 ICU survivors. Methods: This was a multicenter randomized controlled trial. Patients were randomized to either the ICU-VR (intervention) or the control group. All patients were invited to an COVID-19 post-ICU follow-up clinic 3 months after hospital discharge, during which patients in the intervention group received ICU-VR. One month and 3 months later (4 and 6 months after hospital discharge), mental health, quality of life, perceived quality, satisfaction with, and rating of ICU aftercare were scored using questionnaires. Results: Eighty-nine patients (median age 58 years; 63 males, 70%) were included. The prevalence and severity of psychological distress were limited throughout follow-up, and no differences in psychological distress or quality of life were observed between the groups. ICU-VR improved satisfaction with (mean score 8.7, SD 1.6 vs 7.6, SD 1.6 [ICU-VR vs control]; t64=?2.82, P=.006) and overall rating of ICU aftercare (mean overall rating of aftercare 8.9, SD 0.9 vs 7.8, SD 1.7 [ICU-VR vs control]; t64=?3.25; P=.002) compared to controls. ICU-VR added to the quality of ICU aftercare according to 81% of the patients, and all patients would recommend ICU-VR to other ICU survivors. Conclusions: ICU-VR is a feasible and acceptable innovative method to improve satisfaction with and rating of ICU aftercare and adds to its perceived quality. We observed a low prevalence of psychological distress after ICU treatment for COVID-19, and ICU-VR did not improve psychological recovery or quality of life. Future research is needed to confirm our results in other critical illness survivors to potentially facilitate ICU-VR?s widespread availability and application during follow-up. Trial Registration: Netherlands Trial Register NL8835; https://www.trialregister.nl/trial/8835 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-021-05271-z UR - https://www.jmir.org/2022/1/e32368 UR - http://dx.doi.org/10.2196/32368 UR - http://www.ncbi.nlm.nih.gov/pubmed/34978530 ID - info:doi/10.2196/32368 ER - TY - JOUR AU - Hennemann, Severin AU - Kuhn, Sebastian AU - Witthöft, Michael AU - Jungmann, M. Stefanie PY - 2022/1/31 TI - Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients JO - JMIR Ment Health SP - e32832 VL - 9 IS - 1 KW - mHealth KW - symptom checker KW - diagnostics KW - mental disorders KW - psychotherapy KW - mobile phone N2 - Background: Digital technologies have become a common starting point for health-related information-seeking. Web- or app-based symptom checkers aim to provide rapid and accurate condition suggestions and triage advice but have not yet been investigated for mental disorders in routine health care settings. Objective: This study aims to test the diagnostic performance of a widely available symptom checker in the context of formal diagnosis of mental disorders when compared with therapists? diagnoses based on structured clinical interviews. Methods: Adult patients from an outpatient psychotherapy clinic used the app-based symptom checker Ada?check your health (ADA; Ada Health GmbH) at intake. Accuracy was assessed as the agreement of the first and 1 of the first 5 condition suggestions of ADA with at least one of the interview-based therapist diagnoses. In addition, sensitivity, specificity, and interrater reliabilities (Gwet first-order agreement coefficient [AC1]) were calculated for the 3 most prevalent disorder categories. Self-reported usability (assessed using the System Usability Scale) and acceptance of ADA (assessed using an adapted feedback questionnaire) were evaluated. Results: A total of 49 patients (30/49, 61% women; mean age 33.41, SD 12.79 years) were included in this study. Across all patients, the interview-based diagnoses matched ADA?s first condition suggestion in 51% (25/49; 95% CI 37.5-64.4) of cases and 1 of the first 5 condition suggestions in 69% (34/49; 95% CI 55.4-80.6) of cases. Within the main disorder categories, the accuracy of ADA?s first condition suggestion was 0.82 for somatoform and associated disorders, 0.65 for affective disorders, and 0.53 for anxiety disorders. Interrater reliabilities ranged from low (AC1=0.15 for anxiety disorders) to good (AC1=0.76 for somatoform and associated disorders). The usability of ADA was rated as high in the System Usability Scale (mean 81.51, SD 11.82, score range 0-100). Approximately 71% (35/49) of participants would have preferred a face-to-face over an app-based diagnostic. Conclusions: Overall, our findings suggest that a widely available symptom checker used in the formal diagnosis of mental disorders could provide clinicians with a list of condition suggestions with moderate-to-good accuracy. However, diagnostic performance was heterogeneous between disorder categories and included low interrater reliability. Although symptom checkers have some potential to complement the diagnostic process as a screening tool, the diagnostic performance should be tested in larger samples and in comparison with further diagnostic instruments. UR - https://mental.jmir.org/2022/1/e32832 UR - http://dx.doi.org/10.2196/32832 UR - http://www.ncbi.nlm.nih.gov/pubmed/35099395 ID - info:doi/10.2196/32832 ER - TY - JOUR AU - Buck, Benjamin AU - Nguyen, Janelle AU - Porter, Shelan AU - Ben-Zeev, Dror AU - Reger, M. Greg PY - 2022/1/28 TI - FOCUS mHealth Intervention for Veterans With Serious Mental Illness in an Outpatient Department of Veterans Affairs Setting: Feasibility, Acceptability, and Usability Study JO - JMIR Ment Health SP - e26049 VL - 9 IS - 1 KW - mHealth KW - veterans KW - schizophrenia KW - serious mental illness KW - mobile phone N2 - Background: Veterans with serious mental illnesses (SMIs) face barriers to accessing in-person evidence-based interventions that improve illness management. Mobile health (mHealth) has been demonstrated to be feasible, acceptable, effective, and engaging among individuals with SMIs in community mental health settings. mHealth for SMIs has not been tested within the Department of Veterans Affairs (VA). Objective: This study examines the feasibility, acceptability, and preliminary effectiveness of an mHealth intervention for SMI in the context of VA outpatient care. Methods: A total of 17 veterans with SMIs were enrolled in a 1-month pilot trial of FOCUS, a smartphone-based self-management intervention for SMI. At baseline and posttest, they completed measures examining symptoms and functional recovery. The participants provided qualitative feedback related to the usability and acceptability of the intervention. Results: Veterans completed on an average of 85.0 (SD 96.1) interactions with FOCUS over the 1-month intervention period. They reported high satisfaction, usability, and acceptability, with nearly all participants (16/17, 94%) reporting that they would recommend the intervention to a fellow veteran. Clinicians consistently reported finding mHealth-related updates useful for informing their care. Qualitative feedback indicated that veterans thought mHealth complemented their existing VA services well and described potential opportunities to adapt FOCUS to specific subpopulations (eg, combat veterans) as well as specific delivery modalities (eg, groups). In the 1-month period, the participants experienced small improvements in self-assessed recovery, auditory hallucinations, and quality of life. Conclusions: The FOCUS mHealth intervention is feasible, acceptable, and usable among veterans. Future work should develop and examine VA-specific implementation approaches of FOCUS for this population. UR - https://mental.jmir.org/2022/1/e26049 UR - http://dx.doi.org/10.2196/26049 UR - http://www.ncbi.nlm.nih.gov/pubmed/35089151 ID - info:doi/10.2196/26049 ER - TY - JOUR AU - Freyer-Adam, Jennis AU - Baumann, Sophie AU - Bischof, Gallus AU - Staudt, Andreas AU - Goeze, Christian AU - Gaertner, Beate AU - John, Ulrich PY - 2022/1/28 TI - Social Equity in the Efficacy of Computer-Based and In-Person Brief Alcohol Interventions Among General Hospital Patients With At-Risk Alcohol Use: A Randomized Controlled Trial JO - JMIR Ment Health SP - e31712 VL - 9 IS - 1 KW - brief alcohol intervention KW - electronic KW - eHealth KW - digital KW - motivational interviewing KW - socioeconomic status KW - equity KW - social inequality KW - transtheoretical model KW - moderator KW - mental health KW - public health KW - alcohol interventions KW - digital intervention KW - digital health intervention KW - alcohol use N2 - Background: Social equity in the efficacy of behavior change intervention is much needed. While the efficacy of brief alcohol interventions (BAIs), including digital interventions, is well established, particularly in health care, the social equity of interventions has been sparsely investigated. Objective: We aim to investigate whether the efficacy of computer-based versus in-person delivered BAIs is moderated by the participants? socioeconomic status (ie, to identify whether general hospital patients with low-level education and unemployed patients may benefit more or less from one or the other way of delivery compared to patients with higher levels of education and those that are employed). Methods: Patients with nondependent at-risk alcohol use were identified through systematic offline screening conducted on 13 general hospital wards. Patients were approached face-to-face and asked to respond to an app for self-assessment provided by a mobile device. In total, 961 (81% of eligible participants) were randomized and received their allocated intervention: computer-generated and individually tailored feedback letters (CO), in-person counseling by research staff trained in motivational interviewing (PE), or assessment only (AO). CO and PE were delivered on the ward and 1 and 3 months later, were based on the transtheoretical model of intentional behavior change and required the assessment of intervention data prior to each intervention. In CO, the generation of computer-based feedback was created automatically. The assessment of data and sending out feedback letters were assisted by the research staff. Of the CO and PE participants, 89% (345/387) and 83% (292/354) received at least two doses of intervention, and 72% (280/387) and 54% (191/354) received all three doses of intervention, respectively. The outcome was change in grams of pure alcohol per day after 6, 12, 18, and 24 months, with the latter being the primary time-point of interest. Follow-up interviewers were blinded. Study group interactions with education and employment status were tested as predictors of change in alcohol use using latent growth modeling. Results: The efficacy of CO and PE did not differ by level of education (P=.98). Employment status did not moderate CO efficacy (Ps?.66). Up to month 12 and compared to employed participants, unemployed participants reported significantly greater drinking reductions following PE versus AO (incidence rate ratio 0.44, 95% CI 0.21-0.94; P=.03) and following PE versus CO (incidence rate ratio 0.48, 95% CI 0.24?0.96; P=.04). After 24 months, these differences were statistically nonsignificant (Ps?.31). Conclusions: Computer-based and in-person BAI worked equally well independent of the patient?s level of education. Although findings indicate that in the short-term, unemployed persons may benefit more from BAI when delivered in-person rather than computer-based, the findings suggest that both BAIs have the potential to work well among participants with low socioeconomic status. Trial Registration: ClinicalTrials.gov NCT01291693; https://clinicaltrials.gov/ct2/show/NCT01291693 UR - https://mental.jmir.org/2022/1/e31712 UR - http://dx.doi.org/10.2196/31712 UR - http://www.ncbi.nlm.nih.gov/pubmed/35089156 ID - info:doi/10.2196/31712 ER - TY - JOUR AU - Dunn, Taylor AU - Howlett, E. Susan AU - Stanojevic, Sanja AU - Shehzad, Aaqib AU - Stanley, Justin AU - Rockwood, Kenneth PY - 2022/1/27 TI - Patterns of Symptom Tracking by Caregivers and Patients With Dementia and Mild Cognitive Impairment: Cross-sectional Study JO - J Med Internet Res SP - e29219 VL - 24 IS - 1 KW - dementia KW - mild cognitive impairment KW - real-world evidence KW - patient-centric outcomes KW - machine learning KW - dementia stage KW - Alzheimer disease KW - symptom tracking N2 - Background: Individuals with dementia and mild cognitive impairment (MCI) experience a wide variety of symptoms and challenges that trouble them. To address this heterogeneity, numerous standardized tests are used for diagnosis and prognosis. myGoalNav Dementia is a web-based tool that allows individuals with impairments and their caregivers to identify and track outcomes of greatest importance to them, which may be a less arbitrary and more sensitive way of capturing meaningful change. Objective: We aim to explore the most frequent and important symptoms and challenges reported by caregivers and people with dementia and MCI and how this varies according to disease severity. Methods: This cross-sectional study involved 3909 web-based myGoalNav users (mostly caregivers of people with dementia or MCI) who completed symptom profiles between 2006 and 2019. To make a symptom profile, users selected their most personally meaningful or troublesome dementia-related symptoms to track over time. Users were also asked to rank their chosen symptoms from least to most important, which we called the symptom potency. As the stage of disease for these web-based users was unknown, we applied a supervised staging algorithm, previously trained on clinician-derived data, to classify each profile into 1 of 4 stages: MCI and mild, moderate, and severe dementia. Across these stages, we compared symptom tracking frequency, symptom potency, and the relationship between frequency and potency. Results: Applying the staging algorithm to the 3909 user profiles resulted in 917 (23.46%) MCI, 1596 (40.83%) mild dementia, 514 (13.15%) moderate dementia, and 882 (22.56%) severe dementia profiles. We found that the most frequent symptoms in MCI and mild dementia profiles were similar and comprised early hallmarks of dementia (eg, recent memory and language difficulty). As the stage increased to moderate and severe, the most frequent symptoms were characteristic of loss of independent function (eg, incontinence) and behavioral problems (eg, aggression). The most potent symptoms were similar between stages and generally reflected disruptions in everyday life (eg, problems with hobbies or games, travel, and looking after grandchildren). Symptom frequency was negatively correlated with potency at all stages, and the strength of this relationship increased with increasing disease severity. Conclusions: Our results emphasize the importance of patient-centricity in MCI and dementia studies and illustrate the valuable real-world evidence that can be collected with digital tools. Here, the most frequent symptoms across the stages reflected our understanding of the typical disease progression. However, the symptoms that were ranked as most personally important by users were generally among the least frequently selected. Through individualization, patient-centered instruments such as myGoalNav can complement standardized measures by capturing these infrequent but potent outcomes. UR - https://www.jmir.org/2022/1/e29219 UR - http://dx.doi.org/10.2196/29219 UR - http://www.ncbi.nlm.nih.gov/pubmed/35084341 ID - info:doi/10.2196/29219 ER - TY - JOUR AU - Serrano-Ripoll, J. Maria AU - Zamanillo-Campos, Rocío AU - Fiol-DeRoque, A. Maria AU - Castro, Adoración AU - Ricci-Cabello, Ignacio PY - 2022/1/27 TI - Impact of Smartphone App?Based Psychological Interventions for Reducing Depressive Symptoms in People With Depression: Systematic Literature Review and Meta-analysis of Randomized Controlled Trials JO - JMIR Mhealth Uhealth SP - e29621 VL - 10 IS - 1 KW - smartphone technology KW - mental health interventions KW - depression KW - eHealth KW - mHealth KW - apps KW - systematic review KW - meta-analysis KW - mobile phone N2 - Background: Depression is a serious, disabling mental disorder that severely affects quality of life. Patients with depression often do not receive adequate treatment. App-based psychotherapy is considered to have great potential to treat depression owing to its reach and easy accessibility. Objective: We aim to analyze the impact of app-based psychological interventions for reducing depressive symptoms in people with depression. Methods: We conducted a systematic literature review and meta-analysis. We searched Medline, Embase, PsycINFO, Web of Science, and Cochrane Central Register of Controlled Trials from inception to December 23, 2020. We selected randomized controlled trials to examine the impact of app-based psychological interventions for reducing depressive symptoms in people with depression. Study selection, data extraction, and critical appraisal (using the Cochrane Risk of Bias tool for randomized studies and the ROBINS-I tool for nonrandomized studies) were conducted independently by 2 reviewers. Where possible, we pooled data using random effects meta-analyses to obtain estimates of the effect size of the intervention. We conducted post hoc meta-regression analyses to explore the factors associated with intervention success. Results: After screening 3468 unique references retrieved from bibliographic searches and assessing the eligibility of 79 full texts, we identified 12 trials (2859 participants) evaluating 14 different interventions. Of 14 trials, 7 (58%) were conducted in the United States; 3 (25%) trials, in Asia (Japan, South Korea, and China); 1 (8%) trial, in Australia; and 1 (8%) trial, in Germany. Of the 12 trials, 5 (42%) trials presented a low risk of bias. The mean duration of the interventions was 6.6 (SD 2.8) weeks. Two-thirds of the interventions were based on cognitive behavioral therapy alone or included it in combination with cognitive control therapy, positive psychology, brief behavioral activation, or mindfulness- and acceptance-based therapy. With no evidence of publication bias, a pooled analysis of 83% (10/12) of the trials and 86% (12/14) of the interventions showed that app-based interventions, compared with a control group receiving usual care or minimal intervention, produced a moderate reduction in depressive symptoms (standardized mean difference [SMD] ?0.51, 95% CI ?0.69 to ?0.33; 2018/2859, 70.58% of the participants; I2=70%). Our meta-regression analyses indicated that there was a greater reduction in symptoms of depression (P=.04) in trials that included participants with moderate to severe depression (SMD ?0.67, 95% CI ?0.79 to ?0.55), compared with trials with participants exhibiting mild to moderate depression (SMD ?0.15, 95% CI ?0.43 to ?0.12). Conclusions: App-based interventions targeted at people with depression produce moderate reductions in the symptoms of depression. More methodologically robust trials are needed to confirm our findings, determine which intervention features are associated with greater improvements, and identify those populations most likely to benefit from this type of intervention. Trial Registration: PROSPERO CRD42019145689; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=145689 UR - https://mhealth.jmir.org/2022/1/e29621 UR - http://dx.doi.org/10.2196/29621 UR - http://www.ncbi.nlm.nih.gov/pubmed/35084346 ID - info:doi/10.2196/29621 ER - TY - JOUR AU - Birnbaum, L. Michael AU - Abrami, Avner AU - Heisig, Stephen AU - Ali, Asra AU - Arenare, Elizabeth AU - Agurto, Carla AU - Lu, Nathaniel AU - Kane, M. John AU - Cecchi, Guillermo PY - 2022/1/24 TI - Acoustic and Facial Features From Clinical Interviews for Machine Learning?Based Psychiatric Diagnosis: Algorithm Development JO - JMIR Ment Health SP - e24699 VL - 9 IS - 1 KW - audiovisual patterns KW - speech analysis KW - facial analysis KW - psychiatry KW - schizophrenia spectrum disorders KW - bipolar disorder KW - symptom prediction KW - diagnostic prediction KW - machine learning KW - audiovisual KW - speech KW - schizophrenia KW - spectrum disorders N2 - Background: In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions. Objective: We aimed to investigate whether reliable inferences?psychiatric signs, symptoms, and diagnoses?can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder. Methods: We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation. Results: The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner?pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61). Conclusions: This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis. UR - https://mental.jmir.org/2022/1/e24699 UR - http://dx.doi.org/10.2196/24699 UR - http://www.ncbi.nlm.nih.gov/pubmed/35072648 ID - info:doi/10.2196/24699 ER - TY - JOUR AU - Parra, Federico AU - Benezeth, Yannick AU - Yang, Fan PY - 2022/1/24 TI - Automatic Assessment of Emotion Dysregulation in American, French, and Tunisian Adults and New Developments in Deep Multimodal Fusion: Cross-sectional Study JO - JMIR Ment Health SP - e34333 VL - 9 IS - 1 KW - emotion dysregulation KW - deep multimodal fusion KW - small data KW - psychometrics N2 - Background: Emotion dysregulation is a key dimension of adult psychological functioning. There is an interest in developing a computer-based, multimodal, and automatic measure. Objective: We wanted to train a deep multimodal fusion model to estimate emotion dysregulation in adults based on their responses to the Multimodal Developmental Profile, a computer-based psychometric test, using only a small training sample and without transfer learning. Methods: Two hundred and forty-eight participants from 3 different countries took the Multimodal Developmental Profile test, which exposed them to 14 picture and music stimuli and asked them to express their feelings about them, while the software extracted the following features from the video and audio signals: facial expressions, linguistic and paralinguistic characteristics of speech, head movements, gaze direction, and heart rate variability derivatives. Participants also responded to the brief version of the Difficulties in Emotional Regulation Scale. We separated and averaged the feature signals that corresponded to the responses to each stimulus, building a structured data set. We transformed each person?s per-stimulus structured data into a multimodal codex, a grayscale image created by projecting each feature?s normalized intensity value onto a cartesian space, deriving each pixel?s position by applying the Uniform Manifold Approximation and Projection method. The codex sequence was then fed to 2 network types. First, 13 convolutional neural networks dealt with the spatial aspect of the problem, estimating emotion dysregulation by analyzing each of the codified responses. These convolutional estimations were then fed to a transformer network that decoded the temporal aspect of the problem, estimating emotional dysregulation based on the succession of responses. We introduce a Feature Map Average Pooling layer, which computes the mean of the convolved feature maps produced by our convolution layers, dramatically reducing the number of learnable weights and increasing regularization through an ensembling effect. We implemented 8-fold cross-validation to provide a good enough estimation of the generalization ability to unseen samples. Most of the experiments mentioned in this paper are easily replicable using the associated Google Colab system. Results: We found an average Pearson correlation (r) of 0.55 (with an average P value of <.001) between ground truth emotion dysregulation and our system?s estimation of emotion dysregulation. An average mean absolute error of 0.16 and a mean concordance correlation coefficient of 0.54 were also found. Conclusions: In psychometry, our results represent excellent evidence of convergence validity, suggesting that the Multimodal Developmental Profile could be used in conjunction with this methodology to provide a valid measure of emotion dysregulation in adults. Future studies should replicate our findings using a hold-out test sample. Our methodology could be implemented more generally to train deep neural networks where only small training samples are available. UR - https://mental.jmir.org/2022/1/e34333 UR - http://dx.doi.org/10.2196/34333 UR - http://www.ncbi.nlm.nih.gov/pubmed/35072643 ID - info:doi/10.2196/34333 ER - TY - JOUR AU - Pszczolkowski, Stefan AU - Cottam, J. William AU - Briley, M. Paul AU - Iwabuchi, J. Sarina AU - Kaylor-Hughes, Catherine AU - Shalabi, Abdulrhman AU - Babourina-Brooks, Ben AU - Berrington, Adam AU - Barber, Shaun AU - Suazo Di Paola, Ana AU - Blamire, Andrew AU - McAllister-Williams, Hamish R. AU - Parikh, Jehill AU - Abdelghani, Mohamed AU - Matthäus, Lars AU - Hauffe, Ralf AU - Liddle, Peter AU - Auer, P. Dorothee AU - Morriss, Richard PY - 2022/1/20 TI - Connectivity-Guided Theta Burst Transcranial Magnetic Stimulation Versus Repetitive Transcranial Magnetic Stimulation for Treatment-Resistant Moderate to Severe Depression: Magnetic Resonance Imaging Protocol and SARS-CoV-2?Induced Changes for a Randomized Double-blind Controlled Trial JO - JMIR Res Protoc SP - e31925 VL - 11 IS - 1 KW - depression KW - magnetic resonance imaging KW - image-guidance KW - personalized medicine KW - transcranial magnetic stimulation N2 - Background: Depression is a substantial health and economic burden. In approximately one-third of patients, depression is resistant to first-line treatment; therefore, it is essential to find alternative treatments. Transcranial magnetic stimulation (TMS) is a neuromodulatory treatment involving the application of magnetic pulses to the brain that is approved in the United Kingdom and the United States in treatment-resistant depression. This trial aims to compare the clinical effectiveness, cost-effectiveness, and mechanism of action of standard treatment repetitive TMS (rTMS) targeted at the F3 electroencephalogram site with a newer treatment?a type of TMS called theta burst stimulation (TBS) targeted based on measures of functional brain connectivity. This protocol outlines brain imaging acquisition and analysis for the Brain Imaging Guided Transcranial Magnetic Stimulation in Depression (BRIGhTMIND) study trial that is used to create personalized TMS targets and answer the proposed mechanistic hypotheses. Objective: The aims of the imaging arm of the BRIGhTMIND study are to identify functional and neurochemical brain signatures indexing the treatment mechanisms of rTMS and connectivity-guided intermittent theta burst TMS and to identify imaging-based markers predicting response to treatment. Methods: The study is a randomized double-blind controlled trial with 1:1 allocation to either 20 sessions of TBS or standard rTMS. Multimodal magnetic resonance imaging (MRI) is acquired for each participant at baseline (before TMS treatment) with T1-weighted and task-free functional MRI during rest used to estimate TMS targets. For participants enrolled in the mechanistic substudy, additional diffusion-weighted sequences are acquired at baseline and at posttreatment follow-up 16 weeks after treatment randomization. Core data sets of T1-weighted and task-free functional MRI during rest are acquired for all participants and are used to estimate TMS targets. Additional sequences of arterial spin labeling, magnetic resonance spectroscopy, and diffusion-weighted images are acquired depending on the recruitment site for mechanistic evaluation. Standard rTMS treatment is targeted at the F3 electrode site over the left dorsolateral prefrontal cortex, whereas TBS treatment is guided using the coordinate of peak effective connectivity from the right anterior insula to the left dorsolateral prefrontal cortex. Both treatment targets benefit from the level of MRI guidance, but only TBS is provided with precision targeting based on functional brain connectivity. Results: Recruitment began in January 2019 and is ongoing. Data collection is expected to continue until January 2023. Conclusions: This trial will determine the impact of precision MRI guidance on rTMS treatment and assess the neural mechanisms underlying this treatment in treatment-resistant depressed patients. Trial Registration: ISRCTN Registry ISRCTN19674644; https://www.isrctn.com/ISRCTN19674644 International Registered Report Identifier (IRRID): DERR1-10.2196/31925 UR - https://www.researchprotocols.org/2022/1/e31925 UR - http://dx.doi.org/10.2196/31925 UR - http://www.ncbi.nlm.nih.gov/pubmed/35049517 ID - info:doi/10.2196/31925 ER - TY - JOUR AU - Dominiak, Monika AU - Kaczmarek-Majer, Katarzyna AU - Antosik-Wójci?ska, Z. Anna AU - Opara, R. Karol AU - Olwert, Anna AU - Radziszewska, Weronika AU - Hryniewicz, Olgierd AU - ?wi?cicki, ?ukasz AU - Wojnar, Marcin AU - Mierzejewski, Pawe? PY - 2022/1/19 TI - Behavioral and Self-reported Data Collected From Smartphones for the Assessment of Depressive and Manic Symptoms in Patients With Bipolar Disorder: Prospective Observational Study JO - J Med Internet Res SP - e28647 VL - 24 IS - 1 KW - bipolar disorder KW - generalized linear model KW - mixed-effects regression KW - classification KW - manic episodes KW - depressive episodes KW - smartphone KW - behavioral markers KW - mHealth KW - remote monitoring N2 - Background: Smartphones allow for real-time monitoring of patients? behavioral activities in a naturalistic setting. These data are suggested as markers for the mental state of patients with bipolar disorder (BD). Objective: We assessed the relations between data collected from smartphones and the clinically rated depressive and manic symptoms together with the corresponding affective states in patients with BD. Methods: BDmon, a dedicated mobile app, was developed and installed on patients? smartphones to automatically collect the statistics about their phone calls and text messages as well as their self-assessments of sleep and mood. The final sample for the numerical analyses consisted of 51 eligible patients who participated in at least two psychiatric assessments and used the BDmon app (mean participation time, 208 [SD 132] days). In total, 196 psychiatric assessments were performed using the Hamilton Depression Rating Scale and the Young Mania Rating Scale. Generalized linear mixed-effects models were applied to quantify the strength of the relation between the daily statistics on the behavioral data collected automatically from smartphones and the affective symptoms and mood states in patients with BD. Results: Objective behavioral data collected from smartphones were found to be related with the BD states as follows: (1) depressed patients tended to make phone calls less frequently than euthymic patients (?=?.064, P=.01); (2) the number of incoming answered calls during depression was lower than that during euthymia (?=?.15, P=.01) and, concurrently, missed incoming calls were more frequent and increased as depressive symptoms intensified (?=4.431, P<.001; ?=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (?=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to that in the euthymic state (?=3.53, P=.01) and positively correlated to the severity of symptoms (?=2.991, P=.02); (5) the variability of the duration of the outgoing calls was higher in manic/mixed states (?=.0012, P=.045) and positively correlated to the severity of symptoms (?=.0017, P=.02); and (6) the number and length of the sent text messages was higher in manic/mixed states as compared to that in the euthymic state (?=.031, P=.01; ?=.015, P=.01; respectively) and positively correlated to the severity of manic symptoms (?=.116, P<.001; ?=.022, P<.001; respectively). We also observed that self-assessment of mood was lower in depressive (?=?1.452, P<.001) and higher in manic states (?=.509, P<.001). Conclusions: Smartphone-based behavioral parameters are valid markers for assessing the severity of affective symptoms and discriminating between mood states in patients with BD. This technology opens a way toward early detection of worsening of the mental state and thereby increases the patient?s chance of improving in the course of the illness. UR - https://www.jmir.org/2022/1/e28647 UR - http://dx.doi.org/10.2196/28647 UR - http://www.ncbi.nlm.nih.gov/pubmed/34874015 ID - info:doi/10.2196/28647 ER - TY - JOUR AU - Chan, W. William AU - Fitzsimmons-Craft, E. Ellen AU - Smith, C. Arielle AU - Firebaugh, Marie-Laure AU - Fowler, A. Lauren AU - DePietro, Bianca AU - Topooco, Naira AU - Wilfley, E. Denise AU - Taylor, Barr C. AU - Jacobson, C. Nicholas PY - 2022/1/19 TI - The Challenges in Designing a Prevention Chatbot for Eating Disorders: Observational Study JO - JMIR Form Res SP - e28003 VL - 6 IS - 1 KW - chatbot KW - eating disorders KW - digital mental health KW - prevention KW - intervention development N2 - Background: Chatbots have the potential to provide cost-effective mental health prevention programs at scale and increase interactivity, ease of use, and accessibility of intervention programs. Objective: The development of chatbot prevention for eating disorders (EDs) is still in its infancy. Our aim is to present examples of and solutions to challenges in designing and refining a rule-based prevention chatbot program for EDs, targeted at adult women at risk for developing an ED. Methods: Participants were 2409 individuals who at least began to use an EDs prevention chatbot in response to social media advertising. Over 6 months, the research team reviewed up to 52,129 comments from these users to identify inappropriate responses that negatively impacted users? experience and technical glitches. Problems identified by reviewers were then presented to the entire research team, who then generated possible solutions and implemented new responses. Results: The most common problem with the chatbot was a general limitation in understanding and responding appropriately to unanticipated user responses. We developed several workarounds to limit these problems while retaining some interactivity. Conclusions: Rule-based chatbots have the potential to reach large populations at low cost but are limited in understanding and responding appropriately to unanticipated user responses. They can be most effective in providing information and simple conversations. Workarounds can reduce conversation errors. UR - https://formative.jmir.org/2022/1/e28003 UR - http://dx.doi.org/10.2196/28003 UR - http://www.ncbi.nlm.nih.gov/pubmed/35044314 ID - info:doi/10.2196/28003 ER - TY - JOUR AU - Sierk, Anika AU - Travers, Eoin AU - Economides, Marcos AU - Loe, Sheng Bao AU - Sun, Luning AU - Bolton, Heather PY - 2022/1/17 TI - A New Digital Assessment of Mental Health and Well-being in the Workplace: Development and Validation of the Unmind Index JO - JMIR Ment Health SP - e34103 VL - 9 IS - 1 KW - mental health KW - well-being KW - mHealth KW - measurement N2 - Background: Unmind is a workplace, digital, mental health platform with tools to help users track, maintain, and improve their mental health and well-being (MHWB). Psychological measurement plays a key role on this platform, providing users with insights on their current MHWB, the ability to track it over time, and personalized recommendations, while providing employers with aggregate information about the MHWB of their workforce. Objective: Due to the limitations of existing measures for this purpose, we aimed to develop and validate a novel well-being index for digital use, to capture symptoms of common mental health problems and key aspects of positive well-being. Methods: In Study 1A, questionnaire items were generated by clinicians and screened for face validity. In Study 1B, these items were presented to a large sample (n=1104) of UK adults, and exploratory factor analysis was used to reduce the item pool and identify coherent subscales. In Study 2, the final measure was presented to a new nationally representative UK sample (n=976), along with a battery of existing measures, with 238 participants retaking the Umind Index after 1 week. The factor structure and measurement invariance of the Unmind Index was evaluated using confirmatory factor analysis, convergent and discriminant validity by estimating correlations with existing measures, and reliability by examining internal consistency and test-retest intraclass correlations. Results: Studies 1A and 1B yielded a 26-item measure with 7 subscales: Calmness, Connection, Coping, Happiness, Health, Fulfilment, and Sleep. Study 2 showed that the Unmind Index is fitted well by a second-order factor structure, where the 7 subscales all load onto an overall MHWB factor, and established measurement invariance by age and gender. Subscale and total scores correlate well with existing mental health measures and generally diverge from personality measures. Reliability was good or excellent across all subscales. Conclusions: The Unmind Index is a robust measure of MHWB that can help to identify target areas for intervention in nonclinical users of a mental health app. We argue that there is value in measuring mental ill health and mental well-being together, rather than treating them as separate constructs. UR - https://mental.jmir.org/2022/1/e34103 UR - http://dx.doi.org/10.2196/34103 UR - http://www.ncbi.nlm.nih.gov/pubmed/35037895 ID - info:doi/10.2196/34103 ER - TY - JOUR AU - Knyahnytska, Yuliya AU - Zomorrodi, Reza AU - Kaster, Tyler AU - Voineskos, Daphne AU - Trevizol, Alisson AU - Blumberger, Daniel PY - 2022/1/17 TI - The Safety, Clinical, and Neurophysiological Effects of Intranasal Ketamine in Patients Who Do Not Respond to Electroconvulsive Therapy: Protocol for a Pilot, Open-Label Clinical Trial JO - JMIR Res Protoc SP - e30163 VL - 11 IS - 1 KW - intranasal KW - racemic ketamine KW - NMDA antagonist KW - treatment resistant depression KW - electroconvulsive therapy nonresponders KW - drug KW - treatment KW - ketamine KW - depression KW - mental health KW - safety KW - neurophysiological KW - side effect KW - biomarker KW - clinical trial KW - alternative N2 - Background: Major depressive disorder is among the most disabling illnesses worldwide, with a lifetime prevalence of 16.2%. Research suggests that 20% to 40% of patients with depression do not respond to pharmacotherapy, developing treatment-resistant depression. Electroconvulsive therapy is the gold standard for treating individuals with treatment-resistant depression, with remission rates of approximately 75% to 90%. However, 10% to 25% of patients do not respond to electroconvulsive therapy, and many are unable to tolerate it due to the side effects. Both groups are considered to be patients who do not respond to electroconvulsive therapy, because both groups continue to exhibit symptoms of severe depression, have a limited number of treatment options available, and are in need of rapid treatment. Ketamine, an N-methyl-D-aspartate receptor antagonist, has been shown to exert rapid antidepressant effects in patients with treatment-resistant depression when administered in subanesthetic doses through 40-minute intravenous infusions. Recently, a ketamine compound, esketamine (Spravato), that is administered through the intranasal route received regulatory approval by the US Food and Drug Administration and Health Canada to treat depression. However, esketamine is challenging to access due to high costs and limited availability. Racemic ketamine (rketamine) is cheap and easy to access; however, the effects in patients who have not responded to electroconvulsive therapy have yet to be understood or tested. This study will use transcranial magnetic stimulation to study mechanisms of human brain cortical physiology at the systemic level to identify neurobiomarkers of response. Objective: The objective of this open-label pilot clinical trial is to test the feasibility and safety of intranasal ketamine in patients who have not responded to electroconvulsive therapy. The primary outcome is to determine the feasibility of a larger randomized controlled trial to test the efficacy of intranasal ketamine for patients who have not responded to electroconvulsive therapy for clinical indicators in unipolar depression. The secondary outcome is to determine the preliminary effects of an intervention on clinical outcomes, such as depressive symptoms, suicidal ideation, and quality of living. The third outcome is to explore neurophysiological changes as measured by transcranial magnetic stimulation electromyography and electroencephalography to measure changes in cortical excitability as potential predictors of clinical response. Methods: A sterile solution of racemic ketamine hydrochloride will be administered twice per week for 4 weeks (8 sessions) intranasally to patients with treatment-resistant depression who did not respond to or could not tolerate an acute course of electroconvulsive therapy. We will recruit 25 adults (24-65 years old) over the course of 2 years from an academic psychiatric hospital in Toronto, Canada. Results: This study has received ethics approval, and funding has been secured. The study is currently active. Conclusions: This is the first study to test repeated doses of intranasal rketamine in patients who have not responded to electroconvulsive therapy for depression. Results from this study will (1) inform the development of a larger adequately powered randomized controlled trial to test the efficacy of intranasal ketamine for depression and (2) determine potential neurophysiological markers of clinical response. Trial Registration: Clinical Trials.gov NCT05137938; http://clinicaltrials.gov/ct2/show/NCT05137938 International Registered Report Identifier (IRRID): PRR1-10.2196/30163 UR - https://www.researchprotocols.org/2022/1/e30163 UR - http://dx.doi.org/10.2196/30163 UR - http://www.ncbi.nlm.nih.gov/pubmed/34882570 ID - info:doi/10.2196/30163 ER - TY - JOUR AU - Possemato, Kyle AU - Wu, Justina AU - Greene, Carolyn AU - MacQueen, Rex AU - Blonigen, Daniel AU - Wade, Michael AU - Owen, Jason AU - Keane, Terence AU - Brief, Deborah AU - Lindley, Steven AU - Prins, Annabel AU - Mackintosh, Margaret-Anne AU - Carlson, Eve PY - 2022/1/13 TI - Web-Based Problem-solving Training With and Without Peer Support in Veterans With Unmet Mental Health Needs: Pilot Study of Feasibility, User Acceptability, and Participant Engagement JO - J Med Internet Res SP - e29559 VL - 24 IS - 1 KW - problem-solving training KW - mHealth KW - peer specialists KW - veterans N2 - Background: eHealth tools have the potential to meet the mental health needs of individuals who experience barriers to accessing in-person treatment. However, most users have less than optimal engagement with eHealth tools. Coaching from peer specialists may increase their engagement with eHealth. Objective: This pilot study aims to test the feasibility and acceptability of a novel, completely automated web-based system to recruit, screen, enroll, assess, randomize, and then deliver an intervention to a national sample of military veterans with unmet mental health needs; investigate whether phone-based peer support increases the use of web-based problem-solving training compared with self-directed use; and generate hypotheses about potential mechanisms of action for problem-solving and peer support for future full-scale research. Methods: Veterans (N=81) with unmet mental health needs were recruited via social media advertising and enrolled and randomized to the self-directed use of a web-based problem-solving training called Moving Forward (28/81, 35%), peer-supported Moving Forward (27/81, 33%), or waitlist control (26/81, 32%). The objective use of Moving Forward was measured with the number of log-ins. Participants completed pre- and poststudy measures of mental health symptoms and problem-solving confidence. Satisfaction was also assessed post treatment. Results: Automated recruitment, enrollment, and initial assessment methods were feasible and resulted in a diverse sample of veterans with unmet mental health needs from 38 states. Automated follow-up methods resulted in 46% (37/81) of participants completing follow-up assessments. Peer support was delivered with high fidelity and was associated with favorable participant satisfaction. Participants randomized to receive peer support had significantly more Moving Forward log-ins than those of self-directed Moving Forward participants, and those who received peer support had a greater decrease in depression. Problem-solving confidence was associated with greater Moving Forward use and improvements in mental health symptoms among participants both with and without peer support. Conclusions: Enrolling and assessing individuals in eHealth studies without human contact is feasible; however, different methods or designs are necessary to achieve acceptable participant engagement and follow-up rates. Peer support shows potential for increasing engagement in web-based interventions and reducing symptoms. Future research should investigate when and for whom peer support for eHealth is helpful. Problem-solving confidence should be further investigated as a mechanism of action for web-based problem-solving training. Trial Registration: ClinicalTrials.gov NCT03555435; http://clinicaltrials.gov/ct2/show/NCT03555435 UR - https://www.jmir.org/2022/1/e29559 UR - http://dx.doi.org/10.2196/29559 UR - http://www.ncbi.nlm.nih.gov/pubmed/35023846 ID - info:doi/10.2196/29559 ER - TY - JOUR AU - Stargatt, Jennifer AU - Bhar, Sunil AU - Bhowmik, Jahar AU - Al Mahmud, Abdullah PY - 2022/1/12 TI - Digital Storytelling for Health-Related Outcomes in Older Adults: Systematic Review JO - J Med Internet Res SP - e28113 VL - 24 IS - 1 KW - digital storytelling KW - mental health KW - aging KW - dementia KW - reminiscence KW - memory KW - systematic review KW - older adults N2 - Background: Older adults face a unique set of challenges and may experience a range of psychological comorbidities. Digital storytelling is an emerging tool for sharing and recording lived experiences and may have the potential to support well-being but is yet to be systematically reviewed for use among older adults. Objective: The aim of this review is to examine the methods for creating digital stories, the health-related outcomes associated with creating digital stories, and the potential for implementing digital storytelling with older adults. Methods: We systematically searched electronic databases to identify articles published in English that reported on at least one health-related outcome of digital storytelling for participants aged ?60 years. Data were extracted and synthesized using qualitative content analysis and summarized in tables. The methodological quality of the studies was assessed using the Mixed Methods Appraisal Tool. Results: A total of 8 studies were included in the review. Participants were primarily community-dwelling older adults living with dementia, involving family caregivers and professional care staff. Studies have taken various approaches to digital storytelling and reported diverse benefits associated with digital storytelling, including improvements in mood, memory, social engagement, and quality of relationships. Although the potential for implementation was not widely examined, some studies have presented evidence for acceptability and feasibility. Generally, studies were of high quality, despite the absence of comparator groups and confounder analyses. Conclusions: The evidence reviewed suggests that despite the various approaches taken, digital storytelling shows promise as an effective approach for supporting well-being in older adults. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019145922; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019145922 International Registered Report Identifier (IRRID): RR2-10.2196/15512 UR - https://www.jmir.org/2022/1/e28113 UR - http://dx.doi.org/10.2196/28113 UR - http://www.ncbi.nlm.nih.gov/pubmed/35019845 ID - info:doi/10.2196/28113 ER - TY - JOUR AU - Pereira-Sanchez, Victor AU - Alvarez-Mon, Angel Miguel AU - Horinouchi, Toru AU - Kawagishi, Ryo AU - Tan, J. Marcus P. AU - Hooker, R. Elizabeth AU - Alvarez-Mon, Melchor AU - Teo, R. Alan PY - 2022/1/11 TI - Examining Tweet Content and Engagement of Users With Tweets About Hikikomori in Japanese: Mixed Methods Study of Social Withdrawal JO - J Med Internet Res SP - e31175 VL - 24 IS - 1 KW - hikikomori KW - loneliness KW - social isolation KW - social withdrawal KW - Twitter KW - hidden youth KW - mobile phone N2 - Background: Hikikomori is a form of severe social withdrawal that is particularly prevalent in Japan. Social media posts offer insight into public perceptions of mental health conditions and may also inform strategies to identify, engage, and support hard-to-reach patient populations such as individuals affected by hikikomori. Objective: In this study, we seek to identify the types of content on Twitter related to hikikomori in the Japanese language and to assess Twitter users? engagement with that content. Methods: We conducted a mixed methods analysis of a random sample of 4940 Japanese tweets from February to August 2018 using a hashtag (#hikikomori). Qualitative content analysis included examination of the text of each tweet, development of a codebook, and categorization of tweets into relevant codes. For quantitative analysis (n=4859 tweets), we used bivariate and multivariate logistic regression models, adjusted for multiple comparisons, and estimated the predicted probabilities of tweets receiving engagement (likes or retweets). Results: Our content analysis identified 9 codes relevant to tweets about hikikomori: personal anecdotes, social support, marketing, advice, stigma, educational opportunities, refuge (ibasho), employment opportunities, and medicine and science. Tweets about personal anecdotes were the most common (present in 2747/4859, 56.53% of the tweets), followed by social support (902/4859, 18.56%) and marketing (624/4859, 12.84%). In the adjusted models, tweets coded as stigma had a lower predicted probability of likes (?33 percentage points, 95% CI ?42 to ?23 percentage points; P<.001) and retweets (?11 percentage points, 95% CI ?18 to ?4 percentage points; P<.001), personal anecdotes had a lower predicted probability of retweets (?8 percentage points, 95% CI ?14 to ?3 percentage points; P=.002), marketing had a lower predicted probability of likes (?13 percentage points, 95% CI ?21 to ?6 percentage points; P<.001), and social support had a higher predicted probability of retweets (+15 percentage points, 95% CI 6-24 percentage points; P=.001), compared with all tweets without each of these codes. Conclusions: Japanese tweets about hikikomori reflect a unique array of topics, many of which have not been identified in prior research and vary in their likelihood of receiving engagement. Tweets often contain personal stories of hikikomori, suggesting the potential to identify individuals with hikikomori through Twitter. UR - https://www.jmir.org/2022/1/e31175 UR - http://dx.doi.org/10.2196/31175 UR - http://www.ncbi.nlm.nih.gov/pubmed/35014971 ID - info:doi/10.2196/31175 ER - TY - JOUR AU - Gunn, M. Kate AU - Skaczkowski, Gemma AU - Dollman, James AU - Vincent, D. Andrew AU - Short, E. Camille AU - Brumby, Susan AU - Barrett, Alison AU - Harrison, Nathan AU - Turnbull, Deborah PY - 2022/1/11 TI - Combining Farmers? Preferences With Evidence-Based Strategies to Prevent and Lower Farmers? Distress: Co-design and Acceptability Testing of ifarmwell JO - JMIR Hum Factors SP - e27631 VL - 9 IS - 1 KW - farm KW - agriculture KW - rural KW - drought KW - mental health KW - stress KW - coping KW - online intervention KW - acceptance and commitment therapy N2 - Background: Farming is physically and psychologically hazardous. Farmers face many barriers to help seeking from traditional physical and mental health services; however, improved internet access now provides promising avenues for offering support. Objective: This study aims to co-design with farmers the content and functionality of a website that helps them adopt transferable coping strategies and test its acceptability in the broader farming population. Methods: Research evidence and expert opinions were synthesized to inform key design principles. A total of 18 farmers detailed what they would like from this type of website. Intervention logic and relevant evidence-based strategies were mapped. Website content was drafted and reviewed by 2 independent mental health professionals. A total of 9 farmers provided detailed qualitative feedback on the face validity of the draft content. Subsequently, 9 farmers provided feedback on the website prototype. Following amendments and internal prototype testing and optimization, prototype usability (ie, completion rate) was examined with 157 registered website users who were (105/157, 66.9%) female, aged 21-73 years; 95.5% (149/156) residing in inner regional to very remote Australia, and 68.2% (107/157) ?sheep, cattle and/or grain farmers.? Acceptability was examined with a subset of 114 users who rated at least module 1. Interviews with 108 farmers who did not complete all 5 modules helped determine why, and detailed interviews were conducted with 18 purposively sampled users. Updates were then made according to adaptive trial design methodology. Results: This systematic co-design process resulted in a web-based resource based on acceptance and commitment therapy and designed to overcome barriers to engagement with traditional mental health and well-being strategies?ifarmwell. It was considered an accessible and confidential source of practical and relevant farmer-focused self-help strategies. These strategies were delivered via 5 interactive modules that include written, drawn, and audio- and video-based psychoeducation and exercises, as well as farming-related jokes, metaphors, examples, and imagery. Module 1 included distress screening and information on how to speak to general practitioners about mental health?related concerns (including a personalized conversation script). Modules were completed fortnightly. SMS text messages offered personalized support and reminders. Qualitative interviews and star ratings demonstrated high module acceptability (average 4.06/5 rating) and suggested that additional reminders, higher quality audio recordings, and shorter modules would be useful. Approximately 37.1% (52/140) of users who started module 1 completed all modules, with too busy or not got to it yet being the main reason for non-completion, and previous module acceptability not predicting subsequent module completion. Conclusions: Sequential integration of research evidence, expert knowledge, and farmers? preferences in the co-design process allowed for the development of a self-help intervention that focused on important intervention targets and was acceptable to this difficult-to-engage group. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12617000506392; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372526 UR - https://humanfactors.jmir.org/2022/1/e27631 UR - http://dx.doi.org/10.2196/27631 UR - http://www.ncbi.nlm.nih.gov/pubmed/35014963 ID - info:doi/10.2196/27631 ER - TY - JOUR AU - Sutherland, Stephanie AU - Jeong, Dahn AU - Cheng, Michael AU - St-Jean, Mireille AU - Jalali, Alireza PY - 2022/1/7 TI - Perceptions of Educational Needs in an Era of Shifting Mental Health Care to Primary Care: Exploratory Pilot Study JO - JMIR Form Res SP - e32422 VL - 6 IS - 1 KW - mental health KW - Canada KW - qualitative research KW - caregiver KW - family physician KW - mentorship N2 - Background: There is an unmet need for mental health care in Canada. Primary care providers such as general practitioners and family physicians are the essential part of mental health care services; however, mental health is often underestimated and underprioritized by family physicians. It is currently not known what is required to increase care providers? willingness, comfort, and skills to adequately provide care to patients who present with mental health issues. Objective: The aim of this study was to understand the need of caregivers (family members overseeing care of an individual with a mental health diagnosis) and family physicians regarding the care and medical management of individuals with mental health conditions. Methods: A needs assessment was designed to understand the educational needs of caregivers and family physicians regarding the provision of mental health care, specifically to seek advice on the format and delivery mode for an educational curriculum to be accessed by both stakeholder groups. Exploratory qualitative interviews were conducted, and data were collected and analyzed iteratively until thematic saturation was achieved. Results: Caregivers of individuals with mental health conditions (n=24) and family physicians (n=10) were interviewed. Both the caregivers and the family physicians expressed dissatisfaction with the status quo regarding the provision of mental health care at the family physician?s office. They stated that there was a need for more educational materials as well as additional support. The caregivers expressed a general lack of confidence in family physicians to manage their son?s or daughter?s mental health condition, while family physicians sought more networking opportunities to improve and facilitate the provision of mental health care. Conclusions: Robust qualitative studies are necessary to identify the educational and medical management needs of caregivers and family physicians. Understanding each other?s perspectives is an essential first step to collaboratively designing, implementing, and subsequently evaluating community-based mental health care. Fortunately, there are initiatives underway to address these need areas (eg, websites such as the eMentalHealth, as well as the mentorship and collaborative care network), and information from this study can help inform the gaps in those existing initiatives. UR - https://formative.jmir.org/2022/1/e32422 UR - http://dx.doi.org/10.2196/32422 UR - http://www.ncbi.nlm.nih.gov/pubmed/34994704 ID - info:doi/10.2196/32422 ER - TY - JOUR AU - Breitinger, Scott AU - Gardea-Resendez, Manuel AU - Langholm, Carsten AU - Xiong, Ashley AU - Laivell, Joseph AU - Stoppel, Cynthia AU - Harper, Laura AU - Volety, Rama AU - Walker, Alex AU - D'Mello, Ryan AU - Byun, Soo Andrew Jin AU - Zandi, Peter AU - Goes, S. Fernando AU - Frye, Mark AU - Torous, John PY - 2023/12/29 TI - Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study JO - J Med Internet Res SP - e47006 VL - 25 KW - mood disorders KW - depression KW - bipolar disorder KW - digital health KW - digital phenotyping KW - mobile apps KW - patient-generated health data KW - wearable devices N2 - Background: In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. Objective: This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. Methods: We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. Results: We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. Conclusions: Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites. UR - https://www.jmir.org/2023/1/e47006 UR - http://dx.doi.org/10.2196/47006 UR - http://www.ncbi.nlm.nih.gov/pubmed/38157233 ID - info:doi/10.2196/47006 ER - TY - JOUR AU - Smith, A. Katharine AU - Ostinelli, G. Edoardo AU - Ede, Roger AU - Allard, Lisa AU - Thomson, Michaela AU - Hewitt, Kiran AU - Brown, Petra AU - Zangani, Caroline AU - Jenkins, Matthew AU - Hinze, Verena AU - Ma, George AU - Pothulu, Prajnesh AU - Henshall, Catherine AU - Malhi, S. Gin AU - Every-Palmer, Susanna AU - Cipriani, Andrea PY - 2023/12/22 TI - Assessing the Impact of Evidence-Based Mental Health Guidance During the COVID-19 Pandemic: Systematic Review and Qualitative Evaluation JO - JMIR Ment Health SP - e52901 VL - 10 KW - evidence synthesis KW - guidelines KW - mental health KW - systematic review KW - focus group KW - survey KW - COVID-19 KW - pandemic KW - digital health KW - eHealth KW - mobile phone N2 - Background: During the COVID-19 pandemic, the Oxford Precision Psychiatry Lab (OxPPL) developed open-access web-based summaries of mental health care guidelines (OxPPL guidance) in key areas such as digital approaches and telepsychiatry, suicide and self-harm, domestic violence and abuse, perinatal care, and vaccine hesitancy and prioritization in the context of mental illness, to inform timely clinical decision-making. Objective: This study aimed to evaluate the practice of creating evidence-based health guidelines during health emergencies using the OxPPL guidance as an example. An international network of clinical sites and colleagues (in Australia, New Zealand, and the United Kingdom) including clinicians, researchers, and experts by experience aimed to (1) evaluate the clinical impact of the OxPPL guidance, as an example of an evidence-based summary of guidelines; (2) review the literature for other evidence-based summaries of COVID-19 guidelines regarding mental health care; and (3) produce a framework for response to future global health emergencies. Methods: The impact and clinical utility of the OxPPL guidance were assessed using clinicians? feedback via an international survey and focus groups. A systematic review (protocol registered on Open Science Framework) identified summaries or syntheses of guidelines for mental health care during and after the COVID-19 pandemic and assessed the accuracy of the methods used in the OxPPL guidance by identifying any resources that the guidance had not included. Results: Overall, 80.2% (146/182) of the clinicians agreed or strongly agreed that the OxPPL guidance answered important clinical questions, 73.1% (133/182) stated that the guidance was relevant to their service, 59.3% (108/182) said that the guidelines had or would have a positive impact on their clinical practice, 42.9% (78/182) that they had shared or would share the guidance, and 80.2% (146/182) stated that the methodology could be used during future health crises. The focus groups found that the combination of evidence-based knowledge, clinical viewpoint, and visibility was crucial for clinical implementation. The systematic review identified 2543 records, of which 2 syntheses of guidelines met all the inclusion criteria, but only 1 (the OxPPL guidance) used evidence-based methodology. The review showed that the OxPPL guidance had included the majority of eligible guidelines, but 6 were identified that had not been included. Conclusions: The study identified an unmet need for web-based, evidence-based mental health care guidance during the COVID-19 pandemic. The OxPPL guidance was evaluated by clinicians as having a real-world clinical impact. Robust evidence-based methodology and expertise in mental health are necessary, but easy accessibility is also needed, and digital technology can materially help. Further health emergencies are inevitable and now is the ideal time to prepare, including addressing the training needs of clinicians, patients, and carers, especially in areas such as telepsychiatry and digital mental health. For future planning, guidance should be widely disseminated on an international platform, with allocated resources to support adaptive updates. UR - https://mental.jmir.org/2023/1/e52901 UR - http://dx.doi.org/10.2196/52901 UR - http://www.ncbi.nlm.nih.gov/pubmed/38133912 ID - info:doi/10.2196/52901 ER - TY - JOUR AU - Bufano, Pasquale AU - Laurino, Marco AU - Said, Sara AU - Tognetti, Alessandro AU - Menicucci, Danilo PY - 2023/12/13 TI - Digital Phenotyping for Monitoring Mental Disorders: Systematic Review JO - J Med Internet Res SP - e46778 VL - 25 KW - digital phenotyping KW - mobile KW - mental health KW - smartphone KW - mobile sensing KW - passive sensing KW - active sensing KW - digital phenotype KW - digital biomarker KW - mobile phone N2 - Background: The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status. Objective: This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature. Methods: Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework. Results: We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations. Conclusions: Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation). UR - https://www.jmir.org/2023/1/e46778 UR - http://dx.doi.org/10.2196/46778 UR - http://www.ncbi.nlm.nih.gov/pubmed/38090800 ID - info:doi/10.2196/46778 ER - TY - JOUR AU - Rioux, Charlie AU - Fulp, C. Delaney AU - Haley, N. Parker AU - LaBelle, L. Jenna AU - Aasted, E. Mary AU - Lambert, K. Kasie AU - Donohue, T. Madison AU - Mafu, T. Nkatheko PY - 2023/12/6 TI - Phenotypic Environmental Sensitivity and Mental Health During Pregnancy and Post Partum: Protocol for the Experiences of Pregnancy Longitudinal Cohort Study JO - JMIR Res Protoc SP - e49243 VL - 12 KW - anxiety KW - biological sensitivity to context KW - depression KW - diathesis-stress KW - highly sensitive person KW - moderation KW - resilience KW - sensory processing reactivity KW - substance use KW - vantage sensitivity N2 - Background: Mental health problems during pregnancy and post partum are common and associated with negative short- and long-term impacts on pregnant individuals, obstetric outcomes, and child socioemotional development. Socio-environmental factors are important predictors of perinatal mental health, but the effects of the environment on mental health are heterogeneous. The differential susceptibility theory and the environmental sensitivity framework suggest that individuals differ in their degree of sensitivity to positive and negative environments, which can be captured by individual phenotypes such as temperament and personality. While there is strong evidence for these models in childhood, few studies examined them in adults, and they were not examined in pregnancy. Objective: The primary objective of the Experiences of Pregnancy study is to explore whether childhood and current environments are associated with mental health and well-being in pregnancy and whether these effects depend on individual sensitivity phenotypes (personality). This study also aims to gather important psychosocial and health data for potential secondary data analyses and integrative data analyses. Methods: We will conduct a longitudinal cohort study. The study was not registered elsewhere, other than this protocol. Participants will be recruited through social media advertisements linking to the study website, followed by an eligibility call on Zoom (Zoom Video Communications). Participants must be aged 18 years or older, currently residing in the United States as citizens or permanent residents, and currently planning to continue the pregnancy. A minimum of 512 participants will be recruited based on power analyses for the main objectives. Since the data will also be a resource for secondary analyses, up to 1000 participants will be recruited based on the available budget. Participants will be in their first trimester of pregnancy, and they will be followed at each trimester and once post partum. Data will be obtained through self-reported questionnaires assessing demographic factors; pregnancy-related factors; delivery, labor, and birth outcomes; early infant feeding; individual personality factors; childhood and current environments; mental health and well-being; attachment; and infant temperament. A series of measures were taken to safeguard the study from web robots and fraudulent participants, as well as to reduce legal and social risks for participants following Dobbs v. Jackson. Results: The study received ethics approval in April 2023 from the University of Oklahoma-Norman Campus Institutional Review Board. Recruitment occurred from May to August 2023, with 3 follow-ups occurring over 10 months. Conclusions: The Experiences of Pregnancy study will extend theories of environmental sensitivity, mainly applied in children to the perinatal period. This will help better understand individual sensitivity factors associated with risk, resilience, plasticity, and receptivity to negative and positive environmental influences during pregnancy for pregnant individuals. International Registered Report Identifier (IRRID): PRR1-10.2196/49243 UR - https://www.researchprotocols.org/2023/1/e49243 UR - http://dx.doi.org/10.2196/49243 UR - http://www.ncbi.nlm.nih.gov/pubmed/38055312 ID - info:doi/10.2196/49243 ER - TY - JOUR AU - Wang, Yi AU - Yu, Yide AU - Liu, Yue AU - Ma, Yan AU - Pang, Cheong-Iao Patrick PY - 2023/12/5 TI - Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach JO - JMIR Ment Health SP - e49894 VL - 10 KW - artificial intelligence KW - AI KW - mental disorder KW - psychotherapy effectiveness KW - deep learning KW - machine learning KW - natural language processing KW - NLP KW - data imbalance KW - model fusion N2 - Background: After the COVID-19 pandemic, the conflict between limited mental health care resources and the rapidly growing number of patients has become more pronounced. It is necessary for psychologists to borrow artificial intelligence (AI)?based methods to analyze patients? satisfaction with drug treatment for those undergoing mental illness treatment. Objective: Our goal was to construct highly accurate and transferable models for predicting the satisfaction of patients with mental illness with medication by analyzing their own experiences and comments related to medication intake. Methods: We extracted 41,851 reviews in 20 categories of disorders related to mental illnesses from a large public data set of 161,297 reviews in 16,950 illness categories. To discover a more optimal structure of the natural language processing models, we proposed the Unified Interchangeable Model Fusion to decompose the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT), support vector machine, and random forest (RF) models into 2 modules, the encoder and the classifier, and then reconstruct fused ?encoder+classifer? models to accurately evaluate patients? satisfaction. The fused models were divided into 2 categories in terms of model structures, traditional machine learning?based models and neural network?based models. A new loss function was proposed for those neural network?based models to overcome overfitting and data imbalance. Finally, we fine-tuned the fused models and evaluated their performance comprehensively in terms of F1-score, accuracy, ? coefficient, and training time using 10-fold cross-validation. Results: Through extensive experiments, the transformer bidirectional encoder+RF model outperformed the state-of-the-art BERT, MentalBERT, and other fused models. It became the optimal model for predicting the patients? satisfaction with drug treatment. It achieved an average graded F1-score of 0.872, an accuracy of 0.873, and a ? coefficient of 0.806. This model is suitable for high-standard users with sufficient computing resources. Alternatively, it turned out that the word-embedding encoder+RF model showed relatively good performance with an average graded F1-score of 0.801, an accuracy of 0.812, and a ? coefficient of 0.695 but with much less training time. It can be deployed in environments with limited computing resources. Conclusions: We analyzed the performance of support vector machine, RF, BERT, MentalBERT, and all fused models and identified the optimal models for different clinical scenarios. The findings can serve as evidence to support that the natural language processing methods can effectively assist psychologists in evaluating the satisfaction of patients with drug treatment programs and provide precise and standardized solutions. The Unified Interchangeable Model Fusion provides a different perspective on building AI models in mental health and has the potential to fuse the strengths of different components of the models into a single model, which may contribute to the development of AI in mental health. UR - https://mental.jmir.org/2023/1/e49894 UR - http://dx.doi.org/10.2196/49894 UR - http://www.ncbi.nlm.nih.gov/pubmed/38051580 ID - info:doi/10.2196/49894 ER - TY - JOUR AU - Kim, Seoyun AU - Cha, Junyeop AU - Kim, Dongjae AU - Park, Eunil PY - 2023/11/30 TI - Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts JO - J Med Internet Res SP - e49074 VL - 25 KW - mental health KW - sentiment analysis KW - mental disorder KW - text analysis KW - NLP KW - natural language processing KW - clustering N2 - Background: Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits. Objective: In this study, we analyzed the distinctive linguistic characteristics in users? posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. Methods: Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health?related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. Results: We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. Conclusions: By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients? mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online. UR - https://www.jmir.org/2023/1/e49074 UR - http://dx.doi.org/10.2196/49074 UR - http://www.ncbi.nlm.nih.gov/pubmed/38032730 ID - info:doi/10.2196/49074 ER - TY - JOUR AU - Petcu, Catalina AU - Boukhelif, Ikram AU - Davis, Veena AU - Shamsi, Hamda AU - Al-Assi, Marwa AU - Miladi, Anis AU - Khaled, M. Salma PY - 2023/11/27 TI - Design and Implementation of Survey Quality Control System for Qatar?s First National Mental Health Survey: Case Study JO - JMIR Form Res SP - e37653 VL - 7 KW - World Mental Health Survey KW - quality control indicators KW - Middle East KW - phone interview KW - case study KW - COVID-19 N2 - Background: All World Mental Health (WMH) Surveys apply high standards of data quality. To date, most of the published quality control (QC) procedures for these surveys were in relation to face-to-face interviews. However, owing to the social restrictions that emerged from the COVID-19 pandemic, telephone interviews are the most effective alternative for conducting complex probability-based large-scale surveys. Objective: In this paper, we present the QC system implemented in the WMH Qatar Survey, the first WMH Survey conducted during the COVID-19 pandemic in the Middle East. The objective of the QC process was to acquire high data quality through the reduction of random errors and bias in data collection. Methods: The QC design and procedures in this study were adapted to the telephone survey mode in response to the COVID-19 pandemic. We focus on the design of the QC indicator system and its implementation, including the investigation process, monitoring interviewers? performance during survey fielding and applying quality-informed interventions. Results: The study team investigated 11,035 flags triggered during the 2 waves of the survey data collection. The most triggered flags were related to short question administration duration and multiple visits to the same survey questions or responses. Live monitoring of the interviews helped in understanding why certain duration-related flags were triggered and the interviewing patterns of the interviewers. Corrective and preventive actions were taken against interviewers? behaviors based on the investigation of triggered flags per interviewer and live call monitoring of interviews. Although, in most cases, the interviewers required refresher training sessions and feedback to improve their performance, several interviewers discontinued work because of low productivity and a high number of triggered flags. Conclusions: The specific QC procedures implemented in the course of the WMH Qatar Survey were essential for successfully meeting the target number of interviews (N=5000). The QC strategies and the new indicators customized for telephone interviews contributed to the flag investigation and verification process. The QC data presented in this study shed light on the rigorous methods and quality monitoring processes in the course of conducting a large-scale national survey on sensitive topics during the COVID-19 pandemic. UR - https://formative.jmir.org/2023/1/e37653 UR - http://dx.doi.org/10.2196/37653 UR - http://www.ncbi.nlm.nih.gov/pubmed/37906213 ID - info:doi/10.2196/37653 ER - TY - JOUR AU - Arias de la Torre, Jorge AU - Vilagut, Gemma AU - Ronaldson, Amy AU - Bakolis, Ioannis AU - Dregan, Alex AU - Navarro-Mateu, Fernando AU - Pérez, Katherine AU - Szücs, Anna AU - Bartoll-Roca, Xavier AU - Molina, J. Antonio AU - Elices, Matilde AU - Pérez-Solá, Víctor AU - Martín, Vicente AU - Serrano-Blanco, Antoni AU - Valderas, M. Jose AU - Alonso, Jordi PY - 2023/11/23 TI - Reconsidering the Use of Population Health Surveys for Monitoring of Mental Health JO - JMIR Public Health Surveill SP - e48138 VL - 9 KW - mental health KW - public heath KW - burden KW - health surveys KW - monitoring KW - status KW - data collection KW - electronic health record KW - challenges KW - assessment tool KW - population KW - population health survey UR - https://publichealth.jmir.org/2023/1/e48138 UR - http://dx.doi.org/10.2196/48138 UR - http://www.ncbi.nlm.nih.gov/pubmed/37995112 ID - info:doi/10.2196/48138 ER - TY - JOUR AU - El Dahr, Yola AU - Perquier, Florence AU - Moloney, Madison AU - Woo, Guyyunge AU - Dobrin-De Grace, Roksana AU - Carvalho, Daniela AU - Addario, Nicole AU - Cameron, E. Emily AU - Roos, E. Leslie AU - Szatmari, Peter AU - Aitken, Madison PY - 2023/11/9 TI - Feasibility of Using Research Electronic Data Capture (REDCap) to Collect Daily Experiences of Parent-Child Dyads: Ecological Momentary Assessment Study JO - JMIR Form Res SP - e42916 VL - 7 KW - ambulatory assessment KW - children KW - ecological momentary assessment KW - longitudinal KW - parents KW - survey N2 - Background: Intensive longitudinal data collection, including ecological momentary assessment (EMA), has the potential to reduce recall biases, collect more ecologically valid data, and increase our understanding of dynamic associations between variables. EMA is typically administered using an application that is downloaded on participants? devices, which presents cost and privacy concerns that may limit its use. Research Electronic Data Capture (REDCap), a web-based survey application freely available to nonprofit organizations, may allow researchers to overcome these barriers; however, at present, little guidance is available to researchers regarding the setup of EMA in REDCap, especially for those who are new to using REDCap or lack advanced programming expertise. Objective: We provide an example of a simplified EMA setup in REDCap. This study aims to demonstrate the feasibility of this approach. We provide information on survey completion and user behavior in a sample of parents and children recruited across Canada. Methods: We recruited 66 parents and their children (aged 9-13 years old) from an existing longitudinal cohort study to participate in a study on risk and protective factors for children?s mental health. Parents received survey prompts (morning and evening) by email or SMS text message for 14 days, twice daily. Each survey prompt contained 2 sections, one for parents and one for children to complete. Results: The completion rates were good (mean 82%, SD 8%) and significantly higher on weekdays than weekends and in dyads with girls than dyads with boys. Children were available to respond to their own survey questions most of the time (in 1134/1498, 75.7% of surveys submitted). The number of assessments submitted was significantly higher, and response times were significantly faster among participants who selected SMS text message survey notifications compared to email survey notifications. The average response time was 47.0 minutes after the initial survey notification, and the use of reminder messages increased survey completion. Conclusions: Our results support the feasibility of using REDCap for EMA studies with parents and children. REDCap also has features that can accommodate EMA studies by recruiting participants across multiple time zones and providing different survey delivery methods. Offering the option of SMS text message survey notifications and reminders may be an important way to increase completion rates and the timeliness of responses. REDCap is a potentially useful tool for researchers wishing to implement EMA in settings in which cost or privacy are current barriers. Researchers should weigh these benefits with the potential limitations of REDCap and this design, including staff time to set up, monitor, and clean the data outputs of the project. UR - https://formative.jmir.org/2023/1/e42916 UR - http://dx.doi.org/10.2196/42916 UR - http://www.ncbi.nlm.nih.gov/pubmed/37943593 ID - info:doi/10.2196/42916 ER - TY - JOUR AU - Gregory, E. Megan AU - Sova, N. Lindsey AU - Huerta, R. Timothy AU - McAlearney, Scheck Ann PY - 2023/11/1 TI - Implications for Electronic Surveys in Inpatient Settings Based on Patient Survey Response Patterns: Cross-Sectional Study JO - J Med Internet Res SP - e48236 VL - 25 KW - surveys KW - patient satisfaction KW - patient experience KW - patient surveys KW - electronic survey KW - cross-sectional study KW - quality of life KW - mental health KW - symptoms KW - data quality KW - hospitalization N2 - Background:  Surveys of hospitalized patients are important for research and learning about unobservable medical issues (eg, mental health, quality of life, and symptoms), but there has been little work examining survey data quality in this population whose capacity to respond to survey items may differ from the general population. Objective:  The aim of this study is to determine what factors drive response rates, survey drop-offs, and missing data in surveys of hospitalized patients. Methods:  Cross-sectional surveys were distributed on an inpatient tablet to patients in a large, midwestern US hospital. Three versions were tested: 1 with 174 items and 2 with 111 items; one 111-item version had missing item reminders that prompted participants when they did not answer items. Response rate, drop-off rate (abandoning survey before completion), and item missingness (skipping items) were examined to investigate data quality. Chi-square tests, Kaplan-Meyer survival curves, and distribution charts were used to compare data quality among survey versions. Response duration was computed for each version. Results: Overall, 2981 patients responded. Response rate did not differ between the 174- and 111-item versions (81.7% vs 83%, P=.53). Drop-off was significantly reduced when the survey was shortened (65.7% vs 20.2% of participants dropped off, P<.001). Approximately one-quarter of participants dropped off by item 120, with over half dropping off by item 158. The percentage of participants with missing data decreased substantially when missing item reminders were added (77.2% vs 31.7% of participants, P<.001). The mean percentage of items with missing data was reduced in the shorter survey (40.7% vs 20.3% of items missing); with missing item reminders, the percentage of items with missing data was further reduced (20.3% vs 11.7% of items missing). Across versions, for the median participant, each item added 24.6 seconds to a survey?s duration. Conclusions:  Hospitalized patients may have a higher tolerance for longer surveys than the general population, but surveys given to hospitalized patients should have a maximum of 120 items to ensure high rates of completion. Missing item prompts should be used to reduce missing data. Future research should examine generalizability to nonhospitalized individuals. UR - https://www.jmir.org/2023/1/e48236 UR - http://dx.doi.org/10.2196/48236 UR - http://www.ncbi.nlm.nih.gov/pubmed/37910163 ID - info:doi/10.2196/48236 ER - TY - JOUR AU - Vaezipour, Atiyeh AU - Aldridge, Danielle AU - Koenig, Sebastian AU - Burns, Clare AU - Baghaei, Nilufar AU - Theodoros, Deborah AU - Russell, Trevor PY - 2023/10/31 TI - Rehabilitation Supported by Immersive Virtual Reality for Adults With Communication Disorders: Semistructured Interviews and Usability Survey Study JO - JMIR Rehabil Assist Technol SP - e46959 VL - 10 KW - communication disorders KW - speech and language therapy KW - rehabilitation KW - virtual reality KW - human-computer interaction KW - technology acceptance KW - acceptance KW - communication KW - therapy KW - usefulness KW - usability KW - survey KW - barrier KW - mobile phone N2 - Background: Individuals who have acquired communication disorders often struggle to transfer the skills they learn during therapy sessions to real-life situations. Immersive virtual reality (VR) technology has the potential to create realistic communication environments that can be used both in clinical settings and for practice at home by individuals with communication disorders. Objective: This research aims to enhance our understanding of the acceptance, usefulness, and usability of a VR application (SIM:Kitchen), designed for communication rehabilitation. Additionally, this research aims to identify the perceived barriers and benefits of using VR technology from the perspective of individuals with acquired communication disorders. Methods: Semistructured interviews and usability surveys were conducted with 10 individuals with acquired neurogenic communication disorders aged 46-81 (mean 58, SD 9.57) years after trialing an immersive VR application. The audio-recorded interviews were transcribed and analyzed to identify themes. Results: The quantitative data regarding the usability of the system associated with participants? immersion experience in the VR application were promising. Findings from semistructured interviews are discussed across five key thematic areas including (1) participant?s attitude toward VR, (2) perceived usefulness of the VR system, (3) perceived ease of use of the VR system, (4) their willingness to continue using VR, and (5) the factors they perceived as challenges or facilitators to adopting this VR technology. Conclusions: Overall, participants in this study found the VR experience to be enjoyable and were impressed by the realism of the VR application designed for communication rehabilitation. This study highlighted personally relevant, immersive VR interventions with different levels of task difficulty that could enhance technology uptake in the context of communication rehabilitation. However, it is essential that VR hand controller technology is refined to be more naturalistic in movement and able to accommodate user capabilities. UR - https://rehab.jmir.org/2023/1/e46959 UR - http://dx.doi.org/10.2196/46959 UR - http://www.ncbi.nlm.nih.gov/pubmed/37906228 ID - info:doi/10.2196/46959 ER - TY - JOUR AU - Engineer, Margi AU - Kot, Sushant AU - Dixon, Emma PY - 2023/10/25 TI - Investigating the Readability and Linguistic, Psychological, and Emotional Characteristics of Digital Dementia Information Written in the English Language: Multitrait-Multimethod Text Analysis JO - JMIR Form Res SP - e48143 VL - 7 KW - natural language processing KW - consumer health information KW - readability KW - Alzheimer disease and related dementias KW - caregivers N2 - Background: Past research in the Western context found that people with dementia search for digital dementia information in peer-reviewed medical research articles, dementia advocacy and medical organizations, and blogs written by other people with dementia. This past work also demonstrated that people with dementia do not perceive English digital dementia information as emotionally or cognitively accessible. Objective: In this study, we sought to investigate the readability; linguistic, psychological, and emotional characteristics; and target audiences of digital dementia information. We conducted a textual analysis of 3 different types of text-based digital dementia information written in English: 300 medical articles, 35 websites, and 50 blogs. Methods: We assessed the text?s readability using the Flesch Reading Ease and Flesch-Kincaid Grade Level measurements, as well as tone, analytical thinking, clout, authenticity, and word frequencies using a natural language processing tool, Linguistic Inquiry and Word Count Generator. We also conducted a thematic analysis to categorize the target audiences for each information source and used these categorizations for further statistical analysis. Results: The median Flesch-Kincaid Grade Level readability score and Flesch Reading Ease score for all types of information (N=1139) were 12.1 and 38.6, respectively, revealing that the readability scores of all 3 information types were higher than the minimum requirement. We found that medical articles had significantly (P=.05) higher word count and analytical thinking scores as well as significantly lower clout, authenticity, and emotional tone scores than websites and blogs. Further, blogs had significantly (P=.48) higher word count and authenticity scores but lower analytical scores than websites. Using thematic analysis, we found that most of the blogs (156/227, 68.7%) and web pages (399/612, 65.2%) were targeted at people with dementia. Website information targeted at a general audience had significantly lower readability scores. In addition, website information targeted at people with dementia had higher word count and lower emotional tone ratings. The information on websites targeted at caregivers had significantly higher clout and lower authenticity scores. Conclusions: Our findings indicate that there is an abundance of digital dementia information written in English that is targeted at people with dementia, but this information is not readable by a general audience. This is problematic considering that people with <12 years of education are at a higher risk of developing dementia. Further, our findings demonstrate that digital dementia information written in English has a negative tone, which may be a contributing factor to the mental health crisis many people with dementia face after receiving a diagnosis. Therefore, we call for content creators to lower readability scores to make the information more accessible to a general audience and to focus their efforts on providing information in a way that does not perpetuate overly negative narratives of dementia. UR - https://formative.jmir.org/2023/1/e48143 UR - http://dx.doi.org/10.2196/48143 UR - http://www.ncbi.nlm.nih.gov/pubmed/37878351 ID - info:doi/10.2196/48143 ER - TY - JOUR AU - Groot, Julia AU - MacLellan, Alexander AU - Butler, Madelaine AU - Todor, Elisa AU - Zulfiqar, Mahnoor AU - Thackrah, Timothy AU - Clarke, Christopher AU - Brosnan, Mark AU - Ainsworth, Ben PY - 2023/10/19 TI - The Effectiveness of Fully Automated Digital Interventions in Promoting Mental Well-Being in the General Population: Systematic Review and Meta-Analysis JO - JMIR Ment Health SP - e44658 VL - 10 KW - mental well-being KW - promotion KW - intervention KW - digital KW - web-based KW - apps KW - mobile phone N2 - Background: Recent years have highlighted an increasing need to promote mental well-being in the general population. This has led to a rapidly growing market for fully automated digital mental well-being tools. Although many individuals have started using these tools in their daily lives, evidence on the overall effectiveness of digital mental well-being tools is currently lacking. Objective: This study aims to review the evidence on the effectiveness of fully automated digital interventions in promoting mental well-being in the general population. Methods: Following the preregistration of the systematic review protocol on PROSPERO, searches were carried out in MEDLINE, Web of Science, Cochrane, PsycINFO, PsycEXTRA, Scopus, and ACM Digital (initial searches in February 2022; updated in October 2022). Studies were included if they contained a general population sample and a fully automated digital intervention that exclusively used psychological mental well-being promotion activities. Two reviewers, blinded to each other?s decisions, conducted data selection, extraction, and quality assessment of the included studies. Narrative synthesis and a random-effects model of per-protocol data were adopted. Results: We included 19 studies that involved 7243 participants. These studies included 24 fully automated digital mental well-being interventions, of which 15 (63%) were included in the meta-analysis. Compared with no intervention, there was a significant small effect of fully automated digital mental well-being interventions on mental well-being in the general population (standardized mean difference 0.19, 95% CI 0.04-0.33; P=.02). Specifically, mindfulness-, acceptance-, commitment-, and compassion-based interventions significantly promoted mental well-being in the general population (P=.006); insufficient evidence was available for positive psychology and cognitive behavioral therapy?based interventions; and contraindications were found for integrative approaches. Overall, there was substantial heterogeneity, which could be partially explained by the intervention duration, comparator, and study outcomes. The risk of bias was high, and confidence in the quality of the evidence was very low (Grading of Recommendations, Assessment, Development, and Evaluations), primarily because of the high rates of study dropout (average 37%; range 0%-85%) and suboptimal intervention adherence (average 40%). Conclusions: This study provides a novel contribution to knowledge regarding the effectiveness, strengths, and weaknesses of fully automated digital mental well-being interventions in the general population. Future research and practice should consider these findings when developing fully automated digital mental well-being tools. In addition, research should aim to investigate positive psychology and cognitive behavioral therapy?based tools as well as develop further strategies to improve adherence and reduce dropout in fully automated digital mental well-being interventions. Finally, it should aim to understand when and for whom these interventions are particularly beneficial. Trial Registration: PROSPERO CRD42022310702; https://tinyurl.com/yc7tcwy7 UR - https://mental.jmir.org/2023/1/e44658 UR - http://dx.doi.org/10.2196/44658 UR - http://www.ncbi.nlm.nih.gov/pubmed/37856172 ID - info:doi/10.2196/44658 ER - TY - JOUR AU - Ryan, T. Arthur AU - Stearns-Yoder, A. Kelly AU - Brenner, A. Lisa PY - 2023/10/18 TI - Real-World User Demographics of Three Web-Based Digital Mental Health Interventions Provided by the US Department of Veterans Affairs: Observational Study Using Web Analytics Data JO - JMIR Form Res SP - e48365 VL - 7 KW - digital intervention KW - unguided KW - web-based KW - internet-delivered KW - mental health KW - veterans KW - Google Analytics KW - insomnia KW - anger KW - depression KW - mobile phone N2 - Background: Unguided digital mental health interventions (UDMHIs) have the potential to provide low-cost and effective mental health care at scale. Controlled trials have demonstrated the efficacy of UDMHIs to address mental health symptoms and conditions. However, few previous publications have described the demographics of real-world users of UDMHIs that are freely available to the public. The US Department of Veterans Affairs has created and hosts several UDMHIs on its Veteran Training Portal website. These web-based, free-to-use, and publicly available UDMHIs include Path to Better Sleep, Anger and Irritability Management Skills, and Moving Forward, which focus on insomnia, problematic anger, and depression symptoms, respectively. Objective: This study aimed to examine the user demographics of these 3 UDMHIs in the year 2021. In addition, it aimed to compare the age and gender distribution of the users of those 3 UDMHIs with one another and with the age and gender distribution of the total US veteran population. Methods: Google Analytics was used to collect user data for each of the 3 UDMHIs. The age and gender distribution of the users of each UDMHI was compared with that of the other UDMHIs as well as with that of the overall US veteran population using chi-square tests. Information on the total number of users, the country they were in, and the devices they used to access the UDMHIs was also collected and reported. Results: In 2021, the 3 UDMHIs together recorded 29,306 unique users. The estimated age range and gender were available for 24.12% (7068/29,306) of those users. Each UDMHI?s age and gender distribution significantly differed from that of the other UDMHIs and from that of the overall US veteran population (P<.001 on all chi-square tests). Women and younger age groups were overrepresented among UDMHI users compared with the overall US veteran population. The majority of devices used to access the UDMHIs were desktop or laptop devices, although a substantial proportion of devices used were mobile devices (10,199/29,752, 34.28%). Most users (27,789/29,748, 93.41%) were located in the United States, with users from Canada, the United Kingdom, and Australia accounting for another 2.61% (775/29,748) of total users. Conclusions: Our use of Google Analytics data provided useful information about the users of 3 free and publicly available UDMHIs provided by the US Department of Veterans Affairs. Although our findings should be considered in light of the limitations of autonomously collected web analytics data, they still offer useful information for health care policy makers, administrators, and UDMHI developers. UR - https://formative.jmir.org/2023/1/e48365 UR - http://dx.doi.org/10.2196/48365 UR - http://www.ncbi.nlm.nih.gov/pubmed/37851501 ID - info:doi/10.2196/48365 ER - TY - JOUR AU - Shalev, Ligat AU - Bistre, Moises AU - Lubin, Gadi AU - Avirame, Keren AU - Raskin, Sergey AU - Linkovski, Omer AU - Eitan, Renana AU - Rose, J. Adam PY - 2023/10/17 TI - Enabling Expedited Disposition of Emergencies Using Telepsychiatry in Israel: Protocol for a Hybrid Implementation Study JO - JMIR Res Protoc SP - e49405 VL - 12 KW - eHealth KW - telepsychiatry KW - digital health service KW - emergency department KW - mental health KW - implementation science KW - Promoting Action on Research Implementation in Health Services KW - organizational innovation N2 - Background: Telepsychiatry is the use of virtual communication, such as a video link, to deliver mental health assessment, treatment, and follow-up. Previous studies have shown telepsychiatry to be feasible, accurate compared with in-person practice, and satisfying for psychiatrists and patients. Telepsychiatry has also been associated with reduced waiting times for evaluation and, in some studies, lower admission rates. However, most previous studies focused on using telepsychiatry in community settings and not on involuntary admission. Objective: The aim of this study is to examine the effectiveness and implementation process of patient assessment for involuntary admissions in the psychiatric emergency department (ED) using a video link. Methods: This type 1 hybrid implementation study will examine telepsychiatry effectiveness and the implementation process, by comparing telepsychiatry (n=240) with historical controls who had a face-to-face evaluation (n=240) during the previous, usual care period in 5 psychiatric EDs in Israel. A temporary waiver of the standing policy requiring in-person evaluations only, for the purpose of research, was obtained from the Israeli Ministry of Health. During the telepsychiatry phase, clinical staff and patients will join a video call from the ED, while the attending physician will log in elsewhere. The Promoting Action on Research Implementation in Health Services (PARIHS) framework will guide the evaluation of the telepsychiatry implementation process in the ED. PARIHS has the following 3 constructs: (1) evidence: staff's opinions regarding the innovation?s viability and practicality, their satisfaction levels with its use, and patients' perceptions of the change; (2) context: level of approval of new strategies in the ED, decision-making processes, and the manner in which clinical teams converse and work together; (3) facilitation: adequacy of the facilitation efforts using champions reports. Primary clinical outcomes include ED length of stay and violent incidents obtained from medical records. Results: This study received Helsinki approval from the Ethics Committee of Abarbanel Mental Health Center (174; March 13, 2023), Jerusalem Mental Health Center (22-21; November 6, 2022), Lev-Hasharon Mental Health Medical Center (LH12023; February 12, 2023), Tel-Aviv Medical Center (TLV-22-0656; January 3, 2023), and Sha'ar Menashe (1-4-23; April 18, 2023). Data collection began in July 2023 in 2 study sites and will begin soon at the others. Conclusions: Telepsychiatry could have significant benefits for patients in the psychiatric ED. Examining telepsychiatry effectiveness in the ED, in addition to identifying the facilitators and barriers of implementing it in different emergency settings, will facilitate better policy decisions regarding its implementation. Trial Registration: ClinicalTrials.gov NCT05771545; https://clinicaltrials.gov/study/NCT05771545 International Registered Report Identifier (IRRID): DERR1-10.2196/49405 UR - https://www.researchprotocols.org/2023/1/e49405 UR - http://dx.doi.org/10.2196/49405 UR - http://www.ncbi.nlm.nih.gov/pubmed/37847548 ID - info:doi/10.2196/49405 ER - TY - JOUR AU - Hoffman, Valerie AU - Flom, Megan AU - Mariano, Y. Timothy AU - Chiauzzi, Emil AU - Williams, Andre AU - Kirvin-Quamme, Andrew AU - Pajarito, Sarah AU - Durden, Emily AU - Perski, Olga PY - 2023/10/13 TI - User Engagement Clusters of an 8-Week Digital Mental Health Intervention Guided by a Relational Agent (Woebot): Exploratory Study JO - J Med Internet Res SP - e47198 VL - 25 KW - anxiety KW - clustering KW - depression KW - digital health KW - digital mental health intervention KW - mental health KW - relational agents KW - user engagement N2 - Background: With the proliferation of digital mental health interventions (DMHIs) guided by relational agents, little is known about the behavioral, cognitive, and affective engagement components associated with symptom improvement over time. Obtaining a better understanding could lend clues about recommended use for particular subgroups of the population, the potency of different intervention components, and the mechanisms underlying the intervention?s success. Objective: This exploratory study applied clustering techniques to a range of engagement indicators, which were mapped to the intervention?s active components and the connect, attend, participate, and enact (CAPE) model, to examine the prevalence and characterization of each identified cluster among users of a relational agent-guided DMHI. Methods: We invited adults aged 18 years or older who were interested in using digital support to help with mood management or stress reduction through social media to participate in an 8-week DMHI guided by a natural language processing?supported relational agent, Woebot. Users completed assessments of affective and cognitive engagement, working alliance as measured by goal and task working alliance subscale scores, and enactment (ie, application of therapeutic recommendations in real-world settings). The app passively collected data on behavioral engagement (ie, utilization). We applied agglomerative hierarchical clustering analysis to the engagement indicators to identify the number of clusters that provided the best fit to the data collected, characterized the clusters, and then examined associations with baseline demographic and clinical characteristics as well as mental health outcomes at week 8. Results: Exploratory analyses (n=202) supported 3 clusters: (1) ?typical utilizers? (n=81, 40%), who had intermediate levels of behavioral engagement; (2) ?early utilizers? (n=58, 29%), who had the nominally highest levels of behavioral engagement in week 1; and (3) ?efficient engagers? (n=63, 31%), who had significantly higher levels of affective and cognitive engagement but the lowest level of behavioral engagement. With respect to mental health baseline and outcome measures, efficient engagers had significantly higher levels of baseline resilience (P<.001) and greater declines in depressive symptoms (P=.01) and stress (P=.01) from baseline to week 8 compared to typical utilizers. Significant differences across clusters were found by age, gender identity, race and ethnicity, sexual orientation, education, and insurance coverage. The main analytic findings remained robust in sensitivity analyses. Conclusions: There were 3 distinct engagement clusters found, each with distinct baseline demographic and clinical traits and mental health outcomes. Additional research is needed to inform fine-grained recommendations regarding optimal engagement and to determine the best sequence of particular intervention components with known potency. The findings represent an important first step in disentangling the complex interplay between different affective, cognitive, and behavioral engagement indicators and outcomes associated with use of a DMHI incorporating a natural language processing?supported relational agent. Trial Registration: ClinicalTrials.gov NCT05672745; https://classic.clinicaltrials.gov/ct2/show/NCT05672745 UR - https://www.jmir.org/2023/1/e47198 UR - http://dx.doi.org/10.2196/47198 UR - http://www.ncbi.nlm.nih.gov/pubmed/37831490 ID - info:doi/10.2196/47198 ER - TY - JOUR AU - Francis-Oliviero, Florence AU - Loubières, Céline AU - Grové, Christine AU - Marinucci, Alexandra AU - Shankland, Rebecca AU - Salamon, Réda AU - Perez, Emmanuelle AU - Garancher, Laure AU - Galera, Cédric AU - Gaillard, Elsa AU - Orri, Massimiliano AU - González-Caballero, Luis Juan AU - Montagni, Ilaria PY - 2023/10/5 TI - Improving Children?s Mental Health Literacy Through the Cocreation of an Intervention and Scale Validation: Protocol for the CHILD-Mental Health Literacy Research Study JO - JMIR Res Protoc SP - e51096 VL - 12 KW - child KW - mental health KW - literacy KW - intervention KW - scale N2 - Background: Children?s mental health is a public health priority, with 1 in 5 European children younger than 12 years having a behavioral, developmental, or psychological disorder. Mental health literacy (MHL) is a modifiable determinant of mental health, promoting psychological well-being and reducing mental health problems. Despite its significance, no interventions or scales currently exist for increasing and measuring MHL in this population. Objective: This study has dual objectives: (1) cocreating and evaluating an intervention on children?s MHL, and (2) developing and validating a scale that measures children?s MHL. Methods: Our study focuses on children aged 9-11 years attending primary school classes in various settings, including urban and rural areas, and priority education zones within a French department. Using a participatory research approach, we will conduct workshops involving children, parents, teachers, and 1 artist to cocreate an intervention comprising multiple tools (eg, a pedagogical kit and videos). This intervention will undergo initial evaluation in 4 classes through observations, interviews, and satisfaction questionnaires to assess its viability. Concurrently, the artist will collaborate with children to create the initial version of the CHILD-MHL scale, which will then be administered to 300 children. Psychometric analyses will validate the scale. Subsequently, we will conduct a cluster randomized controlled trial involving a minimum of 20 classes, using the CHILD-MHL scale scores as the primary end point to evaluate the intervention?s efficacy. Additional interviews will complement this mixed methods evaluation. Both the intervention and the scale are grounded in the Child-Focused MHL model. Results: The first tool of the intervention is the pedagogical kit Le Jardin du Dedans, supported by the public organization Psycom Santé Mentale Info and endorsed by UNICEF (United Nations Children?s Fund) France. The second tool is a handbook by the Pan American Health Organization and the World Health Organization, which is addressed to teachers to sensitize them to children?s mental health problems. The third is a 5-page supplementary leaflet produced by the nongovernmental organization The Ink Link, which teaches children the notion of MHL. Finally, we produced 56 items of the MHL Scale and listed existing education policies for children?s mental health. Conclusions: After its robust evaluation, the intervention could be extended to several schools in France. The scale will be the first in the world to measure children?s MHL. It will be used not only to evaluate interventions but also to provide data for decision makers to include MHL in all educational policies. Both the intervention and the scale could be translated into other languages. International Registered Report Identifier (IRRID): PRR1-10.2196/51096 UR - https://www.researchprotocols.org/2023/1/e51096 UR - http://dx.doi.org/10.2196/51096 UR - http://www.ncbi.nlm.nih.gov/pubmed/37796588 ID - info:doi/10.2196/51096 ER - TY - JOUR AU - Kim, Hyeonseong AU - Jeong, Seohyun AU - Hwang, Inae AU - Sung, Kiyoung AU - Moon, Woori AU - Shin, Min-Sup PY - 2023/9/29 TI - Validation of a Brief Internet-Based Self-Report Measure of Maladaptive Personality and Interpersonal Schema: Confirmatory Factor Analysis JO - Interact J Med Res SP - e48425 VL - 12 KW - maladaptive schema KW - measure of schema KW - self-report measure KW - internet-based measure KW - digital mental health care KW - interpersonal schema N2 - Background: Existing digital mental health interventions mainly focus on the symptoms of specific mental disorders, but do not focus on Maladaptive Personalities and Interpersonal Schemas (MPISs). As an initial step toward considering personalities and schemas in intervention programs, there is a need for the development of tools for measuring core personality traits and interpersonal schemas known to cause psychological discomfort among potential users of digital mental health interventions. Thus, the MPIS was developed. Objective: The objectives of this study are to validate the MPIS by comparing 2 models of the MPIS factor structure and to understand the characteristics of the MPIS by assessing its correlations with other measures. Methods: Data were collected from 234 participants who were using web-based community sites in South Korea, including university students, graduate students, working professionals, and homemakers. All the data were gathered through web-based surveys. Confirmatory factor analysis was used to compare a single-factor model with a 5-factor model. Reliability and correlation analyses with other scales were performed. Results: The results of confirmatory factor analysis indicated that the 5-factor model (?2550=1278.1; Tucker-Lewis index=0.80; comparative fit index=0.81; and Root Mean Square Error of Approximation=0.07) was more suitable than the single-factor model (?2560=2341.5; Tucker-Lewis index=0.52; comparative fit index=0.54; and Root Mean Square Error of Approximation=0.11) for measuring maladaptive personality traits and interpersonal relationship patterns. The internal consistency of each factor of the MPIS was good (Cronbach ?=.71-.88), and the correlations with existing measures were statistically significant. The MPIS is a validated 35-item tool for measuring 5 essential personality traits and interpersonal schemas in adults aged 18-39 years. Conclusions: This study introduced the MPIS, a concise and effective questionnaire capable of measuring maladaptive personality traits and interpersonal relationship schemas. Through analysis, the MPIS was shown to reliably assess these psychological constructs and validate them. Its web-based accessibility and reduced item count make it a valuable tool for mental health assessment. Future applications include its integration into digital mental health care services, allowing easy web-based administration and aiding in the classification of psychological therapy programs based on the obtained results. Trial Registration: ClinicalTrials.gov NCT05952063; https://www.clinicaltrials.gov/study/NCT05952063 UR - https://www.i-jmr.org/2023/1/e48425 UR - http://dx.doi.org/10.2196/48425 UR - http://www.ncbi.nlm.nih.gov/pubmed/37773606 ID - info:doi/10.2196/48425 ER - TY - JOUR AU - Forman-Hoffman, L. Valerie AU - Pirner, C. Maddison AU - Flom, Megan AU - Kirvin-Quamme, Andrew AU - Durden, Emily AU - Kissinger, A. Jennifer AU - Robinson, Athena PY - 2023/9/27 TI - Engagement, Satisfaction, and Mental Health Outcomes Across Different Residential Subgroup Users of a Digital Mental Health Relational Agent: Exploratory Single-Arm Study JO - JMIR Form Res SP - e46473 VL - 7 KW - adoption KW - anxiety KW - chatbot KW - cognitive behavioral therapy KW - conversational agent KW - CBT KW - depression KW - digital health KW - medically underserved area KW - mental health KW - mhealth KW - mobile app KW - mobile health KW - mobile phone KW - mood KW - psychotherapy KW - relational agent KW - rural KW - satisfaction KW - smartphone app KW - smartphone KW - underserved KW - usage KW - vulnerable N2 - Background: Mental illness is a pervasive worldwide public health issue. Residentially vulnerable populations, such as those living in rural medically underserved areas (MUAs) or mental health provider shortage areas (MHPSAs), face unique access barriers to mental health care. Despite the growth of digital mental health interventions using relational agent technology, little is known about their use patterns, efficacy, and favorability among residentially vulnerable populations. Objective: This study aimed to explore differences in app use, therapeutic alliance, mental health outcomes, and satisfaction across residential subgroups (metropolitan, nonmetropolitan, or rural), MUAs (yes or no), and MHPSAs (yes or no) among users of a smartphone-based, digital mental health intervention, Woebot LIFE (WB-LIFE). WB-LIFE was designed to help users better understand and manage their moods and features a relational agent, Woebot, that converses through text-based messages. Methods: We used an exploratory study that examined data from 255 adults enrolled in an 8-week, single-arm trial of WB-LIFE. Analyses compared levels of app use and therapeutic alliance total scores as well as subscales (goal, task, and bond), mental health outcomes (depressive and anxiety symptoms, stress, resilience, and burnout), and program satisfaction across residential subgroups. Results: Few study participants resided in nonmetropolitan (25/255, 10%) or rural (3/255, 1%) areas, precluding estimates across this variable. Despite a largely metropolitan sample, nearly 39% (99/255) resided in an MUA and 55% (141/255) in an MHPSA. There were no significant differences in app use or satisfaction by MUA or MHPSA status. There also were no differences in depressive symptoms, anxiety, stress, resilience, or burnout, with the exception of MUA participants having higher baseline depressive symptoms among those starting in the moderate range or higher (Patient Health Questionnaire-8 item scale?10) than non-MUA participants (mean 16.50 vs 14.41, respectively; P=.01). Although working alliance scores did not differ by MHPSA status, those who resided in an MUA had higher goal (2-tailed t203.47=2.21; P=.03), and bond (t203.47=1.94; P=.05) scores at day 3 (t192.98=2.15; P=.03), and higher goal scores at week 8 (t186.19=2.28; P=.02) as compared with those not living in an MUA. Conclusions: Despite the study not recruiting many participants from rural or nonmetropolitan populations, sizable proportions resided in an MUA or an MHPSA. Analyses revealed few differences in app use, therapeutic alliance, mental health outcomes (including baseline levels), or satisfaction across MUA or MHPSA status over the 8-week study. Findings suggest that vulnerable residential populations may benefit from using digital agent?guided cognitive behavioral therapy. Trial Registration: ClinicalTrials.gov NCT05672745; https://clinicaltrials.gov/study/NCT05672745 UR - https://formative.jmir.org/2023/1/e46473 UR - http://dx.doi.org/10.2196/46473 UR - http://www.ncbi.nlm.nih.gov/pubmed/37756047 ID - info:doi/10.2196/46473 ER - TY - JOUR AU - Sayer, A. Nina AU - Nelson, B. David AU - Gradus, L. Jaimie AU - Sripada, K. Rebecca AU - Murdoch, Maureen AU - Teo, R. Alan AU - Orazem, J. Robert AU - Cerel, Julie PY - 2023/9/26 TI - The Effects of Suicide Exposure on Mental Health Outcomes Among Post-9/11 Veterans: Protocol for an Explanatory, Sequential, Mixed Methods Study JO - JMIR Res Protoc SP - e51324 VL - 12 KW - veterans KW - suicide KW - death KW - posttraumatic stress disorder KW - bereavement KW - health services N2 - Background: The toll associated with suicide goes well beyond the individual who died. This study focuses on a risk factor for veteran suicide that has received little previous empirical attention?exposure to the suicide death of another person. Objective: The study?s primary objective is to describe the mental health outcomes associated with suicide exposure among veterans who served on active duty after September 2001 (?post-9/11?). The secondary objective is to elucidate why some veterans develop persistent problems following suicide exposure, whereas others do not. Methods: This is an explanatory, sequential, mixed methods study of a nationally representative sample of post-9/11 veterans enrolled in Department of Veterans Affairs (VA) health care. Our sampling strategy was designed for adequate representation of female and American Indian and Alaska Native veterans to allow for examination of associations between suicide exposure and outcomes within these groups. Primary outcomes comprise mental health problems associated with trauma and loss (posttraumatic stress disorder and prolonged grief disorder) and suicide precursors (suicidal ideation, attempts, and planning). Data collection will be implemented in 3 waves. During wave 1, we will field a brief survey to a national probability sample to assess exposure history (suicide, other sudden death, or neither) and exposure characteristics (eg, closeness with the decedent) among 11,400 respondents. In wave 2, we will include 39.47% (4500/11,400) of the wave-1 respondents, stratified by exposure history (suicide, other sudden death, or neither), to assess health outcomes and other variables of interest. During wave 3, we will conduct interviews with a purposive subsample of 32 respondents exposed to suicide who differ in mental health outcomes. We will supplement the survey and interview data with VA administrative data identifying diagnoses, reported suicide attempts, and health care use. Results: The study began on July 1, 2022, and will end on June 30, 2026. This is the only national, population-based study of suicide exposure in veterans and the first one designed to study differences based on sex and race. Comparing those exposed to suicide with those exposed to sudden death for reasons other than suicide (eg, combat) and those unexposed to any sudden death may allow for the identification of the common and unique contribution of suicide exposure to outcomes and help seeking. Conclusions: Integrating survey, qualitative, and VA administrative data to address significant knowledge gaps regarding the effects of suicide exposure in a national sample will lay the foundation for interventions to address the needs of individuals affected by a suicide death, including female and American Indian and Alaska Native veterans. International Registered Report Identifier (IRRID): DERR1-10.2196/51324 UR - https://www.researchprotocols.org/2023/1/e51324 UR - http://dx.doi.org/10.2196/51324 UR - http://www.ncbi.nlm.nih.gov/pubmed/37751271 ID - info:doi/10.2196/51324 ER - TY - JOUR AU - Bizzotto, Nicole AU - Schulz, Johannes Peter AU - de Bruijn, Gert-Jan PY - 2023/9/18 TI - The ?Loci? of Misinformation and Its Correction in Peer- and Expert-Led Online Communities for Mental Health: Content Analysis JO - J Med Internet Res SP - e44656 VL - 25 KW - online communities KW - social media KW - mental health KW - misinformation KW - empowerment KW - content analysis KW - online community KW - infodemiology KW - information seeking KW - help seeking KW - information behavior KW - online search KW - search query KW - information quality KW - information accuracy N2 - Background: Mental health problems are recognized as a pressing public health issue, and an increasing number of individuals are turning to online communities for mental health to search for information and support. Although these virtual platforms have the potential to provide emotional support and access to anecdotal experiences, they can also present users with large amounts of potentially inaccurate information. Despite the importance of this issue, limited research has been conducted, especially on the differences that might emerge due to the type of content moderation of online communities: peer-led or expert-led. Objective: We aim to fill this gap by examining the prevalence, the communicative context, and the persistence of mental health misinformation on Facebook online communities for mental health, with a focus on understanding the mechanisms that enable effective correction of inaccurate information and differences between expert-led and peer-led groups. Methods: We conducted a content analysis of 1534 statements (from 144 threads) in 2 Italian-speaking Facebook groups. Results: The study found that an alarming number of comments (26.1%) contained medically inaccurate information. Furthermore, nearly 60% of the threads presented at least one misinformation statement without any correction attempt. Moderators were more likely to correct misinformation than members; however, they were not immune to posting content containing misinformation, which was an unexpected finding. Discussions about aspects of treatment (including side effects or treatment interruption) significantly increased the probability of encountering misinformation. Additionally, the study found that misinformation produced in the comments of a thread, rather than as the first post, had a lower probability of being corrected, particularly in peer-led communities. Conclusions: The high prevalence of misinformation in online communities, particularly when left uncorrected, underscores the importance of conducting additional research to identify effective mechanisms to prevent its spread. This is especially important given the study?s finding that misinformation tends to be more prevalent around specific ?loci? of discussion that, once identified, can serve as a starting point to develop strategies for preventing and correcting misinformation within them. UR - https://www.jmir.org/2023/1/e44656 UR - http://dx.doi.org/10.2196/44656 UR - http://www.ncbi.nlm.nih.gov/pubmed/37721800 ID - info:doi/10.2196/44656 ER - TY - JOUR AU - Lynham, Joanne Amy AU - Jones, R. Ian AU - Walters, R. James T. PY - 2023/9/13 TI - Cardiff Online Cognitive Assessment in a National Sample: Cross-Sectional Web-Based Study JO - J Med Internet Res SP - e46675 VL - 25 KW - cognition KW - digital assessment KW - mental health KW - mobile phone KW - normative data KW - web-based KW - cognitive assessment KW - CONCA N2 - Background: Psychiatric disorders are associated with cognitive impairment. We have developed a web-based, 9-task cognitive battery to measure the core domains affected in people with psychiatric disorders. To date, this assessment has been used to collect data on a clinical sample of participants with psychiatric disorders. Objective: The aims of this study were (1) to establish a briefer version of the battery (called the Cardiff Online Cognitive Assessment [CONCA]) that can give a valid measure of cognitive ability (?g?) and (2) to collect normative data and demonstrate CONCA?s application in a health population sample. Methods: Based on 6 criteria and data from our previous study, we selected 5 out of the original 9 tasks to include in CONCA. These included 3 core tasks that were sufficient to derive a measure of ?g? and 2 optional tasks. Participants from a web-based national cohort study (HealthWise Wales) were invited to complete CONCA. Completion rates, sample characteristics, performance distributions, and associations between cognitive performance and demographic characteristics and mental health measures were examined. Results: A total of 3679 participants completed at least one CONCA task, of which 3135 completed all 3 core CONCA tasks. Performance on CONCA was associated with age (B=?0.05, SE 0.002; P<.001), device (tablet computer: B=?0.26, SE 0.05; P<.001; smartphone: B=?0.46, SE 0.05; P<.001), education (degree: B=1.68, SE 0.14; P<.001), depression symptoms (B=?0.04, SE 0.01; P<.001), and anxiety symptoms (B=?0.04, SE 0.01; P<.001). Conclusions: CONCA provides a valid measure of ?g,? which can be derived using as few as 3 tasks that take no more than 15 minutes. Performance on CONCA showed associations with demographic characteristics in the expected direction and was associated with current depression and anxiety symptoms. The effect of device on cognitive performance is an important consideration for research using web-based assessments. UR - https://www.jmir.org/2023/1/e46675 UR - http://dx.doi.org/10.2196/46675 UR - http://www.ncbi.nlm.nih.gov/pubmed/37703073 ID - info:doi/10.2196/46675 ER - TY - JOUR AU - Zhang, Xiaolong AU - Lewis, Shôn AU - Carter, Lesley-Anne AU - Bucci, Sandra PY - 2023/9/12 TI - A Digital System (YouXin) to Facilitate Self-Management by People With Psychosis in China: Protocol for a Nonrandomized Validity and Feasibility Study With a Mixed Methods Design JO - JMIR Res Protoc SP - e45170 VL - 12 KW - psychosis KW - self-management KW - digital KW - smartphone app KW - eHealth KW - mHealth KW - China N2 - Background: Psychosis is one of the most disabling mental health conditions and causes significant personal, social, and economic burden. Accurate and timely symptom monitoring is critical to offering prompt and time-sensitive clinical services. Digital health is a promising solution for the barriers encountered by conventional symptom monitoring approaches, including accessibility, the ecological validity of assessments, and recall bias. However, to date, there has been no digital health technology developed to support self-management for people with psychosis in China. Objective: We report the study protocol to evaluate the validity, feasibility, acceptability, usability, and safety of a symptom self-monitoring smartphone app (YouXin; Chinese name ??) for people with psychosis in China. Methods: This is a nonrandomized validity and feasibility study with a mixed methods design. The study was approved by the University of Manchester and Beijing Anding Hospital Research Ethics Committee. YouXin is a smartphone app designed to facilitate symptom self-monitoring for people with psychosis. YouXin has 2 core functions: active monitoring of symptoms (ie, smartphone survey) and passive monitoring of behavioral activity (ie, passive data collection via embedded smartphone sensors). The development process of YouXin utilized a systematic coproduction approach. A series of coproduction consultation meetings was conducted by the principal researcher with service users and clinicians to maximize the usability and acceptability of the app for end users. Participants with psychosis aged 16 years to 65 years were recruited from Beijing Anding Hospital, Beijing, China. All participants were invited to use the YouXin app to self-monitor symptoms for 4 weeks. At the end of the 4-week follow-up, we invited participants to take part in a qualitative interview to explore the acceptability of the app and trial procedures postintervention. Results: Recruitment to the study was initiated in August 2022. Of the 47 participants who were approached for the study from August 2022 to October 2022, 41 participants agreed to take part in the study. We excluded 1 of the 41 participants for not meeting the inclusion criteria, leaving a total of 40 participants who began the study. As of December 2022, 40 participants had completed the study, and the recruitment was complete. Conclusions: This study is the first to develop and test a symptom self-monitoring app specifically designed for people with psychosis in China. If the study shows the feasibility of YouXin, a potential future direction is to integrate the app into clinical workflows to facilitate digital mental health care for people with psychosis in China. This study will inform improvements to the app, trial procedures, and implementation strategies with this population. Moreover, the findings of this trial could lead to optimization of digital health technologies designed for people with psychosis in China. International Registered Report Identifier (IRRID): DERR1-10.2196/45170 UR - https://www.researchprotocols.org/2023/1/e45170 UR - http://dx.doi.org/10.2196/45170 UR - http://www.ncbi.nlm.nih.gov/pubmed/37698905 ID - info:doi/10.2196/45170 ER - TY - JOUR AU - Huffman, Landry AU - Lawrence-Sidebottom, Darian AU - Huberty, Jennifer AU - Beatty, Clare AU - Roots, Monika AU - Roots, Kurt AU - Parikh, Amit AU - Guerra, Rachael PY - 2023/9/6 TI - Satisfaction, Perceived Usefulness, and Therapeutic Alliance as Correlates of Participant Engagement in a Pediatric Digital Mental Health Intervention: Cross-Sectional Questionnaire Study JO - JMIR Form Res SP - e49384 VL - 7 KW - service satisfaction KW - satisfaction KW - patient-provider KW - adolescent KW - child KW - children KW - youth KW - mental health KW - perceived usefulness KW - internet-based coaching KW - coach KW - coaching KW - internet-based therapy KW - collaborative care KW - digital mental health intervention KW - engagement N2 - Background: Although evidence suggests that digital mental health interventions (DMHIs) are effective alternatives to traditional mental health care, participant engagement continues to be an issue, especially for pediatric DMHIs. Extant studies of DMHIs among adults suggest that participants? satisfaction, perceived usefulness, and therapeutic alliance are closely tied to engagement. However, these associations have not been investigated among children and adolescents involved in DMHIs. Objective: To address these gaps in extant DMHI research, the purpose of this study was to (1) develop and implement a measure to assess satisfaction, perceived usefulness, and therapeutic alliance among children and adolescents participating in a DMHI and (2) investigate satisfaction, perceived usefulness, and therapeutic alliance as correlates of children?s and adolescents? engagement in the DMHI. Methods: Members (children and adolescents) of a pediatric DMHI who had completed at least one session with a care provider (eg, coach or therapist) were eligible for inclusion in the study. Adolescent members and caregivers of children completed a survey assessing satisfaction with service, perceived usefulness of care, and therapeutic alliance with care team members. Results: This study provides evidence for the reliability and validity of an adolescent- and caregiver-reported user experience assessment in a pediatric DMHI. Moreover, our findings suggest that adolescents' and caregivers? satisfaction and perceived usefulness are salient correlates of youths? engagement with a DMHI. Conclusions: This study provides valuable preliminary evidence that caregivers? satisfaction and perceived usefulness are salient correlates of youths? engagement with a DMHI. Although further research is required, these findings offer preliminary evidence that caregivers play a critical role in effectively increasing engagement among children and adolescents involved in DMHIs. UR - https://formative.jmir.org/2023/1/e49384 UR - http://dx.doi.org/10.2196/49384 UR - http://www.ncbi.nlm.nih.gov/pubmed/37672321 ID - info:doi/10.2196/49384 ER - TY - JOUR AU - Parkes, Steven AU - Croak, Bethany AU - Brooks, K. Samantha AU - Stevelink, M. Sharon A. AU - Leightley, Daniel AU - Fear, T. Nicola AU - Rafferty, Laura AU - Greenberg, Neil PY - 2023/8/28 TI - Evaluating a Smartphone App (MeT4VeT) to Support the Mental Health of UK Armed Forces Veterans: Feasibility Randomized Controlled Trial JO - JMIR Ment Health SP - e46508 VL - 10 KW - military KW - veteran KW - mental health KW - military to civilian transition KW - digital health KW - mobile apps KW - smartphone KW - mobile phone KW - mobile health KW - mHealth KW - digital intervention KW - support KW - app KW - feasibility KW - acceptability KW - engagement KW - usability N2 - Background: Previous research demonstrates that less than 50% of military veterans experiencing mental health difficulties seek formal support. Veterans often struggle to identify problems as mental health difficulties. In addition, they may fail to recognize the need for support before reaching a crisis point and face difficulties navigating care pathways to access support. Objective: A feasibility trial was conducted to assess a novel digital smartphone app (Mental Health Toolkit for Veterans Project [MeT4VeT]) for UK Armed Forces (UKAF) veterans experiencing mental health difficulties. The trial aimed to explore the feasibility and acceptability of trial procedures for a later randomized controlled trial (RCT) and to assess the acceptability of the MeT4VeT app. Methods: Participants were recruited at UK military medical centers, by advertising on social media, and through veteran third-sector organizations between February and November 2021, and assessed for eligibility (male, owned a smartphone, served at least 2 years in the UKAF, left the UKAF within the last 2 years, not undertaking formal mental health treatment). Eligible participants were assigned, on a 1:1 ratio, to either the intervention group (full app) or a control group (noninteractive app with signposting information). Three key objectives were determined a priori to assess the practicality of running an RCT including an assessment of recruitment and retention, evaluation of the technical app delivery and measurement processes, and acceptability and usability of the intervention. Results: In total, 791 individuals completed the participant information sheet, of which 261 (33%) were ineligible, 377 (48%) declined or were unable to be contacted for consent, and 103 (13%) did not download the app or complete the baseline measures. Of this, 50 participants completed baseline measures and were randomly assigned to the intervention group (n=24) or the control group (n=26). The trial was effective at enabling both the technical delivery of the intervention and collection of outcome measures, with improvements in mental health demonstrated for the intervention group from baseline to the 3-month follow-up. Recruitment and retention challenges were highlighted with only 50 out of the 530 eligible participants enrolled in the trial. The acceptability and usability of the MeT4VeT app were generally supported, and it was reported to be a useful, accessible way for veterans to monitor and manage their mental health. Conclusions: The results highlighted that further work is needed to refine recruitment processes and maintain engagement with the app. Following this, an RCT can be considered to robustly assess the ability of the app to positively affect mental health outcomes indicated within this trial. Trial Registration: ClinicalTrials.gov NCT05993676; https://clinicaltrials.gov/ct2/show/NCT05993676 UR - https://mental.jmir.org/2023/1/e46508 UR - http://dx.doi.org/10.2196/46508 UR - http://www.ncbi.nlm.nih.gov/pubmed/37639295 ID - info:doi/10.2196/46508 ER - TY - JOUR AU - Vial, Stéphane AU - Boudhraâ, Sana AU - Dumont, Mathieu AU - Tremblay, Melanie AU - Riendeau, Sophie PY - 2023/8/22 TI - Developing A Mobile App With a Human-Centered Design Lens to Improve Access to Mental Health Care (Mentallys Project): Protocol for an Initial Co-Design Process JO - JMIR Res Protoc SP - e47220 VL - 12 KW - co-design KW - human-centered design KW - e-mental health KW - design expertise KW - user engagement KW - patient-centered design KW - imaginary prototype N2 - Background: Co-design is one of the human-centered design approaches that allows end users to significantly and positively impact the design of mental health technologies. It is a promising approach to foster user acceptance and engagement in digital mental health solutions. Surprisingly, there is a lack of understanding of what co-design is in this field. In this paper, co-design is approached as a cocreation process involving persons with a lived experience of mental health problems, health professionals, and design experts who lead and facilitate the overall creative process. Objective: This paper describes an initial co-design research protocol for the development of a mobile app that aims to improve access to mental health care. It highlights the characteristics of a co-design approach in e?mental health rooted in human-centered design and led by design experts alongside health experts. The paper focuses on the first steps (phase 1) of the co-design process of the ongoing Mentallys project. Methods: This Mentallys project will be located in Montréal (Quebec, Canada). The method approach will be based on the ?method stories,? depicting the ?making of? this project and reflecting adjustments needed to the protocol throughout the project in specific situations. Phase 1 of the process will focus on the desirability of the app. Targeted participants will include people with a lived experience of mental health problems, peer support workers and clinicians, and 3 facilitators (all design experts or researchers). Web-based sessions will be organized because of the COVID-19 pandemic, using Miro (RealtimeBoard Inc) and Zoom (Zoom Video Communications, Inc). Data collection will be based on the comments, thoughts, and new ideas of participants around the imaginary prototypes. Thematic analysis will be carried out after each session to inform a new version of the prototype. Results: We conducted 2 stages in phase 1 of the process. During stage 1, we explored ideas through group co-design workshops (divergent thinking). Six co-design workshops were held: 2 with only clinicians (n=7), 2 with peer support workers (n=5) and people with a lived experience of mental health problems (n=2), and 2 with all of them (n=14). A total of 6 facilitators participated in conducting activities in subgroups. During stage 2, ideas were refined through 10 dyad co-design sessions (convergent thinking). Stage 2 involved 3 participants (n=3) and 1 facilitator. Thematic analysis was performed after stage 1, while analytic questioning is being performed for stage 2. Both stages allowed several iterations of the prototypes. Conclusions: The design of the co-design process, the leadership of the design expertise throughout the process, and the different forms of co-design activities are key elements in this project. We highly recommend that health researchers partner with professional designers or design researchers who are familiar with co-design. International Registered Report Identifier (IRRID): DERR1-10.2196/47220 UR - https://www.researchprotocols.org/2023/1/e47220 UR - http://dx.doi.org/10.2196/47220 UR - http://www.ncbi.nlm.nih.gov/pubmed/37606978 ID - info:doi/10.2196/47220 ER - TY - JOUR AU - de Winter, P. Remco F. AU - Meijer, M. Connie AU - Enterman, H. John AU - Kool-Goudzwaard, Nienke AU - Gemen, Manuela AU - van den Bos, T. Anne AU - Steentjes, Danielle AU - van Son, E. Gabrielle AU - Hazewinkel, C. Mirjam AU - de Beurs, P. Derek AU - de Groot, H. Marieke PY - 2023/8/11 TI - A Clinical Model for the Differentiation of Suicidality: Protocol for a Usability Study of the Proposed Model JO - JMIR Res Protoc SP - e45438 VL - 12 KW - differentiation KW - suicidality KW - suicidal behavior KW - subtype KW - subcategory KW - category KW - categories KW - categorize KW - subcategories KW - validation study KW - mental health KW - suicide KW - suicidal KW - classification KW - psychiatry KW - classify KW - psychiatric KW - suicide prevention KW - suicidal ideation KW - mental illness KW - suicidal thought KW - dying KW - perceptual disintegration KW - PD KW - primary depressive cognition KW - PDC KW - psychosocial turmoil KW - inadequate communication KW - intraclass correlation coefficients KW - ICC N2 - Background: Even though various types of suicidality are observed in clinical practice, suicidality is still considered a uniform concept. To distinguish different types of suicidality and consequently improve detection and management of suicidality, we developed a clinical differentiation model for suicidality. We believe that the model allows for a more targeted assessment of suicidal conditions and improves the use of evidence-based treatment strategies. The differentiation model is based on the experience with suicidality that we have encountered in clinical practice. This model distinguishes 4 subtypes of entrapment leading to suicidality. The earliest description of this model and a proposal for usability research has been previously presented in a book chapter. Objective: In this study, we present the most recent version of the 4-type differentiation model of suicidality and a protocol for a study into the usability of the proposed model. Methods: The 4-type differentiation model of suicidality distinguishes the following subtypes: (1) perceptual disintegration, (2) primary depressive cognition, (3) psychosocial turmoil, and (4) inadequate coping or communication. We plan to test the usability of the 4 subtypes in a pilot study of 25 cases, and subsequently, we will include 75 cases in a follow-up study. We looked at the case notes of 100 anonymized patients with suicidality who presented to mental health care emergency service in The Hague International Center. The summary and conclusions of the letters sent to the patients? general practitioners after suicide risk assessment will be independently rated by 3 psychiatrists and 3 nurse-scientists for absolute and dimensional scores. The Suicidality Differentiation version 2 (SUICIDI-II) instrument, developed for this study, is used for rating all the cases. Intraclass correlation coefficients for absolute and dimensional scores will be calculated to examine type agreement between raters to examine the usability of the model and the feasibility of the SUICIDI-II instrument. Results: We consider the model tentatively valid if the intraclass correlation coefficients are ?0.70. Subsequently, if the model turns out to be valid, we plan to rate 75 other cases in a follow-up study, according to a similar or adjusted procedure. Study results are expected to be published by the end of 2023. Conclusions: The theoretical roots of the differentiation model stem from classic and contemporary theoretical models of suicidality and from our clinical practice experiences with suicidal behaviors. We believe that this model can be used to adjust the diagnosis, management, treatment, and research of suicidality, in addition to distinguishing different dynamics between practitioners and patients with suicidality and their families. International Registered Report Identifier (IRRID): DERR1-10.2196/45438 UR - https://www.researchprotocols.org/2023/1/e45438 UR - http://dx.doi.org/10.2196/45438 UR - http://www.ncbi.nlm.nih.gov/pubmed/37566444 ID - info:doi/10.2196/45438 ER - TY - JOUR AU - Nghiem, Jodie AU - Adler, A. Daniel AU - Estrin, Deborah AU - Livesey, Cecilia AU - Choudhury, Tanzeem PY - 2023/8/10 TI - Understanding Mental Health Clinicians? Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study JO - JMIR Form Res SP - e47380 VL - 7 KW - digital technology KW - clinical decision support KW - mobile health KW - mHealth KW - qualitative research KW - mental health KW - clinician KW - perception KW - patient-generated health data KW - mobile app KW - digital app KW - wearables KW - mobile phone N2 - Background: Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used. Objective: We conducted a qualitative study to understand mental health clinicians? perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants? current experiences with and visions for using passive PGHD. Methods: Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes. Results: Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven?we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data?participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients? mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action?participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy?participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use. Conclusions: Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data?sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians. UR - https://formative.jmir.org/2023/1/e47380 UR - http://dx.doi.org/10.2196/47380 UR - http://www.ncbi.nlm.nih.gov/pubmed/37561561 ID - info:doi/10.2196/47380 ER - TY - JOUR AU - Virk, Punit AU - Arora, Ravia AU - Burt, Heather AU - Finnamore, Caitlin AU - Gadermann, Anne AU - Barbic, Skye AU - Doan, Quynh PY - 2023/8/9 TI - Evaluating the Psychometric Properties and Clinical Utility of a Digital Psychosocial Self-Screening Tool (HEARTSMAP-U) for Postsecondary Students: Prospective Cohort Study JO - JMIR Ment Health SP - e48709 VL - 10 KW - mental health KW - screening KW - validity KW - postsecondary students KW - clinical utility N2 - Background: Existing screening tools for mental health issues among postsecondary students have several challenges, including a lack of standardization and codevelopment by students. HEARTSMAP-U was adapted to address these issues. Objective: This study aimed to evaluate the suitability of HEARTSMAP-U as a self-screening tool for psychosocial issues among postsecondary students by evaluating its validity evidence and clinical utility. Methods: A prospective cohort study was conducted with University of British Columbia Vancouver students to evaluate HEARTSMAP-U?s predictive validity and convergent validity. Participating students completed baseline and 3-month follow-up assessments via HEARTSMAP-U and a clinician-administered interview. Results: In a diverse student sample (n=100), HEARTSMAP-U demonstrated high sensitivity (95%-100%) in identifying any psychiatric concerns that were flagged by a research clinician, with lower specificity (21%-25%). Strong convergent validity (r=0.54-0.68) was demonstrated when relevant domains and sections of HEARTSMAP-U were compared with those of other conceptually similar instruments. Conclusions: This preliminary evaluation suggests that HEARTSMAP-U may be suitable for screening in the postsecondary educational setting. However, a larger-scale evaluation is necessary to confirm and expand on these findings. UR - https://mental.jmir.org/2023/1/e48709 UR - http://dx.doi.org/10.2196/48709 UR - http://www.ncbi.nlm.nih.gov/pubmed/37556180 ID - info:doi/10.2196/48709 ER - TY - JOUR AU - Viduani, Anna AU - Cosenza, Victor AU - Fisher, L. Helen AU - Buchweitz, Claudia AU - Piccin, Jader AU - Pereira, Rivka AU - Kohrt, A. Brandon AU - Mondelli, Valeria AU - van Heerden, Alastair AU - Araújo, Matsumura Ricardo AU - Kieling, Christian PY - 2023/8/7 TI - Assessing Mood With the Identifying Depression Early in Adolescence Chatbot (IDEABot): Development and Implementation Study JO - JMIR Hum Factors SP - e44388 VL - 10 KW - depression KW - adolescent KW - ambulatory assessment KW - chatbot KW - smartphone KW - digital mental health KW - mobile phone N2 - Background: Mental health status assessment is mostly limited to clinical or research settings, but recent technological advances provide new opportunities for measurement using more ecological approaches. Leveraging apps already in use by individuals on their smartphones, such as chatbots, could be a useful approach to capture subjective reports of mood in the moment. Objective: This study aimed to describe the development and implementation of the Identifying Depression Early in Adolescence Chatbot (IDEABot), a WhatsApp-based tool designed for collecting intensive longitudinal data on adolescents? mood. Methods: The IDEABot was developed to collect data from Brazilian adolescents via WhatsApp as part of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study. It supports the administration and collection of self-reported structured items or questionnaires and audio responses. The development explored WhatsApp?s default features, such as emojis and recorded audio messages, and focused on scripting relevant and acceptable conversations. The IDEABot supports 5 types of interactions: textual and audio questions, administration of a version of the Short Mood and Feelings Questionnaire, unprompted interactions, and a snooze function. Six adolescents (n=4, 67% male participants and n=2, 33% female participants) aged 16 to 18 years tested the initial version of the IDEABot and were engaged to codevelop the final version of the app. The IDEABot was subsequently used for data collection in the second- and third-year follow-ups of the IDEA-RiSCo study. Results: The adolescents assessed the initial version of the IDEABot as enjoyable and made suggestions for improvements that were subsequently implemented. The IDEABot?s final version follows a structured script with the choice of answer based on exact text matches throughout 15 days. The implementation of the IDEABot in 2 waves of the IDEA-RiSCo sample (140 and 132 eligible adolescents in the second- and third-year follow-ups, respectively) evidenced adequate engagement indicators, with good acceptance for using the tool (113/140, 80.7% and 122/132, 92.4% for second- and third-year follow-up use, respectively), low attrition (only 1/113, 0.9% and 1/122, 0.8%, respectively, failed to engage in the protocol after initial interaction), and high compliance in terms of the proportion of responses in relation to the total number of elicited prompts (12.8, SD 3.5; 91% out of 14 possible interactions and 10.57, SD 3.4; 76% out of 14 possible interactions, respectively). Conclusions: The IDEABot is a frugal app that leverages an existing app already in daily use by our target population. It follows a simple rule-based approach that can be easily tested and implemented in diverse settings and possibly diminishes the burden of intensive data collection for participants by repurposing WhatsApp. In this context, the IDEABot appears as an acceptable and potentially scalable tool for gathering momentary information that can enhance our understanding of mood fluctuations and development. UR - https://humanfactors.jmir.org/2023/1/e44388 UR - http://dx.doi.org/10.2196/44388 UR - http://www.ncbi.nlm.nih.gov/pubmed/37548996 ID - info:doi/10.2196/44388 ER - TY - JOUR AU - Spadaro, Benedetta AU - Martin-Key, A. Nayra AU - Funnell, Erin AU - Bená?ek, Ji?í AU - Bahn, Sabine PY - 2023/8/7 TI - Opportunities for the Implementation of a Digital Mental Health Assessment Tool in the United Kingdom: Exploratory Survey Study JO - JMIR Form Res SP - e43271 VL - 7 KW - assessment KW - digital mental health KW - development KW - implementation KW - mental health KW - provision KW - support KW - mobile phone N2 - Background: Every year, one-fourth of the people in the United Kingdom experience diagnosable mental health concerns, yet only a proportion receive a timely diagnosis and treatment. With novel developments in digital technologies, the potential to increase access to mental health assessments and triage is promising. Objective: This study aimed to investigate the current state of mental health provision in the United Kingdom and understand the utility of, and interest in, digital mental health technologies. Methods: A web-based survey was generated using Qualtrics XM. Participants were recruited via social media. Data were explored using descriptive statistics. Results: The majority of the respondents (555/618, 89.8%) had discussed their mental health with a general practitioner. More than three-fourths (503/618, 81.4%) of the respondents had been diagnosed with a mental health disorder, with the most common diagnoses being depression and generalized anxiety disorder. Diagnostic waiting times from first contact with a health care professional varied by diagnosis. Neurodevelopmental disorders (30/56, 54%), bipolar disorder (25/52, 48%), and personality disorders (48/101, 47.5%) had the longest waiting times, with almost half (103/209, 49.3%) of these diagnoses taking >6 months. Participants stated that waiting times resulted in symptoms worsening (262/353, 74.2%), lower quality of life (166/353, 47%), and the necessity to seek emergency care (109/353, 30.9%). Of the 618 participants, 386 (62.5%) stated that they felt that their mental health symptoms were not always taken seriously by their health care provider and 297 (48.1%) were not given any psychoeducational information. The majority of the respondents (416/595, 77.5%) did not have the chance to discuss mental health support and treatment options. Critically, 16.1% (96/595) did not find any treatment or support provided at all helpful, with 63% (48/76) having discontinued treatment with no effective alternatives. Furthermore, 88.3% (545/617) of the respondents) had sought help on the web regarding mental health symptoms, and 44.4% (272/612) had used a web application or smartphone app for their mental health. Psychoeducation (364/596, 61.1%), referral to a health care professional (332/596, 55.7%), and symptom monitoring (314/596, 52.7%) were the most desired app features. Only 6.8% (40/590) of the participants said that they would not be interested in using a mental health assessment app. Respondents were the most interested to receive an overall severity score of their mental health symptoms (441/546, 80.8%) and an indication of whether they should seek mental health support (454/546, 83.2%). Conclusions: Key gaps in current UK mental health care provision are highlighted. Assessment and treatment waiting times together with a lack of information regarding symptoms and treatment options translated into poor care experiences. The participants? responses provide proof-of-concept support for the development of a digital mental health assessment app and valuable recommendations regarding desirable app features. UR - https://formative.jmir.org/2023/1/e43271 UR - http://dx.doi.org/10.2196/43271 UR - http://www.ncbi.nlm.nih.gov/pubmed/37549003 ID - info:doi/10.2196/43271 ER - TY - JOUR AU - Kangarloo, Tairmae AU - Mote, Jasmine AU - Abplanalp, Samuel AU - Gold, Alisa AU - James, Peter AU - Gard, David AU - Fulford, Daniel PY - 2023/8/3 TI - The Influence of Greenspace Exposure on Affect in People With and Those Without Schizophrenia: Exploratory Study JO - JMIR Form Res SP - e44323 VL - 7 KW - greenspace KW - mental health KW - mobile technology KW - affect KW - smartphone KW - sensing KW - schizophrenia KW - natural vegetation KW - exposure KW - assessment KW - mechanism N2 - Background: Exposure to natural vegetation (ie, ?greenspace?) is related to beneficial outcomes, including higher positive and lower negative affect, in individuals with and those without mental health concerns. Researchers have yet to examine dynamic associations between greenspace exposure and affect within individuals over time. Smartphone-based ecological momentary assessment (EMA) and passive sensors (eg, GPS, microphone) allow for frequent sampling of data that may reveal potential moment-to-moment mechanisms through which greenspace exposure impacts mental health. Objective: In this study, we examined associations between greenspace exposure and affect (both self-reported and inferred through speech) in people with and those without schizophrenia spectrum disorder (SSD) at the daily level using smartphones. Methods: Twenty people with SSD and 14 healthy controls reported on their current affect 3 times per day over 7 days using smartphone-based EMA. Affect expressed through speech was labeled from ambient audio data collected via the phone?s microphone using Linguistic Inquiry and Word Count (LIWC). Greenspace exposure, defined as the normalized difference vegetation index (NDVI), was quantified based on continuous geo-location data collected from the phone?s GPS. Results: Overall, people with SSD used significantly more positive affect words (P=.04) and fewer anger words (P=.04) than controls. Groups did not significantly differ in mean EMA-reported positive or negative affect, LIWC total word count, or NDVI exposure. Greater greenspace exposure showed small to moderate associations with lower EMA-reported negative affect across groups. In controls, greenspace exposure on a given day was associated with significantly lower EMA-reported anxiety on that day (b=?0.40, P=.03, 95% CI ?0.76 to ?0.04) but significantly higher use of negative affect words (b=0.66, P<.001, 95% CI 0.29-1.04). There were no significant associations between greenspace exposure and affect at the daily level among participants with SSD. Conclusions: Our findings speak to the utility of passive and active smartphone assessments for identifying potential mechanisms through which greenspace exposure influences mental health. We identified preliminary evidence that greenspace exposure could be associated with improved mental health by reducing experiences of negative affect. Future directions will focus on furthering our understanding of the relationship between greenspace exposure and affect on individuals with and those without SSD. UR - https://formative.jmir.org/2023/1/e44323 UR - http://dx.doi.org/10.2196/44323 UR - http://www.ncbi.nlm.nih.gov/pubmed/37535418 ID - info:doi/10.2196/44323 ER - TY - JOUR AU - Wrightson-Hester, Aimee-Rose AU - Anderson, Georgia AU - Dunstan, Joel AU - McEvoy, M. Peter AU - Sutton, J. Christopher AU - Myers, Bronwyn AU - Egan, Sarah AU - Tai, Sara AU - Johnston-Hollitt, Melanie AU - Chen, Wai AU - Gedeon, Tom AU - Mansell, Warren PY - 2023/7/21 TI - An Artificial Therapist (Manage Your Life Online) to Support the Mental Health of Youth: Co-Design and Case Series JO - JMIR Hum Factors SP - e46849 VL - 10 KW - mental health KW - conversational agents KW - chatbots KW - young people KW - acceptability KW - feasibility KW - co-design KW - artificial therapist KW - artificial intelligence KW - youth KW - child KW - adolescent KW - chatbot KW - Manage Your Life Online KW - MYLO KW - support KW - mobile phone N2 - Background: The prevalence of child and adolescent mental health issues is increasing faster than the number of services available, leading to a shortfall. Mental health chatbots are a highly scalable method to address this gap. Manage Your Life Online (MYLO) is an artificially intelligent chatbot that emulates the method of levels therapy. Method of levels is a therapy that uses curious questioning to support the sustained awareness and exploration of current problems. Objective: This study aimed to assess the feasibility and acceptability of a co-designed interface for MYLO in young people aged 16 to 24 years with mental health problems. Methods: An iterative co-design phase occurred over 4 months, in which feedback was elicited from a group of young people (n=7) with lived experiences of mental health issues. This resulted in the development of a progressive web application version of MYLO that could be used on mobile phones. We conducted a case series to assess the feasibility and acceptability of MYLO in 13 young people over 2 weeks. During this time, the participants tested MYLO and completed surveys including clinical outcomes and acceptability measures. We then conducted focus groups and interviews and used thematic analysis to obtain feedback on MYLO and identify recommendations for further improvements. Results: Most participants were positive about their experience of using MYLO and would recommend MYLO to others. The participants enjoyed the simplicity of the interface, found it easy to use, and rated it as acceptable using the System Usability Scale. Inspection of the use data found evidence that MYLO can learn and adapt its questioning in response to user input. We found a large effect size for the decrease in participants? problem-related distress and a medium effect size for the increase in their self-reported tendency to resolve goal conflicts (the proposed mechanism of change) in the testing phase. Some patients also experienced a reliable change in their clinical outcome measures over the 2 weeks. Conclusions: We established the feasibility and acceptability of MYLO. The initial outcomes suggest that MYLO has the potential to support the mental health of young people and help them resolve their own problems. We aim to establish whether the use of MYLO leads to a meaningful reduction in participants? symptoms of depression and anxiety and whether these are maintained over time by conducting a randomized controlled evaluation trial. UR - https://humanfactors.jmir.org/2023/1/e46849 UR - http://dx.doi.org/10.2196/46849 UR - http://www.ncbi.nlm.nih.gov/pubmed/37477969 ID - info:doi/10.2196/46849 ER - TY - JOUR AU - Greene, Brittney AU - Bernardo, Leah AU - Thompson, Morgan AU - Loughead, James AU - Ashare, Rebecca PY - 2023/7/21 TI - Behavioral Economic Strategies to Improve Enrollment Rates in Clinical Research: Embedded Recruitment Pilot Trial JO - JMIR Form Res SP - e47121 VL - 7 KW - behavior change KW - behavioral economics KW - clinical trials KW - contingency management KW - evidence based KW - information provision KW - recruitment KW - retention KW - SMS text messaging KW - study within a trial KW - SWAT N2 - Background: Nearly 1 in 3 clinical trials end prematurely due to underenrollment. Strategies to enhance recruitment are often implemented without scientific rigor to evaluate efficacy. Evidence-based, cost-effective behavioral economic strategies designed to influence decision-making may be useful to promote clinical trial enrollment. Objective: This study evaluated 2 behavioral economic strategies to improve enrollment and retention rates across 4 clinical trials: information provision (IP) and contingency management (CM; ie, lottery). IP targets descriptive and injunctive norms about participating in research and CM provides participants incentives to reinforce a target behavior. Methods: A sample of 212 participants was enrolled across 4 clinical trials focused on tobacco use: 2 focused on HIV and 2 focused on neuroimaging. The CM condition included a lottery: for each study visit completed, participants received 5 ?draws? from a bowl containing 500 ?chips? valued at US $0, US $1, US $5, or US $100. In the IP condition, text messages that targeted injunctive norms about research (eg, ?Many find it a rewarding way to advance science and be part of a community?) were sent through the Way to Health platform before all study visits. Participants were randomized to 1 of 4 conditions: IP, CM, IP+CM, or standard recruitment (SR). We performed logistic regression, controlling for sex and study, with condition as a between-subject predictor. Outcomes were the percentage of participants who attended a final eligibility visit (primary), met intent-to-treat (ITT) criteria (secondary), and completed the study (secondary). Recruitment was evaluated by the percentage of participants who attended a final eligibility visit, enrollment by ITT status, and retention by the percentage of participants who completed the study. Results: Rates of attending the eligibility visit and meeting ITT status were 58.9% (33/56) and 33.9% (19/56) for IP+CM; 45.5% (25/55) and 18.2% (10/55) for IP only; 41.5% (22/53) and 18.9% (10/53) for CM only; and 37.5% (18/48) and 12.5% (6/48) for SR, respectively. In the logistic regression, females were more likely to meet ITT status than males (odds ratio [OR] 2.7, 95% CI 1.2-5.7; P=.01). The IP+CM group was twice as likely to attend the final eligibility visit than the SR group (OR 2.4, 95% CI 1.1-5.2; P=.04). The IP+CM group was also significantly more likely to reach ITT status than the SR condition (OR 3.9, 95% CI 1.3-11.1; P=.01). Those who received any active intervention (IP, CM, or IP+CM) had a higher study completion rate (33/53, 63.5%) compared to those who received SR (5/12, 41.7%), but this difference was not significant (P=.26). Conclusions: Combining IP and CM strategies may motivate participants to participate in research and improve recruitment and retention rates. Evidence from this study provides preliminary support for the utility of behavioral economics strategies to improve enrollment and reduce attrition in clinical trials. UR - https://formative.jmir.org/2023/1/e47121 UR - http://dx.doi.org/10.2196/47121 UR - http://www.ncbi.nlm.nih.gov/pubmed/37477975 ID - info:doi/10.2196/47121 ER - TY - JOUR AU - Frank, C. Adam AU - Li, Ruibei AU - Peterson, S. Bradley AU - Narayanan, S. Shrikanth PY - 2023/7/18 TI - Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review JO - JMIR Ment Health SP - e45572 VL - 10 KW - wearable KW - smartphone KW - obsessive-compulsive disorder KW - OCD KW - digital KW - phenotype KW - biomarker KW - mobile phone N2 - Background: Smartphones and wearable biosensors can continuously and passively measure aspects of behavior and physiology while also collecting data that require user input. These devices can potentially be used to monitor symptom burden; estimate diagnosis and risk for relapse; predict treatment response; and deliver digital interventions in patients with obsessive-compulsive disorder (OCD), a prevalent and disabling psychiatric condition that often follows a chronic and fluctuating course and may uniquely benefit from these technologies. Objective: Given the speed at which mobile and wearable technologies are being developed and implemented in clinical settings, a continual reappraisal of this field is needed. In this scoping review, we map the literature on the use of wearable devices and smartphone-based devices or apps in the assessment, monitoring, or treatment of OCD. Methods: In July 2022 and April 2023, we conducted an initial search and an updated search, respectively, of multiple databases, including PubMed, Embase, APA PsycINFO, and Web of Science, with no restriction on publication period, using the following search strategy: (?OCD? OR ?obsessive? OR ?obsessive-compulsive?) AND (?smartphone? OR ?phone? OR ?wearable? OR ?sensing? OR ?biofeedback? OR ?neurofeedback? OR ?neuro feedback? OR ?digital? OR ?phenotyping? OR ?mobile? OR ?heart rate variability? OR ?actigraphy? OR ?actimetry? OR ?biosignals? OR ?biomarker? OR ?signals? OR ?mobile health?). Results: We analyzed 2748 articles, reviewed the full text of 77 articles, and extracted data from the 25 articles included in this review. We divided our review into the following three parts: studies without digital or mobile intervention and with passive data collection, studies without digital or mobile intervention and with active or mixed data collection, and studies with a digital or mobile intervention. Conclusions: Use of mobile and wearable technologies for OCD has developed primarily in the past 15 years, with an increasing pace of related publications. Passive measures from actigraphy generally match subjective reports. Ecological momentary assessment is well tolerated for the naturalistic assessment of symptoms, may capture novel OCD symptoms, and may also document lower symptom burden than retrospective recall. Digital or mobile treatments are diverse; however, they generally provide some improvement in OCD symptom burden. Finally, ongoing work is needed for a safe and trusted uptake of technology by patients and providers. UR - https://mental.jmir.org/2023/1/e45572 UR - http://dx.doi.org/10.2196/45572 UR - http://www.ncbi.nlm.nih.gov/pubmed/37463010 ID - info:doi/10.2196/45572 ER - TY - JOUR AU - Lorenzo-Luaces, Lorenzo AU - Howard, Jacqueline PY - 2023/7/7 TI - Efficacy of an Unguided, Digital Single-Session Intervention for Internalizing Symptoms in Web-Based Workers: Randomized Controlled Trial JO - J Med Internet Res SP - e45411 VL - 25 KW - internet-based cognitive behavioral therapy KW - iCBT KW - depression KW - transdiagnostic processes KW - emotion regulation N2 - Background: The Common Elements Toolbox (COMET) is an unguided digital single-session intervention (SSI) based on principles of cognitive behavioral therapy and positive psychology. Although unguided digital SSIs have shown promise in the treatment of youth psychopathology, the data are more mixed regarding their efficacy in adults. Objective: This study aimed to investigate the efficacy of COMET-SSI versus a waiting list control in depression and other transdiagnostic mental health outcomes for Prolific participants with a history of psychopathology. Methods: We conducted an investigator-blinded, preregistered randomized controlled trial comparing COMET-SSI (n=409) with an 8-week waiting list control (n=419). Participants were recruited from the web-based workspace Prolific and assessed for depression, anxiety, work and social functioning, psychological well-being, and emotion regulation at baseline and at 2, 4, and 8 weeks after the intervention. The main outcomes were short-term (2 weeks) and long-term (8 weeks) changes in depression and anxiety. The secondary outcomes were the 8-week changes in work and social functioning, well-being, and emotion regulation. Analyses were conducted according to the intent-to-treat principle with imputation, without imputation, and using a per-protocol sample. In addition, we conducted sensitivity analyses to identify inattentive responders. Results: The sample comprised 61.9% (513/828) of women, with a mean age of 35.75 (SD 11.93) years. Most participants (732/828, 88.3%) met the criteria for screening for depression or anxiety using at least one validated screening scale. A review of the text data suggested that adherence to the COMET-SSI was near perfect, there were very few inattentive respondents, and satisfaction with the intervention was high. However, despite being powered to detect small effects, there were negligible differences between the conditions in the various outcomes at the various time points, even when focusing on subsets of individuals with more severe symptoms. Conclusions: Our results do not support the use of the COMET-SSI in adult Prolific participants. Future work should explore alternate ways of intervening with paid web-based participants, including matching individuals to SSIs they may be most responsive to. Trial Registration: ClinicalTrials.gov NCT05379881, https://clinicaltrials.gov/ct2/show/NCT05379881 UR - https://www.jmir.org/2023/1/e45411 UR - http://dx.doi.org/10.2196/45411 UR - http://www.ncbi.nlm.nih.gov/pubmed/37418303 ID - info:doi/10.2196/45411 ER - TY - JOUR AU - Booth, Frederick AU - Potts, Courtney AU - Bond, Raymond AU - Mulvenna, Maurice AU - Kostenius, Catrine AU - Dhanapala, Indika AU - Vakaloudis, Alex AU - Cahill, Brian AU - Kuosmanen, Lauri AU - Ennis, Edel PY - 2023/7/6 TI - A Mental Health and Well-Being Chatbot: User Event Log Analysis JO - JMIR Mhealth Uhealth SP - e43052 VL - 11 KW - mental well-being KW - positive psychology KW - data analysis KW - health care KW - event log analysis KW - ecological momentary assessment KW - conversational user interface KW - user behavior KW - conversational agent KW - user interface KW - user data KW - digital health application KW - mobile health app KW - digital intervention N2 - Background: Conversational user interfaces, or chatbots, are becoming more popular in the realm of digital health and well-being. While many studies focus on measuring the cause or effect of a digital intervention on people?s health and well-being (outcomes), there is a need to understand how users really engage and use a digital intervention in the real world. Objective: In this study, we examine the user logs of a mental well-being chatbot called ChatPal, which is based on the concept of positive psychology. The aim of this research is to analyze the log data from the chatbot to provide insight into usage patterns, the different types of users using clustering, and associations between the usage of the app?s features. Methods: Log data from ChatPal was analyzed to explore usage. A number of user characteristics including user tenure, unique days, mood logs recorded, conversations accessed, and total number of interactions were used with k-means clustering to identify user archetypes. Association rule mining was used to explore links between conversations. Results: ChatPal log data revealed 579 individuals older than 18 years used the app with most users being female (n=387, 67%). User interactions peaked around breakfast, lunchtime, and early evening. Clustering revealed 3 groups including ?abandoning users? (n=473), ?sporadic users? (n=93), and ?frequent transient users? (n=13). Each cluster had distinct usage characteristics, and the features were significantly different (P<.001) across each group. While all conversations within the chatbot were accessed at least once by users, the ?treat yourself like a friend? conversation was the most popular, which was accessed by 29% (n=168) of users. However, only 11.7% (n=68) of users repeated this exercise more than once. Analysis of transitions between conversations revealed strong links between ?treat yourself like a friend,? ?soothing touch,? and ?thoughts diary? among others. Association rule mining confirmed these 3 conversations as having the strongest linkages and suggested other associations between the co-use of chatbot features. Conclusions: This study has provided insight into the types of people using the ChatPal chatbot, patterns of use, and associations between the usage of the app?s features, which can be used to further develop the app by considering the features most accessed by users. UR - https://mhealth.jmir.org/2023/1/e43052 UR - http://dx.doi.org/10.2196/43052 UR - http://www.ncbi.nlm.nih.gov/pubmed/37410539 ID - info:doi/10.2196/43052 ER - TY - JOUR AU - Beuthin, Oliver AU - Bhui, Kamaldeep AU - Yu, Ly-Mee AU - Shahid, Sadiya AU - Almidani, Louay AU - Bilalaga, Malak Mariah AU - Hussein, Roshan AU - Harba, Alnarjes AU - Nasser, Yasmine PY - 2023/6/22 TI - Culturally Adapting a Digital Intervention to Reduce Suicidal Ideation for Syrian Asylum Seekers and Refugees in the United Kingdom: Protocol for a Qualitative Study JO - JMIR Res Protoc SP - e47627 VL - 12 KW - cultural adaptation KW - digital mental health KW - suicidal ideation KW - refugee mental health KW - Syrian refugee KW - experience-based co-design KW - mental health KW - suicide KW - suicidal KW - refugee KW - immigrant KW - ethnic minority KW - asylum KW - user experience KW - cultural KW - Syria KW - Syrian KW - refugees N2 - Background: The conflict in Syria has produced the largest forced displacement crisis since the Second World War. As a result, Syrians have experienced various stressors across the migratory process, putting them at an increased risk of developing mental health issues, including, crucially, suicidal ideation (SI). Despite their high rates of SI across Europe, there remain various barriers to accessing treatment. One way to increase access is the use of culturally adapted digital interventions, which have already shown potential for other minority populations. To culturally adapt the intervention, further research is needed to better understand Syrian asylum seekers? and refugees? cultural conceptualizations, coping strategies, and help-seeking behavior for SI. To do so, this study will use a unique cultural adaptation framework to intervene at points of lived experience with the migratory process where Syrian culture and signs of psychopathology converge. Likewise, co-design events will be used to adapt points of experience with the intervention where Syrian culture and the intervention conflict. As the first cultural adaption of a digital SI intervention for Syrian asylum seekers and refugees, this study will hopefully encourage further development of culturally sensitive interventions for the largest refugee population in the United Kingdom and the world. Objective: The objective of the study is to increase access to mental health treatment for Syrian asylum seekers and refugees in the United Kingdom by culturally adapting a digital intervention to reduce SI. Methods: The study will use experience-based co-design, an action research method, to culturally adapt a digital intervention to reduce SI for Syrian asylum seekers and refugees in the United Kingdom. This will involve conducting 20-30 interviews to understand their lived experiences with the migratory process, cultural conceptualizations of mental health and SI, coping strategies, mental health help-seeking behavior, and perceptions of digital mental health interventions. In addition, 3 co-design events with 6 participants in each will be held to collaboratively adapt the intervention. Touchpoints and themes extracted from each phase will be prioritized by a community panel before adapting the intervention. Results: The study began in November 2022 and will continue until the last co-design event in August 2023. The results of the study will then be published by December 2023. Conclusions: Access to treatment for some of the most severe mental health issues is still limited for Syrian asylum seekers and refugees in the United Kingdom. Cultural adaptations of digital interventions developed for general populations have the potential to increase access to treatment for this population. Specifically, adapting the intervention for Syrian asylum seekers? and refugees? experiences with SI in relation to their lived experience with the migratory process may enable greater recruitment and adherence for users of various cultural and ethnic subgroups and levels of SI. International Registered Report Identifier (IRRID): DERR1-10.2196/47627 UR - https://www.researchprotocols.org/2023/1/e47627 UR - http://dx.doi.org/10.2196/47627 UR - http://www.ncbi.nlm.nih.gov/pubmed/37347522 ID - info:doi/10.2196/47627 ER - TY - JOUR AU - Mathur, Sonal AU - Weiss, A. Helen AU - Neuman, Melissa AU - Field, P. Andy AU - Leurent, Baptiste AU - Shetty, Tejaswi AU - J, E. James AU - Nair, Pooja AU - Mathews, Rhea AU - Malik, Kanika AU - Michelson, Daniel AU - Patel, Vikram PY - 2023/6/13 TI - Coach-Supported Versus Self-guided Digital Training Course for a Problem-solving Psychological Intervention for Nonspecialists: Protocol for a Pre-Post Nested Randomized Controlled Trial JO - JMIR Res Protoc SP - e41981 VL - 12 KW - adolescent mental health KW - capacity building KW - digital training KW - India KW - problem-solving intervention KW - randomized controlled trial N2 - Background: Psychosocial interventions delivered by nonspecialists can be effective at reducing common adolescent mental health problems in low-resource settings. However, there is a lack of evidence on resource-efficient methods for building capacity to deliver these interventions. Objective: The objective of this study is to evaluate the effects of a digital training (DT) course, delivered in a self-guided format or with coaching, on nonspecialists? competency to deliver a problem-solving intervention intended for adolescents with common mental health problems in India. Methods: We will conduct a pre-post study with a nested parallel, 2-arm, individually randomized controlled trial. The study aims to recruit 262 participants, randomized 1:1 to receive either a self-guided DT course or a DT course with weekly individualized coaching provided remotely by telephone. In both arms, the DT will be accessed over 4 to 6 weeks. Participants will be nonspecialists (ie, without prior practice-based training in psychological therapies) recruited from among university students and affiliates of nongovernmental organizations in Delhi and Mumbai, India. Results: Outcomes will be assessed at baseline and 6 weeks post randomization using a knowledge-based competency measure that incorporates a multiple-choice quiz format. The primary hypothesis is that self-guided DT will lead to increased competency scores among novices with no prior experience of delivering psychotherapies. The secondary hypothesis is that digital training with coaching will have an incremental effect on competency scores compared with DT alone. The first participant was enrolled on April 4, 2022. Conclusions: The study will address an evidence gap on the effectiveness of training methods for nonspecialist providers of adolescent mental health interventions in low-resource settings. The findings from this study will be used to support wider efforts to scale up evidence-based mental health interventions for young people. Trial Registration: ClinicalTrials.gov NCT05290142; https://clinicaltrials.gov/ct2/show/NCT05290142 International Registered Report Identifier (IRRID): DERR1-10.2196/41981 UR - https://www.researchprotocols.org/2023/1/e41981 UR - http://dx.doi.org/10.2196/41981 UR - http://www.ncbi.nlm.nih.gov/pubmed/37310781 ID - info:doi/10.2196/41981 ER - TY - JOUR AU - Singh, Shifali AU - Strong, Roger AU - Xu, Irene AU - Fonseca, M. Luciana AU - Hawks, Zoe AU - Grinspoon, Elizabeth AU - Jung, Lanee AU - Li, Frances AU - Weinstock, S. Ruth AU - Sliwinski, J. Martin AU - Chaytor, S. Naomi AU - Germine, T. Laura PY - 2023/6/2 TI - Ecological Momentary Assessment of Cognition in Clinical and Community Samples: Reliability and Validity Study JO - J Med Internet Res SP - e45028 VL - 25 KW - ecological momentary assessment KW - cognition KW - digital neuropsychology KW - remote assessment KW - digital technology KW - type 1 diabetes, teleneuropsychology KW - reliability KW - validity KW - cognitive functioning KW - psychological KW - physiological KW - glucose KW - community N2 - Background: The current methods of evaluating cognitive functioning typically rely on a single time point to assess and characterize an individual?s performance. However, cognitive functioning fluctuates within individuals over time in relation to environmental, psychological, and physiological contexts. This limits the generalizability and diagnostic utility of single time point assessments, particularly among individuals who may exhibit large variations in cognition depending on physiological or psychological context (eg, those with type 1 diabetes [T1D], who may have fluctuating glucose concentrations throughout the day). Objective: We aimed to report the reliability and validity of cognitive ecological momentary assessment (EMA) as a method for understanding between-person differences and capturing within-person variation in cognition over time in a community sample and sample of adults with T1D. Methods: Cognitive performance was measured 3 times a day for 15 days in the sample of adults with T1D (n=198, recruited through endocrinology clinics) and for 10 days in the community sample (n=128, recruited from TestMyBrain, a web-based citizen science platform) using ultrabrief cognitive tests developed for cognitive EMA. Our cognitive EMA platform allowed for remote, automated assessment in participants? natural environments, enabling the measurement of within-person cognitive variation without the burden of repeated laboratory or clinic visits. This allowed us to evaluate reliability and validity in samples that differed in their expected degree of cognitive variability as well as the method of recruitment. Results: The results demonstrate excellent between-person reliability (ranging from 0.95 to 0.99) and construct validity of cognitive EMA in both the sample of adults with T1D and community sample. Within-person reliability in both samples (ranging from 0.20 to 0.80) was comparable with that observed in previous studies in healthy older adults. As expected, the full-length baseline and EMA versions of TestMyBrain tests correlated highly with one another and loaded together on the expected cognitive domains when using exploratory factor analysis. Interruptions had higher negative impacts on accuracy-based outcomes (?=?.34 to ?.26; all P values <.001) than on reaction time?based outcomes (?=?.07 to ?.02; P<.001 to P=.40). Conclusions: We demonstrated that ultrabrief mobile assessments are both reliable and valid across 2 very different clinic versus community samples, despite the conditions in which cognitive EMAs are administered, which are often associated with more noise and variability. The psychometric characteristics described here should be leveraged appropriately depending on the goals of the cognitive assessment (eg, diagnostic vs everyday functioning) and the population being studied. UR - https://www.jmir.org/2023/1/e45028 UR - http://dx.doi.org/10.2196/45028 UR - http://www.ncbi.nlm.nih.gov/pubmed/37266996 ID - info:doi/10.2196/45028 ER - TY - JOUR AU - Wang, Siqin AU - Ning, Huan AU - Huang, Xiao AU - Xiao, Yunyu AU - Zhang, Mengxi AU - Yang, Fan Ellie AU - Sadahiro, Yukio AU - Liu, Yan AU - Li, Zhenlong AU - Hu, Tao AU - Fu, Xiaokang AU - Li, Zi AU - Zeng, Ye PY - 2023/6/2 TI - Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022 JO - J Med Internet Res SP - e47225 VL - 25 KW - suicide KW - suicidal ideation KW - suicide-risk identification KW - natural language processing KW - social media KW - Japan N2 - Background: Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people?s expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. Objective: This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. Methods: This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. Results: Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. Conclusions: Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner. UR - https://www.jmir.org/2023/1/e47225 UR - http://dx.doi.org/10.2196/47225 UR - http://www.ncbi.nlm.nih.gov/pubmed/37267022 ID - info:doi/10.2196/47225 ER - TY - JOUR AU - La Sala, Louise AU - Pirkis, Jane AU - Cooper, Charlie AU - Hill, M. Nicole T. AU - Lamblin, Michelle AU - Rajaram, Gowri AU - Rice, Simon AU - Teh, Zoe AU - Thorn, Pinar AU - Zahan, Rifat AU - Robinson, Jo PY - 2023/5/19 TI - Acceptability and Potential Impact of the #chatsafe Suicide Postvention Response Among Young People Who Have Been Exposed to Suicide: Pilot Study JO - JMIR Hum Factors SP - e44535 VL - 10 KW - youth KW - suicide KW - social media KW - suicide postvention KW - suicide prevention KW - contagion KW - postvention N2 - Background: Young people are more likely to be affected by suicide contagion, and there are concerns about the role social media plays in the development and maintenance of suicide clusters or in facilitating imitative suicidal behavior. However, social media also presents an opportunity to provide real-time and age-appropriate suicide prevention information, which could be an important component of suicide postvention activities. Objective: This study aimed to test an intervention designed to equip young people to communicate safely online about suicide (#chatsafe) with a sample of young people who had recently been exposed to a suicide or suicide attempt, with a view to determining the role social media can play as part of a postvention response. Methods: A sample of 266 young people from Australia, aged 16 to 25 years, were recruited to participate in the study. They were eligible if they had been exposed to a suicide or knew of a suicide attempt in the past 2 years. All participants received the #chatsafe intervention, which comprised 6 pieces of social media content that were sent to them weekly via direct message through Instagram, Facebook, or Snapchat. Participants were assessed on a range of outcome measures (social media use, willingness to intervene against suicide, internet self-efficacy, confidence, and safety when communicating about suicide on social media platforms) at baseline, immediately after the intervention, and at 4-week follow-up. Results: After the 6-week #chatsafe intervention, participants reported substantial improvements in their willingness to intervene against suicide online, their internet self-efficacy, and their perceived confidence and safety when communicating about suicide online. Overall, the participants reported that it was appropriate to receive the #chatsafe intervention via social media, and no iatrogenic effects were recorded. Conclusions: The findings suggest that it is safe and acceptable to disseminate suicide prevention information entirely via social media among young people who have recently been exposed to a suicide or suicide attempt. Interventions such as #chatsafe could potentially mitigate the risk of distress and future suicidal behavior in young people by improving the quality and safety of online communication about suicide and, as such, can be an important component of delivering a postvention response to young people. UR - https://humanfactors.jmir.org/2023/1/e44535 UR - http://dx.doi.org/10.2196/44535 UR - http://www.ncbi.nlm.nih.gov/pubmed/37204854 ID - info:doi/10.2196/44535 ER - TY - JOUR AU - Braund, A. Taylor AU - O?Dea, Bridianne AU - Bal, Debopriyo AU - Maston, Kate AU - Larsen, Mark AU - Werner-Seidler, Aliza AU - Tillman, Gabriel AU - Christensen, Helen PY - 2023/5/15 TI - Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study JO - JMIR Ment Health SP - e44986 VL - 10 KW - adolescents KW - anxiety KW - depression KW - digital phenotype KW - keystroke dynamics KW - keystroke metadata KW - smartphone KW - students N2 - Background: Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. Objective: In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. Methods: A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children?s Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. Results: Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. Conclusions: Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. Trial Registration: Australian and New Zealand Clinical Trial Registry, ACTRN12619000855123; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377664&isReview=true UR - https://mental.jmir.org/2023/1/e44986 UR - http://dx.doi.org/10.2196/44986 UR - http://www.ncbi.nlm.nih.gov/pubmed/37184904 ID - info:doi/10.2196/44986 ER - TY - JOUR AU - Peretz, Gal AU - Taylor, Barr C. AU - Ruzek, I. Josef AU - Jefroykin, Samuel AU - Sadeh-Sharvit, Shiri PY - 2023/5/15 TI - Machine Learning Model to Predict Assignment of Therapy Homework in Behavioral Treatments: Algorithm Development and Validation JO - JMIR Form Res SP - e45156 VL - 7 KW - deep learning KW - empirically-based practice KW - natural language processing KW - behavioral treatment KW - machine learning KW - homework KW - treatment fidelity KW - artificial intelligence KW - intervention KW - therapy KW - mental health KW - mHealth N2 - Background: Therapeutic homework is a core element of cognitive and behavioral interventions, and greater homework compliance predicts improved treatment outcomes. To date, research in this area has relied mostly on therapists? and clients? self-reports or studies carried out in academic settings, and there is little knowledge on how homework is used as a treatment intervention in routine clinical care. Objective: This study tested whether a machine learning (ML) model using natural language processing could identify homework assignments in behavioral health sessions. By leveraging this technology, we sought to develop a more objective and accurate method for detecting the presence of homework in therapy sessions. Methods: We analyzed 34,497 audio-recorded treatment sessions provided in 8 behavioral health care programs via an artificial intelligence (AI) platform designed for therapy provided by Eleos Health. Therapist and client utterances were captured and analyzed via the AI platform. Experts reviewed the homework assigned in 100 sessions to create classifications. Next, we sampled 4000 sessions and labeled therapist-client microdialogues that suggested homework to train an unsupervised sentence embedding model. This model was trained on 2.83 million therapist-client microdialogues. Results: An analysis of 100 random sessions found that homework was assigned in 61% (n=61) of sessions, and in 34% (n=21) of these cases, more than one homework assignment was provided. Homework addressed practicing skills (n=34, 37%), taking action (n=26, 28.5%), journaling (n=17, 19%), and learning new skills (n=14, 15%). Our classifier reached a 72% F1-score, outperforming state-of-the-art ML models. The therapists reviewing the microdialogues agreed in 90% (n=90) of cases on whether or not homework was assigned. Conclusions: The findings of this study demonstrate the potential of ML and natural language processing to improve the detection of therapeutic homework assignments in behavioral health sessions. Our findings highlight the importance of accurately capturing homework in real-world settings and the potential for AI to support therapists in providing evidence-based care and increasing fidelity with science-backed interventions. By identifying areas where AI can facilitate homework assignments and tracking, such as reminding therapists to prescribe homework and reducing the charting associated with homework, we can ultimately improve the overall quality of behavioral health care. Additionally, our approach can be extended to investigate the impact of homework assignments on therapeutic outcomes, providing insights into the effectiveness of specific types of homework. UR - https://formative.jmir.org/2023/1/e45156 UR - http://dx.doi.org/10.2196/45156 UR - http://www.ncbi.nlm.nih.gov/pubmed/37184927 ID - info:doi/10.2196/45156 ER - TY - JOUR AU - Dechsling, Anders AU - Cogo-Moreira, Hugo AU - Gangestad, Spydevold Jonathan AU - Johannessen, Nettum Sandra AU - Nordahl-Hansen, Anders PY - 2023/5/11 TI - Evaluating the Feasibility of Emotion Expressions in Avatars Created From Real Person Photos: Pilot Web-Based Survey of Virtual Reality Software JO - JMIR Form Res SP - e44632 VL - 7 KW - avatar KW - emotion recognition KW - emotion KW - face KW - facial expression KW - facial KW - images KW - real images KW - software KW - virtual reality N2 - Background: The availability and potential of virtual reality (VR) has led to an increase of its application. VR is suggested to be helpful in training elements of social competence but with an emphasis on interventions being tailored. Recognizing facial expressions is an important social skill and thus a target for training. Using VR in training these skills could have advantages over desktop alternatives. Children with autism, for instance, appear to prefer avatars over real images when assessing facial expressions. Available software provides the opportunity to transform profile pictures into avatars, thereby giving the possibility of tailoring according to an individual?s own environment. However, the emotions provided by such software should be validated before application. Objective: Our aim was to investigate whether available software is a quick, easy, and viable way of providing emotion expressions in avatars transformed from real images. Methods: A total of 401 participants from a general population completed a survey on the web containing 27 different images of avatars transformed, using a software, from real images. We calculated the reliability of each image and their level of difficulty using a structural equation modeling approach. We used Bayesian confirmatory factor analysis testing under a multidimensional first-order correlated factor structure where faces showing the same emotions represented a latent variable. Results: Few emotions were correctly perceived and rated as higher than other emotions. The factor loadings indicating the discrimination of the image were around 0.7, which means 49% shared variance with the latent factor that the face is linked with. The standardized thresholds indicating the difficulty level of the images are mostly around average, and the highest correlation is between faces showing happiness and anger. Conclusions: Only using a software to transform profile pictures to avatars is not sufficient to provide valid emotion expressions. Adjustments are needed to increase faces? discrimination (eg, increasing reliabilities). The faces showed average levels of difficulty, meaning that they are neither very difficult nor very easy to perceive, which fits a general population. Adjustments should be made for specific populations and when applying this technology in clinical practice. UR - https://formative.jmir.org/2023/1/e44632 UR - http://dx.doi.org/10.2196/44632 UR - http://www.ncbi.nlm.nih.gov/pubmed/37166970 ID - info:doi/10.2196/44632 ER - TY - JOUR AU - Baba, Ayako AU - Bunji, Kyosuke PY - 2023/5/10 TI - Prediction of Mental Health Problem Using Annual Student Health Survey: Machine Learning Approach JO - JMIR Ment Health SP - e42420 VL - 10 KW - student counseling KW - health survey KW - machine learning KW - mental health problem KW - response time N2 - Background: One of the reasons why students go to counseling is being called on based on self-reported health survey results. However, there is no concordant standard for such calls. Objective: This study aims to develop a machine learning (ML) model to predict students? mental health problems in 1 year and the following year using the health survey?s content and answering time (response time, response time stamp, and answer date). Methods: Data were obtained from the responses of 3561 (62.58%) of 5690 undergraduate students from University A in Japan (a national university) who completed the health survey in 2020 and 2021. We performed 2 analyses; in analysis 1, a mental health problem in 2020 was predicted from demographics, answers for the health survey, and answering time in the same year, and in analysis 2, a mental health problem in 2021 was predicted from the same input variables as in analysis 1. We compared the results from different ML models, such as logistic regression, elastic net, random forest, XGBoost, and LightGBM. The results with and without answering time conditions were compared using the adopted model. Results: On the basis of the comparison of the models, we adopted the LightGBM model. In this model, both analyses and conditions achieved adequate performance (eg, Matthews correlation coefficient [MCC] of with answering time condition in analysis 1 was 0.970 and MCC of without answering time condition in analysis 1 was 0.976; MCC of with answering time condition in analysis 2 was 0.986 and that of without answering time condition in analysis 2 was 0.971). In both analyses and in both conditions, the response to the questions about campus life (eg, anxiety and future) had the highest impact (Gain 0.131-0.216; Shapley additive explanations 0.018-0.028). Shapley additive explanations of 5 to 6 input variables from questions about campus life were included in the top 10. In contrast to our expectation, the inclusion of answering time?related variables did not exhibit substantial improvement in the prediction of students? mental health problems. However, certain variables generated based on the answering time are apparently helpful in improving the prediction and affecting the prediction probability. Conclusions: These results demonstrate the possibility of predicting mental health across years using health survey data. Demographic and behavioral data, including answering time, were effective as well as self-rating items. This model demonstrates the possibility of synergistically using the characteristics of health surveys and advantages of ML. These findings can improve health survey items and calling criteria. UR - https://mental.jmir.org/2023/1/e42420 UR - http://dx.doi.org/10.2196/42420 UR - http://www.ncbi.nlm.nih.gov/pubmed/37163323 ID - info:doi/10.2196/42420 ER - TY - JOUR AU - Lundin, M. Robert AU - Yeap, Yuhern AU - Menkes, B. David PY - 2023/5/5 TI - Adverse Effects of Virtual and Augmented Reality Interventions in Psychiatry: Systematic Review JO - JMIR Ment Health SP - e43240 VL - 10 KW - virtual reality KW - augmented reality KW - mental health KW - side effects KW - adverse events KW - hardware KW - VR KW - software KW - AR KW - cybersickness KW - reporting standards N2 - Background: Virtual reality (VR) and augmented reality (AR) are emerging treatment modalities in psychiatry, which are capable of producing clinical outcomes broadly comparable to those achieved with standard psychotherapies. Objective: Because the side effect profile associated with the clinical use of VR and AR remains largely unknown, we systematically reviewed available evidence of their adverse effects. Methods: A systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework across 3 mental health databases (PubMed, PsycINFO, and Embase) to identify VR and AR interventions targeting mental health diagnoses. Results: Of 73 studies meeting the inclusion criteria, 7 reported worsening clinical symptoms or an increased fall risk. Another 21 studies reported ?no adverse effects? but failed to identify obvious adverse effects, mainly cybersickness, documented in their results. More concerningly, 45 of the 73 studies made no mention of adverse effects whatsoever. Conclusions: An appropriate screening tool would help ensure that VR adverse effects are correctly identified and reported. UR - https://mental.jmir.org/2023/1/e43240 UR - http://dx.doi.org/10.2196/43240 UR - http://www.ncbi.nlm.nih.gov/pubmed/37145841 ID - info:doi/10.2196/43240 ER - TY - JOUR AU - Carrotte, Elise AU - Hopgood, Fincina AU - Blanchard, Michelle AU - Groot, Christopher AU - Phillips, Lisa PY - 2023/5/5 TI - A New Podcast for Reducing Stigma Against People Living With Complex Mental Health Issues: Co-design Study JO - JMIR Form Res SP - e44412 VL - 7 KW - mental illness stigma KW - co-design KW - podcasting KW - participatory methods KW - attitude change N2 - Background: Mental illness stigma refers to damaging stereotypes and emotional responses around the experience of mental health issues. Media-based interventions have the potential to reduce the public?s stigmatizing attitudes by improving mental health literacy, emotional appeal, and the intimacy of address. As audio-based media facilitating storytelling, podcasts show potential for reducing stigma; however, it is unclear what features could make a podcast effective or engaging. Objective: The Co-Design and Anti-Stigma Podcast Research (CASPR) study aimed to collaborate with key target audience members to inform the development of a new podcast. This podcast primarily aims to reduce listeners? stigmatizing attitudes toward people living with complex mental health issues. Methods: This study was adapted from Experience-Based Co-Design methodology. The first part, information gathering, involved a web-based mixed methods survey with 629 Australian podcast listeners to explore their interest and concerns around podcasts. Then, a series of focus groups were held with a purposive sample of 25 participants to explore the potential benefits and challenges of the podcast format. Focus group participants included people with lived experience of complex mental health issues, media and communications professionals, health care professionals, and people interested in workplace mental health. The second part, co-design, constituted 3 meetings of a co-design committee with 10 participants drawn from the focus groups to design the podcast using brainstorming and decision-making activities. Results: Most survey respondents (537/629, 85.3%) indicated a willingness to listen to a podcast about experiences of mental illness stigma; participants indicated preference for semistructured episodes and a mixture of light and serious content. Focus group participants identified potential challenges with appealing to listeners, making the content emotionally resonant and engaging, and translation to listeners? attitude change. The co-design committee collaborated to achieve consensus on the focus of individual episodes: domains where stigma and discrimination are common, such as workplaces and health care settings; the structure of individual episodes: storyboards that centralize guests with lived experience, featuring explicit discussions around stigma and discrimination; and overarching content principles, including a sincere, empathetic, and hopeful tone; using plain language; having clear calls to action; and providing listener resources. Conclusions: The co-design process informed a podcast design that features lived experience narratives with an explicit focus on stigma and discrimination, highlighting the realities of stigma while acknowledging progress in the space and how listeners can contribute toward social change. This study allowed for an in-depth discussion around the strengths and limitations of such a podcast according to different target audience members. The co-design committee designed key elements of a podcast that has the potential to minimize the limitations of the format while embracing the benefits of podcast-based storytelling. Once produced, the podcast will be evaluated for its impact on attitude change. UR - https://formative.jmir.org/2023/1/e44412 UR - http://dx.doi.org/10.2196/44412 UR - http://www.ncbi.nlm.nih.gov/pubmed/37145860 ID - info:doi/10.2196/44412 ER - TY - JOUR AU - Wen, Bingyang AU - Wang, Ning AU - Subbalakshmi, Koduvayur AU - Chandramouli, Rajarathnam PY - 2023/5/2 TI - Revealing the Roles of Part-of-Speech Taggers in Alzheimer Disease Detection: Scientific Discovery Using One-Intervention Causal Explanation JO - JMIR Form Res SP - e36590 VL - 7 KW - explainable machine learning KW - Alzheimer disease KW - natural language processing KW - causal inference N2 - Background: Recently, rich computational methods that use deep learning or machine learning have been developed using linguistic biomarkers for the diagnosis of early-stage Alzheimer disease (AD). Moreover, some qualitative and quantitative studies have indicated that certain part-of-speech (PoS) features or tags could be good indicators of AD. However, there has not been a systematic attempt to discover the underlying relationships between PoS features and AD. Moreover, there has not been any attempt to quantify the relative importance of PoS features in detecting AD. Objective: Our goal was to disclose the underlying relationship between PoS features and AD, understand whether PoS features are useful in AD diagnosis, and explore which PoS features play a vital role in the diagnosis. Methods: The DementiaBank, containing 1049 transcripts from 208 patients with AD and 243 transcripts from 104 older control individuals, was used. A total of 27 PoS features were extracted from each record. Then, the relationship between AD and each of the PoS features was explored. A transformer-based deep learning model for AD prediction using PoS features was trained. Then, a global explainable artificial intelligence method was proposed and used to discover which PoS features were the most important in AD diagnosis using the transformer-based predictor. A global (model-level) feature importance measure was derived as a summary from the local (example-level) feature importance metric, which was obtained using the proposed causally aware counterfactual explanation method. The unique feature of this method is that it considers causal relations among PoS features and can, hence, preclude counterfactuals that are improbable and result in more reliable explanations. Results: The deep learning?based AD predictor achieved an accuracy of 92.2% and an F1-score of 0.955 when distinguishing patients with AD from healthy controls. The proposed explanation method identified 12 PoS features as being important for distinguishing patients with AD from healthy controls. Of these 12 features, 3 (25%) have been identified by other researchers in previous works in psychology and natural language processing. The remaining 75% (9/12) of PoS features have not been previously identified. We believe that this is an interesting finding that can be used in creating tests that might aid in the diagnosis of AD. Note that although our method is focused on PoS features, it should be possible to extend it to more types of features, perhaps even those derived from other biomarkers, such as syntactic features. Conclusions: The high classification accuracy of the proposed deep learner indicates that PoS features are strong clues in AD diagnosis. There are 12 PoS features that are strongly tied to AD, and because language is a noninvasive and potentially cheap method for detecting AD, this work shows some promising directions in this field. UR - https://formative.jmir.org/2023/1/e36590 UR - http://dx.doi.org/10.2196/36590 UR - http://www.ncbi.nlm.nih.gov/pubmed/37129944 ID - info:doi/10.2196/36590 ER - TY - JOUR AU - Andrade, C. Fernanda AU - Erwin, Savannah AU - Burnell, Kaitlyn AU - Jackson, Jalisa AU - Storch, Marley AU - Nicholas, Julia AU - Zucker, Nancy PY - 2023/4/28 TI - Intervening on Social Comparisons on Social Media: Electronic Daily Diary Pilot Study JO - JMIR Ment Health SP - e42024 VL - 10 KW - social media KW - social comparison KW - young adults KW - social savoring KW - intervention KW - self-esteem KW - depression N2 - Background: Literature has underscored the dark aspects of social media use, including associations with depressive symptoms, feelings of social isolation, and diminished self-esteem. Social comparison, the process of evaluating oneself relative to another person, is thought to contribute to these negative experiences such that people with a stronger tendency to compare themselves with others are particularly susceptible to the detrimental effects of social media. Social media as a form of social connection and communication is nevertheless an inevitable?and arguably integral?part of life, particularly for young adults. Therefore, there is a need to investigate strategies that could alter the manner in which people interact with social media to minimize its detrimental effects and maximize the feelings of affiliation and connection. Objective: This pilot study examined the feasibility, acceptability, and effectiveness of a brief web-based intervention designed to alter engagement with social media and promote psychological well-being by encouraging social savoring as an alternative to social comparison. Social savoring was operationalized as experiencing joyful emotions related to the happiness of someone else?s experiences (ie, feeling happy for someone else). Methods: Following an intensive longitudinal design, 55 college students (mean age 19.29, SD 0.93 years; n=43, 78% women and n=23, 42% White) completed baseline measures (individual differences, psychological well-being, connectedness, and social media use) and then 14 days of daily surveys on their social media activity and well-being. On day 8, the group that was randomized to receive the intervention watched a video instructing them on the skill of social savoring and was asked to practice this skill during days 8 to 14. Results: Overall, participants reported positive perceptions of the intervention. Participants who watched the intervention video reported significantly higher performance self-esteem (P=.02) at posttest than those in the control condition, after controlling for baseline levels. Participants also reported significantly higher state self-esteem (P=.01) on days in which they engaged in more social savoring while using social media, and the use of social savoring increased significantly (P=.01) over time, suggesting that participants found it helpful. Participants in both conditions reported significantly lower levels of social comparison (control: P=.01; intervention: P=.002) and higher levels of connectedness (control: P<.001; intervention: P=.001) at posttest than at baseline. Conclusions: Initial evidence from this pilot study suggests that a web-based social savoring intervention may help minimize the potentially harmful consequences of social media use, at least in some domains. Future work is needed to examine the effectiveness and acceptance of this intervention in different age groups and in clinical samples that are in part characterized by higher levels of comparison with others (eg, people with eating disorders). UR - https://mental.jmir.org/2023/1/e42024 UR - http://dx.doi.org/10.2196/42024 UR - http://www.ncbi.nlm.nih.gov/pubmed/37115607 ID - info:doi/10.2196/42024 ER - TY - JOUR AU - Aasen, Jan AU - Galaaen, Kari AU - Nilsson, Fredrik AU - Sørensen, Torgeir AU - Lien, Lars AU - Leonhardt, Marja PY - 2023/4/27 TI - Promoting Social Participation and Recovery Using Virtual Reality?Based Interventions Among People With Mental Health and Substance Use Disorders: Qualitative Study JO - JMIR Form Res SP - e46136 VL - 7 KW - mental health disorders and substance use disorders KW - MHD KW - SUD KW - recovery KW - social participation KW - social functioning impairments KW - virtual reality?based interventions KW - VRI KW - reflexive thematic analysis KW - qualitative study N2 - Background: People with mental health disorders (MHDs) and substance use disorders (SUDs) are a highly vulnerable group, particularly affected by social exclusion, marginalization, and disconnectedness. Virtual reality technology holds a potential for simulating social environments and interactions to mitigate the social barriers and marginalization faced by people recovering from MHDs and SUDs. However, it is still unclear how we can harness the greater ecological validity of virtual reality?based interventions targeting social and functional impairments in individuals with MHDs and SUDs. Objective: The aim of this paper was to explore how service providers in community-based MHD and SUD health care services perceive the barriers to social participation among adults recovering from MHDs and SUDs to provide a broader understanding of how learning experiences can be modeled to promote social participation in virtual reality environments. Methods: Two semistructured, open-ended, and dual-moderator focus group interviews were conducted with participants representing different community-based MHD and SUD health care services. Service providers were recruited from their MHD and SUD services in our collaborating municipality in Eastern Norway. We recruited the first participant group at a municipal MHD and SUD assisted living facility for service users with ongoing excessive substance use and severe social dysfunctionality. We recruited the second participant group at a community-based follow-up care service aimed at clients with a broad range of MHDs and SUDs and various levels of social functioning. The qualitative data extracted in the interviews were analyzed, using reflexive thematic analysis. Results: The analysis of the service providers? perceptions of the barriers to social participation among clients with MHDs and SUDs revealed the following five main themes: challenging or lacking social connections, impaired cognitive functions, negative self-perception, impaired personal functioning, and insufficient social security. The barriers identified are interrelated in a cluster of cognitive, socioemotional, and functional impairments, leading to a severe and diverse complex of barriers to social participation. Conclusions: Social participation relies on people?s capability to use their present social opportunities. Promoting basic human functioning is key to promoting social participation among people with MHDs and SUDs. The findings in this study indicate a need to address cognitive functioning, socioemotional learning, instrumental skills, and complex social functions to meet the complexity and diversity of the identified barriers to social functioning in our target group. Virtual reality?based interventions for promoting social participation should be sequenced into distinct scenarios dedicated to specific learning goals to build complex learning in a step-by-step process based on successively more complex levels of human and social functioning. UR - https://formative.jmir.org/2023/1/e46136 UR - http://dx.doi.org/10.2196/46136 UR - http://www.ncbi.nlm.nih.gov/pubmed/37104000 ID - info:doi/10.2196/46136 ER - TY - JOUR AU - Sudre, Gustavo AU - Bagi?, I. Anto AU - Becker, T. James AU - Ford, P. John PY - 2023/4/27 TI - An Emerging Screening Method for Interrogating Human Brain Function: Tutorial JO - JMIR Form Res SP - e37269 VL - 7 KW - screening KW - brain function KW - cognition KW - magnetoencephalography KW - MEG KW - neuroimaging KW - tutorial KW - tool KW - cognitive test KW - signal KW - cognitive function UR - https://formative.jmir.org/2023/1/e37269 UR - http://dx.doi.org/10.2196/37269 UR - http://www.ncbi.nlm.nih.gov/pubmed/37103988 ID - info:doi/10.2196/37269 ER - TY - JOUR AU - Lind, N. Monika AU - Kahn, E. Lauren AU - Crowley, Ryann AU - Reed, Wyatt AU - Wicks, Geordie AU - Allen, B. Nicholas PY - 2023/4/26 TI - Reintroducing the Effortless Assessment Research System (EARS) JO - JMIR Ment Health SP - e38920 VL - 10 KW - mobile sensing KW - passive sensing KW - personal sensing KW - digital phenotyping KW - ecological momentary assessment KW - digital mental health UR - https://mental.jmir.org/2023/1/e38920 UR - http://dx.doi.org/10.2196/38920 UR - http://www.ncbi.nlm.nih.gov/pubmed/37099361 ID - info:doi/10.2196/38920 ER - TY - JOUR AU - Lawrence-Sidebottom, Darian AU - Huffman, Goodgame Landry AU - Huberty, Jennifer AU - Beatty, Clare AU - Roots, Monika AU - Roots, Kurt AU - Parikh, Amit AU - Guerra, Rachael AU - Weiser, Jaclyn PY - 2023/4/26 TI - Using Digital Measurement?Based Care to Address Symptoms of Inattention, Hyperactivity, and Opposition in Youth: Retrospective Analysis of Bend Health JO - JMIR Form Res SP - e46578 VL - 7 KW - digital mental health intervention KW - attention-deficit/hyperactivity disorder KW - opposition defiance disorder KW - attention deficit KW - collaborative care KW - behavioral care KW - mental health KW - adolescent KW - child KW - hyperactivity KW - hyperactive KW - inattention KW - ADHD KW - use KW - caregiver KW - behavioral problem N2 - Background: Attention-deficit/hyperactivity disorder (ADHD) and associated behavioral disorders are highly prevalent in children and adolescents, yet many of them do not receive the care they need. Digital mental health interventions (DMHIs) may address this need by providing accessible and high-quality care. Given the necessity for high levels of caregiver and primary care practitioner involvement in addressing ADHD symptoms and behavioral problems, collaborative care interventions that adopt a whole-family approach may be particularly well suited to reduce symptoms of inattention, hyperactivity, and opposition in children and adolescents. Objective: The purpose of this study is to use member (ie, child and adolescent) data from Bend Health, Inc, a collaborative care DMHI that uses a whole-family approach to address child and adolescent mental health concerns, to (1) determine the effects of a collaborative care DMHI on inattention, hyperactivity, and oppositional symptoms in children and adolescents and (2) assess whether the effects of a collaborative care DMHI vary across ADHD subtypes and demographic factors. Methods: Caregivers of children and adolescents with elevated symptoms of inattention, hyperactivity, or opposition assessed their children?s symptom severity approximately every 30 days while participating in Bend Health, Inc. Data from 107 children and adolescents aged 6-17 years who exhibited clinically elevated symptoms at baseline were used to assess symptom severity across monthly assessments (inattention symptom group: n=91, 85.0%; hyperactivity symptom group: n=48, 44.9%; oppositional symptom group: n=70, 65.4%). The majority of the sample exhibited elevated symptoms of at least 2 symptom types at baseline (n=67, 62.6%). Results: Members received care for up to 5.52 months and attended between 0 and 10 coaching, therapy, or psychiatry sessions through Bend Health, Inc. For those with at least 2 assessments, 71.0% (n=22) showed improvements in inattention symptoms, 60.0% (n=9) showed improvements in hyperactivity symptoms, and 60.0% (n=12) showed improvements in oppositional symptoms. When considering group-level change over time, symptom severity decreased over the course of treatment with Bend Health, Inc, for inattention (average decrease=3.51 points, P=.001) and hyperactivity (average decrease=3.07 points, P=.049) but not for oppositional symptoms (average decrease=0.70 points, P=.26). There was a main effect of the duration of care on symptom severity (P<.001) such that each additional month of care was associated with lower symptom scores. Conclusions: This study offers promising early evidence that collaborative care DHMIs may facilitate improvements in ADHD symptoms among children and adolescents, addressing the growing need for accessible and high-quality care for behavioral health problems in the United States. However, additional follow-up studies bolstered by larger samples and control groups are necessary to further establish the robustness of these findings. UR - https://formative.jmir.org/2023/1/e46578 UR - http://dx.doi.org/10.2196/46578 UR - http://www.ncbi.nlm.nih.gov/pubmed/37099379 ID - info:doi/10.2196/46578 ER - TY - JOUR AU - Langener, Simon AU - Kolkmeier, Jan AU - VanDerNagel, Joanne AU - Klaassen, Randy AU - van Manen, Jeannette AU - Heylen, Dirk PY - 2023/4/26 TI - Development of an Alcohol Refusal Training in Immersive Virtual Reality for Patients With Mild to Borderline Intellectual Disability and Alcohol Use Disorder: Cocreation With Experts in Addiction Care JO - JMIR Form Res SP - e42523 VL - 7 KW - virtual reality KW - conversational agent KW - embodied agent KW - persuasion KW - peer pressure KW - addiction KW - alcohol KW - intellectual disability N2 - Background: People with mild to borderline intellectual disability (MBID; IQ=50-85) are at risk for developing an alcohol use disorder (AUD). One factor contributing to this risk is sensitivity to peer pressure. Hence, tailored trainings are needed to practice alcohol refusal in impacted patients. Immersive virtual reality (IVR) appears promising to engage patients in dialogs with virtual humans, allowing to practice alcohol refusal realistically. However, requirements for such an IVR have not been studied for MBID/AUD. Objective: This study aims to develop an IVR alcohol refusal training for patients with MBID and AUD. In this work, we cocreated our peer pressure simulation with experienced experts in addiction care. Methods: We followed the Persuasive System Design (PSD) model to develop our IVR alcohol refusal training. With 5 experts from a Dutch addiction clinic for patients with MBID, we held 3 focus groups to design the virtual environment, persuasive virtual human(s), and persuasive dialog. Subsequently, we developed our initial IVR prototype and conducted another focus group to evaluate IVR and procedures for clinical usage, resulting in our final peer pressure simulation. Results: Our experts described visiting a friend at home with multiple friends as the most relevant peer pressure situation in the clinical setting. Based on the identified requirements, we developed a social-housing apartment with multiple virtual friends present. Moreover, we embedded a virtual man with generic appearance to exert peer pressure using a persuasive dialog. Patients can respond to persuasive attempts by selecting (refusal) responses with varying degrees of risk for relapse in alcohol use. Our evaluation showed that experts value a realistic and interactable IVR. However, experts identified lacking persuasive design elements, such as paralanguage, for our virtual human. For clinical usage, a user-centered customization is needed to prevent adverse effects. Further, interventions should be therapist delivered to avoid try-and-error in patients with MBID. Lastly, we identified factors for immersion, as well as facilitators and barriers for IVR accessibility. Conclusions: Our work establishes an initial PSD for IVR for alcohol refusal trainings in patients with MBID and AUD. With this, scholars can create comparable simulations by performing an analogous cocreation, replicate findings, and identify active PSD elements. For peer pressure, conveying emotional information in a virtual human?s voice (eg, paralanguage) seems vital. However, previous rapport building may be needed to ensure that virtual humans are perceived as cognitively capable entities. Future work should validate our PSD with patients and start developing IVR treatment protocols using interdisciplinary teams. UR - https://formative.jmir.org/2023/1/e42523 UR - http://dx.doi.org/10.2196/42523 UR - http://www.ncbi.nlm.nih.gov/pubmed/37099362 ID - info:doi/10.2196/42523 ER - TY - JOUR AU - Martínez-Miranda, Juan AU - Meza Magallanes, Janet Martha AU - Silva-Peña, Cándido AU - Mercado Rivas, Xitlali Martha AU - Figueroa-Varela, Rocío María del AU - Sánchez Aranda, Lidiana Magda PY - 2023/4/25 TI - A Computational Platform to Support the Detection, Follow-up, and Epidemiological Surveillance of Mental Health and Substance Use Disorders: Protocol for a Development and Evaluation Study JO - JMIR Res Protoc SP - e44607 VL - 12 KW - computational platform KW - mental health KW - substance use KW - epidemiological surveillance KW - digital screening KW - substance use disorder KW - suicidal behavior KW - level of health care KW - mHealth KW - substance abuse KW - addiction KW - mental health and addiction KW - addict KW - IVDU KW - intravenous drug KW - opioid N2 - Background: According to the World Health Organization, approximately 15% of the global population is affected by mental health or substance use disorders. These conditions contribute significantly to the global disease burden, which has worsened because of the direct and indirect effects of COVID-19. In Mexico, a quarter of the population between the ages of 18 and 65 years who reside in urban areas present a mental health condition. The presence of a mental or substance abuse disorder is behind a significant percentage of suicidal behaviors in Mexico, where only 1 in 5 of those who have these disorders receive any treatment. Objective: This study aims to develop, deploy, and evaluate a computational platform to support the early detection and intervention of mental and substance use disorders in secondary and high schools as well as primary care units. The platform also aims to facilitate monitoring, treatment, and epidemiological surveillance ultimately helping specialized health units at the secondary level of care. Methods: The development and evaluation of the proposed computational platform will run during 3 stages. In stage 1, the identification of the functional and user requirements and the implementation of the modules to support the screening, follow-up, treatment, and epidemiological surveillance will be performed. In stage 2, the initial deployment of the screening module will be carried out in a set of secondary and high schools, as well as the deployment of the modules to support the follow-up, treatment, and epidemiological surveillance processes in primary and secondary care health units. In parallel, during stage 2, patient applications to support early interventions and continuous monitoring will also be developed. Finally, during stage 3, the deployment of the complete platform will be performed jointly with a quantitative and qualitative evaluation. Results: The screening process has started, and 6 schools have been currently enrolled. As of February 2023, a total of 1501 students have undergone screening, and the referral of those students presenting a risk in mental health or substance use to primary care units has also started. The development, deployment, and evaluation of all the modules of the proposed platform are expected to be completed by late 2024. Conclusions: The expected results of this study are to impact a better integration between the different levels of health care, from early detection to follow-up and epidemiological surveillance of mental and substance use disorders contributing to reducing the gap in the attention to these problems in the community. International Registered Report Identifier (IRRID): DERR1-10.2196/44607 UR - https://www.researchprotocols.org/2023/1/e44607 UR - http://dx.doi.org/10.2196/44607 UR - http://www.ncbi.nlm.nih.gov/pubmed/37097718 ID - info:doi/10.2196/44607 ER - TY - JOUR AU - Onie, Sandersan AU - Berlinquette, Patrick AU - Holland, Sarah AU - Livingstone, Nicola AU - Finemore, Coco AU - Gale, Nyree AU - Elder, Emma AU - Laggis, George AU - Heffernan, Cassandra AU - Armstrong, Oliver Susanne AU - Theobald, Adam AU - Josifovski, Natasha AU - Torok, Michelle AU - Shand, Fiona AU - Larsen, Mark PY - 2023/4/20 TI - Suicide Prevention Using Google Ads: Randomized Controlled Trial Measuring Engagement JO - JMIR Ment Health SP - e42316 VL - 10 KW - suicide prevention KW - suicide KW - suicidal KW - self harm KW - digital advertising KW - Google Ads KW - search KW - suicide hotline KW - advertise KW - advertising KW - campaign KW - mental health KW - prevention KW - digital intervention KW - online intervention N2 - Background: Studies have shown that individuals may search for suicide-related terms on the internet prior to an attempt. Objective: Thus, across 2 studies, we investigated engagement with an advertisement campaign designed to reach individuals contemplating suicide. Methods: First, we designed the campaign to focus on crisis, running a campaign for 16 days in which crisis-related keywords would trigger an ad and landing page to help individuals find the national suicide hotline number. Second, we expanded the campaign to also help individuals contemplating suicide, running the campaign for 19 days with a wider range of keywords through a co-designed website with a wider range of offerings (eg, lived experience stories). Results: In the first study, the ad was shown 16,505 times and was clicked 664 times (4.02% click rate). There were 101 calls to the hotline. In the second study, the ad was shown 120,881 times and clicked 6227 times (5.15% click rate); of these 6227 clicks, there were 1419 (22.79%) engagements with the site, a substantially higher rate than the industry average of 3%. The number of clicks on the ad was high despite a suicide hotline banner likely being present. Conclusions: Search advertisements are a quick, far-reaching, and cost-efficient way of reaching those contemplating suicide and are needed despite suicide hotline banners being present. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12623000084684; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=385209 UR - https://mental.jmir.org/2023/1/e42316 UR - http://dx.doi.org/10.2196/42316 UR - http://www.ncbi.nlm.nih.gov/pubmed/37079348 ID - info:doi/10.2196/42316 ER - TY - JOUR AU - Huffman, Goodgame Landry AU - Lawrence-Sidebottom, Darian AU - Huberty, Jennifer AU - Roots, Monika AU - Roots, Kurt AU - Parikh, Amit AU - Guerra, Rachael AU - Weiser, Jaclyn PY - 2023/4/20 TI - Using Digital Measurement?Based Care for the Treatment of Anxiety and Depression in Children and Adolescents: Observational Retrospective Analysis of Bend Health Data JO - JMIR Pediatr Parent SP - e46154 VL - 6 KW - digital mental health intervention KW - anxiety KW - depression KW - child KW - adolescent KW - collaborative care KW - mental health KW - caregiver KW - pediatric KW - youth KW - demographic KW - health outcome KW - retrospective KW - treatment KW - e-mental health KW - symptoms N2 - Background: A growing body of evidence supports the efficacy of measurement-based care (MBC) for children and adolescents experiencing mental health concerns, particularly anxiety and depression. In recent years, MBC has increasingly transitioned to web-based spaces in the form of digital mental health interventions (DMHIs), which render high-quality mental health care more accessible nationwide. Although extant research is promising, the emergence of MBC DMHIs means that much is unknown regarding their effectiveness as a treatment for anxiety and depression, particularly among children and adolescents. Objective: This study uses preliminary data from children and adolescents participating in an MBC DMHI administered by Bend Health Inc, a mental health care provider that uses a collaborative care model to assess changes in anxiety and depressive symptoms during participation in the MBC DMHI. Methods: Caregivers of children and adolescents participating in Bend Health Inc for anxiety or depressive symptoms reported measures of their children?s symptoms every 30 days throughout the duration of participation in Bend Health Inc. Data from 114 children (age 6-12 years) and adolescents (age 13-17 years) were used for the analyses (anxiety symptom group: n=98, depressive symptom group: n=61). Results: Among children and adolescents participating in care with Bend Health Inc, 73% (72/98) exhibited improvements in anxiety symptoms and 73% (44/61) exhibited improvement in depressive symptoms, as indicated by either a decrease in symptom severity or screening out of completing the complete assessment. Among those with complete assessment data, group-level anxiety symptom T-scores exhibited a moderate decrease of 4.69 points (P=.002) from the first to the last assessment. However, members? depressive symptom T-scores remained largely stable throughout their involvement. Conclusions: As increasing numbers of young people and families seek DMHIs over traditional mental health treatments due to their accessibility and affordability, this study offers promising early evidence that youth anxiety symptoms decrease during involvement in an MBC DMHI such as Bend Health Inc. However, further analyses with enhanced longitudinal symptom measures are necessary to determine whether depressive symptoms show similar improvements among those involved in Bend Health Inc. UR - https://pediatrics.jmir.org/2023/1/e46154 UR - http://dx.doi.org/10.2196/46154 UR - http://www.ncbi.nlm.nih.gov/pubmed/37079366 ID - info:doi/10.2196/46154 ER - TY - JOUR AU - Jabir, Ishqi Ahmad AU - Martinengo, Laura AU - Lin, Xiaowen AU - Torous, John AU - Subramaniam, Mythily AU - Tudor Car, Lorainne PY - 2023/4/19 TI - Evaluating Conversational Agents for Mental Health: Scoping Review of Outcomes and Outcome Measurement Instruments JO - J Med Internet Res SP - e44548 VL - 25 KW - conversational agent KW - chatbot KW - mental health KW - mHealth KW - mobile health KW - taxonomy KW - outcomes KW - core outcome set N2 - Background: Rapid proliferation of mental health interventions delivered through conversational agents (CAs) calls for high-quality evidence to support their implementation and adoption. Selecting appropriate outcomes, instruments for measuring outcomes, and assessment methods are crucial for ensuring that interventions are evaluated effectively and with a high level of quality. Objective: We aimed to identify the types of outcomes, outcome measurement instruments, and assessment methods used to assess the clinical, user experience, and technical outcomes in studies that evaluated the effectiveness of CA interventions for mental health. Methods: We undertook a scoping review of the relevant literature to review the types of outcomes, outcome measurement instruments, and assessment methods in studies that evaluated the effectiveness of CA interventions for mental health. We performed a comprehensive search of electronic databases, including PubMed, Cochrane Central Register of Controlled Trials, Embase (Ovid), PsychINFO, and Web of Science, as well as Google Scholar and Google. We included experimental studies evaluating CA mental health interventions. The screening and data extraction were performed independently by 2 review authors in parallel. Descriptive and thematic analyses of the findings were performed. Results: We included 32 studies that targeted the promotion of mental well-being (17/32, 53%) and the treatment and monitoring of mental health symptoms (21/32, 66%). The studies reported 203 outcome measurement instruments used to measure clinical outcomes (123/203, 60.6%), user experience outcomes (75/203, 36.9%), technical outcomes (2/203, 1.0%), and other outcomes (3/203, 1.5%). Most of the outcome measurement instruments were used in only 1 study (150/203, 73.9%) and were self-reported questionnaires (170/203, 83.7%), and most were delivered electronically via survey platforms (61/203, 30.0%). No validity evidence was cited for more than half of the outcome measurement instruments (107/203, 52.7%), which were largely created or adapted for the study in which they were used (95/107, 88.8%). Conclusions: The diversity of outcomes and the choice of outcome measurement instruments employed in studies on CAs for mental health point to the need for an established minimum core outcome set and greater use of validated instruments. Future studies should also capitalize on the affordances made available by CAs and smartphones to streamline the evaluation and reduce participants? input burden inherent to self-reporting. UR - https://www.jmir.org/2023/1/e44548 UR - http://dx.doi.org/10.2196/44548 UR - http://www.ncbi.nlm.nih.gov/pubmed/37074762 ID - info:doi/10.2196/44548 ER - TY - JOUR AU - Yeo, GeckHong AU - Loo, Gladys AU - Oon, Matt AU - Pang, Rachel AU - Ho, Dean PY - 2023/4/18 TI - A Digital Peer Support Platform to Translate Online Peer Support for Emerging Adult Mental Well-being: Randomized Controlled Trial JO - JMIR Ment Health SP - e43956 VL - 10 KW - mental health KW - digital health KW - peer support intervention KW - peer emotional disclosure KW - randomized controlled trial N2 - Background: Emerging adulthood (ages 19 to 25 years) is a developmental phase that is marked by increased mental health conditions, especially depression and anxiety. A growing body of work indicates that digital peer emotional support has positive implications for the psychological functioning of emerging adults. There is burgeoning interest among health care professionals, educational stakeholders, and policy makers in understanding the implementation and clinical effectiveness, as well as the associated mechanism of change, of digital peer support as an intervention. Objective: This randomized controlled trial (RCT) examined the effectiveness of a digital peer support intervention over a digital platform?Acceset?for emerging adult psychological well-being with 3 primary aims. First, we evaluated the implementation effectiveness of digital peer support training for individuals providing support (befrienders) and of the digital platform for peer support. Second, we assessed the clinical outcomes of digital peer support in terms of the intervening effect on emerging adult psychological well-being. Third, we investigated the mechanism of change linking the digital peer support intervention to emerging adult psychological well-being. Methods: This RCT involving 100 emerging adults from the National University of Singapore follows the published protocol for this trial. Results: This RCT found effectiveness in digital peer support training?specifically, befrienders? peer support responses demonstrating significantly higher post- than pretraining scores in selfhood (posttraining score: mean 62.83, SD 10.18, and SE 1.72; pretraining score: mean 54.86, SD 7.32, and SE 1.24; t34=3.88; P<.001). The digital peer support intervention demonstrated clinical effectiveness in enhancing selfhood, compassion, and mindfulness and lowering depressive and anxiety symptoms among seekers in the intervention group after the intervention (mean 7.15, SD 5.14; SE 0.88) than among seekers in the waitlist control group before the intervention (mean 11.75, SD 6.72; SE 0.89; t89=3.44; P<.001). The effect of the intervention on seekers? psychological well-being was sustained beyond the period of the intervention. The mechanism of change revealed that seekers? engagement with the intervention had both immediate and prospective implications for their psychological well-being. Conclusions: This RCT of a digital peer support intervention for emerging adult psychological well-being harnesses the interventional potential of 4 components of psychological well-being and elucidated a mechanism of change. By incorporating and validating the digital features and process of a peer support platform, our RCT provides the parameters and conditions for deploying an effective and novel digital peer support intervention for emerging adult psychological well-being in real-world settings. Trial Registration: ClinicalTrials.gov NCT05083676; https://clinicaltrials.gov/ct2/show/NCT05083676 UR - https://mental.jmir.org/2023/1/e43956 UR - http://dx.doi.org/10.2196/43956 UR - http://www.ncbi.nlm.nih.gov/pubmed/36756843 ID - info:doi/10.2196/43956 ER - TY - JOUR AU - Costello, Jeremy AU - Kaur, Manpreet AU - Reformat, Z. Marek AU - Bolduc, V. Francois PY - 2023/4/17 TI - Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study JO - J Med Internet Res SP - e45268 VL - 25 KW - knowledge graph KW - natural language processing KW - neurodevelopmental disorders KW - autism spectrum disorder KW - intellectual disability KW - attention deficit hyperactivity disorder KW - named entity recognition KW - topic modeling KW - aggregation operator N2 - Background: Patients and families need to be provided with trusted information more than ever with the abundance of online information. Several organizations aim to build databases that can be searched based on the needs of target groups. One such group is individuals with neurodevelopmental disorders (NDDs) and their families. NDDs affect up to 18% of the population and have major social and economic impacts. The current limitations in communicating information for individuals with NDDs include the absence of shared terminology and the lack of efficient labeling processes for web resources. Because of these limitations, health professionals, support groups, and families are unable to share, combine, and access resources. Objective: We aimed to develop a natural language?based pipeline to label resources by leveraging standard and free-text vocabularies obtained through text analysis, and then represent those resources as a weighted knowledge graph. Methods: Using a combination of experts and service/organization databases, we created a data set of web resources for NDDs. Text from these websites was scraped and collected into a corpus of textual data on NDDs. This corpus was used to construct a knowledge graph suitable for use by both experts and nonexperts. Named entity recognition, topic modeling, document classification, and location detection were used to extract knowledge from the corpus. Results: We developed a resource annotation pipeline using diverse natural language processing algorithms to annotate web resources and stored them in a structured knowledge graph. The graph contained 78,181 annotations obtained from the combination of standard terminologies and a free-text vocabulary obtained using topic modeling. An application of the constructed knowledge graph is a resource search interface using the ordered weighted averaging operator to rank resources based on a user query. Conclusions: We developed an automated labeling pipeline for web resources on NDDs. This work showcases how artificial intelligence?based methods, such as natural language processing and knowledge graphs for information representation, can enhance knowledge extraction and mobilization, and could be used in other fields of medicine. UR - https://www.jmir.org/2023/1/e45268 UR - http://dx.doi.org/10.2196/45268 UR - http://www.ncbi.nlm.nih.gov/pubmed/37067865 ID - info:doi/10.2196/45268 ER - TY - JOUR AU - Pardini, Susanna AU - Gabrielli, Silvia AU - Olivetto, Silvia AU - Fusina, Francesca AU - Dianti, Marco AU - Forti, Stefano AU - Lancini, Cristina AU - Novara, Caterina PY - 2023/4/17 TI - Personalized, Naturalistic Virtual Reality Scenarios Coupled With Web-Based Progressive Muscle Relaxation Training for the General Population: Protocol for a Proof-of-Principle Randomized Controlled Trial JO - JMIR Res Protoc SP - e44183 VL - 12 KW - digital health KW - progressive muscular relaxation technique KW - mental well-being KW - virtual reality therapy KW - anxiety KW - relaxation KW - e-therapy KW - e-Health KW - virtual reality KW - tool KW - symptoms KW - depression KW - quality of life KW - coping N2 - Background: Virtual reality (VR) is an innovative tool that can facilitate exposure to either stressful or relaxing stimuli and enables individuals who have difficulties visualizing scenes to be involved in a more realistic sensorimotor experience. It also facilitates multisensory stimulation, a sense of presence, and achievement of relaxation. VR scenarios representing visual and auditory elements of natural relaxing environments can facilitate the learning of relaxation techniques such as the progressive muscle relaxation technique (PMRT). A complementary standardized technique deployed to reduce anxiety symptoms is the integration of PMRT and guided imagery (GI). Exposure to a pleasant imaginary environment helps the establishment of an association between a relaxing scenario and the relaxation technique, consequently promoting relaxation. Empirical evidence has shown that VR scenarios can increase the effects of relaxation techniques by enabling people to experience emotional conditions in more vivid settings. Objective: The main aim of this pilot study protocol is to investigate the impact on state anxiety of PMRT, associated with a personalized relaxing scenario in VR, and the role of VR scenarios in facilitating the recall of relaxing images and a sense of presence. A secondary aim is to understand if relaxing sessions administered via Zoom are more effective for managing anxiety and stress than a procedural setting based on audio-track guidance. Methods: Based on a longitudinal, between-subject design, 108 university students will be randomly exposed to one of three experimental conditions: (1) PMRT via Zoom and GI exposure, (2) PMRT via Zoom and personalized VR exposure, and (3) PMRT based on audio-track guidance and personalized VR exposure. Individuals are assessed before and after 7 training sessions based on self-report questionnaires investigating anxiety, depression, quality of life, coping strategies, sense of presence, engagement, and side effects related to VR exposure. Heart rate data are also detected by an Mi Band 2 sensor. Results: The experimental procedure is ongoing. In this paper, preliminary data from a sample of 40 participants will be illustrated. The experimental phase is expected to conclude in May 2023, and the final results of the research will be presented in June 2023. Conclusions: The results of this study will help shape the experimental design to apply it on a subsequent randomized controlled trial, also considering clinical samples. This work is expected to measure whether VR is a more engaging and helpful technique in promoting relaxation and decreasing anxiety levels than GI, by making the visualization process easier and by helping people to face more realistic sensory experiences. Assessing the efficacy of the PMRT in alternative delivery modes may extend its applications, especially in situations where the standard procedure is more challenging to be administered. To our knowledge, no equivalent study has been published so far on this matter. Trial Registration: ClinicalTrials.gov NCT05478941; https://clinicaltrials.gov/ct2/show/NCT05478941 International Registered Report Identifier (IRRID): DERR1-10.2196/44183 UR - https://www.researchprotocols.org/2023/1/e44183 UR - http://dx.doi.org/10.2196/44183 UR - http://www.ncbi.nlm.nih.gov/pubmed/37067881 ID - info:doi/10.2196/44183 ER - TY - JOUR AU - Abd-alrazaq, Alaa AU - Abuelezz, Israa AU - AlSaad, Rawan AU - Al-Jafar, Eiman AU - Ahmed, Arfan AU - Aziz, Sarah AU - Nashwan, Abdulqadir AU - Sheikh, Javaid PY - 2023/4/12 TI - Serious Games for Learning Among Older Adults With Cognitive Impairment: Systematic Review and Meta-analysis JO - J Med Internet Res SP - e43607 VL - 25 KW - serious games KW - cognitive training KW - learning KW - exergames KW - mild cognitive impairment KW - Alzheimer disease KW - dementia KW - systematic reviews KW - meta-analysis KW - mobile phone N2 - Background: Learning disabilities are among the major cognitive impairments caused by aging. Among the interventions used to improve learning among older adults are serious games, which are participative electronic games designed for purposes other than entertainment. Although some systematic reviews have examined the effectiveness of serious games on learning, they are undermined by some limitations, such as focusing on older adults without cognitive impairments, focusing on particular types of serious games, and not considering the comparator type in the analysis. Objective: This review aimed to evaluate the effectiveness of serious games on verbal and nonverbal learning among older adults with cognitive impairment. Methods: Eight electronic databases were searched to retrieve studies relevant to this systematic review and meta-analysis. Furthermore, we went through the studies that cited the included studies and screened the reference lists of the included studies and relevant reviews. Two reviewers independently checked the eligibility of the identified studies, extracted data from the included studies, and appraised their risk of bias and the quality of the evidence. The results of the included studies were summarized using a narrative synthesis or meta-analysis, as appropriate. Results: Of the 559 citations retrieved, 11 (2%) randomized controlled trials (RCTs) ultimately met all eligibility criteria for this review. A meta-analysis of 45% (5/11) of the RCTs revealed that serious games are effective in improving verbal learning among older adults with cognitive impairment in comparison with no or sham interventions (P=.04), and serious games do not have a different effect on verbal learning between patients with mild cognitive impairment and those with Alzheimer disease (P=.89). A meta-analysis of 18% (2/11) of the RCTs revealed that serious games are as effective as conventional exercises in promoting verbal learning (P=.98). We also found that serious games outperformed no or sham interventions (4/11, 36%; P=.03) and conventional cognitive training (2/11, 18%; P<.001) in enhancing nonverbal learning. Conclusions: Serious games have the potential to enhance verbal and nonverbal learning among older adults with cognitive impairment. However, our findings remain inconclusive because of the low quality of evidence, the small sample size in most of the meta-analyzed studies (6/8, 75%), and the paucity of studies included in the meta-analyses. Thus, until further convincing proof of their effectiveness is offered, serious games should be used to supplement current interventions for verbal and nonverbal learning rather than replace them entirely. Further studies are needed to compare serious games with conventional cognitive training and conventional exercises, as well as different types of serious games, different platforms, different intervention periods, and different follow-up periods. Trial Registration: PROSPERO CRD42022348849; https://tinyurl.com/y6yewwfa UR - https://www.jmir.org/2023/1/e43607 UR - http://dx.doi.org/10.2196/43607 UR - http://www.ncbi.nlm.nih.gov/pubmed/37043277 ID - info:doi/10.2196/43607 ER - TY - JOUR AU - Braun, Pia AU - Atik, Ece AU - Guthardt, Lisa AU - Apolinário-Hagen, Jennifer AU - Schückes, Magnus PY - 2023/4/12 TI - Barriers to and Facilitators of a Blended Cognitive Behavioral Therapy Program for Depression and Anxiety Based on Experiences of University Students: Qualitative Interview Study JO - JMIR Form Res SP - e45970 VL - 7 KW - digital therapeutics KW - blended cognitive behavioral therapy KW - bCBT KW - depression KW - anxiety KW - acceptance KW - user experiences KW - university students KW - mobile phone N2 - Background: Blended cognitive behavioral therapy (bCBT) programs have been proposed to increase the acceptance and adoption of digital therapeutics (DTx) such as digital health apps. These programs allow for more personalized care by combining regular face-to-face therapy sessions with DTx. However, facilitators of and barriers to the use of DTx in bCBT programs have rarely been examined among students, who are particularly at risk for developing symptoms of depression and anxiety disorders. Objective: This study aimed to evaluate the facilitators of and barriers to the use of a bCBT program with the elona therapy app among university students with mild to moderate depression or anxiety symptoms. Methods: Semistructured interviews were conducted via videoconference between January 2022 and April 2022 with 102 students (mean age 23.93, SD 3.63 years; 89/102, 87.2% female) from universities in North Rhine-Westphalia, Germany, after they had completed weekly individual cognitive behavioral therapy sessions (25 minutes each) via videoconference for 6 weeks and regularly used the depression (n=67, 65.7%) or anxiety (n=35, 34.3%) module of the app. The interviews were coded based on grounded theory. Results: Many participants highlighted the intuitive handling of the app and indicated that they perceived it as a supportive tool between face-to-face sessions. Participants listed other benefits, such as increased self-reflection and disorder-specific knowledge as well as the transfer of the content of therapy sessions into their daily lives. Some stated that they would have benefited from more personalized and interactive tasks. In general, participants mentioned the time requirement, increased use of the smartphone, and the feeling of being left alone with potentially arising emotions while working on tasks for the next therapy session as possible barriers to the use of the app. Data security was not considered a major concern. Conclusions: Students mostly had positive attitudes toward elona therapy as part of the bCBT program. Our study shows that DTx complementing face-to-face therapy sessions can be perceived as a helpful tool for university students with mild to moderate anxiety or depression symptoms in their daily lives. Future research could elaborate on whether bCBT programs might also be suitable for students with more severe symptoms of mental disorders. In addition, the methods by which such bCBT programs could be incorporated into the university context to reach students in need of psychological support should be explored. UR - https://formative.jmir.org/2023/1/e45970 UR - http://dx.doi.org/10.2196/45970 UR - http://www.ncbi.nlm.nih.gov/pubmed/37043272 ID - info:doi/10.2196/45970 ER - TY - JOUR AU - Nikolajski, Cara AU - O'Brien, Julia AU - Nardo, Emily AU - Szigethy, Eva AU - Jonassaint, Charles PY - 2023/4/6 TI - Tailoring a Digital Mental Health Program for Patients With Sickle Cell Disease: Qualitative Study JO - JMIR Ment Health SP - e44216 VL - 10 KW - mental health KW - sickle cell disease KW - digital health KW - cognitive behavioral therapy KW - digital cognitive behavioral therapy N2 - Background: Depression and other mental health disorders are prevalent among people living with chronic health conditions. Although digital cognitive behavioral therapy (CBT) is considered an effective treatment, African American individuals are less likely to engage in and adhere to digital therapies for mental health disorders compared with White individuals. Objective: The aim of this study was to understand digital CBT mental health treatment perceptions and preferences of African American individuals with sickle cell disease (SCD). Methods: African American individuals with SCD from various US locations were invited to participate in a series of focus groups. Participants were introduced to a health coach?supported mental health app and then asked a series of questions about the usability and appeal of the program as well as, more generally, what would make a digital mental health program effective for them. The authors reviewed the focus group transcripts and conducted a qualitative analysis of the results. Results: A total of 25 people participated in 5 focus groups. Overall, 5 primary themes emerged regarding how app content and related coaching could be modified to enhance digital CBT engagement. These themes included connection with others living with SCD, the personalization of app content and coaching, characteristics of coaches, journaling and pain tracking, and considerations for optimal engagement. Conclusions: Enhancing the user experience by making digital CBT tools relevant to patient populations is critical for optimizing program engagement and its uptake. Our findings highlight potential strategies to modify and design digital CBT tools for users with SCD and may also be applicable to patients with other chronic conditions. Trial Registration: ClinicalTrials.gov NCT04587661; https://clinicaltrials.gov/ct2/show/NCT04587661 UR - https://mental.jmir.org/2023/1/e44216 UR - http://dx.doi.org/10.2196/44216 UR - http://www.ncbi.nlm.nih.gov/pubmed/37023443 ID - info:doi/10.2196/44216 ER - TY - JOUR AU - Ramos, Giovanni AU - Montoya, Kay Amanda AU - Hammons, Renee Hayley AU - Smith, Danielle AU - Chavira, April Denise AU - Rith-Najarian, Rose Leslie PY - 2023/4/6 TI - Digital Intervention Barriers Scale?7 (DIBS-7): Development, Evaluation, and Preliminary Validation JO - JMIR Form Res SP - e40509 VL - 7 KW - barriers KW - development KW - digital mental health intervention KW - measure KW - psychometrics KW - scale KW - validation N2 - Background: The translation of mental health services into digital formats, deemed digital mental health interventions (DMHIs), has the potential to address long-standing obstacles to accessing care. However, DMHIs have barriers of their own that impact enrollment, adherence, and attrition in these programs. Unlike in traditional face-to-face therapy, there is a paucity of standardized and validated measures of barriers in DMHIs. Objective: In this study, we describe the preliminary development and evaluation of such a scale, the Digital Intervention Barriers Scale-7 (DIBS-7). Methods: Following an iterative QUAN ? QUAL mixed methods approach, item generation was guided by qualitative analysis of feedback from participants (n=259) who completed a DMHI trial for anxiety and depression and identified barriers related to self-motivation, ease of use, acceptability, and comprehension of tasks. Item refinement was achieved through DMHI expert review. A final item pool was administered to 559 treatment completers (mean age 23.02 years; 438/559, 78.4% female; 374/559, 69.9% racially or ethnically minoritized). Exploratory factor analyses and confirmatory factor analyses were estimated to determine the psychometric properties of the measure. Finally, criterion-related validity was examined by estimating partial correlations between the DIBS-7 mean score and constructs related to treatment engagement in DMHIs. Results: Statistical analyses estimated a 7-item unidimensional scale with high internal consistency (?=.82, ?=0.89). Preliminary criterion-related validity was supported by significant partial correlations between the DIBS-7 mean score and treatment expectations (pr=?0.25), number of modules with activity (pr=?0.55), number of weekly check-ins (pr=?0.28), and treatment satisfaction (pr=?0.71). Conclusions: Overall, these results provide preliminary support for the use of the DIBS-7 as a potentially useful short scale for clinicians and researchers interested in measuring an important variable often associated with treatment adherence and outcomes in DMHIs. UR - https://formative.jmir.org/2023/1/e40509 UR - http://dx.doi.org/10.2196/40509 UR - http://www.ncbi.nlm.nih.gov/pubmed/37023417 ID - info:doi/10.2196/40509 ER - TY - JOUR AU - Daemen, Maud AU - van Amelsvoort, Therese AU - AU - Reininghaus, Ulrich PY - 2023/4/5 TI - Momentary Self-esteem as a Process Underlying the Association Between Childhood Trauma and Psychosis: Experience Sampling Study JO - JMIR Ment Health SP - e34147 VL - 10 KW - psychosis KW - self-esteem KW - childhood trauma KW - childhood adversity KW - experience sampling method KW - ecological momentary assessment N2 - Background: Exposure to childhood trauma is associated with an increased risk of developing and maintaining psychotic symptoms later in life. Self-esteem might be an important psychological process underlying the association between childhood trauma and psychosis, but there is only limited evidence to support this claim, especially in daily life. Objective: In this study, we aimed to investigate whether exposure to childhood trauma (physical, emotional, and sexual abuse and physical and emotional neglect) moderates the cross-sectional and temporal associations between self-esteem and psychotic experiences in patients with psychotic disorders, their first-degree relatives, and controls. Methods: We assessed momentary self-esteem and psychotic experiences in daily life using the experience sampling method in 139 patients with psychotic disorders, 118 first-degree relatives of patients with psychotic disorders, and 111 controls. Childhood trauma was measured using the Childhood Trauma Questionnaire. We fitted linear mixed models and added two-way and three-way interaction terms to test the hypotheses. Results: The association between momentary self-esteem and psychotic experiences in daily life was modified by prior exposure to high versus low levels of several types of childhood trauma, that is, physical (?22=24.9, family-wise error-corrected P<.001) and sexual abuse (?22=15.9, P<.001) and physical neglect (?22=116.7, P<.001). Specifically, momentary self-esteem was associated with more intense psychotic experiences in patients exposed to high versus low levels of physical neglect, in relatives exposed to high versus low levels of physical abuse, and in relatives and controls exposed to high versus low levels of sexual abuse. When investigating temporal order, the results showed no evidence that childhood trauma modified the temporal associations between self-esteem at tn-1 and psychotic experiences at tn or those between psychotic experiences at tn-1 and self-esteem at tn. Conclusions: The association between self-esteem and psychotic experiences in daily life was found to be stronger in those exposed to high versus low levels of several types of childhood trauma (ie, physical abuse, sexual abuse, and physical neglect). UR - https://mental.jmir.org/2023/1/e34147 UR - http://dx.doi.org/10.2196/34147 UR - http://www.ncbi.nlm.nih.gov/pubmed/37018034 ID - info:doi/10.2196/34147 ER - TY - JOUR AU - Teferra, Gashaw Bazen AU - Rose, Jonathan PY - 2023/3/28 TI - Predicting Generalized Anxiety Disorder From Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation Study JO - JMIR Ment Health SP - e44325 VL - 10 KW - mental health KW - generalized anxiety disorder KW - impromptu speech KW - linguistic features KW - anxiety prediction KW - neural networks KW - natural language processing KW - transformer models KW - mobile phone N2 - Background: The ability to automatically detect anxiety disorders from speech could be useful as a screening tool for an anxiety disorder. Prior studies have shown that individual words in textual transcripts of speech have an association with anxiety severity. Transformer-based neural networks are models that have been recently shown to have powerful predictive capabilities based on the context of more than one input word. Transformers detect linguistic patterns and can be separately trained to make specific predictions based on these patterns. Objective: This study aimed to determine whether a transformer-based language model can be used to screen for generalized anxiety disorder from impromptu speech transcripts. Methods: A total of 2000 participants provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test (TSST). They also completed the Generalized Anxiety Disorder 7-item (GAD-7) scale. A transformer-based neural network model (pretrained on large textual corpora) was fine-tuned on the speech transcripts and the GAD-7 to predict whether a participant was above or below a screening threshold of the GAD-7. We reported the area under the receiver operating characteristic curve (AUROC) on the test data and compared the results with a baseline logistic regression model using the Linguistic Inquiry and Word Count (LIWC) features as input. Using the integrated gradient method to determine specific words that strongly affect the predictions, we inferred specific linguistic patterns that influence the predictions. Results: The baseline LIWC-based logistic regression model had an AUROC value of 0.58. The fine-tuned transformer model achieved an AUROC value of 0.64. Specific words that were often implicated in the predictions were also dependent on the context. For example, the first-person singular pronoun ?I? influenced toward an anxious prediction 88% of the time and a nonanxious prediction 12% of the time, depending on the context. Silent pauses in speech, also often implicated in predictions, influenced toward an anxious prediction 20% of the time and a nonanxious prediction 80% of the time. Conclusions: There is evidence that a transformer-based neural network model has increased predictive power compared with the single word?based LIWC model. We also showed that the use of specific words in a specific context?a linguistic pattern?is part of the reason for the better prediction. This suggests that such transformer-based models could play a useful role in anxiety screening systems. UR - https://mental.jmir.org/2023/1/e44325 UR - http://dx.doi.org/10.2196/44325 UR - http://www.ncbi.nlm.nih.gov/pubmed/36976636 ID - info:doi/10.2196/44325 ER - TY - JOUR AU - Kobak, Kenneth AU - Shear, Katherine M. AU - Skritskaya, A. Natalia AU - Bloom, Colleen AU - Bottex, Gaelle PY - 2023/3/27 TI - A Web-Based Therapist Training Tutorial on Prolonged Grief Disorder Therapy: Pre-Post Assessment Study JO - JMIR Med Educ SP - e44246 VL - 9 KW - grief KW - prolonged grief disorder KW - evidence-based practice KW - mental health training KW - therapist training KW - new technology KW - web-based training KW - dissemination KW - e-learning N2 - Background: Prolonged grief disorder (PGD) is a newly recognized mental disorder characterized by pervasive intense grief that persists longer than cultural or social expectations and interferes with functioning. The COVID-19 epidemic has resulted in increased rates of PGD, and few clinicians feel confident in treating this condition. PGD therapy (PGDT) is a simple, short-term, and evidence-based treatment developed in tandem with the validation of the PGD diagnosis. To facilitate the dissemination of PGDT training, we developed a web-based therapist tutorial that includes didactic training on PGDT concepts and principles as well as web-based multimedia patient scenarios and examples of clinical implementation of PGDT. Objective: We aimed to evaluate user satisfaction with the tutorial and whether the tutorial increased trainees? knowledge of PGDT principles and procedures. Moreover, we included a small number of pilot questions to evaluate the PGDT-related clinical skills. Methods: This study evaluated tutorial learning using a pre- and poststudy design. Participants were recruited from professional organization mailing lists, announcements to graduates of the Columbia School of Social Work, and through word of mouth. After signing consent, participants completed a brief demographic survey, a 55-item multiple-choice prestudy test on the concepts and principles of PGD and PGDT covered in the tutorial, and a 4-item pilot web-based prestudy test to gauge PGD clinical implementation skills. The link to the course content was then activated, and participants were given 8 weeks to complete the 11-module tutorial containing information, web-based exercises, simulated patient and video examples, and self-tests. Results: Overall, 406 clinicians signed consent, and 236 (58.1%) started the tutorial. Of these, 83.1% (196/236) completed all 11 modules. Trainee scores on our PDGT assessment improved substantially from pretraining to the postmodule assessment, with the total number of correct answers increasing from a mean of 29 (SD 5.5; 52.7% correct) to 36.7 (SD 5.2; 66.7% correct; t195=18.93; P<.001). In addition, the trainee?s implementation scores on 4 clinical vignettes increased from 2.6 (SD 0.7) correct out of 4 to 3.1 (SD 0.4) out of 4 (t188=7.02; P<.001). Effect sizes (Cohen d) were 1.44 (95% CI 1.23-1.65) for PDGT assessment and 1.06 (95% CI 0.84-1.29) for implementation. Trainees found the tutorial interesting, enjoyable, clearly presented, and useful for professional development. They endorsed a mean score of 3.7 (SD 0.47) on a 1 to 4 scale of agreement with recommending the course to others and feeling satisfied with the tutorial, and a mean of 3.3 (SD 0.57) with feeling able to apply the skills with clients. Conclusions: This pilot study provides support for the usefulness of this web-based training for teaching clinicians how to administer PGDT. The addition of patient scenarios for clinical implementation strategies holds promise for increasing the effectiveness of PGDT training and other evidence-based treatments. Trial Registration: ClinicalTrials.gov NCT05121792; https://www.clinicaltrials.gov/ct2/show/NCT05121792 UR - https://mededu.jmir.org/2023/1/e44246 UR - http://dx.doi.org/10.2196/44246 UR - http://www.ncbi.nlm.nih.gov/pubmed/36972105 ID - info:doi/10.2196/44246 ER - TY - JOUR AU - Bautista, Justine AU - Schueller, M. Stephen PY - 2023/3/22 TI - Understanding the Adoption and Use of Digital Mental Health Apps Among College Students: Secondary Analysis of a National Survey JO - JMIR Ment Health SP - e43942 VL - 10 KW - mental health KW - mental health apps KW - college students KW - digital health KW - app KW - anxiety N2 - Background: Increasing rates of mental health diagnoses in college students signal the need for new opportunities to support the mental health of this population. With many mental health apps being efficacious, they may be a promising resource for college campuses to provide support to their students. However, it is important to understand why (or why not) students might want to use apps and their desired features. Objective: Information on students? interest in mental health apps may inform which apps are to be provided and how campuses can support their use. This study aimed to understand the interest and hesitation in app use and the relationship between mental health needs, as defined by depression, anxiety, and positive mental health, and app use. Methods: The web-based Healthy Minds Study collected information on mental health needs, perceptions, and service use across colleges and universities. We used a sample of 989 participants who completed the survey between 2018 and 2020 and an elective module on digital mental health. We analyzed the elective module responses using a mixed methods approach, including both descriptive and inferential statistics, along with thematic coding for open text responses. Results: The Results from this study revealed that anxiety (b=?0.07; P<.001), but not depression (b=0.03; P=.12) and positive mental health (b=?0.02; P=.17), was a significant predictor of app adoption. Prominent qualitative findings indicated that the most desired app features included tips and advice, access to resources and information, and on-demand support that involves interaction throughout the day. The participants also suggested an overall desire for human interaction to be integrated into an app. As predicted, hesitancy was encountered, and the qualitative results suggested that there was a lack of interest in the adoption of mental health app and preference. Conclusions: The findings from this study underscore that simply providing digital mental health apps as tools may be insufficient to support their use in college campuses. Although many students were open to using a mental health app, hesitation and uncertainty were common in the participant responses. Working with colleges and universities to increase digital literacy and provide resources that allow students to gauge when app use is appropriate may be helpful when implementing mental health apps as resources in college campuses. UR - https://mental.jmir.org/2023/1/e43942 UR - http://dx.doi.org/10.2196/43942 UR - http://www.ncbi.nlm.nih.gov/pubmed/36947115 ID - info:doi/10.2196/43942 ER - TY - JOUR AU - Salamanca-Sanabria, Alicia AU - Jabir, Ishqi Ahmad AU - Lin, Xiaowen AU - Alattas, Aishah AU - Kocaballi, Baki A. AU - Lee, Jimmy AU - Kowatsch, Tobias AU - Tudor Car, Lorainne PY - 2023/3/20 TI - Exploring the Perceptions of mHealth Interventions for the Prevention of Common Mental Disorders in University Students in Singapore: Qualitative Study JO - J Med Internet Res SP - e44542 VL - 25 KW - interventions KW - students KW - mobile health KW - mHealth KW - mental health KW - mental disorders KW - university KW - common mental disorders KW - anxiety KW - depression N2 - Background: Mental health interventions delivered through mobile health (mHealth) technologies can increase the access to mental health services, especially among university students. The development of mHealth intervention is complex and needs to be context sensitive. There is currently limited evidence on the perceptions, needs, and barriers related to these interventions in the Southeast Asian context. Objective: This qualitative study aimed to explore the perception of university students and mental health supporters in Singapore about mental health services, campaigns, and mHealth interventions with a focus on conversational agent interventions for the prevention of common mental disorders such as anxiety and depression. Methods: We conducted 6 web-based focus group discussions with 30 university students and one-to-one web-based interviews with 11 mental health supporters consisting of faculty members tasked with student pastoral care, a mental health first aider, counselors, psychologists, a clinical psychologist, and a psychiatrist. The qualitative analysis followed a reflexive thematic analysis framework. Results: The following 6 main themes were identified: a healthy lifestyle as students, access to mental health services, the role of mental health promotion campaigns, preferred mHealth engagement features, factors that influence the adoption of mHealth interventions, and cultural relevance of mHealth interventions. The interpretation of our findings shows that students were reluctant to use mental health services because of the fear of stigma and a possible lack of confidentiality. Conclusions: Study participants viewed mHealth interventions for mental health as part of a blended intervention. They also felt that future mental health mHealth interventions should be more personalized and capable of managing adverse events such as suicidal ideation. UR - https://www.jmir.org/2023/1/e44542 UR - http://dx.doi.org/10.2196/44542 UR - http://www.ncbi.nlm.nih.gov/pubmed/36939808 ID - info:doi/10.2196/44542 ER - TY - JOUR AU - Nataliansyah, Muska M. AU - Merchant, S. Kimberly A. AU - Vakkalanka, Priyanka J. AU - Mack, Luke AU - Parsons, Seth AU - Ward, M. Marcia PY - 2023/3/20 TI - Virtual Partnership Addressing Mental Health Crises: Mixed Methods Study of a Coresponder Program in Rural Law Enforcement JO - JMIR Ment Health SP - e42610 VL - 10 KW - mental health KW - telehealth KW - rural health KW - coresponder model KW - implementation KW - mixed methods KW - community KW - technology KW - virtual care N2 - Background: A mental health crisis can create challenges for individuals, families, and communities. This multifaceted issue often involves different professionals from law enforcement and health care systems, which may lead to siloed and suboptimal care. The virtual crisis care (VCC) program was developed to provide rural law enforcement with access to behavioral health professionals and facilitated collaborative care via telehealth technology. Objective: This study was designed to evaluate the implementation and use of a VCC program from a telehealth hub for law enforcement in rural areas. Methods: This study used a mixed methods approach. The quantitative data came from the telehealth hub?s electronic record system. The qualitative data came from in-depth interviews with law enforcement in the 18 counties that adopted the VCC program. Results: Across the 181 VCC encounters, the telehealth hub's recommended disposition and the actual disposition were similar for remaining in place (n=141, 77.9%, and n=137, 75.7%, respectively), voluntary admission (n=9, 5.0%, and n=10, 5.5%, respectively), and involuntary committal (IVC; n=27, 14.9%, and n=19, 10.5%, respectively). Qualitative insights related to the VCC program's implementation, use, benefits, and challenges were identified, providing a comprehensive view of the virtual partnership between rural law enforcement and behavioral health professionals. Conclusions: Use of a VCC program likely averts unnecessary IVCs. Law enforcement interviews affirmed the positive impact of VCC due to its ease of use and the benefits it provides to the individuals in need, the first responders involved, law enforcement resources, and the community. UR - https://mental.jmir.org/2023/1/e42610 UR - http://dx.doi.org/10.2196/42610 UR - http://www.ncbi.nlm.nih.gov/pubmed/36939827 ID - info:doi/10.2196/42610 ER - TY - JOUR AU - Langener, M. Anna AU - Stulp, Gert AU - Kas, J. Martien AU - Bringmann, F. Laura PY - 2023/3/17 TI - Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review JO - JMIR Ment Health SP - e42646 VL - 10 KW - social context KW - experience sampling method KW - egocentric network KW - digital phenotyping KW - passive measures KW - ambulatory assessment KW - mobile phone N2 - Background: Social interactions are important for well-being, and therefore, researchers are increasingly attempting to capture people?s social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. The experience sampling method (ESM) is often used in psychology to study the dynamics within a person and the social environment. In addition, passive sensing is often used to capture social behavior via sensors from smartphones or other wearable devices. Furthermore, sociologists use egocentric networks to track how social relationships are changing. Each of these methods is likely to tap into different but important parts of people?s social environment. Thus far, the development and implementation of these methods have occurred mostly separately from each other. Objective: Our aim was to synthesize the literature on how these methods are currently used to capture the changing social environment in relation to well-being and assess how to best combine these methods to study well-being. Methods: We conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results: We included 275 studies. In total, 3 important points follow from our review. First, each method captures a different but important part of the social environment at a different temporal resolution. Second, measures are rarely validated (>70% of ESM studies and 50% of passive sensing studies were not validated), which undermines the robustness of the conclusions drawn. Third, a combination of methods is currently lacking (only 15/275, 5.5% of the studies combined ESM and passive sensing, and no studies combined all 3 methods) but is essential in understanding well-being. Conclusions: We highlight that the practice of using poorly validated measures hampers progress in understanding the relationship between the changing social environment and well-being. We conclude that different methods should be combined more often to reduce the participants? burden and form a holistic perspective on the social environment. UR - https://mental.jmir.org/2023/1/e42646 UR - http://dx.doi.org/10.2196/42646 UR - http://www.ncbi.nlm.nih.gov/pubmed/36930210 ID - info:doi/10.2196/42646 ER - TY - JOUR AU - Cataldo, Francesco AU - Mendoza, Antonette AU - Chang, Shanton AU - Buchanan, George AU - Van Dam, T. Nicholas PY - 2023/3/16 TI - Enhancing Therapeutic Processes in Videoconferencing Psychotherapy: Interview Study of Psychologists? Technological Perspective JO - JMIR Form Res SP - e40542 VL - 7 KW - videoconference psychotherapy KW - therapeutic relationship KW - therapeutic alliance KW - telehealth KW - technology KW - therapeutic processes N2 - Background: The COVID-19 pandemic caused a surge in the use of telehealth platforms. Psychologists have shifted from face-to-face sessions to videoconference sessions. Therefore, essential information that is easily obtainable via in-person sessions may be missing. Consequently, therapeutic work could be compromised. Objective: This study aimed to explore the videoconference psychotherapy (VCP) experiences of psychologists around the world. Furthermore, we aimed to identify technological features that may enhance psychologists? therapeutic work through augmented VCP. Methods: In total, 17 psychologists across the world (n=7, 41% from Australia; n=1, 6% from England; n=5, 29% from Italy; n=1, 6% from Mexico; n=1, 6% from Spain; and n=2, 12% from the United States) were interviewed. We used thematic analysis to examine the data collected from a sample of 17 psychologists. We applied the Chaos Theory to interpret the system dynamics and collected details about the challenges posed by VCP. For collecting further information about the technology and processes involved, we relied on the Input-Process-Output (IPO) model. Results: The analysis resulted in the generation of 9 themes (input themes: psychologists? attitude, trust-reinforcing features, reducing cognitive load, enhancing emotional communication, and engaging features between psychologists and patients; process themes: building and reinforcing trust, decreasing cognitive load, enhancing emotional communication, and increasing psychologist-patient engagement) and 19 subthemes. Psychologists found new strategies to deal with VCP limitations but also reported the need for more technical control to facilitate therapeutic processes. The suggested technologies (eye contact functionality, emergency call functionality, screen control functionality, interactive interface with other apps and software, and zooming in and out functionality) could enhance the presence and dynamic nature of the therapeutic relationship. Conclusions: Psychologists expressed a desire for enhanced control of VCP sessions. Psychologists reported a decreased sense of control within the therapeutic relationship owing to the influence of the VCP system. Great control of the VCP system could better approximate the critical elements of in-person psychotherapy (eg, observation of body language). To facilitate improved control, psychologists would like technology to implement features such as improved eye contact, better screen control, emergency call functionality, ability to zoom in and out, and an interactive interface to communicate with other apps. These results contribute to the general perception of the computer as an actual part of the VCP process. Thus, the computer plays a key role in the communication, rather than remaining as a technical medium. By adopting the IPO model in the VCP environment (VCP-IPO model), the relationship experience may help psychologists have more control in their VCP sessions. UR - https://formative.jmir.org/2023/1/e40542 UR - http://dx.doi.org/10.2196/40542 UR - http://www.ncbi.nlm.nih.gov/pubmed/36927506 ID - info:doi/10.2196/40542 ER - TY - JOUR AU - Dalcin, Taylor Arlene AU - Yuan, T. Christina AU - Jerome, J. Gerald AU - Goldsholl, Stacy AU - Minahan, Eva AU - Gennusa, Joseph AU - Fink, Tyler AU - Gudzune, A. Kimberly AU - Daumit, Lois Gail AU - Dickerson, Faith AU - Thompson, A. David AU - Wang, Nae-Yuh AU - Martino, Steve PY - 2023/3/16 TI - Designing Practical Motivational Interviewing Training for Mental Health Practitioners Implementing Behavioral Lifestyle Interventions: Protocol for 3 Pilot Intervention Studies JO - JMIR Res Protoc SP - e44830 VL - 12 KW - motivational interviewing KW - training KW - cardiovascular disease KW - lifestyle interventions KW - community mental health N2 - Background: Motivational interviewing (MI) is an evidence-based, patient-centered communication method shown to be effective in helping persons with serious mental illness (SMI) to improve health behaviors. In clinical trials where study staff conducted lifestyle interventions incorporating an MI approach, cardiovascular disease (CVD) risk profiles of participants with SMI showed improvement. Given the disproportionate burden of CVD in this population, practitioners who provide somatic and mental health care to persons with SMI are ideally positioned to deliver patient-centered CVD risk reduction interventions. However, the time for MI training (traditionally 16-24 hours), follow-up feedback, and the coaching required to develop and maintain patient-centered skills are significant barriers to incorporating MI when scaling up these evidence-based practices. Objective: We describe the design and development of the following 2 scalable MI training approaches for community mental health practitioners: real-time brief workshops and follow-up asynchronous avatar training. These approaches are being used in 3 different pilot implementation research projects that address weight loss, smoking cessation, and CVD risk reduction in people with SMI who are a part of ALACRITY Center, a research-to-practice translation center funded by the National Institute of Mental Health. Methods: Clinicians and staff in community mental health clinics across Maryland were trained to deliver 3 distinct evidence-based physical health lifestyle interventions using an MI approach to persons with SMI. The real-time brief MI workshop training for ACHIEVE-D weight loss coaches was 4 hours; IMPACT smoking cessation counselors received 2-hour workshops and prescribers received 1-hour workshops; and RHYTHM CVD risk reduction program staff received 4 hours of MI. All workshop trainings occurred over videoconference. The asynchronous avatar training includes 1 common didactic instructional module for the 3 projects and 1 conversation simulation unique to each study?s target behavior. Avatar training is accessible on a commercial website. We plan to assess practitioners? attitudes and beliefs about MI and evaluate the impact of the 2 MI training approaches on their MI skills 3, 6, and 12 months after training using the MI Treatment Integrity 4.2.1 coding tool and the data generated by the avatar-automated scoring system. Results: The ALACRITY Center was funded in August 2018. We have implemented the MI training for 126 practitioners who are currently delivering the 3 implementation projects. We expect the studies to be complete in May 2023. Conclusions: This study will contribute to knowledge about the effect of brief real-time training augmented with avatar skills practice on clinician MI skills. If MI Treatment Integrity scoring shows it to be effective, brief videoconference trainings supplemented with avatar skills practice could be used to train busy community mental health practitioners to use an MI approach when implementing physical health interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/44830 UR - https://www.researchprotocols.org/2023/1/e44830 UR - http://dx.doi.org/10.2196/44830 UR - http://www.ncbi.nlm.nih.gov/pubmed/36927501 ID - info:doi/10.2196/44830 ER - TY - JOUR AU - Davanzo, Antonella AU - d´Huart, Delfine AU - Seker, Süheyla AU - Moessner, Markus AU - Zimmermann, Ronan AU - Schmeck, Klaus AU - Behn, Alex PY - 2023/3/15 TI - Study Features and Response Compliance in Ecological Momentary Assessment Research in Borderline Personality Disorder: Systematic Review and Meta-analysis JO - J Med Internet Res SP - e44853 VL - 25 KW - borderline personality disorder KW - ecological momentary assessment KW - compliance KW - study design features KW - e?mental health KW - mobile phone N2 - Background: Borderline personality disorder (BPD) is characterized by frequent and intense moment-to-moment changes in affect, behavior, identity, and interpersonal relationships, which typically result in significant and negative deterioration of the person?s overall functioning and well-being. Measuring and characterizing the rapidly changing patterns of instability in BPD dysfunction as they occur in a person?s daily life can be challenging. Ecological momentary assessment (EMA) is a method that can capture highly dynamic processes in psychopathology research and, thus, is well suited to study intense variability patterns across areas of dysfunction in BPD. EMA studies are characterized by frequent repeated assessments that are delivered to participants in real-life, real-time settings using handheld devices capable of registering responses to short self-report questions in daily life. Compliance in EMA research is defined as the proportion of prompts answered by the participant, considering all planned prompts sent. Low compliance with prompt schedules can compromise the relative advantages of using this method. Despite the growing EMA literature on BPD in recent years, findings regarding study design features that affect compliance with EMA protocols have not been compiled, aggregated, and estimated. Objective: This systematic meta-analytic review aimed to investigate the relationship between study design features and participant compliance in EMA research of BPD. Methods: A systematic review was conducted on November 12, 2021, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and MOOSE (Meta-analyses of Observational Studies in Epidemiology) guidelines to search for articles featuring EMA studies of BPD that reported compliance rates and included sufficient data to extract relevant design features. For studies with complete data, random-effect models were used to estimate the overall compliance rate and explore its association with design features. Results: In total, 28 peer-reviewed EMA studies comprising 2052 participants were included in the study. Design features (sampling strategy, average prompting frequency, number of items, response window, sampling device, financial incentive, and dropout rate) showed a large variability across studies, and many studies did not report design features. The meta-analytic synthesis was restricted to 64% (18/28) of articles and revealed a pooled compliance rate of 79% across studies. We did not find any significant relationship between design features and compliance rates. Conclusions: Our results show wide variability in the design and reporting of EMA studies assessing BPD. Compliance rates appear to be stable across varying setups, and it is likely that standard design features are not directly responsible for improving or diminishing compliance. We discuss possible nonspecific factors of study design that may have an impact on compliance. Given the promise of EMA research in BPD, we also discuss the importance of unifying standards for EMA reporting so that data stemming from this rich literature can be aggregated and interpreted jointly. UR - https://www.jmir.org/2023/1/e44853 UR - http://dx.doi.org/10.2196/44853 UR - http://www.ncbi.nlm.nih.gov/pubmed/36920466 ID - info:doi/10.2196/44853 ER - TY - JOUR AU - Jafarlou, Salar AU - Lai, Jocelyn AU - Azimi, Iman AU - Mousavi, Zahra AU - Labbaf, Sina AU - Jain, C. Ramesh AU - Dutt, Nikil AU - Borelli, L. Jessica AU - Rahmani, Amir PY - 2023/3/15 TI - Objective Prediction of Next-Day?s Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study JO - JMIR Form Res SP - e39425 VL - 7 KW - wearable devices KW - mental health KW - affective computing N2 - Background: Affective states are important aspects of healthy functioning; as such, monitoring and understanding affect is necessary for the assessment and treatment of mood-based disorders. Recent advancements in wearable technologies have increased the use of such tools in detecting and accurately estimating mental states (eg, affect, mood, and stress), offering comprehensive and continuous monitoring of individuals over time. Objective: Previous attempts to model an individual?s mental state relied on subjective measurements or the inclusion of only a few objective monitoring modalities (eg, smartphones). This study aims to investigate the capacity of monitoring affect using fully objective measurements. We conducted a comparatively long-term (12-month) study with a holistic sampling of participants? moods, including 20 affective states. Methods: Longitudinal physiological data (eg, sleep and heart rate), as well as daily assessments of affect, were collected using 3 modalities (ie, smartphone, watch, and ring) from 20 college students over a year. We examined the difference between the distributions of data collected from each modality along with the differences between their rates of missingness. Out of the 20 participants, 7 provided us with 200 or more days? worth of data, and we used this for our predictive modeling setup. Distributions of positive affect (PA) and negative affect (NA) among the 7 selected participants were observed. For predictive modeling, we assessed the performance of different machine learning models, including random forests (RFs), support vector machines (SVMs), multilayer perceptron (MLP), and K-nearest neighbor (KNN). We also investigated the capability of each modality in predicting mood and the most important features of PA and NA RF models. Results: RF was the best-performing model in our analysis and performed mood and stress (nervousness) prediction with ~81% and ~72% accuracy, respectively. PA models resulted in better performance compared to NA. The order of the most important modalities in predicting PA and NA was the smart ring, phone, and watch, respectively. SHAP (Shapley Additive Explanations) analysis showed that sleep and activity-related features were the most impactful in predicting PA and NA. Conclusions: Generic machine learning?based affect prediction models, trained with population data, outperform existing methods, which use the individual?s historical information. Our findings indicated that our mood prediction method outperformed the existing methods. Additionally, we found that sleep and activity level were the most important features for predicting next-day PA and NA, respectively. UR - https://formative.jmir.org/2023/1/e39425 UR - http://dx.doi.org/10.2196/39425 UR - http://www.ncbi.nlm.nih.gov/pubmed/36920456 ID - info:doi/10.2196/39425 ER - TY - JOUR AU - Trimpop, Franziska Leonie AU - Bielinski, Luisa Laura AU - Berger, Thomas AU - Willutzki, Ulrike PY - 2023/3/15 TI - Evaluation of Two Web-Based Interventions (Res-Up! and REMOTION) in Routine Outpatient Psychotherapy (Therapy Online Plus?TOP): Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e41413 VL - 12 KW - online therapy KW - randomized controlled trial KW - transdiagnostic KW - resilience KW - emotion regulation KW - capitalization KW - compensation KW - intervention KW - psychotherapy KW - Germany KW - treatment KW - mental disorder KW - effectiveness N2 - Background: Only 11%-40% of those with a mental disorder in Germany receive treatment. In many cases, face-to-face psychotherapy is not available because of limited resources, such as an insufficient number of therapists in the area. New approaches to improve the German health care system are needed to counter chronification. Web-based interventions have been shown to be effective as stand-alone and add-on treatments to routine practice. Interventions designed for a wide range of mental disorders such as transdiagnostic interventions are needed to make treatment for mental disorders more accessible and thus shorten waiting times and mitigate the chronification of mental health problems. In general, interventions can be differentiated as having either a capitalization (CAP) focus?thus drawing on already existing strengths?or a compensation (COMP) focus?trying to compensate for deficits. Up to now, the effectiveness of transdiagnostic web-based interventions with either a CAP or a COMP focus has not yet been evaluated. Objective: This study is the first to examine the effectiveness of two transdiagnostic web-based interventions: (1) the activation of resilience and drawing on existing strengths (CAP: Res-Up!) and (2) the improvement of emotion regulation (COMP: REMOTION), compared with care as usual (CAU) in routine outpatient psychotherapy. Methods: Adults with at least 1 mental health disorder will be recruited at 4 outpatient centers in Germany. Participants will then be randomized equally into 1 of the 2 intervention groups Res-Up! (CAP) and REMOTION (COMP) or into the control group (CAU). Assessments will be made at baseline (T0), at 6 weeks after treatment start (T1), and at 12 weeks after treatment start (T2). A primary outcome will be symptom severity (Brief Symptom Inventory-18). Secondary outcomes will focus on emotion regulation and resilience. Results: Participant recruitment and data collection started in April 2020 and were ongoing as of July 2022. We expect participants to benefit more from the interventions than from the CAU control on the dimensions of symptom severity, resilience, and emotion regulation. Furthermore, we expect to find possible differences between CAP and COMP. The results of the study are expected in 2023. Conclusions: This randomized controlled trial will compare CAU with the transdiagnostic web-based interventions Res-Up! and REMOTION, and will thus inform future studies concerning the effectiveness of transdiagnostic web-based interventions in routine outpatient psychotherapy. Trial Registration: ClinicalTrials.gov NCT04352010; https://clinicaltrials.gov/ct2/show/NCT04352010 International Registered Report Identifier (IRRID): DERR1-10.2196/41413 UR - https://www.researchprotocols.org/2023/1/e41413 UR - http://dx.doi.org/10.2196/41413 UR - http://www.ncbi.nlm.nih.gov/pubmed/36920449 ID - info:doi/10.2196/41413 ER - TY - JOUR AU - Nogueira-Leite, Diogo AU - Cruz-Correia, Ricardo PY - 2023/3/14 TI - Attitudes of Physicians and Individuals Toward Digital Mental Health Tools: Protocol for a Web-Based Survey Research Project JO - JMIR Res Protoc SP - e41040 VL - 12 KW - mental health KW - mobile apps KW - digital technology KW - government regulation N2 - Background: Digital transformation is impacting health care delivery. Great market dynamism is bringing opportunities and concerns alike into public discussion. Digital health apps are a vibrant segment where regulation is emerging, with Germany paving the way with its DiGA (Digitale Gesundheitsanwendungen, in German, meaning digital health apps) program. Simultaneously, mental ill-health constitutes a global health concern, and prevalence is expected to worsen due to the COVID-19 pandemic and its containment measures. Portugal and its National Health System may be a useful testbed for digital health interventions. Objective: The paper outlines the protocol for a research project on the attitudes of physicians and potential users toward digital mental health apps to improve access to care, patient outcomes, and reduce the burden of disease of mental ill-health. Methods: Web surveys will be conducted to acquire data from the main stakeholders (physicians and the academic community). Data analysis will replicate the statistical analysis performed in the studies from Dahlhausen and Borghouts to derive conclusions regarding the relative acceptance and likelihood of successful implementation of digital mental health apps in Portugal. Results: The findings of the proposed studies will elicit important information on how physicians and individuals perceive digital mental health app interventions to improve access to care, patient outcomes, and reduce the burden of disease of mental ill-health. Data collection ran between September 26 and November 6, 2022, for the first study and September 20 and October 20, 2022, for the second study. We obtained 160 responses to the first study?s survey and 539 answers to the second study?s survey. Data analysis is concluded, and both studies? results are expected to be published in 2023. Conclusions: The results of the studies projected in this research protocol will have implications for researchers and academia, industry, and policy makers concerning the adoption and implementation of digital health mental apps and associated interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/41040 UR - https://www.researchprotocols.org/2023/1/e41040 UR - http://dx.doi.org/10.2196/41040 UR - http://www.ncbi.nlm.nih.gov/pubmed/36917172 ID - info:doi/10.2196/41040 ER - TY - JOUR AU - Ryan, Thomas Arthur AU - Brenner, Anne Lisa AU - Ulmer, S. Christi AU - Mackintosh, Margaret-Anne AU - Greene, J. Carolyn PY - 2023/3/6 TI - The Use of Evaluation Panels During the Development of a Digital Intervention for Veterans Based on Cognitive Behavioral Therapy for Insomnia: Qualitative Evaluation Study JO - JMIR Form Res SP - e40104 VL - 7 KW - cognitive behavioral therapy for insomnia KW - CBT-I KW - insomnia KW - digital mental health intervention KW - digital MH intervention KW - internet-delivered KW - veterans KW - Path to Better Sleep N2 - Background: Individuals enrolling in the Veterans Health Administration frequently report symptoms consistent with insomnia disorder. Cognitive behavioral therapy for insomnia (CBT-I) is a gold standard treatment for insomnia disorder. While the Veterans Health Administration has successfully implemented a large dissemination effort to train providers in CBT-I, the limited number of trained CBT-I providers continues to restrict the number of individuals who can receive CBT-I. Digital mental health intervention adaptations of CBT-I have been found to have similar efficacy as traditional CBT-I. To help address the unmet need for insomnia disorder treatment, the VA commissioned the creation of a freely available, internet-delivered digital mental health intervention adaptation of CBT-I known as Path to Better Sleep (PTBS). Objective: We aimed to describe the use of evaluation panels composed of veterans and spouses of veterans during the development of PTBS. Specifically, we report on the methods used to conduct the panels, the feedback they provided on elements of the course relevant to user engagement, and how their feedback influenced the design and content of PTBS. Methods: A communications firm was contracted to recruit 3 veteran (n=27) and 2 spouse of veteran (n=18) panels and convene them for three 1-hour meetings. Members of the VA team identified key questions for the panels, and the communications firm prepared facilitator guides to elicit feedback on these key questions. The guides provided a script for facilitators to follow while convening the panels. The panels were telephonically conducted, with visual content displayed via remote presentation software. The communications firm prepared reports summarizing the panelists? feedback during each panel meeting. The qualitative feedback described in these reports served as the raw material for this study. Results: The panel members provided markedly consistent feedback on several elements of PTBS, including recommendations to emphasize the efficacy of CBT-I techniques; clarify and simplify written content as much as possible; and ensure that content is consistent with the lived experiences of veterans. Their feedback was congruent with previous studies on the factors influencing user engagement with digital mental health interventions. Panelist feedback influenced multiple course design decisions, including reducing the effort required to use the course?s sleep diary function, making written content more concise, and selecting veteran testimonial videos that emphasized the benefits of treating chronic insomnia symptoms. Conclusions: The veteran and spouse evaluation panels provided useful feedback during the design of PTBS. This feedback was used to make concrete revisions and design decisions consistent with existing research on improving user engagement with digital mental health interventions. We believe that many of the key feedback messages provided by these evaluation panels could prove useful to other digital mental health intervention designers. UR - https://formative.jmir.org/2023/1/e40104 UR - http://dx.doi.org/10.2196/40104 UR - http://www.ncbi.nlm.nih.gov/pubmed/36877553 ID - info:doi/10.2196/40104 ER - TY - JOUR AU - Smith, Nathan AU - Peters, Dorian AU - Jay, Caroline AU - Sandal, M. Gro AU - Barrett, C. Emma AU - Wuebker, Robert PY - 2023/2/14 TI - Off-World Mental Health: Considerations for the Design of Well-being?Supportive Technologies for Deep Space Exploration JO - JMIR Form Res SP - e37784 VL - 7 KW - long duration space exploration KW - astronaut mental health KW - countermeasures KW - digital design KW - human factors KW - technology UR - https://formative.jmir.org/2023/1/e37784 UR - http://dx.doi.org/10.2196/37784 UR - http://www.ncbi.nlm.nih.gov/pubmed/36787162 ID - info:doi/10.2196/37784 ER - TY - JOUR AU - Tornero-Costa, Roberto AU - Martinez-Millana, Antonio AU - Azzopardi-Muscat, Natasha AU - Lazeri, Ledia AU - Traver, Vicente AU - Novillo-Ortiz, David PY - 2023/2/2 TI - Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review JO - JMIR Ment Health SP - e42045 VL - 10 KW - artificial intelligence KW - mental health KW - health research KW - review methodology KW - systematic review KW - research methodology KW - research quality KW - trial methodology N2 - Background: Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. Objective: This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. Methods: A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. Results: A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment?18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). Conclusions: These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management. UR - https://mental.jmir.org/2023/1/e42045 UR - http://dx.doi.org/10.2196/42045 UR - http://www.ncbi.nlm.nih.gov/pubmed/36729567 ID - info:doi/10.2196/42045 ER - TY - JOUR AU - Han, Nuo AU - Li, Sijia AU - Huang, Feng AU - Wen, Yeye AU - Wang, Xiaoyang AU - Liu, Xiaoqian AU - Li, Linyan AU - Zhu, Tingshao PY - 2023/1/31 TI - Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study JO - J Med Internet Res SP - e41823 VL - 25 KW - mental health KW - psychological well-being KW - social media KW - machine learning KW - domain knowledge KW - mental well being KW - mental wellbeing KW - linguistic KW - predict KW - model KW - ground truth KW - lexicon N2 - Background: Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and monitor online users? PWB in a nonintrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB. Objective: This study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way. Methods: We recruited 1427 participants. Their well-being was evaluated using 6 dimensions of PWB. Their posts on social media were collected, and 6 psychological lexicons were used to extract linguistic features. A multiobjective prediction model was then built with the extracted linguistic features as input and PWB as the output. Further, the validity of the prediction model was confirmed by evaluating the model's discriminant validity, convergent validity, and criterion validity. The reliability of the model was also confirmed by evaluating the split-half reliability. Results: The correlation coefficients between the predicted PWB scores of social media users and the actual scores obtained using the linguistic prediction model of this study were between 0.49 and 0.54 (P<.001), which means that the model had good criterion validity. In terms of the model?s structural validity, it exhibited excellent convergent validity but less than satisfactory discriminant validity. The results also suggested that our model had good split-half reliability levels for every dimension (ranging from 0.65 to 0.85; P<.001). Conclusions: By confirming the availability and stability of the linguistic prediction model, this study verified the predictability of social media corresponding to ground truth well-being data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in nonprofessional settings such as self-testing or a large-scale user study. UR - https://www.jmir.org/2023/1/e41823 UR - http://dx.doi.org/10.2196/41823 UR - http://www.ncbi.nlm.nih.gov/pubmed/36719723 ID - info:doi/10.2196/41823 ER - TY - JOUR AU - Wu, Jingsong AU - Tu, Jingnan AU - Liu, Zhizhen AU - Cao, Lei AU - He, Youze AU - Huang, Jia AU - Tao, Jing AU - Wong, K. Mabel N. AU - Chen, Lidian AU - Lee, C. Tatia M. AU - Chan, H. Chetwyn C. PY - 2023/1/30 TI - An Effective Test (EOmciSS) for Screening Older Adults With Mild Cognitive Impairment in a Community Setting: Development and Validation Study JO - J Med Internet Res SP - e40858 VL - 25 KW - mild cognitive impairment KW - digital assessment KW - digital health KW - community dwelling KW - screening test KW - older adults KW - aging N2 - Background: Early detection of mild cognitive impairment (MCI) symptoms is an important step to its diagnosis and intervention. We developed a new screening test called ?Efficient Online MCI Screening System? (EOmciSS) for use in community-dwelling older adults. It is a self-paced cognitive test to be completed within 10 minutes on tablets or smartphones in homes or care centers for older adults. Objective: This study aims to test the validity of EOmciSS for identifying community-dwelling older adults with MCI risks. Methods: Participants (N=827) completed EOmciSS and other screening tests for MCI. The psychometric properties tested were ?subscale item difficulty,? ?discriminative index,? ?internal consistency,? and ?construct validity.? We also tested between-group discrimination using the cross-validation method in an MCI group and a normal cognitive function (NCF) group. Results: A total of 3 accuracy factors and 1 reaction time factor explained the structure of the 20 item factors. The difficulty level of accuracy factors (ie, ?trail making,? ?clock drawing,? ?cube copying,? ?delayed recall?) was 0.63-0.99, whereas that of the reaction time factor was 0.77-0.95. The discriminative index of the medium-to-high-difficulty item factors was 0.39-0.97. The internal consistency (Cronbach ?) ranged from .41 (for few item factors) to .96. The training data set contained 9 item factors (CC-Acc1, P<.001; CD-Acc1, P=.07; CD-Acc2, P=.06; CD-Acc3, P<.001; TM-Acc4, P=.07; DR-Acc1, P=.03; RS, P=.06; DR-RT1, P=.02; and DR-RT2, P=.05) that were significant predictors for an MCI classification versus NCF classification. Depressive symptoms were identified as significant factors (P<.001) influencing the performance of participants, and were an integral part of our test system. Age (P=.15), number of years of education (P=.18), and proficiency in using an electronic device (P=.39) did not significantly influence the scores nor classification of participants. Application of the MCI/NCF cutoff score (7.90 out of 9.67) to the validation data set yielded an area under the curve of 0.912 (P<.001; 95% CI 0.868-0.955). The sensitivity was 84.9%, specificity was 85.1%, and the Youden index was 0.70. Conclusions: EOmciSS was valid and reliable for identifying older adults with significant risks of MCI. Our results indicate that EOmciSS has higher sensitivity and specificity than those of the Computer-Administered Neuropsychological Screen for Mild Cognitive Impairment and the Computerized Cognitive Screen. The user interface, online operation, and self-paced format allowed the test system to be operated by older adults or their caregivers in different settings (eg, home or care centers for older adults). Depressive symptoms should be an integral part in future MCI screening systems because they influence the test performance and, hence, MCI risk. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000039411; http://www.chictr.org.cn/showprojen.aspx?proj=62903 UR - https://www.jmir.org/2023/1/e40858 UR - http://dx.doi.org/10.2196/40858 UR - http://www.ncbi.nlm.nih.gov/pubmed/36716081 ID - info:doi/10.2196/40858 ER - TY - JOUR AU - van Spaendonck, Zita AU - Leeuwenburgh, Pieter Koen AU - Dremmen, Marjolein AU - van Schuppen, Joost AU - Starreveld, Daniëlle AU - Dierckx, Bram AU - Legerstee, S. Jeroen PY - 2023/1/24 TI - Comparing Smartphone Virtual Reality Exposure Preparation to Care as Usual in Children Aged 6 to 14 Years Undergoing Magnetic Resonance Imaging: Protocol for a Multicenter, Observer-Blinded, Randomized Controlled Trial JO - JMIR Res Protoc SP - e41080 VL - 12 KW - virtual reality KW - VR KW - children KW - anxiety KW - magnetic resonance imaging KW - MRI KW - MRI scans KW - imaging KW - randomized controlled trial KW - MRI preparation KW - smartphone virtual reality KW - smartphone intervention KW - procedural anxiety KW - psychosocial intervention N2 - Background: A magnetic resonance imaging (MRI) procedure can cause preprocedural and periprocedural anxiety in children. Psychosocial interventions are used to prepare children for the procedure to alleviate anxiety, but these interventions are time-consuming and costly, limiting their clinical use. Virtual reality (VR) is a promising way to overcome these limitations in the preparation of children before an MRI scan. Objective: The objective of this study is (1) to develop a VR smartphone intervention to prepare children at home for an MRI procedure; and (2) to examine the effect of the VR intervention in a randomized controlled trial, in which the VR intervention will be compared to care as usual (CAU). CAU involves an information letter about an MRI examination. The primary outcome is the child?s procedural anxiety during the MRI procedure. Secondary outcomes include preprocedural anxiety and parental anxiety. We hypothesize that the VR preparation will result in a higher reduction of the periprocedural anxiety of both parents and children as compared to CAU. Methods: The VR intervention provides a highly realistic and child-friendly representation of an MRI environment. In this randomized controlled trial, 128 children (aged 6 to 14 years) undergoing an MRI scan will be randomly allocated to the VR intervention or CAU. Children in the VR intervention will receive a log-in code for the VR app and are sent cardboard VR glasses. Results: The VR smartphone preparation app was developed in 2020. The recruitment of participants is expected to be completed in December 2022. Data will be analyzed, and scientific papers will be submitted for publication in 2023. Conclusions: The VR smartphone app is expected to significantly reduce pre- and periprocedural anxiety in pediatric patients undergoing an MRI scan. The VR app offers a realistic and child-friendly experience that can contribute to modern care. A smartphone version of the VR app has the advantage that children, and potentially their parents, can get habituated to the VR environment and noises in their own home environment and can do this VR MRI preparation as often and as long as needed. Trial Registration: ISRCTN Registry ISRCTN20976625; https://www.isrctn.com/ISRCTN20976625 International Registered Report Identifier (IRRID): DERR1-10.2196/41080 UR - https://www.researchprotocols.org/2023/1/e41080 UR - http://dx.doi.org/10.2196/41080 UR - http://www.ncbi.nlm.nih.gov/pubmed/36692931 ID - info:doi/10.2196/41080 ER - TY - JOUR AU - Engdahl, Patrik AU - Svedberg, Petra AU - Lexén, Annika AU - Tjörnstrand, Carina AU - Strid, Catharina AU - Bejerholm, Ulrika PY - 2023/1/18 TI - Co-design Process of a Digital Return-to-Work Solution for People With Common Mental Disorders: Stakeholder Perception Study JO - JMIR Form Res SP - e39422 VL - 7 KW - co-design KW - mental health KW - mobile health KW - return-to-work KW - supported employment N2 - Background: Service users and other stakeholders have had few opportunities to influence the design of their mental health and return-to-work services. Likewise, digital solutions often fail to align with stakeholders? needs and preferences, negatively impacting their utility. mWorks is a co-design initiative to create a digital return-to-work solution for persons with common mental disorders that is acceptable and engaging for those receiving and delivering the intervention. Objective: This study aimed to describe stakeholder perceptions and the involvement of a design process during the prototype development of mWorks. Methods: A co-design approach was used during the iterative development of mWorks. Overall, 86 stakeholders were recruited using a combination of purposeful and convenience sampling. Five stakeholder groups represented service users with experience of sick leave and common mental disorders (n=25), return-to-work professionals (n=19), employers (n=1), digital design and system developers (n=4), and members of the public (n=37). Multiple data sources were gathered using 7 iterations, from March 2018 to November 2020. The rich material was organized and analyzed using content analysis to generate themes and categories that represented this study?s findings. Results: The themes revealed the importance of mWorks in empowering service users with a personal digital support solution that engages them back in work. The categories highlighted that mWorks needs to be a self-management tool that enables service users to self-manage as a supplement to traditional return-to-work services. It was also important that content features helped to reshape a positive self-narrative, with a focus on service users? strengths and resources to break the downward spiral of ill health during sick leave. Additional crucial features included helping service users mobilize their own strategies to cope with thoughts and feelings and formulate goals and a plan for their work return. Once testing of the alpha and beta prototypes began, user engagement became the main focus for greater usability. It is critical to facilitate the comprehension and purpose of mWorks, offer clear guidance, and enhance motivational and goal-setting strategies. Conclusions: Stakeholders? experience-based knowledge asserted that mWorks needs to empower service users by providing them with a personal support tool. To enhance return-to-work prospects, users must be engaged in a meaningful manner while focusing on their strengths and resources. UR - https://formative.jmir.org/2023/1/e39422 UR - http://dx.doi.org/10.2196/39422 UR - http://www.ncbi.nlm.nih.gov/pubmed/36652285 ID - info:doi/10.2196/39422 ER - TY - JOUR AU - McGee-Vincent, Pearl AU - Mackintosh, Margaret-Anne AU - Jamison, L. Andrea AU - Juhasz, Katherine AU - Becket-Davenport, Colleen AU - Bosch, Jeane AU - Avery, J. Timothy AU - Glamb, Lauren AU - Hampole, Shilpa PY - 2023/1/12 TI - Training Staff Across the Veterans Affairs Health Care System to Use Mobile Mental Health Apps: A National Quality Improvement Project JO - JMIR Ment Health SP - e41773 VL - 10 KW - mental health KW - mobile apps KW - digital health KW - technology KW - veterans KW - training N2 - Background: The National Center for PTSD, within the Department of Veterans Affairs (VA), has developed a suite of free, publicly available, evidence-informed apps that can reach an increasing number of veterans and bridge gaps in care by providing resources to those who are not engaged in mental health treatment. To expand the reach of these apps, staff across VA service lines learned about these apps, their features and limitations, and how to introduce them to veterans. Objective: This study aimed to develop, disseminate, and evaluate a training for multidisciplinary staff as part of a national quality improvement project to increase the reach of mobile mental health apps as a resource for veterans. Methods: Sites from all of VA?s 18 geographic regions enrolled in this project. At each site, a minimum of 25 VA staff members who had direct contact with veterans, including staff from the mental health service line and all other service lines, were recruited to participate. Training included a 3-hour multidisciplinary core module, and a 1-hour clinical integration module designed specifically for mental health clinicians. Owing to the COVID-19 pandemic, the trainings were adapted to a live, web-based format. Pre- and posttraining surveys assessed program reach (ie, participants enrolled per site), satisfaction, and effectiveness of the training as measured by changes in knowledge, basic skills, and behavioral intentions to use apps with veterans. Results: A total of 1110 participants representing 34 disciplines at 19 VA sites completed the training. Overall, 67% (743/1109) of participants were mental health staff members. Sites averaged 58.4 participants (SD 36.49, median [IQR] 51). Most (961/1024, 93.85%) participants were satisfied with the training and reported that they (941/1018, 92.44%) would recommend it to others. App knowledge scores significantly increased from pretraining (mean 80.8% correct, SD 15.77%) to posttraining (mean 91.1% correct, SD 9.57%; P<.001). At posttraining, participants also reported greater confidence in their ability to show veterans how to download (z=?13.86; P<.001) and use VA mental health apps (z=?15.13; P<.001). There was near universal endorsement by staff for their intentions to recommend apps to veterans as well as their ability to think of at least one specific veteran to whom they could recommend an app. Staff also reported a strong motivation to encourage other VA staff to share apps with veterans. Conclusions: The training far exceeded the initial goals for staff recruitment and training for all three metrics. Overall, 33% (366/1109) of participants came from service lines outside of mental health, indicating the feasibility of introducing these mental health resources during medical appointments and in other contexts. UR - https://mental.jmir.org/2023/1/e41773 UR - http://dx.doi.org/10.2196/41773 UR - http://www.ncbi.nlm.nih.gov/pubmed/36633895 ID - info:doi/10.2196/41773 ER - TY - JOUR AU - Kaiser, Sabine AU - Rye, Marte AU - Jakobsen, Reidar AU - Martinussen, Monica AU - Høgsdal, Helene AU - Kyrrestad, Henriette PY - 2023/1/11 TI - A Universal Mental Health?Promoting Mobile App for Adolescents: Protocol for a Cluster Randomized Controlled Trial JO - JMIR Res Protoc SP - e42119 VL - 12 KW - mental health promotion KW - mobile app KW - mobile phone KW - adolescents KW - Opp KW - teens KW - application KW - effectiveness KW - intervention KW - effect KW - health KW - health promotion N2 - Background: In times of increasing mental health problems among young people, strengthening efforts to improve mental health through mental health promotion and prevention becomes increasingly important. Effective measures that support young people in coping with negative thoughts, feelings, and stress are essential, not just for the individual but also for society. Objective: The aim of this paper is to provide a description of a cluster randomized controlled trial that will be conducted to examine the effectiveness of Opp, a universal mental health?promoting mobile app for adolescents aged 13 to 19 years that provides information and exercises to better cope with stress, negative thoughts, and negative feelings. The protocol was developed in accordance with the SPIRIT checklist. Methods: An effectiveness study will be conducted with 3 measurement points: preintervention (T1), 2 weeks after the intervention (T2), and about 1 month after the intervention (T3). Adolescents will be recruited from middle and high schools in Norway and randomly assigned to the intervention or control groups. Randomization will be conducted on the school level. Opp can be downloaded from the Google Play or App Store but is password protected with a 4-digit code, which will be removed after study completion. Participants in the intervention group will receive a text message with the code to unlock the app. The participants in the intervention group can use Opp without limits on length or time of use. Objective data on how long or how often the participants use the app will not be collected. However, the second and third questionnaires for the intervention group contain app-specific questions on, for example, the use of the app. Results: Recruitment and data collection started in August and September 2022. So far, 381 adolescents have answered the first questionnaire. Data collection was expected to end in December 2022 but has had to be prolonged to approximately June 2023. The results of the study will be available in 2023 at the earliest. Conclusions: This project will contribute unique knowledge to the field, as there are few studies that have examined the effects of universal health-promoting mobile apps for adolescents. However, several limitations have to be taken into account when interpreting the results, such as randomization on the school level, the short time frame in which the study was conducted, and the lack of objective data to monitor the use of the app. Trial Registration: ClinicalTrials.gov NCT05211713; https://www.clinicaltrials.gov/ct2/show/NCT05211713 International Registered Report Identifier (IRRID): PRR1-10.2196/42119 UR - https://www.researchprotocols.org/2023/1/e42119 UR - http://dx.doi.org/10.2196/42119 UR - http://www.ncbi.nlm.nih.gov/pubmed/36630167 ID - info:doi/10.2196/42119 ER - TY - JOUR AU - Zhang, Dongsong AU - Lim, Jaewan AU - Zhou, Lina AU - Dahl, A. Alicia PY - 2021/12/24 TI - Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study JO - JMIR Ment Health SP - e31633 VL - 8 IS - 12 KW - mobile apps KW - mental health KW - privacy concerns KW - privacy protection KW - mobile phone N2 - Background: Mobile mental health systems (MMHS) have been increasingly developed and deployed in support of monitoring, management, and intervention with regard to patients with mental disorders. However, many of these systems rely on patient data collected by smartphones or other wearable devices to infer patients? mental status, which raises privacy concerns. Such a value-privacy paradox poses significant challenges to patients? adoption and use of MMHS; yet, there has been limited understanding of it. Objective: To address the significant literature gap, this research aims to investigate both the antecedents of patients? privacy concerns and the effects of privacy concerns on their continuous usage intention with regard to MMHS. Methods: Using a web-based survey, this research collected data from 170 participants with MMHS experience recruited from online mental health communities and a university community. The data analyses used both repeated analysis of variance and partial least squares regression. Results: The results showed that data type (P=.003), data stage (P<.001), privacy victimization experience (P=.01), and privacy awareness (P=.08) have positive effects on privacy concerns. Specifically, users report higher privacy concerns for social interaction data (P=.007) and self-reported data (P=.001) than for biometrics data; privacy concerns are higher for data transmission (P=.01) and data sharing (P<.001) than for data collection. Our results also reveal that privacy concerns have an effect on attitude toward privacy protection (P=.001), which in turn affects continuous usage intention with regard to MMHS. Conclusions: This study contributes to the literature by deepening our understanding of the data value-privacy paradox in MMHS research. The findings offer practical guidelines for breaking the paradox through the design of user-centered and privacy-preserving MMHS. UR - https://mental.jmir.org/2021/12/e31633 UR - http://dx.doi.org/10.2196/31633 UR - http://www.ncbi.nlm.nih.gov/pubmed/34951604 ID - info:doi/10.2196/31633 ER - TY - JOUR AU - Kruzan, Payne Kaylee AU - Meyerhoff, Jonah AU - Biernesser, Candice AU - Goldstein, Tina AU - Reddy, Madhu AU - Mohr, C. David PY - 2021/12/24 TI - Centering Lived Experience in Developing Digital Interventions for Suicide and Self-injurious Behaviors: User-Centered Design Approach JO - JMIR Ment Health SP - e31367 VL - 8 IS - 12 KW - user-centered design KW - intervention KW - suicide KW - nonsuicidal self-injury KW - lived experience KW - technology-enabled services KW - digital intervention KW - engagement KW - mobile phone N2 - Background: The prevalence of self-injurious thoughts and behaviors (SITB) signals a growing public health crisis. Despite a recognized need for improved and scalable interventions, the field of SITB intervention faces several challenges: existing interventions are often time and resource intensive, most individuals with SITB do not seek formal mental health care, and efficacious treatments are characterized by small effects. Combined, these challenges indicate a need for improved SITB interventions for individuals in formal treatment and those who are not treatment engaged but are at high risk of worsening mental health and future suicide attempts. Objective: We present a methodological approach and set of techniques that may address these challenges by centering the lived experience of individuals with SITB in the process of developing needed services: user-centered design (UCD). Methods: We highlight the value of UCD in the context of digital interventions for SITB by describing the UCD approach and explicating how it can be leveraged to include lived experience throughout the development and evaluation process. We provide a detailed case example highlighting 3 phases of the early development process that can be used to design an intervention that is engaging and meets end-user needs. In addition, we point to novel applications of UCD to complement new directions in SITB research. Results: In this paper, we offer a 2-pronged approach to meet these challenges. First, in terms of addressing access to effective interventions, digital interventions hold promise to extend the reach of evidence-based treatments outside of brick-and-mortar health care settings. Second, to address challenges related to treatment targets and engagement, we propose involving individuals with lived experience in the design and research process. Conclusions: UCD offers a well-developed and systematic process to center the unique needs, preferences, and perceived barriers of individuals with lived SITB experience in the development and evaluation of digital interventions. UR - https://mental.jmir.org/2021/12/e31367 UR - http://dx.doi.org/10.2196/31367 UR - http://www.ncbi.nlm.nih.gov/pubmed/34951602 ID - info:doi/10.2196/31367 ER - TY - JOUR AU - Majid, Shazmin AU - Reeves, Stuart AU - Figueredo, Grazziela AU - Brown, Susan AU - Lang, Alexandra AU - Moore, Matthew AU - Morriss, Richard PY - 2021/12/20 TI - The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review JO - JMIR Ment Health SP - e27991 VL - 8 IS - 12 KW - user-centered design KW - participatory design KW - human-computer interaction KW - patient and public involvement KW - self-monitoring technology KW - bipolar disorder KW - mobile phone N2 - Background: The number of self-monitoring apps for bipolar disorder (BD) is increasing. The involvement of users in human-computer interaction (HCI) research has a long history and is becoming a core concern for designers working in this space. The application of models of involvement, such as user-centered design, is becoming standardized to optimize the reach, adoption, and sustained use of this type of technology. Objective: This paper aims to examine the current ways in which users are involved in the design and evaluation of self-monitoring apps for BD by investigating 3 specific questions: are users involved in the design and evaluation of technology? If so, how does this happen? And what are the best practice ingredients regarding the design of mental health technology? Methods: We reviewed the available literature on self-tracking technology for BD and make an overall assessment of the level of user involvement in design. The findings were reviewed by an expert panel, including an individual with lived experience of BD, to form best practice ingredients for the design of mental health technology. This combines the existing practices of patient and public involvement and HCI to evolve from the generic guidelines of user-centered design and to those that are tailored toward mental health technology. Results: For the first question, it was found that out of the 11 novel smartphone apps included in this review, 4 (36%) self-monitoring apps were classified as having no mention of user involvement in design, 1 (9%) self-monitoring app was classified as having low user involvement, 4 (36%) self-monitoring apps were classified as having medium user involvement, and 2 (18%) self-monitoring apps were classified as having high user involvement. For the second question, it was found that despite the presence of extant approaches for the involvement of the user in the process of design and evaluation, there is large variability in whether the user is involved, how they are involved, and to what extent there is a reported emphasis on the voice of the user, which is the ultimate aim of such design approaches. For the third question, it is recommended that users are involved in all stages of design with the ultimate goal of empowering and creating empathy for the user. Conclusions: Users should be involved early in the design process, and this should not just be limited to the design itself, but also to associated research ensuring end-to-end involvement. Communities in health care?based design and HCI design need to work together to increase awareness of the different methods available and to encourage the use and mixing of the methods as well as establish better mechanisms to reach the target user group. Future research using systematic literature search methods should explore this further. UR - https://mental.jmir.org/2021/12/e27991 UR - http://dx.doi.org/10.2196/27991 UR - http://www.ncbi.nlm.nih.gov/pubmed/34931992 ID - info:doi/10.2196/27991 ER - TY - JOUR AU - Saleem, Maham AU - Kühne, Lisa AU - De Santis, Karolina Karina AU - Christianson, Lara AU - Brand, Tilman AU - Busse, Heide PY - 2021/12/20 TI - Understanding Engagement Strategies in Digital Interventions for Mental Health Promotion: Scoping Review JO - JMIR Ment Health SP - e30000 VL - 8 IS - 12 KW - digital interventions KW - mental health promotion KW - engagement KW - scoping review KW - mobile phone N2 - Background: Digital interventions offer a solution to address the high demand for mental health promotion, especially when facing physical contact restrictions or lacking accessibility. Engagement with digital interventions is critical for their effectiveness; however, retaining users? engagement throughout the intervention is challenging. It remains unclear what strategies facilitate engagement with digital interventions that target mental health promotion. Objective: Our aim is to conduct a scoping review to investigate user engagement strategies and methods to evaluate engagement with digital interventions that target mental health promotion in adults. Methods: This scoping review adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews. The search was conducted in 7 electronic databases from inception to April 2020. The inclusion criteria for studies were as follows: adult (aged ?18 years) users of digital interventions for mental health promotion from the general population; any digital intervention for mental health promotion; and user engagement strategies described in the intervention design. We extracted the following data items: study characteristics, digital intervention (type and engagement strategy), evaluation of engagement strategy (method and result specifying whether the strategy was effective at facilitating engagement), and features of engagement (extent of use and subjective experience of users). Results: A total of 2766 studies were identified, of which 16 (0.58%) met the inclusion criteria. The 16 studies included randomized controlled trials (6/16, 37%), studies analyzing process data (5/16, 31%), observational studies (3/16, 19%), and qualitative studies (2/16, 13%). The digital interventions for mental health promotion were either web based (12/16, 75%) or mobile app based (4/16, 25%). The engagement strategies included personalized feedback about intervention content or users? mental health status; guidance regarding content and progress through e-coaching; social forums, and interactivity with peers; content gamification; reminders; and flexibility and ease of use. These engagement strategies were deemed effective based on qualitative user feedback or responses on questionnaires or tools (4/16, 25%), usability data (5/16, 31%), or both (7/16, 44%). Most studies identified personalized support in the form of e-coaching, peer support through a social platform, personalized feedback, or joint videoconference sessions as an engaging feature. Conclusions: Personalized support during the intervention, access to social support, and personalized feedback seem to promote engagement with digital interventions for mental health promotion. These findings need to be interpreted with caution because the included studies were heterogeneous, had small sample sizes, and typically did not address engagement as the primary outcome. Despite the importance of user engagement for the effectiveness of digital interventions, this field has not yet received much attention. Further research is needed on the effectiveness of different strategies required to facilitate user engagement in digital interventions for mental health promotion. UR - https://mental.jmir.org/2021/12/e30000 UR - http://dx.doi.org/10.2196/30000 UR - http://www.ncbi.nlm.nih.gov/pubmed/34931995 ID - info:doi/10.2196/30000 ER - TY - JOUR AU - Lee, Hojun AU - Choi, JongKwan AU - Jung, Dooyoung AU - Hur, Ji-Won AU - Cho, Chul-Hyun PY - 2021/12/17 TI - The Effects of Virtual Reality Treatment on Prefrontal Cortex Activity in Patients With Social Anxiety Disorder: Participatory and Interactive Virtual Reality Treatment Study JO - J Med Internet Res SP - e31844 VL - 23 IS - 12 KW - anxiety KW - social anxiety disorder KW - virtual reality KW - fNIRS KW - brain activity KW - prefrontal cortex KW - effectiveness N2 - Background: Attempts to use virtual reality (VR) as a treatment for various psychiatric disorders have been made recently, and many researchers have identified the effects of VR in psychiatric disorders. Studies have reported that VR therapy is effective in social anxiety disorder (SAD). However, there is no prior study on the neural correlates of VR therapy in patients with SAD. Objective: The aim of this study is to find the neural correlates of VR therapy by evaluating the treatment effectiveness of VR in patients with SAD using portable functional near-infrared spectroscopy (fNIRS). Methods: Patients with SAD (n=28) were provided with 6 sessions of VR treatment that was developed for exposure to social situations with a recording system of each participant?s self-introduction in VR. After each VR treatment session, the first-person view (video 1) and third-person view (video 2) clips of the participant?s self-introduction were automatically generated. The functional activities of prefrontal regions were measured by fNIRS while watching videos 1 and 2 with a cognitive task, before and after whole VR treatment sessions, and after the first session of VR treatment. We compared the data of fNIRS between patients with SAD and healthy controls (HCs; n=27). Results: We found that reduction in activities of the right frontopolar prefrontal cortex (FPPFC) in HCs was greater than in the SAD group at baseline (t=?2.01, P=.049). Comparing the frontal cortex activation before and after VR treatment sessions in the SAD group showed significant differences in activities of the FPPFC (right: t=?2.93, P<.001; left: t=?2.25, P=.03) and the orbitofrontal cortex (OFC) (right: t=?2.10, P=.045; left: t=?2.21, P=.04) while watching video 2. Conclusions: Activities of the FPPFC and OFC were associated with symptom reduction after VR treatment for SAD. Our study findings might provide a clue to understanding the mechanisms underlying VR treatment for SAD. Trial Registration: Clinical Research Information Service (CRIS) KCT0003854; https://tinyurl.com/559jp2kp UR - https://www.jmir.org/2021/12/e31844 UR - http://dx.doi.org/10.2196/31844 UR - http://www.ncbi.nlm.nih.gov/pubmed/34801979 ID - info:doi/10.2196/31844 ER - TY - JOUR AU - Martinez, Kim AU - Menéndez-Menéndez, Isabel Maria AU - Bustillo, Andres PY - 2021/12/16 TI - Awareness, Prevention, Detection, and Therapy Applications for Depression and Anxiety in Serious Games for Children and Adolescents: Systematic Review JO - JMIR Serious Games SP - e30482 VL - 9 IS - 4 KW - serious games KW - depression KW - anxiety KW - children KW - adolescents KW - virtual reality KW - mental health KW - detection KW - awareness KW - prevention KW - therapy N2 - Background: Depression and anxiety in children and adolescents are major health problems worldwide. In recent years, serious games research has advanced in the development of tools to address these mental health conditions. However, there has not been an extensive analysis of these games, their tendencies, and capacities. Objective: This review aims to gather the most current serious games, published from 2015 to 2020, with a new approach focusing on their applications: awareness, prevention, detection, and therapy. The purpose is also to analyze the implementation, development, and evaluation of these tools to obtain trends, strengths, and weaknesses for future research lines. Methods: The identification of the serious games through a literature search was conducted on the databases PubMed, Scopus, Wiley, Taylor and Francis, Springer, PsycINFO, PsycArticles, Web of Science, and Science Direct. The identified records were screened to include only the manuscripts meeting these criteria: a serious game for PC, smartphone, or virtual reality; developed by research teams; targeting only depression or anxiety or both; aiming specifically at children or adolescents. Results: A total of 34 studies have been found that developed serious games for PC, smartphone, and virtual reality devices and tested them in children and adolescents. Most of the games address both conditions and are applied in prevention and therapy. Nevertheless, there is a trend that anxiety is targeted more in childhood and depression targeted more in adolescence. Regarding design, the game genres arcade minigames, adventure worlds, and social simulations are used, in this order. For implementation, these serious games usually require sessions of 1 hour and are most often played using a PC. Moreover, the common evaluation tools are normalized questionnaires that measure acquisition of skills or reduction of symptoms. Most studies collect and compare these data before and after the participants play. Conclusions: The results show that more awareness and detection games are needed, as well as games that mix the awareness, prevention, detection, and therapy applications. In addition, games for depression and anxiety should equally target all age ranges. For future research, the development and evaluation of serious games should be standardized, so the implementation of serious games as tools would advance. The games should always offer support while playing, in addition to collecting data on participant behavior during the game to better analyze their learning. Furthermore, there is an open line regarding the use of virtual reality for these games due to the capabilities offered by this technology. UR - https://games.jmir.org/2021/4/e30482 UR - http://dx.doi.org/10.2196/30482 UR - http://www.ncbi.nlm.nih.gov/pubmed/34927589 ID - info:doi/10.2196/30482 ER - TY - JOUR AU - Collins-Pisano, Caroline AU - Velez Court, Juan AU - Johnson, Michael AU - Mois, George AU - Brooks, Jessica AU - Myers, Amanda AU - Muralidharan, Anjana AU - Storm, Marianne AU - Wright, Maggie AU - Berger, Nancy AU - Kasper, Ann AU - Fox, Anthony AU - MacDonald, Sandi AU - Schultze, Sarah AU - Fortuna, Karen PY - 2021/12/16 TI - Core Competencies to Promote Consistency and Standardization of Best Practices for Digital Peer Support: Focus Group Study JO - JMIR Ment Health SP - e30221 VL - 8 IS - 12 KW - COVID-19 KW - peer support KW - competencies KW - training KW - digital N2 - Background: As digital peer support is quickly expanding across the globe in the wake of the COVID-19 pandemic, standardization in the training and delivery of digital peer support can advance the professionalism of this field. While telehealth competencies exist for other fields of mental health practice, such as social work, psychiatry, and psychology, limited research has been done to develop and promote digital peer support competencies. Objective: The goal of this study is to introduce the coproduction of core competencies that can guide digital peer support. Methods: Peer support specialists were recruited through an international listserv and participated in a 1-hour virtual focus group. A total of four focus groups were conducted with 59 peer support specialists from 11 US states and three countries. Results: Analysis was conducted using the rigorous and accelerated data reduction (RADaR) technique, and 10 themes were identified: (1) protecting the rights of service users, (2) technical knowledge and skills in the practice of digital peer support, (3) available technologies, (4) equity of access, (5) digital communication skills, (6) performance-based training, (7) self-care, (8) monitoring digital peer support and addressing digital crisis, (9) peer support competencies, and (10) health literacy (emerging). The authors present recommendations based on these themes. Conclusions: The introduction of digital peer support core competencies is an initial first step to promote the standardization of best practices in digital peer support. The established competencies can potentially act as a guide for training and skill development to be integrated into US state peer support specialist competencies and to enhance competencies endorsed by the Substance Abuse and Mental Health Services Administration (SAMHSA). UR - https://mental.jmir.org/2021/12/e30221 UR - http://dx.doi.org/10.2196/30221 UR - http://www.ncbi.nlm.nih.gov/pubmed/34736223 ID - info:doi/10.2196/30221 ER - TY - JOUR AU - Haßdenteufel, Kathrin AU - Lingenfelder, Katrin AU - Schwarze, E. Cornelia AU - Feisst, Manuel AU - Brusniak, Katharina AU - Matthies, Maria Lina AU - Goetz, Maren AU - Wallwiener, Markus AU - Wallwiener, Stephanie PY - 2021/12/10 TI - Evaluation of Repeated Web-Based Screening for Predicting Postpartum Depression: Prospective Cohort Study JO - JMIR Ment Health SP - e26665 VL - 8 IS - 12 KW - postpartum depression KW - Edinburgh Postnatal Depression Scale KW - screening KW - pregnancy KW - algorithm N2 - Background: Postpartum depression (PPD) is a severe mental disorder that often results in poor maternal-infant attachment and negatively impacts infant development. Universal screening has recently been recommended to identify women at risk, but the optimal screening time during pregnancy has not been defined so far. Thus, web-based technologies with widespread use among women of childbearing age create new opportunities to detect pregnancies with a high risk for adverse mental health outcomes at an early stage. Objective: The aim of this study was to stratify the risk for PPD and to determine the optimal screening time during pregnancy by using a web-based screening tool collecting electronic patient-reported outcomes (ePROs) as the basis for a screening algorithm. Methods: In total, 214 women were repeatedly tested for depressive symptoms 5 times during and 3 times after pregnancy by using the Edinburgh Postnatal Depression Scale (EPDS), accessible on a web-based pregnancy platform, developed by the authors of this study. For each prenatal assessment, the area under the curve (AUC), sensitivity, specificity, and predictive values for PPD were calculated. Multivariate logistic regression analyses were applied to identify further potential predictors, such as age, education, parity, relationship quality, and anxiety, to increase predictive accuracy. Results: Digitally collected data from 214 pregnant women were analyzed. The predictive accuracy of depressive symptoms 3 and 6 months postpartum was reasonable to good regarding the screening in the second (AUC=0.85) and third (AUC=0.75) trimester. The multivariate logistic regression analyses resulted in an excellent AUC of 0.93 at 3 months and a good AUC of 0.87 at 6 months postpartum. Conclusions: The best predictive accuracy for PPD has been shown for screening between the 24th and the 28th gestational week (GW) and seems to be beneficial for identifying women at risk. In combination with the aforementioned predictive factors, the discriminatory power improved, particularly at 3 months postpartum. Screening for depression during pregnancy, combined with the women?s personal risk profile, can be used as a starting point for developing a digital screening algorithm. Thereby, web-based assessment tools constitute feasible, efficient, and cost-effective approaches. Thus, they seem to be beneficial in detecting high-risk pregnancies in order to improve maternal and infant birth outcomes in the long term. UR - https://mental.jmir.org/2021/12/e26665 UR - http://dx.doi.org/10.2196/26665 UR - http://www.ncbi.nlm.nih.gov/pubmed/34890349 ID - info:doi/10.2196/26665 ER - TY - JOUR AU - van Genugten, Rosalie Claire AU - Schuurmans, Josien AU - Hoogendoorn, W. Adriaan AU - Araya, Ricardo AU - Andersson, Gerhard AU - Baños, Rosa AU - Botella, Cristina AU - Cerga Pashoja, Arlinda AU - Cieslak, Roman AU - Ebert, Daniel David AU - García-Palacios, Azucena AU - Hazo, Jean-Baptiste AU - Herrero, Rocío AU - Holtzmann, Jérôme AU - Kemmeren, Lise AU - Kleiboer, Annet AU - Krieger, Tobias AU - Smoktunowicz, Ewelina AU - Titzler, Ingrid AU - Topooco, Naira AU - Urech, Antoine AU - Smit, H. Johannes AU - Riper, Heleen PY - 2021/12/6 TI - Examining the Theoretical Framework of Behavioral Activation for Major Depressive Disorder: Smartphone-Based Ecological Momentary Assessment Study JO - JMIR Ment Health SP - e32007 VL - 8 IS - 12 KW - depression KW - behavioral activation KW - theoretical framework KW - ecological momentary assessment KW - random-intercept cross-lagged panel model KW - behavior KW - framework KW - EMA KW - smartphone KW - mental health KW - treatment KW - engagement KW - mood N2 - Background: Behavioral activation (BA), either as a stand-alone treatment or as part of cognitive behavioral therapy, has been shown to be effective for treating depression. The theoretical underpinnings of BA derive from Lewinsohn et al?s theory of depression. The central premise of BA is that having patients engage in more pleasant activities leads to them experiencing more pleasure and elevates their mood, which, in turn, leads to further (behavioral) activation. However, there is a dearth of empirical evidence about the theoretical framework of BA. Objective: This study aims to examine the assumed (temporal) associations of the 3 constructs in the theoretical framework of BA. Methods: Data were collected as part of the ?European Comparative Effectiveness Research on Internet-based Depression Treatment versus treatment-as-usual? trial among patients who were randomly assigned to receive blended cognitive behavioral therapy (bCBT). As part of bCBT, patients completed weekly assessments of their level of engagement in pleasant activities, the pleasure they experienced as a result of these activities, and their mood over the course of the treatment using a smartphone-based ecological momentary assessment (EMA) application. Longitudinal cross-lagged and cross-sectional associations of 240 patients were examined using random intercept cross-lagged panel models. Results: The analyses did not reveal any statistically significant cross-lagged coefficients (all P>.05). Statistically significant cross-sectional positive associations between activities, pleasure, and mood levels were identified. Moreover, the levels of engagement in activities, pleasure, and mood slightly increased over the duration of the treatment. In addition, mood seemed to carry over, over time, while both levels of engagement in activities and pleasurable experiences did not. Conclusions: The results were partially in accordance with the theoretical framework of BA, insofar as the analyses revealed cross-sectional relationships between levels of engagement in activities, pleasurable experiences deriving from these activities, and enhanced mood. However, given that no statistically significant temporal relationships were revealed, no conclusions could be drawn about potential causality. A shorter measurement interval (eg, daily rather than weekly EMA reports) might be more attuned to detecting potential underlying temporal pathways. Future research should use an EMA methodology to further investigate temporal associations, based on theory and how treatments are presented to patients. Trial Registration: ClinicalTrials.gov, NCT02542891, https://clinicaltrials.gov/ct2/show/NCT02542891; German Clinical Trials Register, DRKS00006866, https://tinyurl.com/ybja3xz7; Netherlands Trials Register, NTR4962, https://www.trialregister.nl/trial/4838; ClinicalTrials.Gov, NCT02389660, https://clinicaltrials.gov/ct2/show/NCT02389660; ClinicalTrials.gov, NCT02361684, https://clinicaltrials.gov/ct2/show/NCT02361684; ClinicalTrials.gov, NCT02449447, https://clinicaltrials.gov/ct2/show/NCT02449447; ClinicalTrials.gov, NCT02410616, https://clinicaltrials.gov/ct2/show/NCT02410616; ISRCTN registry, ISRCTN12388725, https://www.isrctn.com/ISRCTN12388725 UR - https://mental.jmir.org/2021/12/e32007 UR - http://dx.doi.org/10.2196/32007 UR - http://www.ncbi.nlm.nih.gov/pubmed/34874888 ID - info:doi/10.2196/32007 ER - TY - JOUR AU - Taira, Kazuya AU - Hosokawa, Rikuya AU - Itatani, Tomoya AU - Fujita, Sumio PY - 2021/12/3 TI - Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model JO - JMIR Public Health Surveill SP - e34016 VL - 7 IS - 12 KW - suicide KW - internet search engine KW - infoveillance KW - query KW - time series analysis KW - vector autoregression model KW - COVID-19 KW - suicide-related terms KW - internet KW - information seeking KW - time series KW - model KW - loneliness KW - mental health KW - prediction KW - Japan KW - behavior KW - trend N2 - Background: The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is important to take timely preventive measures. Objective: This study aims to clarify whether the number of suicides can be predicted by suicide-related search queries used before searching for the keyword ?suicide.? Methods: This study uses the infoveillance approach for suicide in Japan by search trends in search engines. The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender with queries associated with ?suicide? on ?Yahoo! JAPAN Search? from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to suicide were used as search terms before searching for the keyword ?suicide? and extracted and used for analyses: ?abuse?; ?work, don?t want to go?; ?company, want to quit?; ?divorce?; and ?no money.? The augmented Dickey-Fuller and Johansen tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch-Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque-Bera (JB) test were used to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. Results: In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: ?9.24; max tau 3: ?5.38) and women (minimum tau 3: ?9.24; max tau 3: ?5.38) had no unit roots for all variables. In the Johansen test, a cointegration relationship was observed among several variables. The queries used in the converged models were ?divorce? for men (BG-LM test: P=.55; ARCH-LM test: P=.63; JB test: P=.66) and ?no money? for women (BG-LM test: P=.17; ARCH-LM test: P=.15; JB test: P=.10). In the Granger causality test for each variable, ?divorce? was significant for both men (F104=3.29; P=.04) and women (F104=3.23; P=.04). Conclusions: The number of suicides can be predicted by search queries related to the keyword ?suicide.? Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on ?no money? and ?divorce? predicted suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary. UR - https://publichealth.jmir.org/2021/12/e34016 UR - http://dx.doi.org/10.2196/34016 UR - http://www.ncbi.nlm.nih.gov/pubmed/34823225 ID - info:doi/10.2196/34016 ER - TY - JOUR AU - Yan, Mingli AU - Yin, Huiru AU - Meng, Qiuyan AU - Wang, Shuo AU - Ding, Yiwen AU - Li, Guichen AU - Wang, Chunyan AU - Chen, Li PY - 2021/12/3 TI - A Virtual Supermarket Program for the Screening of Mild Cognitive Impairment in Older Adults: Diagnostic Accuracy Study JO - JMIR Serious Games SP - e30919 VL - 9 IS - 4 KW - virtual reality KW - mild cognitive impairment KW - dementia KW - ambient intelligence KW - digital health KW - elderly population KW - aging N2 - Background: Mild cognitive impairment (MCI) is often a precursor of dementia, and patients with MCI develop dementia at a higher rate than healthy older adults. Early detection of cognitive decline at the MCI stage supports better planning of care and interventions. At present, the use of virtual reality (VR) in screening for MCI in older adults is promising, but there is little evidence regarding the use of virtual supermarkets to screen for MCI. Objective: The objectives of this study are to validate a VR game?based test, namely, the Virtual Supermarket Program (VSP), for differentiating patients with MCI and healthy controls and to identify cutoff scores for different age levels. Methods: Subjects were recruited from several nursing homes and communities in Changchun, China. They were divided into a healthy control group (n=64) and an MCI group (n=62). All subjects were administered the VSP and a series of neuropsychological examinations. The study determined the optimal cutoff, discriminating validity, concurrent validity, and retest reliability of the VSP. We used the area under the receiver operating characteristic curve (AUC) to evaluate the discriminating validity and obtain the optimal cutoff values. Pearson correlation analysis and the intraclass correlation coefficient were used to evaluate the concurrent validity and retest reliability, respectively. Results: A cutoff score of 46.4 was optimal for the entire sample, yielding a sensitivity of 85.9% and specificity of 79.0% for differentiating individuals with MCI and healthy controls, and the AUC was 0.870 (95% CI 0.799-0.924). The median index of VSP score was 51.1 (range 42.6-60.0). There was a moderate positive correlation between the VSP total score and Mini-Mental State Examination score (r=0.429, P<.001). There was a strong positive correlation between VSP total score and Montreal Cognitive Assessment score (r=0.645, P<.001). The retest reliability of the VSP was feasible (r=0.588, P=.048). Conclusions: The VSP is interesting and feasible for subjects. It shows high sensitivity and specificity for the identification of MCI in older adults, which makes it a promising screening method. The VSP may be generalized to older adults in other countries, although some cultural adaptation may be necessary. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000040074; https://www.chictr.org.cn/showprojen.aspx?proj=64639 UR - https://games.jmir.org/2021/4/e30919 UR - http://dx.doi.org/10.2196/30919 UR - http://www.ncbi.nlm.nih.gov/pubmed/34870610 ID - info:doi/10.2196/30919 ER - TY - JOUR AU - Piqueras, A. Jose AU - Vidal-Arenas, Verónica AU - Falcó, Raquel AU - Moreno-Amador, Beatriz AU - Marzo, C. Juan AU - Holcomb, M. Juliana AU - Murphy, Michael PY - 2021/12/1 TI - Short Form of the Pediatric Symptom Checklist-Youth Self-Report (PSC-17-Y): Spanish Validation Study JO - J Med Internet Res SP - e31127 VL - 23 IS - 12 KW - PSC-17-Y KW - psychometric properties KW - screening KW - mental problems KW - adolescents KW - adolescent health KW - adolescent medicine KW - psychiatry KW - psychology KW - psychosocial issues N2 - Background: The short form, 17-item version of the Pediatric Symptom Checklist-Youth Self-Report (PSC-17-Y) is a validated measure that assesses psychosocial problems overall (OVR) and in 3 major psychopathological domains (internalizing, externalizing, and attention-deficit/hyperactivity disorder), taking 5-10 min to complete. Prior research has established sound psychometric properties of the PSC-17-Y for English speakers. Objective: This study extends psychometric evidence for the acceptability of the PSC-17-Y in a large sample of Spanish adolescents, providing proof of its reliability and structure, convergent and discriminant validity, and longitudinal and gender invariance. Methods: Data were collected on 5430 adolescents, aged 12-18 years, who filled out the PSC-17-Y twice during 2018-2019 (7-month interval). We calculated the Cronbach alpha and the McDonald omega coefficients to test reliability, the Pearson correlation for convergent (distress) and criterion validity (well-being, quality of life, and socioemotional skills), confirmatory factor analysis (CFA) for structure validity, and multigroup and longitudinal measurement invariance analysis for longitudinal and gender stability. Results: Within structural analysis for the PSC-17-Y, CFA supported a correlated 3-factor solution, which was also invariant longitudinally and across gender. All 3 subscales showed evidence of reliability, with coefficients near or above .70. Moreover, scores of PSC-17-Y subscales were positively related with convergent measures and negatively related with criterion measures. Normative data for the PSC-17-Y are presented in the form of percentiles (75th and 90th). Conclusions: This work provides the first evidence of the reliability and validity of the Spanish version of the PSC-17-Y administered over the internet to assess mental health problems among adolescents, maintaining the same domains as the long version. UR - https://www.jmir.org/2021/12/e31127 UR - http://dx.doi.org/10.2196/31127 UR - http://www.ncbi.nlm.nih.gov/pubmed/34855614 ID - info:doi/10.2196/31127 ER - TY - JOUR AU - Wiebe, E. Deanna AU - Remers, Shannon AU - Nippak, Pria AU - Meyer, Julien PY - 2021/12/1 TI - Evaluation of an Online System for Routine Outcome Monitoring: Cross-sectional Survey Study JO - JMIR Ment Health SP - e29243 VL - 8 IS - 12 KW - routine outcome monitoring KW - progress monitoring and feedback KW - outcome measures KW - web-based outcome monitoring KW - routine outcome monitoring software KW - outcome measurement questionnaire KW - measurement-based care N2 - Background: The use of routine outcome monitoring (ROM) in the treatment of mental health has emerged as a method of improving psychotherapy treatment outcomes. Despite this, very few clinicians regularly use ROM in clinical practice. Online ROM has been suggested as a solution to increase adoption. Objective: The aim of this study is to identify the influence of moving ROM online on client completion rates of self-reported outcome measures and to identify implementation and utilization barriers to online ROM by assessing clinicians? views on their experience using the online system over previous paper-based methods. Methods: Client completion rates of self-reported outcome measures were compared pre- and postimplementation of an online system of ROM. In addition, a survey questionnaire was administered to 324 mental health service providers regarding their perception of the benefits with an online system of ROM. Results: Client completion rates of self-reported measures increased from 15.62% (427/2734) to 53.98% (1267/2347) after they were moved online. Furthermore, 57% (56/98) of service providers found the new system less time-consuming than the previous paper-based ROM, and 64% (63/98) found that it helped monitor clients. However, the perceived value of the system remains in doubt as only 23% (23/98) found it helped them identify clients at risk for treatment failure, and only 18% (18/98) found it strengthened the therapeutic alliance. Conclusions: Although the current study suggests mixed results regarding service providers? views on their experience using an online system for ROM, it has identified barriers and challenges that are actionable for improvement. UR - https://mental.jmir.org/2021/12/e29243 UR - http://dx.doi.org/10.2196/29243 UR - http://www.ncbi.nlm.nih.gov/pubmed/34855615 ID - info:doi/10.2196/29243 ER - TY - JOUR AU - Siriaraya, Panote AU - Visch, Valentijn AU - Boffo, Marilisa AU - Spijkerman, Renske AU - Wiers, Reinout AU - Korrelboom, Kees AU - Hendriks, Vincent AU - Salemink, Elske AU - van Dooren, Marierose AU - Bas, Michael AU - Goossens, Richard PY - 2021/12/1 TI - Game Design in Mental Health Care: Case Study?Based Framework for Integrating Game Design Into Therapeutic Content JO - JMIR Serious Games SP - e27953 VL - 9 IS - 4 KW - design models KW - gamification KW - case studies KW - mental health KW - eHealth UR - https://games.jmir.org/2021/4/e27953 UR - http://dx.doi.org/10.2196/27953 UR - http://www.ncbi.nlm.nih.gov/pubmed/34855611 ID - info:doi/10.2196/27953 ER - TY - JOUR AU - Fisher, B. Lauren AU - Tuchman, Sylvie AU - Curreri, J. Andrew AU - Markgraf, Maggie AU - Nyer, B. Maren AU - Cassano, Paolo AU - Iverson, L. Grant AU - Fava, Maurizio AU - Zafonte, D. Ross AU - Pedrelli, Paola PY - 2021/12/1 TI - Transitioning From In-Person to Remote Clinical Research on Depression and Traumatic Brain Injury During the COVID-19 Pandemic: Study Modifications and Preliminary Feasibility From a Randomized Controlled Pilot Study JO - JMIR Form Res SP - e28734 VL - 5 IS - 12 KW - COVID-19 KW - telemental health KW - clinical trial KW - traumatic brain injury KW - depression KW - cognitive behavioral therapy N2 - Background: Telehealth has provided many researchers, especially those conducting psychosocial research, with the tools necessary to transition from in-person to remote clinical trials during the COVID-19 pandemic. A growing body of research supports the effectiveness of telemental health for a variety of psychiatric conditions, but few studies have examined telemental health for individuals with comorbid medical diagnoses. Furthermore, little is known about the remote implementation of clinical trials examining telemental health interventions. Objective: This paper outlines the procedural modifications used to facilitate conversion of an in-person randomized controlled trial of cognitive behavioral therapy (CBT) for depression in individuals with traumatic brain injury (TBI; CBT-TBI) to a telemental health study administered remotely. Methods: Given the nature of remote implementation and specific challenges experienced by individuals with TBI, considerations related to treatment delivery, remote consent, data management, neuropsychological assessment, safety monitoring, and delivery of supportive material have been discussed. Feasibility, acceptability, and safety were evaluated by examining attendance and participant responses on self-report measures of treatment satisfaction and suicidal behavior. Results: High rates of treatment attendance, assessment completion, study retention, and satisfaction with the intervention and modality were reported by participants who completed at least one telemental health CBT-TBI session. Conclusions: Study modifications are necessary when conducting a study remotely, and special attention should be paid to comorbidities and population-specific challenges (eg, cognitive impairment). Preliminary data support the feasibility, acceptability, and safety of remotely conducting a randomized controlled trial of CBT-TBI. Trial Registration: ClinicalTrials.gov NCT03307070; https://clinicaltrials.gov/ct2/show/NCT03307070 UR - https://formative.jmir.org/2021/12/e28734 UR - http://dx.doi.org/10.2196/28734 UR - http://www.ncbi.nlm.nih.gov/pubmed/34662285 ID - info:doi/10.2196/28734 ER - TY - JOUR AU - Yang, Xue AU - Wong, Man Kei AU - She, Rui AU - Zhao, Chengjia AU - Ding, Nani AU - Xu, Huihui AU - Tu, Xiaolian AU - Lai, Xinyi AU - Zhang, Guohua PY - 2021/11/30 TI - Relationship Between Illness Representations and Symptoms of Internet Gaming Disorder Among Young People: Cross-Lagged Model JO - JMIR Serious Games SP - e28117 VL - 9 IS - 4 KW - illness representations KW - internet gaming disorder KW - youth KW - cross-lagged model N2 - Background: The common-sense model of illness suggests that mental representations of health threats may affect one?s behavioral reactions to them and health status. Internet gaming disorder is a newly defined mental disorder. Illness representations of internet gaming disorder may affect one?s risk of internet gaming disorder. In turn, symptoms of internet gaming disorder may affect one?s perceptions of the disorder. Objective: This study aimed to investigate the relationships between illness representations and symptoms of internet gaming disorder in college students. Methods: A 1-year longitudinal study was conducted with a convenience sample of Chinese college students (n=591; 342/591, 57.9% female). Results: Of the participants, 10.1% (60/591) and 9.1% (54/591) were classified as having probable internet gaming disorder at baseline (T1) and follow-up (T2), respectively. The correlations between some dimensions of illness representations regarding internet gaming disorder (ie, consequence, timeline, personal control, treatment control, and concern) at T1 and symptoms of internet gaming disorder at T2 and between symptoms of internet gaming disorder at T1 and the dimensions of illness representations at T2 (ie, consequence, timeline, personal control, and emotional response) were statistically significant. The cross-lagged model fit the data well ((?2/df=2.28, comparative fit index=.95, root mean square error of approximation=.06) and showed that internet gaming disorder at T1 was positively associated with unfavorable illness representations at T2. Conclusions: Individuals with more severe symptoms of internet gaming disorder had more pessimistic perceptions about the disorder. Such cognitive perceptions may affect one?s emotional and behavioral reactions towards the disorder (eg, greater levels of depression and low self-control intention) and should be modified by educational programs and psychological interventions. UR - https://games.jmir.org/2021/4/e28117 UR - http://dx.doi.org/10.2196/28117 UR - http://www.ncbi.nlm.nih.gov/pubmed/34851298 ID - info:doi/10.2196/28117 ER - TY - JOUR AU - Six, G. Stephanie AU - Byrne, A. Kaileigh AU - Tibbett, P. Thomas AU - Pericot-Valverde, Irene PY - 2021/11/29 TI - Examining the Effectiveness of Gamification in Mental Health Apps for Depression: Systematic Review and Meta-analysis JO - JMIR Ment Health SP - e32199 VL - 8 IS - 11 KW - depression KW - reward KW - gamification KW - mental health apps KW - apps N2 - Background: Previous research showed that computerized cognitive behavioral therapy can effectively reduce depressive symptoms. Some mental health apps incorporate gamification into their app design, yet it is unclear whether features differ in their effectiveness to reduce depressive symptoms over and above mental health apps without gamification. Objective: The aim of this study was to determine whether mental health apps with gamification elements differ in their effectiveness to reduce depressive symptoms when compared to those that lack these elements. Methods: A meta-analysis of studies that examined the effect of app-based therapy, including cognitive behavioral therapy, acceptance and commitment therapy, and mindfulness, on depressive symptoms was performed. A total of 5597 articles were identified via five databases. After screening, 38 studies (n=8110 participants) remained for data extraction. From these studies, 50 total comparisons between postintervention mental health app intervention groups and control groups were included in the meta-analysis. Results: A random effects model was performed to examine the effect of mental health apps on depressive symptoms compared to controls. The number of gamification elements within the apps was included as a moderator. Results indicated a small to moderate effect size across all mental health apps in which the mental health app intervention effectively reduced depressive symptoms compared to controls (Hedges g=?0.27, 95% CI ?0.36 to ?0.17; P<.001). The gamification moderator was not a significant predictor of depressive symptoms (?=?0.03, SE=0.03; P=.38), demonstrating no significant difference in effectiveness between mental health apps with and without gamification features. A separate meta-regression also did not show an effect of gamification elements on intervention adherence (?=?1.93, SE=2.28; P=.40). Conclusions: The results show that both mental health apps with and without gamification elements were effective in reducing depressive symptoms. There was no significant difference in the effectiveness of mental health apps with gamification elements on depressive symptoms or adherence. This research has important clinical implications for understanding how gamification elements influence the effectiveness of mental health apps on depressive symptoms. UR - https://mental.jmir.org/2021/11/e32199 UR - http://dx.doi.org/10.2196/32199 UR - http://www.ncbi.nlm.nih.gov/pubmed/34847058 ID - info:doi/10.2196/32199 ER - TY - JOUR AU - Johansson, Magnus AU - Berman, H. Anne AU - Sinadinovic, Kristina AU - Lindner, Philip AU - Hermansson, Ulric AU - Andréasson, Sven PY - 2021/11/24 TI - Effects of Internet-Based Cognitive Behavioral Therapy for Harmful Alcohol Use and Alcohol Dependence as Self-help or With Therapist Guidance: Three-Armed Randomized Trial JO - J Med Internet Res SP - e29666 VL - 23 IS - 11 KW - alcohol dependence KW - alcohol use disorders KW - internet-based interventions KW - internet-based cognitive behavioral therapy KW - ICBT KW - cognitive behavioral therapy KW - CBT KW - eHealth KW - alcohol use KW - substance abuse KW - outcomes KW - help-seeking behavior KW - mobile phone N2 - Background: Alcohol use is a major contributor to health loss. Many persons with harmful use or alcohol dependence do not obtain treatment because of limited availability or stigma. They may use internet-based interventions as an alternative way of obtaining support. Internet-based interventions have previously been shown to be effective in reducing alcohol consumption in studies that included hazardous use; however, few studies have been conducted with a specific focus on harmful use or alcohol dependence. The importance of therapist guidance in internet-based cognitive behavioral therapy (ICBT) programs is still unclear. Objective: This trial aims to investigate the effects of a web-based alcohol program with or without therapist guidance among anonymous adult help-seekers. Methods: A three-armed randomized controlled trial was conducted to compare therapist-guided ICBT and self-help ICBT with an information-only control condition. Swedish-speaking adult internet users with alcohol dependence (3 or more International Classification of Diseases, Tenth Revision criteria) or harmful alcohol use (alcohol use disorder identification test>15) were included in the study. Participants in the therapist-guided ICBT and self-help ICBT groups had 12-week access to a program consisting of 5 main modules, as well as a drinking calendar with automatic feedback. Guidance was given by experienced therapists trained in motivational interviewing. The primary outcome measure was weekly alcohol consumption in standard drinks (12 g of ethanol). Secondary outcomes were alcohol-related problems measured using the total alcohol use disorder identification test-score, diagnostic criteria for alcohol dependence and alcohol use disorder, depression, anxiety, health, readiness to change, and access to other treatments or support. Follow-up was conducted 3 (posttreatment) and 6 months after recruitment. Results: During the recruitment period, from March 2015 to March 2017, 1169 participants were included. Participants had a mean age of 45 (SD 13) years, and 56.72% (663/1169) were women. At the 3-month follow-up, the therapist-guided ICBT and control groups differed significantly in weekly alcohol consumption (?3.84, 95% Cl ?6.53 to ?1.16; t417=2.81; P=.005; Cohen d=0.27). No significant differences were found in weekly alcohol consumption between the self-help ICBT group and the therapist-guided ICBT at 3 months, between the self-help ICBT and the control group at 3 months, or between any of the groups at the 6-month follow-up. A limitation of the study was the large number of participants who were completely lost to follow-up (477/1169, 40.8%). Conclusions: In this study, a therapist-guided ICBT program was not found to be more effective than the same program in a self-help ICBT version for reducing alcohol consumption or other alcohol-related outcomes. In the short run, therapist-guided ICBT was more effective than information. Only some internet help-seekers may need a multisession program and therapist guidance to change their drinking when they use internet-based interventions. Trial Registration: ClinicalTrials.gov NCT02377726; https://clinicaltrials.gov/ct2/show/NCT02377726 UR - https://www.jmir.org/2021/11/e29666 UR - http://dx.doi.org/10.2196/29666 UR - http://www.ncbi.nlm.nih.gov/pubmed/34821563 ID - info:doi/10.2196/29666 ER - TY - JOUR AU - Saqib, Kiran AU - Khan, Fozia Amber AU - Butt, Ahmad Zahid PY - 2021/11/24 TI - Machine Learning Methods for Predicting Postpartum Depression: Scoping Review JO - JMIR Ment Health SP - e29838 VL - 8 IS - 11 KW - machine learning KW - postpartum depression KW - big data KW - mobile phone N2 - Background: Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely, given the rapid technological developments in recent years. Objective: This study aims to synthesize the literature on ML and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD). Methods: We used a scoping review methodology using the Arksey and O?Malley framework to rapidly map research activity in ML for predicting PPD. Two independent researchers searched PsycINFO, PubMed, IEEE Xplore, and the ACM Digital Library in September 2020 to identify relevant publications in the past 12 years. Data were extracted from the articles? ML model, data type, and study results. Results: A total of 14 studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine and random forest were the most commonly used algorithms in addition to Naive Bayes, regression, artificial neural network, decision trees, and XGBoost (Extreme Gradient Boosting). There was considerable heterogeneity in the best-performing ML algorithm across the selected studies. The area under the receiver operating characteristic curve values reported for different algorithms were support vector machine (range 0.78-0.86), random forest method (0.88), XGBoost (0.80), and logistic regression (0.93). Conclusions: ML algorithms can analyze larger data sets and perform more advanced computations, which can significantly improve the detection of PPD at an early stage. Further clinical research collaborations are required to fine-tune ML algorithms for prediction and treatment. ML might become part of evidence-based practice in addition to clinical knowledge and existing research evidence. UR - https://mental.jmir.org/2021/11/e29838 UR - http://dx.doi.org/10.2196/29838 UR - http://www.ncbi.nlm.nih.gov/pubmed/34822337 ID - info:doi/10.2196/29838 ER - TY - JOUR AU - Kim, Euisung AU - Han, Jieun AU - Choi, Hojin AU - Prié, Yannick AU - Vigier, Toinon AU - Bulteau, Samuel AU - Kwon, Hyun Gyu PY - 2021/11/24 TI - Examining the Academic Trends in Neuropsychological Tests for Executive Functions Using Virtual Reality: Systematic Literature Review JO - JMIR Serious Games SP - e30249 VL - 9 IS - 4 KW - virtual reality KW - neuropsychological test KW - executive function KW - cognitive ability KW - brain disorder KW - immersive KW - digital health KW - cognition KW - academic trends KW - neurology N2 - Background: In neuropsychology, fully immersive virtual reality (VR) has been spotlighted as a promising tool. It is considered that VR not only overcomes the existing limitation of neuropsychological tests but is also appropriate for treating executive functions (EFs) within activities of daily living (ADL) due to its high ecological validity. While fully immersive VR offers new possibilities of neuropsychological tests, there are few studies that overview the intellectual landscape and academic trends in the research related to mainly targeted EFs with fully immersive VR. Objective: The objective of this study is to get an overview of the research trends that use VR in neuropsychological tests and to analyze the research trends using fully immersive VR neuropsychological tests with experimental articles. Methods: This review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles were searched in three web databases using keywords related to VR, EFs, and cognitive abilities. The study was conducted in two steps, keyword analysis and in-depth systematic review. In the web database search from 2000 to 2019, 1167 articles were initially collected, of which 234 articles in the eligibility phase were used to conduct keyword analysis and a total of 47 articles were included for systematic review. Results: In keyword analysis, the number of articles focused on dementia including the keywords ?MCI,? ?SCD,? and ?dementia? were highlighted over the period, rather than other symptoms. In addition, we identified that the use of behavioral and physiological data in virtual environments (VEs) has dramatically increased in recent studies. In the systematic review, we focused on the purpose of study, assessment, treatment, and validation of usability and structure. We found that treatment studies and uncategorized studies including presence and cybersickness issues have emerged in the recent period. In addition, the target symptoms and range of participants were diversified. Conclusions: There has been a continuously increasing interest in dealing with neuropsychology by using fully immersive VR. Target cognitive abilities have been diversified, as well as target symptoms. Moreover, the concept of embodied cognition was transplanted in this research area. UR - https://games.jmir.org/2021/4/e30249 UR - http://dx.doi.org/10.2196/30249 UR - http://www.ncbi.nlm.nih.gov/pubmed/34822341 ID - info:doi/10.2196/30249 ER - TY - JOUR AU - Shin, Bokyoung AU - Oh, Jooyoung AU - Kim, Byung-Hoon AU - Kim, Erin Hesun AU - Kim, Hyunji AU - Kim, Suji AU - Kim, Jae-Jin PY - 2021/11/22 TI - Effectiveness of Self-Guided Virtual Reality?Based Cognitive Behavioral Therapy for Panic Disorder: Randomized Controlled Trial JO - JMIR Ment Health SP - e30590 VL - 8 IS - 11 KW - virtual reality KW - panic disorder KW - cognitive behavioral therapy KW - exposure therapy KW - intervention N2 - Background: Virtual reality (VR) is as effective a technique as traditional cognitive behavioral therapy (CBT) and a promising tool for treating panic disorder symptoms because VR exposure can be safer and has better acceptability than in vivo exposure and is more immersive than exposure through imagination. CBT techniques can be delivered more effectively using VR as well. So far, VR has required high-quality devices, but the development of mobile VR technology has improved user availability. At the same time, a well-structured form of VR can be reproduced and used anywhere. This means that VR can be used to provide a self-guided form of treatment and address the high treatment costs of evidence-based therapy and the lack of professional therapists. This study aimed to investigate the potential of self-guided VR as an alternative to high-cost treatment. Objective: The main goal of this study was to offer data about the efficacy of a mobile app-based self-led VR CBT in the treatment of panic disorder. Methods: A total of 54 subjects with panic disorder were enrolled in this study and randomly assigned to either the VR treatment group or waitlist group. The VR treatment was designed to be total 12 sessions for 4 weeks. The VR treatment consists of 4 steps in which patients are gradually exposed to phobic stimuli while learning to cope with panic symptoms in each stage. The effectiveness of treatment was assessed through the Panic Disorder Severity Scale, Hamilton Rating Scale for Depression, Body Sensations Questionnaire, Albany Panic and Phobia Questionnaire, Anxiety Sensitivity Index, State-Trait Anxiety Inventory, Hospital Anxiety and Depression Scale, Korean Inventory of Social Avoidance and Distress Scale, Korean Inventory of Depressive Symptomatology, and Perceived Stress Scale. In addition, physiological changes using heart rate variability were evaluated. Results: In within-group analyses, the VR treatment group exhibited improvements in panic disorder symptoms, anxiety, and depression after 4 weeks, while the waitlist group did not show any significant improvement. Compared to the waitlist group, the VR treatment group showed significantly greater improvements in the Panic Disorder Severity Scale in both completer analysis and intention-to-treat analysis. Heart rate variability in the VR treatment group showed improvement in normalized high frequency from baseline to postassessment with no significant differences in any outcome measure between groups. Conclusions: The self-guided, mobile app-based VR intervention was effective in the treatment of panic symptoms and restoring the autonomic nervous system demonstrating the validity of the use of VR for self-guided treatment. VR treatment can be a cost-effective therapeutic approach. Trial Registration: ClinicalTrials.gov NCT04985019; https://clinicaltrials.gov/ct2/show/NCT04985019 UR - https://mental.jmir.org/2021/11/e30590 UR - http://dx.doi.org/10.2196/30590 UR - http://www.ncbi.nlm.nih.gov/pubmed/34813486 ID - info:doi/10.2196/30590 ER - TY - JOUR AU - Paetzold, Isabell AU - Hermans, M. Karlijn S. F. AU - Schick, Anita AU - Nelson, Barnaby AU - Velthorst, Eva AU - Schirmbeck, Frederike AU - AU - van Os, Jim AU - Morgan, Craig AU - van der Gaag, Mark AU - de Haan, Lieuwe AU - Valmaggia, Lucia AU - McGuire, Philip AU - Kempton, Matthew AU - Myin-Germeys, Inez AU - Reininghaus, Ulrich PY - 2021/11/19 TI - Momentary Manifestations of Negative Symptoms as Predictors of Clinical Outcomes in People at High Risk for Psychosis: Experience Sampling Study JO - JMIR Ment Health SP - e30309 VL - 8 IS - 11 KW - ecological momentary assessment KW - psychotic disorder KW - psychopathology N2 - Background: Negative symptoms occur in individuals at ultrahigh risk (UHR) for psychosis. Although there is evidence that observer ratings of negative symptoms are associated with level of functioning, the predictive value of subjective experience in daily life for individuals at UHR has not been studied yet. Objective: This study therefore aims to investigate the predictive value of momentary manifestations of negative symptoms for clinical outcomes in individuals at UHR. Methods: Experience sampling methodology was used to measure momentary manifestations of negative symptoms (blunted affective experience, lack of social drive, anhedonia, and social anhedonia) in the daily lives of 79 individuals at UHR. Clinical outcomes (level of functioning, illness severity, UHR status, and transition status) were assessed at baseline and at 1- and 2-year follow-ups. Results: Lack of social drive, operationalized as greater experienced pleasantness of being alone, was associated with poorer functioning at the 2-year follow-up (b=?4.62, P=.01). Higher levels of anhedonia were associated with poorer functioning at the 1-year follow-up (b=5.61, P=.02). Higher levels of social anhedonia were associated with poorer functioning (eg, disability subscale: b=6.36, P=.006) and greater illness severity (b=?0.38, P=.045) at the 1-year follow-up. In exploratory analyses, there was evidence that individuals with greater variability of positive affect (used as a measure of blunted affective experience) experienced a shorter time to remission from UHR status at follow-up (hazard ratio=4.93, P=.005). Conclusions: Targeting negative symptoms in individuals at UHR may help to predict clinical outcomes and may be a promising target for interventions in the early stages of psychosis. UR - https://mental.jmir.org/2021/11/e30309 UR - http://dx.doi.org/10.2196/30309 UR - http://www.ncbi.nlm.nih.gov/pubmed/34807831 ID - info:doi/10.2196/30309 ER - TY - JOUR AU - Dao, Phuong Kim AU - De Cocker, Katrien AU - Tong, Ly Huong AU - Kocaballi, Baki A. AU - Chow, Clara AU - Laranjo, Liliana PY - 2021/11/19 TI - Smartphone-Delivered Ecological Momentary Interventions Based on Ecological Momentary Assessments to Promote Health Behaviors: Systematic Review and Adapted Checklist for Reporting Ecological Momentary Assessment and Intervention Studies JO - JMIR Mhealth Uhealth SP - e22890 VL - 9 IS - 11 KW - ecological momentary assessment KW - ecological momentary intervention KW - behavior change KW - health behavior KW - mHealth KW - mobile health KW - smartphone apps KW - mobile phone N2 - Background: Healthy behaviors are crucial for maintaining a person?s health and well-being. The effects of health behavior interventions are mediated by individual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life. Objective: This systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives. Methods: We searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies. Results: We included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small sample sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physical activity (4/17, 24%), and smoking (2/17, 12%). Of the 17 studies, the 4 (24%) included randomized controlled trials reported nonstatistically significant effects on health behaviors, and 4 (24%) quasi-experimental studies reported statistically significant pre-post improvements in self-reported primary outcomes, namely depressive (P<.001) and psychotic symptoms (P=.03), drinking frequency (P<.001), and eating patterns (P=.01). EMA was commonly used to capture subjective experiences as well as behaviors, whereas sensors were rarely used. Generally, users perceived EMIs to be helpful. Common suggestions for improvement included enhancing personalization, multimedia and interactive capabilities (eg, voice recording), and lowering the EMA reporting burden. EMI and EMA components were rarely reported and were not described in a standardized manner across studies, hampering progress in this field. A reporting checklist was developed to facilitate the interpretation and comparison of findings and enhance the transparency and replicability of future studies using EMAs and EMIs. Conclusions: The use of smartphone-delivered EMIs using self-reported EMAs to promote behavior change is an emerging area of research, with few studies evaluating efficacy. Such interventions could present an opportunity to enhance health but need further assessment in larger participant cohorts and well-designed evaluations following reporting checklists. Future research should explore combining self-reported EMAs of subjective experiences with objective data passively collected via sensors to promote personalization while minimizing user burden, as well as explore different EMA data collection methods (eg, chatbots). Trial Registration: PROSPERO CRD42019138739; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=138739 UR - https://mhealth.jmir.org/2021/11/e22890 UR - http://dx.doi.org/10.2196/22890 UR - http://www.ncbi.nlm.nih.gov/pubmed/34806995 ID - info:doi/10.2196/22890 ER - TY - JOUR AU - Kiekens, Glenn AU - Robinson, Kealagh AU - Tatnell, Ruth AU - Kirtley, J. Olivia PY - 2021/11/19 TI - Opening the Black Box of Daily Life in Nonsuicidal Self-injury Research: With Great Opportunity Comes Great Responsibility JO - JMIR Ment Health SP - e30915 VL - 8 IS - 11 KW - real-time monitoring KW - nonsuicidal self-injury KW - NSSI KW - experience sampling KW - ecological momentary assessment KW - digital psychiatry UR - https://mental.jmir.org/2021/11/e30915 UR - http://dx.doi.org/10.2196/30915 UR - http://www.ncbi.nlm.nih.gov/pubmed/34807835 ID - info:doi/10.2196/30915 ER - TY - JOUR AU - Martel, Rhiannon AU - Shepherd, Matthew AU - Goodyear-Smith, Felicity PY - 2021/11/19 TI - Implementing the Routine Use of Electronic Mental Health Screening for Youth in Primary Care: Systematic Review JO - JMIR Ment Health SP - e30479 VL - 8 IS - 11 KW - adolescent KW - mental health KW - risk behavior KW - screening KW - primary care N2 - Background: Adolescents often present at primary care clinics with nonspecific physical symptoms when, in fact, they have at least 1 mental health or risk behavior (psychosocial) issue with which they would like help but do not disclose to their care provider. Despite global recommendations, over 50% of youths are not screened for mental health and risk behavior issues in primary care. Objective: This review aimed to examine the implementation, acceptability, feasibility, benefits, and barriers of e-screening tools for mental health and risk behaviors among youth in primary care settings. Methods: Electronic databases?MEDLINE, CINAHL, Scopus, and the Cochrane Database of Systematic Reviews?were searched for studies on the routine screening of youth in primary care settings. Screening tools needed to be electronic and screen for at least 1 mental health or risk behavior issue. A total of 11 studies that were reported in 12 articles, of which all were from high-income countries, were reviewed. Results: e-Screening was largely proven to be feasible and acceptable to youth and their primary care providers. Preconsultation e-screening facilitated discussions about sensitive issues and increased disclosure by youth. However, barriers such as the lack of time, training, and discomfort in raising sensitive issues with youth continued to be reported. Conclusions: To implement e-screening, clinicians need to change their behaviors, and e-screening processes must become normalized into their workflows. Co-designing and tailoring screening implementation frameworks to meet the needs of specific contexts may be required to ensure that clinicians overcome initial resistances and perceived barriers and adopt the required processes in their work. UR - https://mental.jmir.org/2021/11/e30479 UR - http://dx.doi.org/10.2196/30479 UR - http://www.ncbi.nlm.nih.gov/pubmed/34807833 ID - info:doi/10.2196/30479 ER - TY - JOUR AU - Zhang, Qi AU - Fu, Yu AU - Lu, Yanhui AU - Zhang, Yating AU - Huang, Qifang AU - Yang, Yajie AU - Zhang, Ke AU - Li, Mingzi PY - 2021/11/17 TI - Impact of Virtual Reality-Based Therapies on Cognition and Mental Health of Stroke Patients: Systematic Review and Meta-analysis JO - J Med Internet Res SP - e31007 VL - 23 IS - 11 KW - virtual reality KW - stroke KW - cognition KW - depression KW - mental health N2 - Background: Stroke remains one of the major chronic illnesses worldwide that health care organizations will need to address for the next several decades. Individuals poststroke are subject to levels of cognitive impairment and mental health problems. Virtual reality (VR)-based therapies are new technologies used for cognitive rehabilitation and the management of psychological outcomes. Objective: This study performed a meta-analysis to evaluate the effects of VR-based therapies on cognitive function and mental health in patients with stroke. Methods: A comprehensive database search was performed using PubMed, MEDLINE (Ovid), Embase, Cochrane Library, and APA PsycINFO databases for randomized controlled trials (RCTs) that studied the effects of VR on patients with stroke. We included trials published up to April 15, 2021, that fulfilled our inclusion and exclusion criteria. The literature was screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.3 software. Results: A total of 894 patients from 23 RCTs were included in our meta-analysis. Compared to traditional rehabilitation therapies, the executive function (standard mean difference [SMD]=0.88, 95% confidence interval [CI]=0.06-1.70, P=.03), memory (SMD=1.44, 95% CI=0.21-2.68, P=.02), and visuospatial function (SMD=0.78, 95% CI=0.23-1.33, P=.006) significantly improved among patients after VR intervention. However, there were no significant differences observed in global cognitive function, attention, verbal fluency, depression, and the quality of life (QoL). Conclusions: The findings of our meta-analysis showed that VR-based therapies are efficacious in improving executive function, memory, and visuospatial function in patients with stroke. For global cognitive function, attention, verbal fluency, depression, and the QoL, further research is required. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021252788; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=252788 UR - https://www.jmir.org/2021/11/e31007 UR - http://dx.doi.org/10.2196/31007 UR - http://www.ncbi.nlm.nih.gov/pubmed/34787571 ID - info:doi/10.2196/31007 ER - TY - JOUR AU - Zong, Hui AU - Hu, Binyang AU - Han, Yang AU - Li, Zuofeng AU - Zhang, Xiaoyan PY - 2021/11/17 TI - Prevalence and Temporal Trends Analysis of Screening and Diagnostic Instruments in Posttraumatic Stress Disorder: Text Mining Study JO - JMIR Ment Health SP - e33599 VL - 8 IS - 11 KW - posttraumatic stress disorder KW - instruments KW - prevalence KW - clinical trials KW - text mining N2 - Background: Various instruments for patient screening and diagnosis have been developed for and applied in posttraumatic stress disorder (PTSD). Objective: This study comprehensively investigates the prevalence and temporal trends of the most widely used instruments in PTSD-related studies. Methods: A total of 1345 files of registered clinical trials from ClinicalTrials.gov and 9422 abstracts from the PubMed database from 2005 to 2020 were downloaded for this study. The instruments applied in clinical trials were manually annotated, and instruments in abstracts were recognized using exact string matching. The prevalence score of an instrument in a certain period was calculated as the number of studies divided by the number of instances of the instrument. By calculating the yearly prevalence index of each instrument, we conducted a trends analysis and compared the trends in index change between instruments. Results: A total of 4178 instrument synonyms were annotated, which were mapped to 1423 unique instruments. In the 16 years from 2005 to 2020, only 10 instruments were used more than once per year; the 4 most used instruments were the PTSD Checklist, the Clinician-Administered PTSD Disorder Scale, the Patient Health Questionnaire, and the Beck Depression Inventory. There were 18 instruments whose yearly prevalence index score exceeded 0.1 at least once during the 16 years. The changes in trends and time points of partial instruments in clinical trials and PubMed abstracts were highly consistent. The average time duration of a PTSD-related trial was 1495.5 days or approximately 4 years from submission to ClinicalTrial.gov to publication in a journal. Conclusions: The application of widely accepted and appropriate instruments can help improve the reliability of research results in PTSD-related clinical studies. With extensive text data obtained from real clinical trials and published articles, we investigated and compared the usage of instruments in the PTSD research community. UR - https://mental.jmir.org/2021/11/e33599 UR - http://dx.doi.org/10.2196/33599 UR - http://www.ncbi.nlm.nih.gov/pubmed/34666307 ID - info:doi/10.2196/33599 ER - TY - JOUR AU - Yoo, Whi Dong AU - Ernala, Kiranmai Sindhu AU - Saket, Bahador AU - Weir, Domino AU - Arenare, Elizabeth AU - Ali, F. Asra AU - Van Meter, R. Anna AU - Birnbaum, L. Michael AU - Abowd, D. Gregory AU - De Choudhury, Munmun PY - 2021/11/16 TI - Clinician Perspectives on Using Computational Mental Health Insights From Patients? Social Media Activities: Design and Qualitative Evaluation of a Prototype JO - JMIR Ment Health SP - e25455 VL - 8 IS - 11 KW - mental health KW - social media KW - information technology N2 - Background: Previous studies have suggested that social media data, along with machine learning algorithms, can be used to generate computational mental health insights. These computational insights have the potential to support clinician-patient communication during psychotherapy consultations. However, how clinicians perceive and envision using computational insights during consultations has been underexplored. Objective: The aim of this study is to understand clinician perspectives regarding computational mental health insights from patients? social media activities. We focus on the opportunities and challenges of using these insights during psychotherapy consultations. Methods: We developed a prototype that can analyze consented patients? Facebook data and visually represent these computational insights. We incorporated the insights into existing clinician-facing assessment tools, the Hamilton Depression Rating Scale and Global Functioning: Social Scale. The design intent is that a clinician will verbally interview a patient (eg, How was your mood in the past week?) while they reviewed relevant insights from the patient?s social media activities (eg, number of depression-indicative posts). Using the prototype, we conducted interviews (n=15) and 3 focus groups (n=13) with mental health clinicians: psychiatrists, clinical psychologists, and licensed clinical social workers. The transcribed qualitative data were analyzed using thematic analysis. Results: Clinicians reported that the prototype can support clinician-patient collaboration in agenda-setting, communicating symptoms, and navigating patients? verbal reports. They suggested potential use scenarios, such as reviewing the prototype before consultations and using the prototype when patients missed their consultations. They also speculated potential negative consequences: patients may feel like they are being monitored, which may yield negative effects, and the use of the prototype may increase the workload of clinicians, which is already difficult to manage. Finally, our participants expressed concerns regarding the prototype: they were unsure whether patients? social media accounts represented their actual behaviors; they wanted to learn how and when the machine learning algorithm can fail to meet their expectations of trust; and they were worried about situations where they could not properly respond to the insights, especially emergency situations outside of clinical settings. Conclusions: Our findings support the touted potential of computational mental health insights from patients? social media account data, especially in the context of psychotherapy consultations. However, sociotechnical issues, such as transparent algorithmic information and institutional support, should be addressed in future endeavors to design implementable and sustainable technology. UR - https://mental.jmir.org/2021/11/e25455 UR - http://dx.doi.org/10.2196/25455 UR - http://www.ncbi.nlm.nih.gov/pubmed/34783667 ID - info:doi/10.2196/25455 ER - TY - JOUR AU - Klein, Arno AU - Clucas, Jon AU - Krishnakumar, Anirudh AU - Ghosh, S. Satrajit AU - Van Auken, Wilhelm AU - Thonet, Benjamin AU - Sabram, Ihor AU - Acuna, Nino AU - Keshavan, Anisha AU - Rossiter, Henry AU - Xiao, Yao AU - Semenuta, Sergey AU - Badioli, Alessandra AU - Konishcheva, Kseniia AU - Abraham, Ann Sanu AU - Alexander, M. Lindsay AU - Merikangas, R. Kathleen AU - Swendsen, Joel AU - Lindner, B. Ariel AU - Milham, P. Michael PY - 2021/11/11 TI - Remote Digital Psychiatry for Mobile Mental Health Assessment and Therapy: MindLogger Platform Development Study JO - J Med Internet Res SP - e22369 VL - 23 IS - 11 KW - mental health KW - mHealth KW - mobile health KW - digital health KW - eHealth KW - digital psychiatry KW - digital phenotyping KW - teletherapy KW - mobile device KW - mobile phone KW - smartphone KW - ecological momentary assessment KW - ecological momentary intervention KW - EMA KW - EMI KW - ESM KW - experience sampling KW - experience sampling methods N2 - Background: Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health. Objective: This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. Methods: We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform?s configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study. Results: Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users? data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser?based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. Conclusions: We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health?related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population. UR - https://www.jmir.org/2021/11/e22369 UR - http://dx.doi.org/10.2196/22369 UR - http://www.ncbi.nlm.nih.gov/pubmed/34762054 ID - info:doi/10.2196/22369 ER - TY - JOUR AU - Kundu, Anasua AU - Chaiton, Michael AU - Billington, Rebecca AU - Grace, Daniel AU - Fu, Rui AU - Logie, Carmen AU - Baskerville, Bruce AU - Yager, Christina AU - Mitsakakis, Nicholas AU - Schwartz, Robert PY - 2021/11/11 TI - Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review JO - JMIR Med Inform SP - e28962 VL - 9 IS - 11 KW - sexual and gender minorities KW - mental health KW - mental disorders KW - substance-related disorders KW - machine learning N2 - Background: A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods. Objective: This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field. Methods: The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings. Results: We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%). Conclusions: ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health. UR - https://medinform.jmir.org/2021/11/e28962 UR - http://dx.doi.org/10.2196/28962 UR - http://www.ncbi.nlm.nih.gov/pubmed/34762059 ID - info:doi/10.2196/28962 ER - TY - JOUR AU - Przybylko, Geraldine AU - Morton, Darren AU - Morton, Jason AU - Renfrew, Melanie PY - 2021/11/11 TI - The Influence of Gender and Age on the Outcomes of and Adherence to a Digital Interdisciplinary Mental Health Promotion Intervention in an Australasian Nonclinical Setting: Cohort Study JO - JMIR Ment Health SP - e29866 VL - 8 IS - 11 KW - age KW - gender KW - adherence KW - digital health KW - interdisciplinary KW - mental health KW - promotion KW - intervention KW - lifestyle medicine KW - positive psychology KW - multicomponent KW - lifestyle KW - outcome KW - cohort study N2 - Background: The global prevalence of mental health disorders is at a crisis point, particularly in the wake of COVID-19, prompting calls for the development of digital interdisciplinary mental health promotion interventions (MHPIs) for nonclinical cohorts. However, the influence of gender and age on the outcomes of and adherence to MHPIs is not well understood. Objective: The aim of this study was to determine the influence of gender and age on the outcomes of and adherence to a 10-week digital interdisciplinary MHPI that integrates strategies from positive psychology and lifestyle medicine and utilizes persuasive systems design (PSD) principles in a nonclinical setting. Methods: This study involved 488 participants who completed the digital interdisciplinary MHPI. Participants completed a pre and postintervention questionnaire that used: (1) the ?mental health? and ?vitality? subscales from the Short Form 36 (SF-36) Health Survey; (2) the Depression, Anxiety and Stress Scale (DASS-21); and (3) Satisfaction With Life Scale (SWL). Adherence to the digital interdisciplinary MHPI was measured by the number of educational videos the participants viewed and the extent to which they engaged in experiential challenge activities offered as part of the program. Results: On average, the participants (N=488; mean age 47.1 years, SD 14.1; 77.5% women) demonstrated statistically significant improvements in all mental health and well-being outcome measures, and a significant gender and age interaction was observed. Women tended to experience greater improvements than men in the mental health and well-being measures, and older men experienced greater improvements than younger men in the mental health and vitality subscales. Multiple analysis of variance results of the adherence measures indicated a significant difference for age but not gender. No statistically significant interaction between gender and age was observed for adherence measures. Conclusions: Digital interdisciplinary MHPIs that utilize PSD principles can improve the mental health and well-being of nonclinical cohorts, regardless of gender or age. Hence, there may be a benefit in utilizing PSD principles to develop universal MHPIs such as that employed in this study, which can be used across gender and age groups. Future research should examine which PSD principles optimize universal digital interdisciplinary MHPIs. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12619000993190; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377889 and Australian New Zealand Clinical Trials Registry ACTRN12619001009101; http://www.anzctr.org.au/ACTRN12619001009101.aspx UR - https://mental.jmir.org/2021/11/e29866 UR - http://dx.doi.org/10.2196/29866 UR - http://www.ncbi.nlm.nih.gov/pubmed/34762058 ID - info:doi/10.2196/29866 ER - TY - JOUR AU - Chancellor, Stevie AU - Sumner, A. Steven AU - David-Ferdon, Corinne AU - Ahmad, Tahirah AU - De Choudhury, Munmun PY - 2021/11/8 TI - Suicide Risk and Protective Factors in Online Support Forum Posts: Annotation Scheme Development and Validation Study JO - JMIR Ment Health SP - e24471 VL - 8 IS - 11 KW - online communities KW - suicide crisis KW - construct validity KW - annotation scheme KW - Reddit KW - annotation N2 - Background: Online communities provide support for individuals looking for help with suicidal ideation and crisis. As community data are increasingly used to devise machine learning models to infer who might be at risk, there have been limited efforts to identify both risk and protective factors in web-based posts. These annotations can enrich and augment computational assessment approaches to identify appropriate intervention points, which are useful to public health professionals and suicide prevention researchers. Objective: This qualitative study aims to develop a valid and reliable annotation scheme for evaluating risk and protective factors for suicidal ideation in posts in suicide crisis forums. Methods: We designed a valid, reliable, and clinically grounded process for identifying risk and protective markers in social media data. This scheme draws on prior work on construct validity and the social sciences of measurement. We then applied the scheme to annotate 200 posts from r/SuicideWatch?a Reddit community focused on suicide crisis. Results: We documented our results on producing an annotation scheme that is consistent with leading public health information coding schemes for suicide and advances attention to protective factors. Our study showed high internal validity, and we have presented results that indicate that our approach is consistent with findings from prior work. Conclusions: Our work formalizes a framework that incorporates construct validity into the development of annotation schemes for suicide risk on social media. This study furthers the understanding of risk and protective factors expressed in social media data. This may help public health programming to prevent suicide and computational social science research and investigations that rely on the quality of labels for downstream machine learning tasks. UR - https://mental.jmir.org/2021/11/e24471 UR - http://dx.doi.org/10.2196/24471 UR - http://www.ncbi.nlm.nih.gov/pubmed/34747705 ID - info:doi/10.2196/24471 ER - TY - JOUR AU - Stecher, Chad AU - Berardi, Vincent AU - Fowers, Rylan AU - Christ, Jaclyn AU - Chung, Yunro AU - Huberty, Jennifer PY - 2021/11/4 TI - Identifying App-Based Meditation Habits and the Associated Mental Health Benefits: Longitudinal Observational Study JO - J Med Internet Res SP - e27282 VL - 23 IS - 11 KW - behavioral habits KW - habit formation KW - mindfulness meditation KW - mental health KW - mHealth KW - mobile health KW - dynamic time warping KW - mobile phone N2 - Background: Behavioral habits are often initiated by contextual cues that occur at approximately the same time each day; so, it may be possible to identify a reflexive habit based on the temporal similarity of repeated daily behavior. Mobile health tools provide the detailed, longitudinal data necessary for constructing such an indicator of reflexive habits, which can improve our understanding of habit formation and help design more effective mobile health interventions for promoting healthier habits. Objective: This study aims to use behavioral data from a commercial mindfulness meditation mobile phone app to construct an indicator of reflexive meditation habits based on temporal similarity and estimate the association between temporal similarity and meditation app users? perceived health benefits. Methods: App-use data from June 2019 to June 2020 were analyzed for 2771 paying subscribers of a meditation mobile phone app, of whom 86.06% (2359/2771) were female, 72.61% (2012/2771) were college educated, 86.29% (2391/2771) were White, and 60.71% (1664/2771) were employed full-time. Participants volunteered to complete a survey assessing their perceived changes in physical and mental health from using the app. Receiver operating characteristic curve analysis was used to evaluate the ability of the temporal similarity measure to predict future behavior, and variable importance statistics from random forest models were used to corroborate these findings. Logistic regression was used to estimate the association between temporal similarity and self-reported physical and mental health benefits. Results: The temporal similarity of users? daily app use before completing the survey, as measured by the dynamic time warping (DTW) distance between app use on consecutive days, significantly predicted app use at 28 days and at 6 months after the survey, even after controlling for users? demographic and socioeconomic characteristics, total app sessions, duration of app use, and number of days with any app use. In addition, the temporal similarity measure significantly increased in the area under the receiver operating characteristic curve (AUC) for models predicting any future app use in 28 days (AUC=0.868 with DTW and 0.850 without DTW; P<.001) and for models predicting any app use in 6 months (AUC=0.821 with DTW and 0.802 without DTW; P<.001). Finally, a 1% increase in the temporal similarity of users? daily meditation practice with the app over 6 weeks before the survey was associated with increased odds of reporting mental health improvements, with an odds ratio of 2.94 (95% CI 1.832-6.369). Conclusions: The temporal similarity of the meditation app use was a significant predictor of future behavior, which suggests that this measure can identify reflexive meditation habits. In addition, temporal similarity was associated with greater perceived mental health benefits, which demonstrates that additional mental health benefits may be derived from forming reflexive meditation habits. UR - https://www.jmir.org/2021/11/e27282 UR - http://dx.doi.org/10.2196/27282 UR - http://www.ncbi.nlm.nih.gov/pubmed/34734826 ID - info:doi/10.2196/27282 ER - TY - JOUR AU - McIntyre, Heather AU - Loughhead, Mark AU - Hayes, Laura AU - Procter, Gerard Nicholas PY - 2021/11/4 TI - National Disability Insurance Scheme and Lived Experience of People Presenting to the Emergency Department: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e33268 VL - 10 IS - 11 KW - lived experience KW - National Disability Insurance Scheme KW - emergency department KW - psychosocial disability KW - communication pathways N2 - Background: Currently, within Australia, 3.6% of all emergency department (ED) presentations are mental health?related. Information about the context of the person presenting to the ED (beyond immediate needs), including their psychosocial disability (PSD) National Disability Insurance Scheme (NDIS) plan, is reported as incomplete and fragmented. There are missed opportunities for early support and care continuity that could potentially inform ED practitioners to revise current practices. Objective: The aims of this study are: (1) to obtain original data from the lived experience voice of those with the PSD NDIS plan and their experience when presenting to an ED, (2) to gather information from NDIS service providers to reveal communication pathways between the ED and NDIS services, and (3) to gain knowledge from ED clinicians around processes for improving continuity of care and consumer experience. Methods: This inductive, mixed methods phenomenological study will involve data collection analyzed sequentially, with each stage informing future stages of the research. Interviews will focus on the lived experience voice exploring concerns that have led to an ED presentation, alongside an analysis of associated clinical and administrative documentation and communications. Focus groups with NDIS support workers and support coordinators will provide phenomenological data around the experience from their perspective. National quantitative surveys among those with a PSD NDIS plan and emergency services clinicians will provide insight into current practices within community care and ED presentations. The research project design includes a lived experience advisory group who are assisting with the design of the interview and focus group schedules and national surveys, as well as in shaping the interpretation of qualitative information. All transcripts will be subject to thematic analysis to understand individuals? meaning-making of these complex and particular phenomena. The research team includes a lived experience researcher and a lived experience carer (PhD candidate). Results: This study is funded by MIND Australia as a PhD industry scholarship, which commenced in April 2020. A systematic review as a preresearch activity has been completed and is currently under review. The Human Research Ethics Committee of the University of South Australia has approved this project. An advisory group has been selected, and interview, focus group, and survey schedules are currently being codesigned. Recruitment will commence in November 2021. It is envisaged that data collection will be completed by June 2022. Conclusions: Understanding the lived experience of the precare, during care, and postcare stages of ED presentations from the perspective of those with a PSD NDIS plan will inform the research team around current practices and provide information about improvement for pathways of care for consumers and carers, while also informing health policy. International Registered Report Identifier (IRRID): PRR1-10.2196/33268 UR - https://www.researchprotocols.org/2021/11/e33268 UR - http://dx.doi.org/10.2196/33268 UR - http://www.ncbi.nlm.nih.gov/pubmed/34554101 ID - info:doi/10.2196/33268 ER - TY - JOUR AU - Blanco, Ivan AU - Boemo, Teresa AU - Sanchez-Lopez, Alvaro PY - 2021/11/2 TI - An Online Assessment to Evaluate the Role of Cognitive Biases and Emotion Regulation Strategies for Mental Health During the COVID-19 Lockdown of 2020: Structural Equation Modeling Study JO - JMIR Ment Health SP - e30961 VL - 8 IS - 11 KW - COVID-19 KW - emotion regulation KW - cognitive biases KW - psychological adjustment KW - resilience N2 - Background: Extant research supports causal roles of cognitive biases in stress regulation under experimental conditions. However, their contribution to psychological adjustment in the face of ecological major stressors has been largely unstudied. Objective: We developed a novel online method for the ecological examination of attention and interpretation biases during major stress (ie, the COVID-19 lockdown in March/April 2020) and tested their relations with the use of emotion regulation strategies (ie, reappraisal and rumination) to account for individual differences in psychological adjustment to major COVID-19?related stressors (ie, low depression and anxiety, and high well-being and resilience). Methods: Participants completed an online protocol evaluating the psychological impact of COVID-19?related stressors and the use of emotion regulation strategies in response to them, during the initial weeks of the lockdown of March/April 2020. They also completed a new online cognitive task designed to remotely assess attention and interpretation biases for negative information. The psychometric properties of the online cognitive bias assessments were very good, supporting their feasibility for ecological evaluation. Results: Structural equation models showed that negative interpretation bias was a direct predictor of worst psychological adjustment (higher depression and anxiety, and lower well-being and resilience; ?29=7.57; root mean square error of approximation=0.000). Further, rumination mediated the influence of interpretation bias in anxiety (P=.045; 95% CI 0.03-3.25) and resilience (P=.001; 95% CI ?6.34 to ?1.65), whereas reappraisal acted as a mediator of the influence of both attention (P=.047; 95% CI ?38.71 to ?0.16) and interpretation biases (P=.04; 95% CI ?5.25 to ?0.12) in well-being. Conclusions: This research highlights the relevance of individual processes of attention and interpretation during periods of adversity and identifies modifiable protective factors that can be targeted through online interventions. UR - https://mental.jmir.org/2021/11/e30961 UR - http://dx.doi.org/10.2196/30961 UR - http://www.ncbi.nlm.nih.gov/pubmed/34517337 ID - info:doi/10.2196/30961 ER - TY - JOUR AU - Bhattacharjee, Ananya AU - Haque, Taiabul S. M. AU - Hady, Abdul Md AU - Alam, Raihanul S. M. AU - Rabbi, Mashfiqui AU - Kabir, Ashad Muhammad AU - Ahmed, Ishtiaque Syed PY - 2021/11/2 TI - Understanding the Social Determinants of Mental Health of Undergraduate Students in Bangladesh: Interview Study JO - JMIR Form Res SP - e27114 VL - 5 IS - 11 KW - Bangladesh KW - global south KW - social determinant KW - students KW - undergraduate KW - religion KW - women KW - mobile phone N2 - Background: The undergraduate student population has been actively studied in digital mental health research. However, the existing literature primarily focuses on students from high-income nations, and undergraduates from limited-income nations remain understudied. Objective: This study aims to identify the broader social determinants of mental health among undergraduate students in Bangladesh, a limited-income nation in South Asia; study the manifestation of these determinants in their day-to-day lives; and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that affect their mental health. Methods: We conducted a 21-day study with 38 undergraduate students from 7 universities in Bangladesh. We conducted 2 semistructured interviews: one prestudy and one poststudy. During the 21-day study, participants used an Android app to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that the participants could independently review their mood and conversation patterns. Results: Our results show that academics, family, job and economic condition, romantic relationship, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors, as the participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, the participants took certain steps on their own to improve their mental health (eg, reduced the frequency of communication with certain persons). Conclusions: Although some of the factors (eg, academics) were reported in previous studies conducted in the Global North, this paper sheds light on some new issues (eg, extended family problems and religion) that are specific to the context of the Global South. Overall, the findings from this study would provide better insights for researchers to design better solutions to help the younger population from this part of the world. UR - https://formative.jmir.org/2021/11/e27114 UR - http://dx.doi.org/10.2196/27114 UR - http://www.ncbi.nlm.nih.gov/pubmed/34726609 ID - info:doi/10.2196/27114 ER - TY - JOUR AU - Martin-Key, A. Nayra AU - Mirea, Dan-Mircea AU - Olmert, Tony AU - Cooper, Jason AU - Han, Sarah Sung Yeon AU - Barton-Owen, Giles AU - Farrag, Lynn AU - Bell, Emily AU - Eljasz, Pawel AU - Cowell, Daniel AU - Tomasik, Jakub AU - Bahn, Sabine PY - 2021/10/28 TI - Toward an Extended Definition of Major Depressive Disorder Symptomatology: Digital Assessment and Cross-validation Study JO - JMIR Form Res SP - e27908 VL - 5 IS - 10 KW - major depressive disorder KW - subthreshold depression, transdiagnostic symptoms KW - digital assessment KW - digital mental health KW - mobile phone N2 - Background: Diagnosing major depressive disorder (MDD) is challenging, with diagnostic manuals failing to capture the wide range of clinical symptoms that are endorsed by individuals with this condition. Objective: This study aims to provide evidence for an extended definition of MDD symptomatology. Methods: Symptom data were collected via a digital assessment developed for a delta study. Random forest classification with nested cross-validation was used to distinguish between individuals with MDD and those with subthreshold symptomatology of the disorder using disorder-specific symptoms and transdiagnostic symptoms. The diagnostic performance of the Patient Health Questionnaire?9 was also examined. Results: A depression-specific model demonstrated good predictive performance when distinguishing between individuals with MDD (n=64) and those with subthreshold depression (n=140) (area under the receiver operating characteristic curve=0.89; sensitivity=82.4%; specificity=81.3%; accuracy=81.6%). The inclusion of transdiagnostic symptoms of psychopathology, including symptoms of depression, generalized anxiety disorder, insomnia, emotional instability, and panic disorder, significantly improved the model performance (area under the receiver operating characteristic curve=0.95; sensitivity=86.5%; specificity=90.8%; accuracy=89.5%). The Patient Health Questionnaire?9 was excellent at identifying MDD but overdiagnosed the condition (sensitivity=92.2%; specificity=54.3%; accuracy=66.2%). Conclusions: Our findings are in line with the notion that current diagnostic practices may present an overly narrow conception of mental health. Furthermore, our study provides proof-of-concept support for the clinical utility of a digital assessment to inform clinical decision-making in the evaluation of MDD. UR - https://formative.jmir.org/2021/10/e27908 UR - http://dx.doi.org/10.2196/27908 UR - http://www.ncbi.nlm.nih.gov/pubmed/34709182 ID - info:doi/10.2196/27908 ER - TY - JOUR AU - Cilia, Federica AU - Carette, Romuald AU - Elbattah, Mahmoud AU - Dequen, Gilles AU - Guérin, Jean-Luc AU - Bosche, Jérôme AU - Vandromme, Luc AU - Le Driant, Barbara PY - 2021/10/25 TI - Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning JO - JMIR Hum Factors SP - e27706 VL - 8 IS - 4 KW - autism spectrum disorder KW - screening KW - eye tracking KW - data visualization KW - machine learning KW - deep learning KW - AI KW - ASS KW - artificial intelligence KW - ML KW - adolescent KW - diagnosis N2 - Background: The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. Objective: This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening Methods: The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. Results: The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. Conclusions: Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders. UR - https://humanfactors.jmir.org/2021/4/e27706 UR - http://dx.doi.org/10.2196/27706 UR - http://www.ncbi.nlm.nih.gov/pubmed/34694238 ID - info:doi/10.2196/27706 ER - TY - JOUR AU - Bond, Jessica AU - Robotham, Dan AU - Kenny, Alexandra AU - Pinfold, Vanessa AU - Kabir, Thomas AU - Andleeb, Humma AU - Larkin, Michael AU - Martin, L. Jennifer AU - Brown, Susan AU - Bergin, D. Aislinn AU - Petit, Ariane AU - Rosebrock, Laina AU - Lambe, Sinéad AU - Freeman, Daniel AU - Waite, Felicity PY - 2021/10/25 TI - Automated Virtual Reality Cognitive Therapy for People With Psychosis: Protocol for a Qualitative Investigation Using Peer Research Methods JO - JMIR Res Protoc SP - e31742 VL - 10 IS - 10 KW - virtual reality KW - therapy KW - schizophrenia KW - agoraphobia KW - peer research KW - qualitative methods KW - implementation KW - mental health KW - psychosis KW - cognitive therapy N2 - Background: Many people with psychosis experience difficulties in everyday social situations. Anxiety can make life challenging, leading to withdrawal. Cognitive therapy, using active in vivo learning, enables people to overcome fears. These treatments are not readily available to people with psychosis. Automated virtual reality (VR) therapy is a potential route to increase accessibility. The gameChange automated VR cognitive therapy is designed to help people overcome anxious avoidance and build confidence in everyday social situations. A virtual coach guides the person through the treatment. Understanding user experience is key to facilitating future implementation. Peer research methods, in which people with lived experience of the issues being studied are involved in collecting and analyzing data, may be useful in developing this understanding. This encourages researchers to draw on their lived experience to explore participant perspectives and co-create knowledge. Objective: The primary objective is to use a peer research approach to explore the participant experience of a novel automated VR therapy for anxious social avoidance. This includes understanding (1) the experience of anxious social avoidance in people with psychosis, (2) the experience of the gameChange automated VR cognitive therapy, and (3) any potential impact of the therapy in people?s lives. This will inform future implementation strategies. The secondary objective is to explore how peer research can be used to co-create knowledge. Methods: Semistructured interviews will be conducted with approximately 25 people with psychosis participating in the gameChange trial (ISRCTN17308399). Participants will be recruited from the five trial centers based in National Health Service mental health trusts across England. Interviews will be conducted by two researchers. One is a peer researcher with similar lived experience to the trial participants. The other has lived experiences of mental health issues that do not directly overlap with those of the trial participants. Interview questions will focus on an individual?s experience of anxious social avoidance, experiences of participating in the gameChange VR therapy, and any changes or impact following therapy. The interview schedule was developed in collaboration with the gameChange Lived Experience Advisory Panel (LEAP), comprising 10 project advisors with lived experience of psychosis. Interpretative phenomenological analysis and template analysis will be used to explore individual accounts. The LEAP will contribute to the analysis. Results: Data collection will be conducted from April to September 2021, and analysis will be conducted from June to October 2021. As of September 28, 2021, 20 participants had been interviewed, and coding is underway. Conclusions: The study, employing a peer research approach, may provide a unique insight into the experiences of anxious social avoidance in people with psychosis and its treatment using automated VR therapy. This will inform potential future implementation of VR automated therapies in mental health services. International Registered Report Identifier (IRRID): DERR1-10.2196/31742 UR - https://www.researchprotocols.org/2021/10/e31742 UR - http://dx.doi.org/10.2196/31742 UR - http://www.ncbi.nlm.nih.gov/pubmed/34694236 ID - info:doi/10.2196/31742 ER - TY - JOUR AU - Moon, C. Khatiya AU - Van Meter, R. Anna AU - Kirschenbaum, A. Michael AU - Ali, Asra AU - Kane, M. John AU - Birnbaum, L. Michael PY - 2021/10/22 TI - Internet Search Activity of Young People With Mood Disorders Who Are Hospitalized for Suicidal Thoughts and Behaviors: Qualitative Study of Google Search Activity JO - JMIR Ment Health SP - e28262 VL - 8 IS - 10 KW - suicide KW - mood disorders KW - depression KW - internet KW - search engine KW - Google search KW - digital health KW - mobile health KW - adolescent KW - young adult N2 - Background: Little is known about the internet search activity of people with suicidal thoughts and behaviors (STBs). This data source has the potential to inform both clinical and public health efforts, such as suicide risk assessment and prevention. Objective: We aimed to evaluate the internet search activity of suicidal young people to find evidence of suicidal ideation and behavioral health?related content. Methods: Individuals aged between 15 and 30 years (N=43) with mood disorders who were hospitalized for STBs provided access to their internet search history. Searches that were conducted in the 3-month period prior to hospitalization were extracted and manually evaluated for search themes related to suicide and behavioral health. Results: A majority (27/43, 63%) of participants conducted suicide-related searches. Participants searched for information that exactly matched their planned or chosen method of attempting suicide in 21% (9/43) of cases. Suicide-related search queries also included unusual suicide methods and references to suicide in popular culture. A majority of participants (33/43, 77%) had queries related to help-seeking themes, including how to find inpatient and outpatient behavioral health care. Queries related to mood and anxiety symptoms were found among 44% (19/43) of participants and included references to panic disorder, the inability to focus, feelings of loneliness, and despair. Queries related to substance use were found among 44% (19/43) of participants. Queries related to traumatic experiences were present among 33% (14/43) of participants. Few participants conducted searches for crisis hotlines (n=3). Conclusions: Individuals search the internet for information related to suicide prior to hospitalization for STBs. The improved understanding of the search activity of suicidal people could inform outreach, assessment, and intervention strategies for people at risk. Access to search data may also benefit the ongoing care of suicidal patients. UR - https://mental.jmir.org/2021/10/e28262 UR - http://dx.doi.org/10.2196/28262 UR - http://www.ncbi.nlm.nih.gov/pubmed/34677139 ID - info:doi/10.2196/28262 ER - TY - JOUR AU - Hudon, Alexandre AU - Beaudoin, Mélissa AU - Phraxayavong, Kingsada AU - Dellazizzo, Laura AU - Potvin, Stéphane AU - Dumais, Alexandre PY - 2021/10/22 TI - Use of Automated Thematic Annotations for Small Data Sets in a Psychotherapeutic Context: Systematic Review of Machine Learning Algorithms JO - JMIR Ment Health SP - e22651 VL - 8 IS - 10 KW - psychotherapy KW - artificial intelligence KW - automated text classification KW - machine learning KW - systematic review N2 - Background: A growing body of literature has detailed the use of qualitative analyses to measure the therapeutic processes and intrinsic effectiveness of psychotherapies, which yield small databases. Nonetheless, these approaches have several limitations and machine learning algorithms are needed. Objective: The objective of this study is to conduct a systematic review of the use of machine learning for automated text classification for small data sets in the fields of psychiatry, psychology, and social sciences. This review will identify available algorithms and assess if automated classification of textual entities is comparable to the classification done by human evaluators. Methods: A systematic search was performed in the electronic databases of Medline, Web of Science, PsycNet (PsycINFO), and Google Scholar from their inception dates to 2021. The fields of psychiatry, psychology, and social sciences were selected as they include a vast array of textual entities in the domain of mental health that can be reviewed. Additional records identified through cross-referencing were used to find other studies. Results: This literature search identified 5442 articles that were eligible for our study after the removal of duplicates. Following abstract screening, 114 full articles were assessed in their entirety, of which 107 were excluded. The remaining 7 studies were analyzed. Classification algorithms such as naive Bayes, decision tree, and support vector machine classifiers were identified. Support vector machine is the most used algorithm and best performing as per the identified articles. Prediction classification scores for the identified algorithms ranged from 53%-91% for the classification of textual entities in 4-7 categories. In addition, 3 of the 7 studies reported an interjudge agreement statistic; these were consistent with agreement statistics for text classification done by human evaluators. Conclusions: A systematic review of available machine learning algorithms for automated text classification for small data sets in several fields (psychiatry, psychology, and social sciences) was conducted. We compared automated classification with classification done by human evaluators. Our results show that it is possible to automatically classify textual entities of a transcript based solely on small databases. Future studies are nevertheless needed to assess whether such algorithms can be implemented in the context of psychotherapies. UR - https://mental.jmir.org/2021/10/e22651 UR - http://dx.doi.org/10.2196/22651 UR - http://www.ncbi.nlm.nih.gov/pubmed/34677133 ID - info:doi/10.2196/22651 ER - TY - JOUR AU - Lustig, Andrew AU - Brookes, Gavin AU - Hunt, Daniel PY - 2021/10/21 TI - Social Semiotics of Gangstalking Evidence Videos on YouTube: Multimodal Discourse Analysis of a Novel Persecutory Belief System JO - JMIR Ment Health SP - e30311 VL - 8 IS - 10 KW - internet KW - discourse analysis KW - psychosis KW - delusion KW - semiotics KW - linguistics KW - computer-mediated communication KW - schizophrenia KW - eHealth KW - video KW - communication KW - YouTube KW - social media KW - discourse KW - mental health N2 - Background: Gangstalking refers to a novel persecutory belief system wherein sufferers believe that they are being followed, watched, and harassed by a vast network of people in their community who have been recruited as complicit perpetrators. They are frequently diagnosed as mentally ill, although they reject this formulation. Those affected by this belief system self-identify as targeted individuals (TIs). They seek to prove the veracity of their persecution and dispute the notion that they are mentally ill by posting videos online that purport to provide evidence of their claims. Objective: The objective of the study was to characterize the multimodal social semiotic practices used in gangstalking evidence videos. Methods: We assembled a group of 50 evidence videos posted on YouTube by self-identified TIs and performed a multimodal social semiotic discourse analysis using a grounded theory approach to data analysis. Results: TIs accomplished several social and interpersonal tasks in the videos. They constructed their own identity as subjects of persecution and refuted the notion that they suffered from mental illness. They also cultivated positive ambient affiliation with viewers of the videos but manifested hostility toward people who appeared in the videos. They made extensive use of multimodal deixis to generate salience and construe the gangstalking belief system. The act of filming itself was a source of conflict and served as a self-fulfilling prophecy; filming was undertaken to neutrally record hostility directed toward video bloggers (vloggers). However, the act of filming precipitated the very behaviors that they set out to document. Finally, the act of filming was also regarded as an act of resistance and empowerment by vloggers. Conclusions: These data provide insight into a novel persecutory belief system. Interpersonal concerns are important for people affected, and they construe others as either sympathetic or hostile. They create positive ambient affiliation with viewers. We found that vloggers use multimodal deixis to illustrate the salience of the belief system. The videos highlighted the Derridean concept of différance, wherein the meaning of polysemous signifiers is deferred without definitive resolution. This may be important in communicating with people and patients with persecutory belief systems. Clinicians may consider stepping away from the traditional true/false dichotomy endorsed by psychiatric classification systems and focus on the ambiguity in semiotic systems generally and in persecutory belief systems specifically. UR - https://mental.jmir.org/2021/10/e30311 UR - http://dx.doi.org/10.2196/30311 UR - http://www.ncbi.nlm.nih.gov/pubmed/34673523 ID - info:doi/10.2196/30311 ER - TY - JOUR AU - Kemp, Jessica AU - Chorney, Jill AU - Kassam, Iman AU - MacDonald, Julie AU - MacDonald, Tara AU - Wozney, Lori AU - Strudwick, Gillian PY - 2021/10/19 TI - Learning About the Current State of Digital Mental Health Interventions for Canadian Youth to Inform Future Decision-Making: Mixed Methods Study JO - J Med Internet Res SP - e30491 VL - 23 IS - 10 KW - youth mental health KW - digital mental health KW - COVID-19 KW - digital mental health interventions KW - e-mental health N2 - Background: The COVID-19 pandemic has increased the demand for youth mental health services in Canada as disruptions to clinical care continue to persist due to the risk of transmission and exposure to the virus. Digital mental health interventions, including web-based resources and mobile apps, have provided opportunities to support youth mental health remotely across Canada. There is a need to better understand how these digital interventions are being selected, recommended, and used in various regions across Canada. Objective: A national jurisdictional scan was completed to (1) determine what web-based programs, apps, and websites are promoted and licensed in Canada for youth mental health; (2) identify criteria and decision-making processes that Canadian jurisdictions use to select web-based programs, apps, and websites for youth mental health; and (3) identify upcoming trends, innovations, and digital mental health possibilities that are emerging in the youth sector. Methods: The aims of the jurisdictional scan were addressed through a review of related academic and grey literature; stakeholder interviews, including individuals involved in various areas of the youth mental health sector; and a social media review of pertinent Twitter content. Results: A total of 66 web-based resources and apps were identified for use by youth in Canada. 16 stakeholder interviews were completed and included discussions with researchers, clinicians, youth organizations, and others involved in digital interventions for youth mental health. These discussions identified a limited use of frameworks used to guide decision-making processes when selecting digital interventions. Many clinicians agreed on a similar set of eligibility requirements for youth mental health apps and digital resources, such as the evidence base and cultural relevance of the intervention. Stakeholders also identified upcoming trends and innovations in the youth digital mental health space, including artificial intelligence, digital phenotyping, and personalized therapy. Over 4 weeks, 2184 tweets were reviewed to identify and compare global and national trends and innovations involving digital mental health and youth. Key trends included the promotion of regional chat services as well as the effects of the COVID-19 pandemic on youth mental health and access to care. Conclusions: As organizations begin to plan for the delivery of mental health care following the pandemic, there are concerns about the sustainability of these digital mental health interventions as well as a need for services to be more informed by the experiences and preferences of youth. UR - https://www.jmir.org/2021/10/e30491 UR - http://dx.doi.org/10.2196/30491 UR - http://www.ncbi.nlm.nih.gov/pubmed/34665141 ID - info:doi/10.2196/30491 ER - TY - JOUR AU - Mayer, Gwendolyn AU - Hummel, Svenja AU - Gronewold, Nadine AU - Oetjen, Neele AU - Hilbel, Thomas AU - Schultz, Jobst-Hendrik PY - 2021/10/18 TI - Validity and Reliability of the Self-administered Psycho-TherApy-SystemS (SELFPASS) Item Pool for the Daily Mood Tracking of Depressive Symptoms: Cross-sectional Web-Based Survey JO - JMIR Ment Health SP - e29615 VL - 8 IS - 10 KW - self-management KW - mood tracking KW - validity KW - reliability KW - item pool KW - questionnaire KW - depression KW - anxiety KW - mood assessment N2 - Background: e-Mental health apps targeting depression have gained increased attention in mental health care. Daily self-assessment is an essential part of e-mental health apps. The Self-administered Psycho-TherApy-SystemS (SELFPASS) app is a self-management app to manage depressive and comorbid anxiety symptoms of patients with a depression diagnosis. A self-developed item pool with 40 depression items and 12 anxiety items is included to provide symptom-specific suggestions for interventions. However, the psychometric properties of the item pool have not yet been evaluated. Objective: The aim of this study is to investigate the validity and reliability of the SELFPASS item pool. Methods: A weblink with the SELFPASS item pool and validated mood assessment scales was distributed to healthy subjects and patients who had received a diagnosis of a depressive disorder within the last year. Two scores were derived from the SELFPASS item pool: SELFPASS depression (SP-D) and SELFPASS anxiety (SP-A). Reliability was examined using Cronbach ?. Construct validity was assessed through Pearson correlations with the Patient Health Questionnaire-9 (PHQ-9), the General Anxiety Disorder Scale-7 (GAD-7), and the WHO-5-Wellbeing-Scale (WHO-5). Logistic regression analysis was performed as an indicator for concurrent criterion validity of SP-D and SP-A. Factor analysis was performed to provide information about the underlying factor structure of the item pool. Item-scale correlations were calculated in order to determine item quality. Results: A total of 284 participants were included, with 192 (67.6%) healthy subjects and 92 (32.4%) patients. Cronbach ? was set to .94 for SP-D and ?=.88 for SP-A. We found significant positive correlations between SP-D and PHQ-9 scores (r=0.87; P<.001) and between SP-A and GAD-7 scores (r=0.80; P<.001), and negative correlations between SP-D and WHO-5 scores (r=?0.80; P<.001) and between SP-A and WHO-5 scores (r=?0.69; P<.001). Increasing scores of SP-D and SP-A led to increased odds of belonging to the patient group (SP-D: odds ratio 1.03, 95% CI 1.01-1.05; P<.001; SP-A: 1.05, 1.05-1.01; P=.01). The item pool yielded 2 factors: one that consisted of mood-related items and another with somatic-related items. Conclusions: The SELFPASS item pool showed good psychometric properties in terms of reliability, construct, and criterion validity. The item pool is an appropriate source for daily mood tracking in future e-mental health apps among patients with depression. Our study provides general recommendations for future developments as well as recommendations within the item pool. UR - https://mental.jmir.org/2021/10/e29615 UR - http://dx.doi.org/10.2196/29615 UR - http://www.ncbi.nlm.nih.gov/pubmed/34661547 ID - info:doi/10.2196/29615 ER - TY - JOUR AU - Wade, E. Natasha AU - Ortigara, M. Joseph AU - Sullivan, M. Ryan AU - Tomko, L. Rachel AU - Breslin, J. Florence AU - Baker, C. Fiona AU - Fuemmeler, F. Bernard AU - Delrahim Howlett, Katia AU - Lisdahl, M. Krista AU - Marshall, T. Andrew AU - Mason, J. Michael AU - Neale, C. Michael AU - Squeglia, M. Lindsay AU - Wolff-Hughes, L. Dana AU - Tapert, F. Susan AU - Bagot, S. Kara AU - PY - 2021/10/18 TI - Passive Sensing of Preteens? Smartphone Use: An Adolescent Brain Cognitive Development (ABCD) Cohort Substudy JO - JMIR Ment Health SP - e29426 VL - 8 IS - 10 KW - preadolescents KW - smartphone use KW - passive sensing KW - screen use KW - screen time KW - mobile phone N2 - Background: Concerns abound regarding childhood smartphone use, but studies to date have largely relied on self-reported screen use. Self-reporting of screen use is known to be misreported by pediatric samples and their parents, limiting the accurate determination of the impact of screen use on social, emotional, and cognitive development. Thus, a more passive, objective measurement of smartphone screen use among children is needed. Objective: This study aims to passively sense smartphone screen use by time and types of apps used in a pilot sample of children and to assess the feasibility of passive sensing in a larger longitudinal sample. Methods: The Adolescent Brain Cognitive Development (ABCD) study used passive, objective phone app methods for assessing smartphone screen use over 4 weeks in 2019-2020 in a subsample of 67 participants (aged 11-12 years; 31/67, 46% female; 23/67, 34% White). Children and their parents both reported average smartphone screen use before and after the study period, and they completed a questionnaire regarding the acceptability of the study protocol. Descriptive statistics for smartphone screen use, app use, and protocol feasibility and acceptability were reviewed. Analyses of variance were run to assess differences in categorical app use by demographics. Self-report and parent report were correlated with passive sensing data. Results: Self-report of smartphone screen use was partly consistent with objective measurement (r=0.49), although objective data indicated that children used their phones more than they reported. Passive sensing revealed the most common types of apps used were for streaming (mean 1 hour 57 minutes per day, SD 1 hour 32 minutes), communication (mean 48 minutes per day, SD 1 hour 17 minutes), gaming (mean 41 minutes per day, SD 41 minutes), and social media (mean 36 minutes per day, SD 1 hour 7 minutes). Passive sensing of smartphone screen use was generally acceptable to children (43/62, 69%) and parents (53/62, 85%). Conclusions: The results of passive, objective sensing suggest that children use their phones more than they self-report. Therefore, use of more robust methods for objective data collection is necessary and feasible in pediatric samples. These data may then more accurately reflect the impact of smartphone screen use on behavioral and emotional functioning. Accordingly, the ABCD study is implementing a passive sensing protocol in the full ABCD cohort. Taken together, passive assessment with a phone app provided objective, low-burden, novel, informative data about preteen smartphone screen use. UR - https://mental.jmir.org/2021/10/e29426 UR - http://dx.doi.org/10.2196/29426 UR - http://www.ncbi.nlm.nih.gov/pubmed/34661541 ID - info:doi/10.2196/29426 ER - TY - JOUR AU - Spadaro, Benedetta AU - Martin-Key, A. Nayra AU - Bahn, Sabine PY - 2021/10/13 TI - Building the Digital Mental Health Ecosystem: Opportunities and Challenges for Mobile Health Innovators JO - J Med Internet Res SP - e27507 VL - 23 IS - 10 KW - digital implementation KW - digital mental health KW - digital psychiatry KW - digital technology KW - viewpoint UR - https://www.jmir.org/2021/10/e27507 UR - http://dx.doi.org/10.2196/27507 UR - http://www.ncbi.nlm.nih.gov/pubmed/34643537 ID - info:doi/10.2196/27507 ER - TY - JOUR AU - Hu, Xiao-Su AU - Beard, Katherine AU - Sherbel, Catherine Mary AU - Nascimento, D. Thiago AU - Petty, Sean AU - Pantzlaff, Eddie AU - Schwitzer, David AU - Kaciroti, Niko AU - Maslowski, Eric AU - Ashman, M. Lawrence AU - Feinberg, E. Stephen AU - DaSilva, F. Alexandre PY - 2021/10/12 TI - Brain Mechanisms of Virtual Reality Breathing Versus Traditional Mindful Breathing in Pain Modulation: Observational Functional Near-infrared Spectroscopy Study JO - J Med Internet Res SP - e27298 VL - 23 IS - 10 KW - virtual reality breathing KW - traditional mindful breathing KW - pain KW - functional near-infrared spectroscopy KW - mobile phone N2 - Background: Pain is a complex experience that involves sensory-discriminative and cognitive-emotional neuronal processes. It has long been known across cultures that pain can be relieved by mindful breathing (MB). There is a common assumption that MB exerts its analgesic effect through interoception. Interoception refers to consciously refocusing the mind?s attention to the physical sensation of internal organ function. Objective: In this study, we dissect the cortical analgesic processes by imaging the brains of healthy subjects exposed to traditional MB (TMB) and compare them with another group for which we augmented MB to an outside sensory experience via virtual reality breathing (VRB). Methods: The VRB protocol involved in-house?developed virtual reality 3D lungs that synchronized with the participants? breathing cycles in real time, providing them with an immersive visual-auditory exteroception of their breathing. Results: We found that both breathing interventions led to a significant increase in pain thresholds after week-long practices, as measured by a thermal quantitative sensory test. However, the underlying analgesic brain mechanisms were opposite, as revealed by functional near-infrared spectroscopy data. In the TMB practice, the anterior prefrontal cortex uniquely modulated the premotor cortex. This increased its functional connection with the primary somatosensory cortex (S1), thereby facilitating the S1-based sensory-interoceptive processing of breathing but inhibiting its other role in sensory-discriminative pain processing. In contrast, virtual reality induced an immersive 3D exteroception with augmented visual-auditory cortical activations, which diminished the functional connection with the S1 and consequently weakened the pain processing function of the S1. Conclusions: In summary, our study suggested two analgesic neuromechanisms of VRB and TMB practices?exteroception and interoception?that distinctively modulated the S1 processing of the ascending noxious inputs. This is in line with the concept of dualism (Yin and Yang). UR - https://www.jmir.org/2021/10/e27298 UR - http://dx.doi.org/10.2196/27298 UR - http://www.ncbi.nlm.nih.gov/pubmed/34636731 ID - info:doi/10.2196/27298 ER - TY - JOUR AU - Hood, M. Anna AU - Stotesbury, Hanne AU - Murphy, Jennifer AU - Kölbel, Melanie AU - Slee, April AU - Springall, Charlie AU - Paradis, Matthew AU - Corral-Frías, Saraí Nadia AU - Reyes-Aguilar, Azalea AU - Cuellar Barboza, B. Alfredo AU - Noser, E. Amy AU - Gomes, Stacey AU - Mitchell, Monica AU - Watkins, M. Sharon AU - Butsch Kovacic, Melinda AU - Kirkham, J. Fenella AU - Crosby, E. Lori PY - 2021/10/7 TI - Attitudes About COVID-19 and Health (ATTACH): Online Survey and Mixed Methods Study JO - JMIR Ment Health SP - e29963 VL - 8 IS - 10 KW - COVID-19 KW - mental health KW - international KW - mitigation strategies KW - deprivation N2 - Background: Behavioral mitigation strategies to slow the spread of COVID-19 have resulted in sweeping lifestyle changes, with short- and long-term psychological, well-being, and quality of life implications. The Attitudes About COVID-19 and Health (ATTACH) study focuses on understanding attitudes and beliefs while considering the impact on mental and physical health and the influence of broader demographic and geographic factors on attitudes, beliefs, and mental health burden. Objective: In this assessment of our first wave of data collection, we provide baseline cohort description of the ATTACH study participants in the United Kingdom, the United States, and Mexico. Additionally, we assess responses to daily poll questions related to COVID-19 and conduct a cross-sectional analysis of baseline assessments collected in the UK between June 26 and October 31, 2020. Methods: The ATTACH study uses smartphone app technology and online survey data collection. Participants completed poll questions related to COVID-19 2 times daily and a monthly survey assessing mental health, social isolation, physical health, and quality of life. Poll question responses were graphed using 95% Clopper?Pearson (exact) tests with 95% CIs. Pearson correlations, hierarchical linear regression analyses, and generalized linear models assessed relationships, predictors of self-reported outcomes, and group differences, respectively. Results: By October 31, 2020, 1405, 80, and 90 participants had consented to participate in the UK, United States, and Mexico, respectively. Descriptive data for the UK daily poll questions indicated that participants generally followed social distancing measures, but worry and negative impacts on families increased as the pandemic progressed. Although participants generally reported feeling that the reasons for current measures had been made clear, there was low trust that the government was doing everything in its power to meet public needs. In the UK, 1282 participants also completed a monthly survey (94.99% [1326/1396] White, 72.22% [1014/1404] female, and 20.12% [277/1377] key or essential workers); 18.88% (242/1282) of UK participants reported a preexisting mental health disorder, 31.36% (402/1282) reported a preexisting chronic medical illness, and 35.11% (493/1404) were aged over 65; 57.72% (740/1282) of participants reported being more sedentary since the pandemic began, and 41.89% (537/1282) reported reduced access to medical care. Those with poorer mental health outcomes lived in more deprived neighborhoods, in larger households (Ps<.05), had more preexisting mental health disorders and medical conditions, and were younger than 65 years (all Ps<.001). Conclusions: Communities who have been exposed to additional harm during the COVID-19 pandemic were experiencing worse mental outcomes. Factors including having a medical condition, or living in a deprived neighborhood or larger household were associated with heightened risk. Future longitudinal studies should investigate the link between COVID-19 exposure, mental health, and sociodemographic and residential characteristics. UR - https://mental.jmir.org/2021/10/e29963 UR - http://dx.doi.org/10.2196/29963 UR - http://www.ncbi.nlm.nih.gov/pubmed/34357877 ID - info:doi/10.2196/29963 ER - TY - JOUR AU - Shaukat-Jali, Ruksana AU - van Zalk, Nejra AU - Boyle, Edward David PY - 2021/10/7 TI - Detecting Subclinical Social Anxiety Using Physiological Data From a Wrist-Worn Wearable: Small-Scale Feasibility Study JO - JMIR Form Res SP - e32656 VL - 5 IS - 10 KW - social anxiety KW - wearable sensors KW - physiological measurement KW - machine learning KW - young adults KW - mental health KW - mHealth KW - new methods KW - anxiety KW - wearable KW - sensor KW - digital phenotyping KW - digital biomarkers N2 - Background: Subclinical (ie, threshold) social anxiety can greatly affect young people?s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment, which would be greatly beneficial for persons with social anxiety, society, and health care services. Nevertheless, indicators such as skin temperature measured by wrist-worn sensors have not been used in prior work on physiological social anxiety detection. Objective: This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including heart rate, skin temperature, and electrodermal activity (EDA). Methods: Young adults (N=12) with self-reported subclinical social anxiety (measured using the widely used self-reported version of the Liebowitz Social Anxiety Scale) participated in an impromptu speech task. Physiological data were collected using an E4 Empatica wearable device. Using the preprocessed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours (KNN) were used to develop models for 3 different contexts. Models were trained to differentiate (1) between baseline and socially anxious states, (2) among baseline, anticipation anxiety, and reactive anxiety states, and (3) social anxiety among individuals with social anxiety of differing severity. The predictive capability of the singular modalities was also explored in each of the 3 supervised learning experiments. The generalizability of the developed models was evaluated using 10-fold cross-validation as a performance index. Results: With modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when differentiating between baseline and socially anxious states. Models trained to differentiate among baseline, anticipation anxiety, and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Furthermore, the models developed to differentiate between social anxiety experienced by individuals with anxiety of differing severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating anxiety among individuals with social anxiety of differing severity. Conclusions: The results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors. UR - https://formative.jmir.org/2021/10/e32656 UR - http://dx.doi.org/10.2196/32656 UR - http://www.ncbi.nlm.nih.gov/pubmed/34617905 ID - info:doi/10.2196/32656 ER - TY - JOUR AU - Mouchabac, Stephane AU - Leray, Philippe AU - Adrien, Vladimir AU - Gollier-Briant, Fanny AU - Bonnot, Olivier PY - 2021/9/30 TI - Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations JO - J Med Internet Res SP - e24560 VL - 23 IS - 9 KW - suicide KW - bayesian network KW - smartphone application KW - digital psychiatry KW - artificial intelligence N2 - Background: Recently, artificial intelligence technologies and machine learning methods have offered attractive prospects to design and manage crisis response processes, especially in suicide crisis management. In other domains, most algorithms are based on big data to help diagnose and suggest rational treatment options in medicine. But data in psychiatry are related to behavior and clinical evaluation. They are more heterogeneous, less objective, and incomplete compared to other fields of medicine. Consequently, the use of psychiatric clinical data may lead to less accurate and sometimes impossible-to-build algorithms and provide inefficient digital tools. In this case, the Bayesian network (BN) might be helpful and accurate when constructed from expert knowledge. Medical Companion is a government-funded smartphone application based on repeated questions posed to the subject and algorithm-matched advice to prevent relapse of suicide attempts within several months. Objective: Our paper aims to present our development of a BN algorithm as a medical device in accordance with the American Psychiatric Association digital healthcare guidelines and to provide results from a preclinical phase. Methods: The experts are psychiatrists working in university hospitals who are experienced and trained in managing suicidal crises. As recommended when building a BN, we divided the process into 2 tasks. Task 1 is structure determination, representing the qualitative part of the BN. The factors were chosen for their known and demonstrated link with suicidal risk in the literature (clinical, behavioral, and psychometrics) and therapeutic accuracy (advice). Task 2 is parameter elicitation, with the conditional probabilities corresponding to the quantitative part. The 4-step simulation (use case) process allowed us to ensure that the advice was adapted to the clinical states of patients and the context. Results: For task 1, in this formative part, we defined clinical questions related to the mental state of the patients, and we proposed specific factors related to the questions. Subsequently, we suggested specific advice related to the patient?s state. We obtained a structure for the BN with a graphical representation of causal relations between variables. For task 2, several runs of simulations confirmed the a priori model of experts regarding mental state, refining the precision of our model. Moreover, we noticed that the advice had the same distribution as the previous state and was clinically relevant. After 2 rounds of simulation, the experts found the exact match. Conclusions: BN is an efficient methodology to build an algorithm for a digital assistant dedicated to suicidal crisis management. Digital psychiatry is an emerging field, but it needs validation and testing before being used with patients. Similar to psychotropics, any medical device requires a phase II (preclinical) trial. With this method, we propose another step to respond to the American Psychiatric Association guidelines. Trial Registration: ClinicalTrials.gov NCT03975881; https://clinicaltrials.gov/ct2/show/NCT03975881 UR - https://www.jmir.org/2021/9/e24560 UR - http://dx.doi.org/10.2196/24560 UR - http://www.ncbi.nlm.nih.gov/pubmed/34591030 ID - info:doi/10.2196/24560 ER - TY - JOUR AU - Rantanen, Teemu AU - Gluschkoff, Kia AU - Silvennoinen, Piia AU - Heponiemi, Tarja PY - 2021/9/21 TI - The Associations Between Mental Health Problems and Attitudes Toward Web-Based Health and Social Care Services: Evidence From a Finnish Population-Based Study JO - J Med Internet Res SP - e28066 VL - 23 IS - 9 KW - digital inclusion KW - digital exclusion KW - digital divide KW - mental health KW - attitudes N2 - Background: The significance of web-based health and social care services has been highlighted in recent years. There is a risk that the digitalization of public services will reinforce the digital and social exclusion of vulnerable groups, such as individuals with mental health problems. Objective: This study aims to examine the associations between mental health problems and attitudes toward web-based health and social care services in the general population. The attitudes measured include lack of interest, perceived need for face-to-face encounters, and concern for safety. The study also evaluates whether sociodemographic characteristics (age, gender, education level, and poverty) modify these associations. Methods: Cross-sectional population-based data were collected from 4495 Finnish adults in 2017. Linear regression was used to examine the main effects and interactions of poor mental health and sociodemographic characteristics on attitudes toward web-based health and social care services. Results: The results show that mental health was associated with attitudes toward web-based health and social care services. Individuals with mental health problems were especially concerned about the safety of web-based services. Poor mental health was independently associated with negative attitudes toward web-based services over the effects of sociodemographic factors. Some of the associations between poor mental health and negative attitudes toward web-based services were stronger among older people and men. With regard to sociodemographic characteristics, particularly higher age, low education, and poverty were associated with negative attitudes toward web-based health and social care services. Conclusions: Poor mental health is associated with negative attitudes toward web-based health and social care services and thus indirectly with exclusion. It seems that being older and being male both reinforce the link between poor mental health and exclusion. In supporting the digital inclusion of people with mental health problems, attention should be paid to guidance and counseling, reliability, and the user-friendliness of web-based services as well as to the prevention of poverty. In addition, it is essential to see web-based services as complementary to, and not a substitute for, face-to-face services. UR - https://www.jmir.org/2021/9/e28066 UR - http://dx.doi.org/10.2196/28066 UR - http://www.ncbi.nlm.nih.gov/pubmed/34546184 ID - info:doi/10.2196/28066 ER - TY - JOUR AU - Shpigelman, Noa Carmit AU - Tal, Amir AU - Zisman-Ilani, Yaara PY - 2021/9/21 TI - Digital Community Inclusion of Individuals With Serious Mental Illness: A National Survey to Map Digital Technology Use and Community Participation Patterns in the Digital Era JO - JMIR Ment Health SP - e28123 VL - 8 IS - 9 KW - mobile health KW - technology KW - digital community participation KW - digital community inclusion KW - serious mental illness KW - recovery N2 - Background: Despite the growing interest in developing and using mobile health (mHealth) and digital technologies in mental health, little is known about the scope and nature of virtual community inclusion. Objective: The overarching goal of this study was to understand and conceptualize virtual community inclusion of individuals with serious mental illness (SMI). Specific objectives of this study were as follows: (1) mapping the prevalence, trends, and experiences related to mHealth and digital technology use among individuals with SMI; (2) comparing patterns of technology use by individuals with and those without SMI; and (3) examining whether use of mHealth and digital technologies predicts recovery among individuals with SMI. Methods: A web-based survey of technology use and virtual participation was developed and distributed among adults with and those without SMI via social media, national email discussion lists, nonprofit organizations, and advocacy groups. Results: A total of 381 adults aged 18 years or older participated in the survey, of whom 199 (52%) identified as having a SMI. Participants with SMI reported significantly greater access to technology and significantly fewer days of face-to-face participation in community activities than those without SMI. Among participants with SMI, greater technology use was positively associated with positive emotions and significantly predicted recovery. Conclusions: This study is the first to explore, map, and conceptualize virtual community inclusion among adults with SMI. Our findings indicate a gap in the literature and research on community inclusion and participation, and emphasize the need for virtual community inclusion, particularly during the COVID-19 pandemic and its future implications. UR - https://mental.jmir.org/2021/9/e28123 UR - http://dx.doi.org/10.2196/28123 UR - http://www.ncbi.nlm.nih.gov/pubmed/34546177 ID - info:doi/10.2196/28123 ER - TY - JOUR AU - Saredakis, Dimitrios AU - Keage, AD Hannah AU - Corlis, Megan AU - Ghezzi, S. Erica AU - Loffler, Helen AU - Loetscher, Tobias PY - 2021/9/20 TI - The Effect of Reminiscence Therapy Using Virtual Reality on Apathy in Residential Aged Care: Multisite Nonrandomized Controlled Trial JO - J Med Internet Res SP - e29210 VL - 23 IS - 9 KW - reminiscence KW - head-mounted display KW - apathy KW - cognitive aging KW - dementia KW - residential facilities KW - virtual reality N2 - Background: Apathy is a frequent and underrecognized neurological disorder symptom. Reduced goal-directed behavior caused by apathy is associated with poor outcomes for older adults in residential aged care. Recommended nonpharmacological treatments include person-centered therapy using information and communication technology. Virtual reality (VR) in the form of head-mounted displays (HMDs) is a fully immersive technology that provides access to a wide range of freely available content. The use of VR as a therapy tool has demonstrated promise in the treatment of posttraumatic stress disorder and anxiety. In addition, VR has been used to improve conditions including depression, anxiety, cognitive function, and balance in older adults with memory deficits, Alzheimer disease, and Parkinson disease. Research using VR for the symptoms of apathy in older adults living in residential aged care facilities is limited. Objective: This study aims to examine whether using HMDs as a tool for reminiscence therapy improves the symptoms of apathy compared with using a laptop computer and physical items with older adults living in residential aged care. Methods: In this multisite trial, 43 participants were allocated to one of three groups: reminiscence therapy intervention using VR in the form of HMDs, reminiscence therapy using a laptop computer supplemented by physical items if required (active control), and a usual care (passive control) group. The primary outcome was apathy, and the secondary outcomes included cognition and depression. The side effects of using HMDs were also measured in the VR group. Results: Mixed model analyses revealed no significant group interaction over time in outcomes between the VR and laptop groups (estimate=?2.24, SE 1.89; t40=?1.18; P=.24). Pooled apathy scores in the two intervention groups compared with the passive control group also revealed no significant group interaction over time (estimate=?0.26, SE 1.66; t40=?0.16; P=.88). There were no significant secondary outcomes. Most participants in the VR group stated that they would prefer to watch content in VR than on a flat screen (?22=11.2; P=.004), side effects from HMD use were negligible to minimal according to the Simulator Sickness Questionnaire cutoff scores. Conclusions: Although there were no significant results in outcome measures, this study found that participants engaged in the research and enjoyed the process of reminiscing using both forms of technology. It was found that VR can be implemented in an aged care setting with correct protocols in place. Providing residents in aged care with a choice of technology may assist in increasing participation in activities. We cannot dismiss the importance of immediate effects while the therapy was in progress, and this is an avenue for future research. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12619001510134; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378564. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2020-046030 UR - https://www.jmir.org/2021/9/e29210 UR - http://dx.doi.org/10.2196/29210 UR - http://www.ncbi.nlm.nih.gov/pubmed/34542418 ID - info:doi/10.2196/29210 ER - TY - JOUR AU - Flanagan, Olivia AU - Chan, Amy AU - Roop, Partha AU - Sundram, Frederick PY - 2021/9/17 TI - Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review JO - JMIR Mhealth Uhealth SP - e24352 VL - 9 IS - 9 KW - smartphone KW - data science KW - speech patterns KW - mood disorders KW - diagnosis KW - monitoring N2 - Background: Mood disorders are commonly underrecognized and undertreated, as diagnosis is reliant on self-reporting and clinical assessments that are often not timely. Speech characteristics of those with mood disorders differs from healthy individuals. With the wide use of smartphones, and the emergence of machine learning approaches, smartphones can be used to monitor speech patterns to help the diagnosis and monitoring of mood disorders. Objective: The aim of this review is to synthesize research on using speech patterns from smartphones to diagnose and monitor mood disorders. Methods: Literature searches of major databases, Medline, PsycInfo, EMBASE, and CINAHL, initially identified 832 relevant articles using the search terms ?mood disorders?, ?smartphone?, ?voice analysis?, and their variants. Only 13 studies met inclusion criteria: use of a smartphone for capturing voice data, focus on diagnosing or monitoring a mood disorder(s), clinical populations recruited prospectively, and in the English language only. Articles were assessed by 2 reviewers, and data extracted included data type, classifiers used, methods of capture, and study results. Studies were analyzed using a narrative synthesis approach. Results: Studies showed that voice data alone had reasonable accuracy in predicting mood states and mood fluctuations based on objectively monitored speech patterns. While a fusion of different sensor modalities revealed the highest accuracy (97.4%), nearly 80% of included studies were pilot trials or feasibility studies without control groups and had small sample sizes ranging from 1 to 73 participants. Studies were also carried out over short or varying timeframes and had significant heterogeneity of methods in terms of the types of audio data captured, environmental contexts, classifiers, and measures to control for privacy and ambient noise. Conclusions: Approaches that allow smartphone-based monitoring of speech patterns in mood disorders are rapidly growing. The current body of evidence supports the value of speech patterns to monitor, classify, and predict mood states in real time. However, many challenges remain around the robustness, cost-effectiveness, and acceptability of such an approach and further work is required to build on current research and reduce heterogeneity of methodologies as well as clinical evaluation of the benefits and risks of such approaches. UR - https://mhealth.jmir.org/2021/9/e24352 UR - http://dx.doi.org/10.2196/24352 UR - http://www.ncbi.nlm.nih.gov/pubmed/34533465 ID - info:doi/10.2196/24352 ER - TY - JOUR AU - Whitton, E. Alexis AU - Hardy, Rebecca AU - Cope, Kate AU - Gieng, Chilin AU - Gow, Leanne AU - MacKinnon, Andrew AU - Gale, Nyree AU - O'Moore, Kathleen AU - Anderson, Josephine AU - Proudfoot, Judith AU - Cockayne, Nicole AU - O'Dea, Bridianne AU - Christensen, Helen AU - Newby, Maree Jill PY - 2021/9/16 TI - Mental Health Screening in General Practices as a Means for Enhancing Uptake of Digital Mental Health Interventions: Observational Cohort Study JO - J Med Internet Res SP - e28369 VL - 23 IS - 9 KW - depression KW - anxiety KW - general practice KW - screening KW - digital mental health N2 - Background: Digital mental health interventions stand to play a critical role in managing the mental health impact of the COVID-19 pandemic. Thus, enhancing their uptake is a key priority. General practitioners (GPs) are well positioned to facilitate access to digital interventions, but tools that assist GPs in identifying suitable patients are lacking. Objective: This study aims to evaluate the suitability of a web-based mental health screening and treatment recommendation tool (StepCare) for improving the identification of anxiety and depression in general practice and, subsequently, uptake of digital mental health interventions. Methods: StepCare screens patients for symptoms of depression (9-item Patient Health Questionnaire) and anxiety (7-item Generalized Anxiety Disorder scale) in the GP waiting room. It provides GPs with stepped treatment recommendations that include digital mental health interventions for patients with mild to moderate symptoms. Patients (N=5138) from 85 general practices across Australia were invited to participate in screening. Results: Screening identified depressive or anxious symptoms in 43.09% (1428/3314) of patients (one-quarter were previously unidentified or untreated). The majority (300/335, 89.6%) of previously unidentified or untreated patients had mild to moderate symptoms and were candidates for digital mental health interventions. Although less than half were prescribed a digital intervention by their GP, when a digital intervention was prescribed, more than two-thirds of patients reported using it. Conclusions: Implementing web-based mental health screening in general practices can provide important opportunities for GPs to improve the identification of symptoms of mental illness and increase patient access to digital mental health interventions. Although GPs prescribed digital interventions less frequently than in-person psychotherapy or medication, the promising rates of uptake by GP-referred patients suggest that GPs can play a critical role in championing digital interventions and maximizing the associated benefits. UR - https://www.jmir.org/2021/9/e28369 UR - http://dx.doi.org/10.2196/28369 UR - http://www.ncbi.nlm.nih.gov/pubmed/34528896 ID - info:doi/10.2196/28369 ER - TY - JOUR AU - Ricard, Joseph Benjamin AU - Hassanpour, Saeed PY - 2021/9/15 TI - Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes JO - J Med Internet Res SP - e27314 VL - 23 IS - 9 KW - social media KW - natural language processing KW - alcohol abuse KW - machine learning N2 - Background: Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth in the United States. Objective: This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System. Methods: The Bidirectional Encoder Representations From Transformers neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled data set of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey. Results: Significant associations were observed: between alcohol-hashtagged tweets and alcohol consumption (P=.01) and heavy drinking (P=.005) but not binge drinking (P=.37), self-reported at the metropolitan-micropolitan statistical area level; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); between alcohol-hashtagged tweets and the number of breweries (P<.001), wineries (P<.001), and beer, wine, and liquor stores (P<.001) but not drinking places (P=.23), per capita at the US county and county-equivalent level; and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P<.001), as well as ethanol consumed from wine (P<.001) and liquor (P=.01) sources but not beer (P=.63), at the US state level. Conclusions: Here, we present a novel natural language processing pipeline developed using Reddit?s alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes. UR - https://www.jmir.org/2021/9/e27314 UR - http://dx.doi.org/10.2196/27314 UR - http://www.ncbi.nlm.nih.gov/pubmed/34524095 ID - info:doi/10.2196/27314 ER - TY - JOUR AU - Flobak, Eivind AU - Nordby, Sektnan Emilie AU - Guribye, Frode AU - Kenter, Robin AU - Nordgreen, Tine AU - Lundervold, J. Astri PY - 2021/9/14 TI - Designing Videos With and for Adults With ADHD for an Online Intervention: Participatory Design Study and Thematic Analysis of Evaluation JO - JMIR Ment Health SP - e30292 VL - 8 IS - 9 KW - participatory design KW - ADHD KW - online intervention KW - video KW - therapeutic content KW - stigma KW - attention deficit hyperactivity disorder KW - design KW - participatory KW - intervention KW - experience KW - mental health N2 - Background: Adults with attention deficit hyperactivity disorder (ADHD) represent a heterogeneous group with both strengths and difficulties associated with the diagnosis. An online intervention attuned to their needs may improve their everyday functioning. When designing online interventions, it is important to adapt the therapeutic content to the values and needs of the target group. Objective: This paper describes and evaluates a participatory process used to produce content for an online intervention for adults with ADHD by producing video vignettes clarifying core training principles grounded in the participants' everyday experiences. Methods: We report on the qualitative data from 2 research phases: the design and evaluation of video vignettes for an online intervention. In the first phase, 12 adults with ADHD, 2 clinicians, and 2 research assistants participated in the production of video vignettes for the online intervention. In the second phase, participants (n=109) gave feedback on the videos as part of a clinical trial of the intervention. A subgroup (n=7) was interviewed in-depth regarding their experiences with the videos. The qualitative data were analyzed using thematic analysis. Results: In the first phase, the participants with ADHD contributed with experiences from challenging everyday situations. In the process, we navigated between therapeutic principles and the participants' experiential perspectives to create content relevant and consistent with the target group's values and experiences. In the second phase, we identified 3 themes related to the participants' experiences and interpretation of the video vignettes: (1) recognition of ADHD-related challenges, (2) connection with the characters and the situations, and (3) video protagonists as companions and role models for change. Conclusions: A participatory design process for designing online mental health interventions can be used to probe and balance between the therapeutic principles defined by clinicians and the participants? experiences with mental health issues in the production of therapeutic content. In our study, the inclusion of video vignettes in an online intervention enabled a contextualized and relevant presentation of everyday experiences and psychosocial factors in the life of an adult with ADHD. Trial Registration: ClinicalTrials.gov NCT04511169; https://clinicaltrials.gov/ct2/show/NCT04511169 UR - https://mental.jmir.org/2021/9/e30292 UR - http://dx.doi.org/10.2196/30292 UR - http://www.ncbi.nlm.nih.gov/pubmed/34519666 ID - info:doi/10.2196/30292 ER - TY - JOUR AU - Stephenson, Callum AU - Malakouti, Niloufar AU - Nashed, Y. Joseph AU - Salomons, Tim AU - Cook, J. Douglas AU - Milev, Roumen AU - Alavi, Nazanin PY - 2021/9/14 TI - Using Electronically Delivered Therapy and Brain Imaging to Understand Obsessive-Compulsive Disorder Pathophysiology: Protocol for a Pilot Study JO - JMIR Res Protoc SP - e30726 VL - 10 IS - 9 KW - mental health KW - obsessive-compulsive disorder KW - cognitive behavioral therapy KW - exposure ritual prevention KW - electronic KW - functional magnetic resonance imaging KW - eHealth KW - brain imaging N2 - Background: Obsessive-compulsive disorder (OCD) is a debilitating and prevalent anxiety disorder. Although the basal ganglia and frontal cortex are the brain regions that are most commonly hypothesized to be involved in OCD, the exact pathophysiology is unknown. By observing the effects of proven treatments on brain activation levels, the cause of OCD can be better understood. Currently, the gold standard treatment for OCD is cognitive behavioral therapy (CBT) with exposure and response prevention. However, this is often temporally and geographically inaccessible, time consuming, and costly. Fortunately, CBT can be effectively delivered using the internet (electronically delivered CBT [e-CBT]) because of its structured nature, thus addressing these barriers. Objective: The aims of this study are to implement an e-CBT program for OCD and to observe its effects on brain activation levels using functional magnetic resonance imaging (MRI). It is hypothesized that brain activation levels in the basal ganglia and frontal cortex will decrease after treatment. Methods: Individuals with OCD will be offered a 16-week e-CBT program with exposure and response prevention mirroring in-person CBT content and administered through a secure web-based platform. The efficacy of the treatment will be evaluated using clinically validated symptomology questionnaires at baseline, at week 8, and after treatment (week 16). Using functional MRI at baseline and after treatment, brain activation levels will be assessed in the resting state and while exposed to anxiety-inducing images (eg, dirty dishes if cleanliness is an obsession). The effects of treatment on brain activation levels and the correlation between symptom changes and activation levels will be analyzed. Results: The study received initial ethics approval in December 2020, and participant recruitment began in January 2021. Participant recruitment has been conducted through social media advertisements, physical advertisements, and physician referrals. To date, 5 participants have been recruited. Data collection is expected to conclude by January 2022, and data analysis is expected to be completed by February 2022. Conclusions: The findings from this study can further our understanding of the causation of OCD and help develop more effective treatments for this disorder. Trial Registration: ClinicalTrials.gov NCT04630197; https://clinicaltrials.gov/ct2/show/NCT04630197. International Registered Report Identifier (IRRID): PRR1-10.2196/30726 UR - https://www.researchprotocols.org/2021/9/e30726 UR - http://dx.doi.org/10.2196/30726 UR - http://www.ncbi.nlm.nih.gov/pubmed/34348889 ID - info:doi/10.2196/30726 ER - TY - JOUR AU - Miguel-Cruz, Antonio AU - Ladurner, Anna-Maria AU - Kohls-Wiebe, Megan AU - Rawani, David PY - 2021/9/14 TI - The Effects of 3D Immersion Technology (3Scape) on Mental Health in Outpatients From a Short-Term Assessment, Rehabilitation, and Treatment Program: Feasibility Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e25017 VL - 10 IS - 9 KW - technology assessment KW - mental health KW - technology for rehabilitation KW - clinical engineering KW - biomedical engineering N2 - Background: Mental health conditions are prevalent among Canadians and are a leading cause of disability. Each year, 1 in 5 Canadians experiences a mental health issue. A total of 5% of people aged ?65 years perceive their mental health as fair or poor, and 6.3% of them have mood disorders. Regarding older adults with cognitive impairments such as dementia, up to 40%-50% of them experience depression at some point. We believe that older adults can benefit significantly from information and telecommunication technologies as a strategy for improving mental health conditions such as depression and anxiety, while simultaneously improving their quality of life. 3Scape Systems Inc is an Alberta-based private company that has produced a series of specialized 3D videos designed to simulate real-life events and engage individuals living with mental health disorders and cognitive impairments such as dementia. Objective: This study aims to explore the trial design and effects of 3Scape videos on older adults? symptoms of depression and anxiety and the efficacy of this technology in improving the quality of life of patients attending the Short-Term Assessment, Rehabilitation, and Treatment Psychiatry Day Hospital program at Glenrose Rehabilitation Hospital and to provide data to estimate the parameters required to design a definitive randomized controlled trial. Methods: The trial will use a randomized controlled design comprising 15 intervention participants and 15 control group participants. The participants will be adults aged ?65 years who are cognitively intact or have minimal cognitive impairment (ie, Montreal Cognitive Assessment score ?18), and are clients of the Short-Term Assessment, Rehabilitation, and Treatment Psychiatry Day Hospital program at Glenrose Rehabilitation Hospital. This study?s primary outcome variables are related to clients? depressive and anxiety symptoms and their quality of life. The control group will receive the standard of care (ie, the Short-Term Assessment, Rehabilitation, and Treatment Psychiatry Day Hospital program at Glenrose Rehabilitation Hospital). The intervention group will receive the same standard of care as the control group and will use 3Scape Systems videos for therapeutic activities. Results: Our study is currently on hold because of the COVID-19 pandemic. The recruitment process is expected to resume by November 2021, and the primary impact analysis is expected to be conducted by February 2022. Conclusions: This study will provide valuable information such as the measurement of comparative intervention effects, perception of older adults and mental health therapists about the 3Scape Systems, the associated costs of treatment, and product costs. This will contribute to the evidence planning process, which will be crucial for the future adoption of 3Scape Systems. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): 93685907; https://www.isrctn.com/ISRCTN93685907. International Registered Report Identifier (IRRID): PRR1-10.2196/25017 UR - https://www.researchprotocols.org/2021/9/e25017 UR - http://dx.doi.org/10.2196/25017 UR - http://www.ncbi.nlm.nih.gov/pubmed/34519669 ID - info:doi/10.2196/25017 ER - TY - JOUR AU - Keyworth, Chris AU - O'Connor, Rory AU - Quinlivan, Leah AU - Armitage, J. Christopher PY - 2021/9/14 TI - Acceptability of a Brief Web-Based Theory-Based Intervention to Prevent and Reduce Self-harm: Mixed Methods Evaluation JO - J Med Internet Res SP - e28349 VL - 23 IS - 9 KW - self-harm KW - implementation intentions KW - acceptability KW - online KW - volitional help sheet KW - digital health KW - mental health N2 - Background: The volitional help sheet (VHS) for self-harm equips people with a means of responding automatically to triggers for self-harm with coping strategies. Although there is some evidence of its efficacy, improving acceptability and making the intervention available in a web-based format may be crucial to increasing effectiveness and reach. Objective: This study aims to use the Theoretical Framework of Acceptability (TFA) to explore the acceptability of the VHS, examine for whom and under what circumstances this intervention is more or less acceptable, and develop a series of recommendations for how the VHS can be used to support people in reducing repeat self-harm. Methods: We explored acceptability in two phases. First, our patient and public involvement partners evaluated the original VHS from a lived experience perspective, which was subsequently translated into a web-based format. Second, a representative sample of adults in the United Kingdom who had previously self-harmed were recruited via a YouGov survey (N=514) and were asked to rate the acceptability of the VHS based on the seven constructs of the TFA, namely, affective attitude, burden, perceived effectiveness, ethicality, intervention coherence, opportunity costs, and self-efficacy. Data were analyzed using descriptive statistics, one-tailed t tests, and binary logistic regression. A directed content analysis approach was used to analyze qualitative data. Results: Participants in the web-based survey rated the VHS as positive (affective attitude; t457=4.72; P<.001); were confident using it (self-efficacy; t457=9.54; P<.001); felt they did not have to give up any benefits, profits, or values when using it (opportunity costs; t439=?15.51; P<.001); understood it and how it worked (intervention coherence; t464=11.90; P<.001); and were confident that it would achieve its purpose (perceived effectiveness; t466=2.04; P=.04). The TFA domain burden appeared to be an important indicator of acceptability. Lower levels of perceived burden when using the VHS tool were more prevalent among younger adults aged 18-24 years (OR 3.63, 95% CI 1.50-8.78), people of White ethnic background (OR 3.02, 95% CI 1.06-8.613), and people without a long-term health condition (OR 1.53, 95% CI 1.01-2.30). Perceived modifications to further improve acceptability included improved formatting (burden), the feature to add new situations and responses or amend existing ones (ethicality), and clearer instructions and further detail about the purpose of the VHS (intervention coherence). Conclusions: Our findings show high levels of acceptability among some people who have previously self-harmed, particularly among younger adults, people of White ethnic backgrounds, and people without long-term health conditions. Future research should aim to improve acceptability among older adults, people from minority ethnic groups, and people with long-term health conditions. UR - https://www.jmir.org/2021/9/e28349 UR - http://dx.doi.org/10.2196/28349 UR - http://www.ncbi.nlm.nih.gov/pubmed/34518153 ID - info:doi/10.2196/28349 ER - TY - JOUR AU - Meyerhoff, Jonah AU - Liu, Tony AU - Kording, P. Konrad AU - Ungar, H. Lyle AU - Kaiser, M. Susan AU - Karr, J. Chris AU - Mohr, C. David PY - 2021/9/3 TI - Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study JO - J Med Internet Res SP - e22844 VL - 23 IS - 9 KW - mHealth KW - personal sensing KW - digital phenotyping KW - passive sensing KW - ecological momentary assessment KW - depression KW - anxiety KW - digital biomarkers KW - mental health assessment KW - mobile device KW - mobile phone KW - internet technology KW - psychiatric disorders N2 - Background: The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort. Objective: This study aims to evaluate whether changes in phone sensor?derived behavioral features were associated with subsequent changes in mental health symptoms. Methods: This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms. Results: A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (Time: r=?0.23, P=.02; Locations: r=?0.36, P<.001), exercise duration (r=0.39; P=.03) and use of active apps (r=?0.31; P<.001). Among the depression and anxiety groups, changes in depression were preceded by changes in GPS features for Locations (r=?0.20; P=.03) and Transitions (r=?0.21; P=.03). Depression changes were not related to subsequent sensor-derived features. The minimal symptoms group showed no significant relationships. There were no associations between sensor-based features and anxiety and minimal associations between sensor-based features and social anxiety. Conclusions: Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms. UR - https://www.jmir.org/2021/9/e22844 UR - http://dx.doi.org/10.2196/22844 UR - http://www.ncbi.nlm.nih.gov/pubmed/34477562 ID - info:doi/10.2196/22844 ER - TY - JOUR AU - Lee, Yun Dong AU - Park, Jimyung AU - Noh, Sung Jai AU - Roh, Woong Hyun AU - Ha, Ho Jae AU - Lee, Young Eun AU - Son, Joon Sang AU - Park, Woong Rae PY - 2021/9/3 TI - Characteristics of Dimensional Psychopathology in Suicidal Patients With Major Psychiatric Disorders and Its Association With the Length of Hospital Stay: Algorithm Validation Study JO - JMIR Ment Health SP - e30827 VL - 8 IS - 9 KW - suicide KW - computed phenotype KW - natural language processing KW - research domain criteria KW - electronic health record N2 - Background: Suicide has emerged as a serious concern for public health; however, only few studies have revealed the differences between major psychiatric disorders and suicide. Recent studies have attempted to quantify research domain criteria (RDoC) into numeric scores to systematically use them in computerized methods. The RDoC scores were used to reveal the characteristics of suicide and its association with major psychiatric disorders. Objective: We intended to investigate the differences in the dimensional psychopathology among hospitalized suicidal patients and the association between the dimensional psychopathology of psychiatric disorders and length of hospital stay. Methods: This retrospective study enrolled hospitalized suicidal patients diagnosed with major psychiatric disorders (depression, schizophrenia, and bipolar disorder) between January 2010 and December 2020 at a tertiary hospital in South Korea. The RDoC scores were calculated using the patients? admission notes. To measure the differences between psychiatric disorder cohorts, analysis of variance and the Cochran Q test were conducted and post hoc analysis for RDoC domains was performed with the independent two-sample t test. A linear regression model was used to analyze the association between the RDoC scores and sociodemographic features and comorbidity index. To estimate the association between the RDoC scores and length of hospital stay, multiple logistic regression models were applied to each psychiatric disorder group. Results: We retrieved 732 admissions for 571 patients (465 with depression, 73 with schizophrenia, and 33 with bipolar disorder). We found significant differences in the dimensional psychopathology according to the psychiatric disorders. The patient group with depression showed the highest negative RDoC domain scores. In the cognitive and social RDoC domains, the groups with schizophrenia and bipolar disorder scored higher than the group with depression. In the arousal RDoC domain, the depression and bipolar disorder groups scored higher than the group with schizophrenia. We identified significant associations between the RDoC scores and length of stay for the depression and bipolar disorder groups. The odds ratios (ORs) of the length of stay were increased because of the higher negative RDoC domain scores in the group with depression (OR 1.058, 95% CI 1.006-1.114) and decreased by higher arousal RDoC domain scores in the group with bipolar disorder (OR 0.537, 95% CI 0.285-0.815). Conclusions: This study showed the association between the dimensional psychopathology of major psychiatric disorders related to suicide and the length of hospital stay and identified differences in the dimensional psychopathology of major psychiatric disorders. This may provide new perspectives for understanding suicidal patients. UR - https://mental.jmir.org/2021/9/e30827 UR - http://dx.doi.org/10.2196/30827 UR - http://www.ncbi.nlm.nih.gov/pubmed/34477555 ID - info:doi/10.2196/30827 ER - TY - JOUR AU - Adanijo, Abimbola AU - McWilliams, Caoimhe AU - Wykes, Til AU - Jilka, Sagar PY - 2021/9/3 TI - Investigating Mental Health Service User Opinions on Clinical Data Sharing: Qualitative Focus Group Study JO - JMIR Ment Health SP - e30596 VL - 8 IS - 9 KW - clinical data KW - data sharing KW - mental health data KW - service users KW - focus groups KW - mental health KW - digital health KW - health records N2 - Background: Sharing patient data can help drive scientific advances and improve patient care, but service users are concerned about how their data are used. When the National Health Service proposes to scrape general practitioner records, it is very important that we understand these concerns in some depth. Objective: This study aims to investigate views of mental health service users on acceptable data sharing to provide clear recommendations for future data sharing systems. Methods: A total of 4 focus groups with 4 member-checking groups were conducted via the internet between October 2020 and March 2021, with a total of 22 service users in the United Kingdom. Thematic analysis was used to identify the themes. Results: Six main themes, with several subthemes were identified, such as the purpose of data sharing?for profit, public good, and continuation of care; discrimination through the misattribution of physical symptoms to mental health conditions (ie, diagnostic overshadowing) alongside the discrimination of individuals or groups within society (ie, institutional discrimination); safeguarding data by preserving anonymity and confidentiality, strengthening security measures, and holding organizations accountable; data accuracy and informed consent?increasing transparency about data use and choice; and incorporating service user involvement in system governance to provide insight and increase security. Conclusions: This study extends the limited research on the views and concerns of mental health service users regarding acceptable data sharing. If adopted, the recommendations should improve the confidence of service users in sharing their data. The five recommendations include screening to ensure that data sharing benefits the public, providing service users with information about how their data are shared and what for, highlighting the existing safeguarding procedures, incorporating service user involvement, and developing tailored training for health care professionals to address issues of diagnostic overshadowing and inaccurate health records. Adopting such systems would aid in data sharing for legitimate interests that will benefit patients and the National Health Service. UR - https://mental.jmir.org/2021/9/e30596 UR - http://dx.doi.org/10.2196/30596 UR - http://www.ncbi.nlm.nih.gov/pubmed/34477558 ID - info:doi/10.2196/30596 ER - TY - JOUR AU - Lau, Nancy AU - Colt, F. Susannah AU - Waldbaum, Shayna AU - O'Daffer, Alison AU - Fladeboe, Kaitlyn AU - Yi-Frazier, P. Joyce AU - McCauley, Elizabeth AU - Rosenberg, R. Abby PY - 2021/8/27 TI - Telemental Health For Youth With Chronic Illnesses: Systematic Review JO - JMIR Ment Health SP - e30098 VL - 8 IS - 8 KW - telehealth care KW - mental health KW - psychosocial issues KW - psychiatry KW - psychology KW - child KW - chronic disease N2 - Background: Children, adolescents, and young adults with chronic conditions experience difficulties coping with disease-related stressors, comorbid mental health problems, and decreased quality of life. The COVID-19 pandemic has led to a global mental health crisis, and telemental health has necessarily displaced in-person care. However, it remains unknown whether such remote interventions are feasible or efficacious. We aimed to fill this research-practice gap. Objective: In this systematic review, we present a synthesis of studies examining the feasibility and efficacy of telemental health interventions for youth aged ?25 years with chronic illnesses. Methods: PubMed, Embase, Web of Science, PsycInfo, and Cochrane Database of Systematic Reviews were searched from 2008 to 2020. We included experimental, quasiexperimental, and observational studies of telemental health interventions designed for children, adolescents, and young adults aged ?25 years with chronic illnesses, in which feasibility or efficacy outcomes were measured. Only English-language publications in peer-reviewed journals were included. We excluded studies of interventions for caregivers or health care providers, mental health problems not in the context of a chronic illness, disease and medication management, and prevention programs for healthy individuals. Results: We screened 2154 unique study records and 109 relevant full-text articles. Twelve studies met the inclusion criteria, and they represented seven unique telemental health interventions. Five of the studies included feasibility outcomes and seven included efficacy outcomes. All but two studies were pilot studies with relatively small sample sizes. Most interventions were based on cognitive behavioral therapy and problem-solving therapy. The subset of studies examining intervention feasibility concluded that telemental health interventions were appropriate, acceptable, and satisfactory to patients and their parents. Technology did not create barriers in access to care. For the subset of efficacy studies, evidence in support of the efficacy of telemental health was mixed. Significant heterogeneity in treatment type, medical diagnoses, and outcomes precluded a meta-analysis. Conclusions: The state of the science for telemental health interventions designed for youth with chronic illnesses is in a nascent stage. Early evidence supports the feasibility of telehealth-based delivery of traditional in-person interventions. Few studies have assessed efficacy, and current findings are mixed. Future research should continue to evaluate whether telemental health may serve as a sustainable alternative to in-person care after the COVID pandemic. UR - https://mental.jmir.org/2021/8/e30098 UR - http://dx.doi.org/10.2196/30098 UR - http://www.ncbi.nlm.nih.gov/pubmed/34448724 ID - info:doi/10.2196/30098 ER - TY - JOUR AU - Zhao, Zhong AU - Tang, Haiming AU - Zhang, Xiaobin AU - Qu, Xingda AU - Hu, Xinyao AU - Lu, Jianping PY - 2021/8/26 TI - Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation JO - J Med Internet Res SP - e29328 VL - 23 IS - 8 KW - autism spectrum disorder KW - eye tracking KW - face-to-face interaction KW - machine learning KW - visual fixation N2 - Background: Previous studies have shown promising results in identifying individuals with autism spectrum disorder (ASD) by applying machine learning (ML) to eye-tracking data collected while participants viewed varying images (ie, pictures, videos, and web pages). Although gaze behavior is known to differ between face-to-face interaction and image-viewing tasks, no study has investigated whether eye-tracking data from face-to-face conversations can also accurately identify individuals with ASD. Objective: The objective of this study was to examine whether eye-tracking data from face-to-face conversations could classify children with ASD and typical development (TD). We further investigated whether combining features on visual fixation and length of conversation would achieve better classification performance. Methods: Eye tracking was performed on children with ASD and TD while they were engaged in face-to-face conversations (including 4 conversational sessions) with an interviewer. By implementing forward feature selection, four ML classifiers were used to determine the maximum classification accuracy and the corresponding features: support vector machine (SVM), linear discriminant analysis, decision tree, and random forest. Results: A maximum classification accuracy of 92.31% was achieved with the SVM classifier by combining features on both visual fixation and session length. The classification accuracy of combined features was higher than that obtained using visual fixation features (maximum classification accuracy 84.62%) or session length (maximum classification accuracy 84.62%) alone. Conclusions: Eye-tracking data from face-to-face conversations could accurately classify children with ASD and TD, suggesting that ASD might be objectively screened in everyday social interactions. However, these results will need to be validated with a larger sample of individuals with ASD (varying in severity and balanced sex ratio) using data collected from different modalities (eg, eye tracking, kinematic, electroencephalogram, and neuroimaging). In addition, individuals with other clinical conditions (eg, developmental delay and attention deficit hyperactivity disorder) should be included in similar ML studies for detecting ASD. UR - https://www.jmir.org/2021/8/e29328 UR - http://dx.doi.org/10.2196/29328 UR - http://www.ncbi.nlm.nih.gov/pubmed/34435957 ID - info:doi/10.2196/29328 ER - TY - JOUR AU - Fu, Guanghui AU - Song, Changwei AU - Li, Jianqiang AU - Ma, Yue AU - Chen, Pan AU - Wang, Ruiqian AU - Yang, Xiang Bing AU - Huang, Zhisheng PY - 2021/8/26 TI - Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study JO - J Med Internet Res SP - e26119 VL - 23 IS - 8 KW - deep learning KW - distant supervision KW - mental health KW - crisis prevention N2 - Background: Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. Objective: We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. Methods: To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). Results: Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. Conclusions: In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide. UR - https://www.jmir.org/2021/8/e26119 UR - http://dx.doi.org/10.2196/26119 UR - http://www.ncbi.nlm.nih.gov/pubmed/34435964 ID - info:doi/10.2196/26119 ER - TY - JOUR AU - Keller, Maria Franziska AU - Dahmen, Alina AU - Derksen, Christina AU - Kötting, Lukas AU - Lippke, Sonia PY - 2021/8/26 TI - Psychosomatic Rehabilitation Patients and the General Population During COVID-19: Online Cross-sectional and Longitudinal Study of Digital Trainings and Rehabilitation Effects JO - JMIR Ment Health SP - e30610 VL - 8 IS - 8 KW - mental health KW - COVID-19 KW - medical rehabilitation KW - psychosomatic rehabilitation KW - internet-delivered digital trainings N2 - Background: The COVID-19 pandemic has largely affected people?s mental health and psychological well-being. Specifically, individuals with a pre-existing mental health disorder seem more impaired by lockdown measures posing as major stress factors. Medical rehabilitation treatment can help people cope with these stressors. The internet and digital apps provide a platform to contribute to regular treatment and to conduct research on this topic. Objective: Making use of internet-based assessments, this study investigated individuals from the general population and patients from medical, psychosomatic rehabilitation clinics. Levels of depression, anxiety, loneliness, and perceived stress during the COVID-19 pandemic, common COVID-19?related worries, and the intention to use digital apps were compared. Furthermore, we investigated whether participating in internet-delivered digital trainings prior to and during patients? rehabilitation stay, as well as the perceived usefulness of digital trainings, were associated with improved mental health after rehabilitation. Methods: A large-scale, online, cross-sectional study was conducted among a study sample taken from the general population (N=1812) in Germany from May 2020 to April 2021. Further, a longitudinal study was conducted making use of the internet among a second study sample of psychosomatic rehabilitation patients at two measurement time points?before (N=1719) and after (n=738) rehabilitation?between July 2020 and April 2021. Validated questionnaires and adapted items were used to assess mental health and COVID-19?related worries. Digital trainings were evaluated. Propensity score matching, multivariate analyses of covariance, an exploratory factor analysis, and hierarchical regression analyses were performed. Results: Patients from the psychosomatic rehabilitation clinics reported increased symptoms with regard to depression, anxiety, loneliness, and stress (F4,2028=183.74, P<.001, ?2p=0.27) compared to the general population. Patients perceived greater satisfaction in communication with health care professionals (F1,837=31.67, P<.001, ?2p=0.04), had lower financial worries (F1,837=38.96, P<.001, ?2p=0.04), but had higher household-related worries (F1,837=5.34, P=.02, ?2p=0.01) compared to the general population. Symptoms of depression, anxiety, loneliness, and perceived stress were lower postrehabilitation (F1,712=23.21, P<.001, ?2p=0.04) than prior to rehabilitation. Psychosomatic patients reported a higher intention to use common apps and digital trainings (F3,2021=51.41, P<.001, ?2p=0.07) than the general population. With regard to digital trainings offered prior to and during the rehabilitation stay, the perceived usefulness of digital trainings on rehabilitation goals was associated with decreased symptoms of depression (?=?.14, P<.001), anxiety (?=?.12, P<.001), loneliness (?=?.18, P<.001), and stress postrehabilitation (?=?.19, P<.001). Participation in digital group therapy for depression was associated with an overall change in depression (F1,725=4.82, P=.03, ?2p=0.01) and anxiety (F1,725=6.22, P=.01, ?2p=0.01) from pre- to postrehabilitation. Conclusions: This study validated the increased mental health constraints of psychosomatic rehabilitation patients in comparison to the general population and the effects of rehabilitation treatment. Digital rehabilitation components are promising tools that could prepare patients for their rehabilitation stay, could integrate well with face-to-face therapy during rehabilitation treatment, and could support aftercare. Trial Registration: ClinicalTrials.gov NCT04453475; https://clinicaltrials.gov/ct2/show/NCT04453475 and ClinicalTrials.gov NCT03855735; https://clinicaltrials.gov/ct2/show/NCT03855735 UR - https://mental.jmir.org/2021/8/e30610 UR - http://dx.doi.org/10.2196/30610 UR - http://www.ncbi.nlm.nih.gov/pubmed/34270444 ID - info:doi/10.2196/30610 ER - TY - JOUR AU - Morant, Nicola AU - Chilman, Natasha AU - Lloyd-Evans, Brynmor AU - Wackett, Jane AU - Johnson, Sonia PY - 2021/8/17 TI - Acceptability of Using Social Media Content in Mental Health Research: A Reflection. Comment on ?Twitter Users? Views on Mental Health Crisis Resolution Team Care Compared With Stakeholder Interviews and Focus Groups: Qualitative Analysis? JO - JMIR Ment Health SP - e32475 VL - 8 IS - 8 KW - Twitter KW - social media KW - qualitative KW - crisis resolution team KW - home treatment team KW - mental health KW - acute care KW - severe mental illness UR - https://mental.jmir.org/2021/8/e32475 UR - http://dx.doi.org/10.2196/32475 UR - http://www.ncbi.nlm.nih.gov/pubmed/34402799 ID - info:doi/10.2196/32475 ER - TY - JOUR AU - Di Matteo, Daniel AU - Fotinos, Kathryn AU - Lokuge, Sachinthya AU - Mason, Geneva AU - Sternat, Tia AU - Katzman, A. Martin AU - Rose, Jonathan PY - 2021/8/13 TI - Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study JO - J Med Internet Res SP - e28918 VL - 23 IS - 8 KW - mobile sensing KW - passive EMA KW - passive sensing KW - psychiatric assessment KW - mood and anxiety disorders KW - mobile apps KW - mhealth KW - mobile phone KW - digital health KW - digital phenotyping N2 - Background: The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals? behaviors to infer their mental states and therefore screen for anxiety disorders and depression. Objective: The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression. Methods: An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated. Results: Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression. Conclusions: We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries. UR - https://www.jmir.org/2021/8/e28918 UR - http://dx.doi.org/10.2196/28918 UR - http://www.ncbi.nlm.nih.gov/pubmed/34397386 ID - info:doi/10.2196/28918 ER - TY - JOUR AU - Manabe, Masae AU - Liew, Kongmeng AU - Yada, Shuntaro AU - Wakamiya, Shoko AU - Aramaki, Eiji PY - 2021/8/12 TI - Estimation of Psychological Distress in Japanese Youth Through Narrative Writing: Text-Based Stylometric and Sentiment Analyses JO - JMIR Form Res SP - e29500 VL - 5 IS - 8 KW - psychological distress KW - youth KW - narratives KW - natural language processing KW - Japan KW - mental health KW - stress KW - distress KW - young adult KW - teenager KW - sentiment N2 - Background: Internalizing mental illnesses associated with psychological distress are often underdetected. Text-based detection using natural language processing (NLP) methods is increasingly being used to complement conventional detection efforts. However, these approaches often rely on self-disclosure through autobiographical narratives that may not always be possible, especially in the context of the collectivistic Japanese culture. Objective: We propose the use of narrative writing as an alternative resource for mental illness detection in youth. Accordingly, in this study, we investigated the textual characteristics of narratives written by youth with psychological distress; our research focuses on the detection of psychopathological tendencies in written imaginative narratives. Methods: Using NLP tools such as stylometric measures and lexicon-based sentiment analysis, we examined short narratives from 52 Japanese youth (mean age 19.8 years, SD 3.1) obtained through crowdsourcing. Participants wrote a short narrative introduction to an imagined story before completing a questionnaire to quantify their tendencies toward psychological distress. Based on this score, participants were categorized into higher distress and lower distress groups. The written narratives were then analyzed using NLP tools and examined for between-group differences. Although outside the scope of this study, we also carried out a supplementary analysis of narratives written by adults using the same procedure. Results: Youth demonstrating higher tendencies toward psychological distress used significantly more positive (happiness-related) words, revealing differences in valence of the narrative content. No other significant differences were observed between the high and low distress groups. Conclusions: Youth with tendencies toward mental illness were found to write more positive stories that contained more happiness-related terms. These results may potentially have widespread implications on psychological distress screening on online platforms, particularly in cultures such as Japan that are not accustomed to self-disclosure. Although the mechanisms that we propose in explaining our results are speculative, we believe that this interpretation paves the way for future research in online surveillance and detection efforts. UR - https://formative.jmir.org/2021/8/e29500 UR - http://dx.doi.org/10.2196/29500 UR - http://www.ncbi.nlm.nih.gov/pubmed/34387556 ID - info:doi/10.2196/29500 ER - TY - JOUR AU - Schueller, M. Stephen AU - Neary, Martha AU - Lai, Jocelyn AU - Epstein, A. Daniel PY - 2021/8/11 TI - Understanding People?s Use of and Perspectives on Mood-Tracking Apps: Interview Study JO - JMIR Ment Health SP - e29368 VL - 8 IS - 8 KW - mental health KW - mobile apps KW - mHealth KW - emotions KW - affect KW - self-tracking N2 - Background: Supporting mental health and wellness is of increasing interest due to a growing recognition of the prevalence and burden of mental health issues. Mood is a central aspect of mental health, and several technologies, especially mobile apps, have helped people track and understand it. However, despite formative work on and dissemination of mood-tracking apps, it is not well understood how mood-tracking apps used in real-world contexts might benefit people and what people hope to gain from them. Objective: To address this gap, the purpose of this study was to understand motivations for and experiences in using mood-tracking apps from people who used them in real-world contexts. Methods: We interviewed 22 participants who had used mood-tracking apps using a semistructured interview and card sorting task. The interview focused on their experiences using a mood-tracking app. We then conducted a card sorting task using screenshots of various data entry and data review features from mood-tracking apps. We used thematic analysis to identify themes around why people use mood-tracking apps, what they found useful about them, and where people felt these apps fell short. Results: Users of mood-tracking apps were primarily motivated by negative life events or shifts in their own mental health that prompted them to engage in tracking and improve their situation. In general, participants felt that using a mood-tracking app facilitated self-awareness and helped them to look back on a previous emotion or mood experience to understand what was happening. Interestingly, some users reported less inclination to document their negative mood states and preferred to document their positive moods. There was a range of preferences for personalization and simplicity of tracking. Overall, users also liked features in which their previous tracked emotions and moods were visualized in figures or calendar form to understand trends. One gap in available mood-tracking apps was the lack of app-facilitated recommendations or suggestions for how to interpret their own data or improve their mood. Conclusions: Although people find various features of mood-tracking apps helpful, the way people use mood-tracking apps, such as avoiding entering negative moods, tracking infrequently, or wanting support to understand or change their moods, demonstrate opportunities for improvement. Understanding why and how people are using current technologies can provide insights to guide future designs and implementations. UR - https://mental.jmir.org/2021/8/e29368 UR - http://dx.doi.org/10.2196/29368 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383678 ID - info:doi/10.2196/29368 ER - TY - JOUR AU - Nickels, Stefanie AU - Edwards, D. Matthew AU - Poole, F. Sarah AU - Winter, Dale AU - Gronsbell, Jessica AU - Rozenkrants, Bella AU - Miller, P. David AU - Fleck, Mathias AU - McLean, Alan AU - Peterson, Bret AU - Chen, Yuanwei AU - Hwang, Alan AU - Rust-Smith, David AU - Brant, Arthur AU - Campbell, Andrew AU - Chen, Chen AU - Walter, Collin AU - Arean, A. Patricia AU - Hsin, Honor AU - Myers, J. Lance AU - Marks Jr, J. William AU - Mega, L. Jessica AU - Schlosser, A. Danielle AU - Conrad, J. Andrew AU - Califf, M. Robert AU - Fromer, Menachem PY - 2021/8/10 TI - Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling JO - JMIR Ment Health SP - e27589 VL - 8 IS - 8 KW - mental health KW - mobile sensing KW - mobile phone KW - mHealth KW - depression KW - location KW - GPS KW - app usage KW - voice diaries KW - adherence KW - engagement KW - mobility KW - sleep KW - physical activity KW - digital phenotyping KW - user-centered design N2 - Background: Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. Objective: This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. Methods: A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). Results: A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary?derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079). Conclusions: This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness. UR - https://mental.jmir.org/2021/8/e27589 UR - http://dx.doi.org/10.2196/27589 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383685 ID - info:doi/10.2196/27589 ER - TY - JOUR AU - Aný?, Ji?í AU - Bak?tein, Eduard AU - Dally, Andrea AU - Koleni?, Marián AU - Hlinka, Jaroslav AU - Hartmannová, Tereza AU - Urbanová, Kate?ina AU - Correll, U. Christoph AU - Novák, Daniel AU - ?paniel, Filip PY - 2021/8/9 TI - Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study JO - JMIR Ment Health SP - e26348 VL - 8 IS - 8 KW - bipolar disorder KW - symptom monitoring KW - ecological mood assessment KW - relapse detection KW - mobile application KW - mobile phone N2 - Background: Self-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick, and scalable digital self-report measures that can also detect relapse are still not available for clinical care. Objective: In this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire?a 10-item, mobile app?based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]). Methods: The content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach ? was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS?15 and MADRS?15) from a nonrelapse (interepisode) state (YMRS<15 and MADRS<15) using a logistic mixed-effects model. Results: A total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (P<.001; bootstrap). Similarly, ASERT mania items were highly associated with YMRS total scores (P<.001; bootstrap). Furthermore, the logistic mixed-effects regression model for scale-based relapse detection showed high detection accuracy in a repeated holdout validation for both depression (accuracy=85%; sensitivity=69.9%; specificity=88.4%; area under the receiver operating characteristic curve=0.880) and mania (accuracy=87.5%; sensitivity=64.9%; specificity=89.9%; area under the receiver operating characteristic curve=0.844). Conclusions: The ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario. UR - https://mental.jmir.org/2021/8/e26348 UR - http://dx.doi.org/10.2196/26348 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383689 ID - info:doi/10.2196/26348 ER - TY - JOUR AU - Wongkoblap, Akkapon AU - Vadillo, A. Miguel AU - Curcin, Vasa PY - 2021/8/6 TI - Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study JO - JMIR Ment Health SP - e19824 VL - 8 IS - 8 KW - depression KW - mental health KW - Twitter KW - social media KW - deep learning KW - anaphora resolution KW - multiple-instance learning KW - depression markers N2 - Background: Mental health problems are widely recognized as a major public health challenge worldwide. This concern highlights the need to develop effective tools for detecting mental health disorders in the population. Social networks are a promising source of data wherein patients publish rich personal information that can be mined to extract valuable psychological cues; however, these data come with their own set of challenges, such as the need to disambiguate between statements about oneself and third parties. Traditionally, natural language processing techniques for social media have looked at text classifiers and user classification models separately, hence presenting a challenge for researchers who want to combine text sentiment and user sentiment analysis. Objective: The objective of this study is to develop a predictive model that can detect users with depression from Twitter posts and instantly identify textual content associated with mental health topics. The model can also address the problem of anaphoric resolution and highlight anaphoric interpretations. Methods: We retrieved the data set from Twitter by using a regular expression or stream of real-time tweets comprising 3682 users, of which 1983 self-declared their depression and 1699 declared no depression. Two multiple instance learning models were developed?one with and one without an anaphoric resolution encoder?to identify users with depression and highlight posts related to the mental health of the author. Several previously published models were applied to our data set, and their performance was compared with that of our models. Results: The maximum accuracy, F1 score, and area under the curve of our anaphoric resolution model were 92%, 92%, and 90%, respectively. The model outperformed alternative predictive models, which ranged from classical machine learning models to deep learning models. Conclusions: Our model with anaphoric resolution shows promising results when compared with other predictive models and provides valuable insights into textual content that is relevant to the mental health of the tweeter. UR - https://mental.jmir.org/2021/8/e19824 UR - http://dx.doi.org/10.2196/19824 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383688 ID - info:doi/10.2196/19824 ER - TY - JOUR AU - Mata-Greve, Felicia AU - Johnson, Morgan AU - Pullmann, D. Michael AU - Friedman, C. Emily AU - Griffith Fillipo, Isabell AU - Comtois, A. Katherine AU - Arean, Patricia PY - 2021/8/5 TI - Mental Health and the Perceived Usability of Digital Mental Health Tools Among Essential Workers and People Unemployed Due to COVID-19: Cross-sectional Survey Study JO - JMIR Ment Health SP - e28360 VL - 8 IS - 8 KW - digital health KW - COVID-19 KW - essential worker KW - unemployed KW - usability KW - user burden KW - mental health KW - e-mental health KW - survey KW - distress N2 - Background: COVID-19 has created serious mental health consequences for essential workers or people who have become unemployed as a result of the pandemic. Digital mental health tools have the potential to address this problem in a timely and efficient manner. Objective: The purpose of this study was to document the extent of digital mental health tool (DMHT) use by essential workers and those unemployed due to COVID-19, including asking participants to rate the usability and user burden of the DMHT they used most to cope. We also explored which aspects and features of DMHTs were seen as necessary for managing stress during a pandemic by having participants design their own ideal DMHT. Methods: A total of 2000 people were recruited from an online research community (Prolific) to complete a one-time survey about mental health symptoms, DMHT use, and preferred digital mental health features. Results: The final sample included 1987 US residents that identified as either an essential worker or someone who was unemployed due to COVID-19. Almost three-quarters of the sample (1479/1987, 74.8%) reported clinically significant emotional distress. Only 14.2% (277/1957) of the sample used a DMHT to cope with stress associated with COVID-19. Of those who used DMHTs to cope with COVID-19, meditation apps were the most common (119/261, 45.6%). Usability was broadly in the acceptable range, although participants unemployed due to COVID-19 were less likely to report user burden with DMHTs than essential workers (t198.1=?3.89, P<.001). Individuals with emotional distress reported higher financial burden for their DMHT than nondistressed individuals (t69.0=?3.21, P=.01). When the sample was provided the option to build their own DMHT, the most desired features were a combination of mindfulness/meditation (1271/1987, 64.0%), information or education (1254/1987, 63.1%), distraction tools (1170/1987, 58.9%), symptom tracking for mood and sleep (1160/1987, 58.4%), link to mental health resources (1140/1987, 57.4%), and positive psychology (1131/1986, 56.9%). Subgroups by employment, distress, and previous DMHT use status had varied preferences. Of those who did not use a DMHT to cope with COVID-19, most indicated that they did not consider looking for such a tool to help with coping (1179/1710, 68.9%). Conclusions: Despite the potential need for DMHTs, this study found that the use of such tools remains similar to prepandemic levels. This study also found that regardless of the level of distress or even past experience using an app to cope with COVID-19, it is possible to develop a COVID-19 coping app that would appeal to a majority of essential workers and unemployed persons. UR - https://mental.jmir.org/2021/8/e28360 UR - http://dx.doi.org/10.2196/28360 UR - http://www.ncbi.nlm.nih.gov/pubmed/34081592 ID - info:doi/10.2196/28360 ER - TY - JOUR AU - Zhang, Yuezhou AU - Folarin, A. Amos AU - Sun, Shaoxiong AU - Cummins, Nicholas AU - Ranjan, Yatharth AU - Rashid, Zulqarnain AU - Conde, Pauline AU - Stewart, Callum AU - Laiou, Petroula AU - Matcham, Faith AU - Oetzmann, Carolin AU - Lamers, Femke AU - Siddi, Sara AU - Simblett, Sara AU - Rintala, Aki AU - Mohr, C. David AU - Myin-Germeys, Inez AU - Wykes, Til AU - Haro, Maria Josep AU - Penninx, H. Brenda W. J. AU - Narayan, A. Vaibhav AU - Annas, Peter AU - Hotopf, Matthew AU - Dobson, B. Richard J. AU - PY - 2021/7/30 TI - Predicting Depressive Symptom Severity Through Individuals? Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study JO - JMIR Mhealth Uhealth SP - e29840 VL - 9 IS - 7 KW - mental health KW - depression KW - digital biomarkers KW - digital phenotyping KW - digital health KW - Bluetooth KW - hierarchical Bayesian model KW - mobile health KW - mHealth KW - monitoring N2 - Background: Research in mental health has found associations between depression and individuals? behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. Objective: This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). Methods: The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals? life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. Results: A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547). Conclusions: Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals? behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings. UR - https://mhealth.jmir.org/2021/7/e29840 UR - http://dx.doi.org/10.2196/29840 UR - http://www.ncbi.nlm.nih.gov/pubmed/34328441 ID - info:doi/10.2196/29840 ER - TY - JOUR AU - Lederman, Reeva AU - D'Alfonso, Simon PY - 2021/7/20 TI - The Digital Therapeutic Alliance: Prospects and Considerations JO - JMIR Ment Health SP - e31385 VL - 8 IS - 7 KW - therapeutic alliance KW - digital therapeutic alliance KW - digital mental health KW - mental health apps KW - teletherapy KW - chatbots UR - https://mental.jmir.org/2021/7/e31385 UR - http://dx.doi.org/10.2196/31385 UR - http://www.ncbi.nlm.nih.gov/pubmed/34283035 ID - info:doi/10.2196/31385 ER - TY - JOUR AU - Philipp-Muller, Emile Aaron AU - Reshetukha, Taras AU - Vazquez, Gustavo AU - Milev, Roumen AU - Armstrong, Dawn AU - Jagayat, Jasleen AU - Alavi, Nazanin PY - 2021/7/20 TI - Combining Ketamine and Internet-Based Cognitive Behavioral Therapy for the Treatment of Posttraumatic Stress Disorder: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e30334 VL - 10 IS - 7 KW - mental health KW - PTSD KW - psychotherapy KW - cognitive behavioral therapy KW - online KW - internet KW - electronic KW - virtual KW - mental health care KW - ketamine N2 - Background: Over one third of patients with posttraumatic stress disorder (PTSD) do not respond to current interventions. Ketamine presents a potential treatment option; however, its effects are temporary. Administering ketamine alongside psychotherapy is one potential means of prolonging its effects; however, only a few studies have investigated this treatment method to date, and none have tested ketamine with internet-based or electronically delivered cognitive behavioral therapy (e-CBT). Objective: This open-label randomized controlled trial aims to assess the efficacy of a combined treatment method of subanesthetic intravenous ketamine and e-CBT for treating patients with PTSD. Methods: In total, 20 patients with refractory PTSD recruited from a community clinic will be randomly assigned to either an experimental group (n=10), receiving a combination of ketamine and therapist-administered e-CBT over 14 weeks, or a waitlist control group (n=10), receiving the experimental treatment after 14 weeks. Both groups will be assessed for the symptoms of PTSD and comorbid disorders before treatment, at two midway points, and at the end of the experiment. Results: PTSD symptoms of participants in the experimental group are expected to improve significantly more than those of participants in the waitlist control group (P=.05) with a large effect size (?2=0.14). Conclusions: This is the first study to assess the relationship between e-CBT and ketamine and their combined ability to treat refractory PTSD. If successful, this study will open web-based, asynchronous therapeutic options for patients with PTSD and will provide new insights into the functional role of glutamate in trauma-related disorders as well as in learning, memory, and fear extinction. Trial Registration: ClinicalTrials.gov NCT04771767; https://clinicaltrials.gov/ct2/show/NCT04771767. International Registered Report Identifier (IRRID): PRR1-10.2196/30334 UR - https://www.researchprotocols.org/2021/7/e30334 UR - http://dx.doi.org/10.2196/30334 UR - http://www.ncbi.nlm.nih.gov/pubmed/34092549 ID - info:doi/10.2196/30334 ER - TY - JOUR AU - Opoku Asare, Kennedy AU - Terhorst, Yannik AU - Vega, Julio AU - Peltonen, Ella AU - Lagerspetz, Eemil AU - Ferreira, Denzil PY - 2021/7/12 TI - Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study JO - JMIR Mhealth Uhealth SP - e26540 VL - 9 IS - 7 KW - mHealth KW - mental health KW - mobile phone KW - digital biomarkers KW - digital phenotyping KW - smartphone KW - supervised machine learning KW - depression N2 - Background: Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression. Objective: The objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression. Methods: Smartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days (SD 17.90; range 8-86). We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression. Results: Of the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants? age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score <10), while 231 (16.81%) were depressed scores (PHQ-8 score ?10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status?normalized entropy and depression (r=0.14, P<.001). LMM demonstrates an intraclass correlation of 0.7584 and a significant positive association between screen status?normalized entropy and depression (?=.48, P=.03). The best ML algorithms achieved the following metrics: precision, 85.55%-92.51%; recall, 92.19%-95.56%; F1, 88.73%-94.00%; area under the curve receiver operating characteristic, 94.69%-99.06%; Cohen ?, 86.61%-92.90%; and accuracy, 96.44%-98.14%. Including age group and gender as predictors improved the ML performances. Screen and internet connectivity features were the most influential in predicting depression. Conclusions: Our findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors? data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring. UR - https://mhealth.jmir.org/2021/7/e26540 UR - http://dx.doi.org/10.2196/26540 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255713 ID - info:doi/10.2196/26540 ER - TY - JOUR AU - Sukunesan, Suku AU - Huynh, Minh AU - Sharp, Gemma PY - 2021/7/9 TI - Examining the Pro-Eating Disorders Community on Twitter Via the Hashtag #proana: Statistical Modeling Approach JO - JMIR Ment Health SP - e24340 VL - 8 IS - 7 KW - Twitter KW - infodemiology KW - eating disorders KW - proana KW - thinspo KW - hashtags KW - transient KW - cybersectarianism N2 - Background: There is increasing concern around communities that promote eating disorders (Pro-ED) on social media sites through messages and images that encourage dangerous weight control behaviors. These communities share group identity formed through interactions between members and can involve the exchange of ?tips,? restrictive dieting plans, extreme exercise plans, and motivating imagery of thin bodies. Unlike Instagram, Facebook, or Tumblr, the absence of adequate policy to moderate Pro-ED content on Twitter presents a unique space for the Pro-ED community to freely communicate. While recent research has identified terms, themes, and common lexicon used within the Pro-ED online community, very few have been longitudinal. It is important to focus upon the engagement of Pro-ED online communities over time to further understand how members interact and stay connected, which is currently lacking. Objective: The purpose of this study was to explore beyond the common messages of Pro-ED on Twitter to understand how Pro-ED communities get traction over time by using the hashtag considered to symbolize the Pro-ED movement, #proana. Our focus was to collect longitudinal data to gain a further understanding of the engagement of Pro-ED communities on Twitter. Methods: Descriptive statistics were used to identify the preferred tweeting style of Twitter users (either as mentioning another user in a tweet or without) as well as their most frequently used hashtag, in addition to #proana. A series of Mann Whitney U tests were then conducted to compare preferred posting style across number of followed, followers, tweets, and favorites. This was followed by linear models using a forward step-wise approach that were applied for Pro-ED Twitter users to examine the factors associated with their number of followers. Results: This study reviewed 11,620 Pro-ED Twitter accounts that posted using the hashtag #proana between September 2015 and July 2018. These profiles then underwent a 2-step screening of inclusion and exclusion criteria to reach the final sample of 967 profiles. Over 90% (10,484/11,620) of the profiles were found to have less than 6 tweets within the 34-month period. Most of the users were identified as preferring a mentioning style of tweeting (718/967, 74.3%) over not mentioning (248/967, 25.7%). Further, #proana and #thinspo were used interchangeably to propagate shared themes, and there was a reciprocal effect between followers and the followed. Conclusions: Our analysis showed that the number of accounts followed and number of Pro-ED tweets posted were significant predictors for the number of followers a user has, compared to likes. Our results could potentially be useful to social media platforms to understand which features could help or otherwise curtail the spread of ED messages and activity. Our findings also show that Pro-ED communities are transient in nature, engaging in superficial discussion threads but resilient, emulating cybersectarian behavior. UR - https://mental.jmir.org/2021/7/e24340 UR - http://dx.doi.org/10.2196/24340 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255707 ID - info:doi/10.2196/24340 ER - TY - JOUR AU - Chilman, Natasha AU - Morant, Nicola AU - Lloyd-Evans, Brynmor AU - Wackett, Jane AU - Johnson, Sonia PY - 2021/6/29 TI - Twitter Users? Views on Mental Health Crisis Resolution Team Care Compared With Stakeholder Interviews and Focus Groups: Qualitative Analysis JO - JMIR Ment Health SP - e25742 VL - 8 IS - 6 KW - Twitter KW - social media KW - qualitative KW - crisis resolution team KW - home treatment team KW - mental health KW - acute care KW - severe mental illness N2 - Background: Analyzing Twitter posts enables rapid access to how issues and experiences are socially shared and constructed among communities of health service users and providers, in ways that traditional qualitative methods may not. Objective: To enrich the understanding of mental health crisis care in the United Kingdom, this study explores views on crisis resolution teams (CRTs) expressed on Twitter. We aim to identify the similarities and differences among views expressed on Twitter compared with interviews and focus groups. Methods: We used Twitter?s advanced search function to retrieve public tweets on CRTs. A thematic analysis was conducted on 500 randomly selected tweets. The principles of refutational synthesis were applied to compare themes with those identified in a multicenter qualitative interview study. Results: The most popular hashtag identified was #CrisisTeamFail, where posts were principally related to poor quality of care and access, particularly for people given a personality disorder diagnosis. Posts about CRTs giving unhelpful self-management advice were common, as were tweets about resource strains on mental health services. This was not identified in the research interviews. Although each source yielded unique themes, there were some overlaps with themes identified via interviews and focus groups, including the importance of rapid access to care. Views expressed on Twitter were generally more critical than those obtained via face-to-face methods. Conclusions: Traditional qualitative studies may underrepresent the views of more critical stakeholders by collecting data from participants accessed via mental health services. Research on social media content can complement traditional or face-to-face methods and ensure that a broad spectrum of viewpoints can inform service development and policy. UR - https://mental.jmir.org/2021/6/e25742 UR - http://dx.doi.org/10.2196/25742 UR - http://www.ncbi.nlm.nih.gov/pubmed/34185017 ID - info:doi/10.2196/25742 ER - TY - JOUR AU - Gould, E. Christine AU - Carlson, Chalise AU - Ma, Flora AU - Forman-Hoffman, Valerie AU - Ranta, Kristian AU - Kuhn, Eric PY - 2021/6/29 TI - Effects of Mobile App?Based Intervention for Depression in Middle-Aged and Older Adults: Mixed Methods Feasibility Study JO - JMIR Form Res SP - e25808 VL - 5 IS - 6 KW - aging KW - depression KW - digital health KW - digital therapeutics KW - mHealth KW - mobile phone N2 - Background: Digital mental health interventions may help middle-aged and older adults with depression overcome barriers to accessing traditional care, but few studies have investigated their use in this population. Objective: This pilot study examines the feasibility, acceptability, and potential efficacy of the Meru Health Program, an 8-week mobile app?delivered intervention. Methods: A total of 20 community-dwelling middle-aged and older adults (age: mean 61.7 years, SD 11.3) with elevated depressive symptoms participated in a single-arm pilot study investigating the Meru Health Program, an app-delivered intervention supported by remote therapists. The program primarily uses mindfulness and cognitive behavioral skills to target depressive symptoms. A semistructured interview was completed at the baseline to establish current psychiatric diagnoses. Depressive symptoms were measured using the Patient Health Questionnaire and Patient-Reported Outcomes Measurement Information System (PROMIS) depression measures. Anxiety symptoms were measured using the Generalized Anxiety Disorder Scale and the PROMIS Anxiety measure. User experience and acceptability were examined through surveys and qualitative interviews. Results: In total, 90% (18/20) of the participants completed the program, with 75% (15/20) completing at least 7 of the 8 introductory weekly lessons. On average, participants completed 60 minutes of practice and exchanged 5 messages with their therapists every week. The app was rated as helpful by 89% (17/19) participants. Significant decreases in depressive (P=.03) and anxiety symptom measures (P=.01) were found; 45% (9/20) of participants showed clinically significant improvement in either depressive symptoms or anxiety symptoms. Conclusions: The findings suggest that the commercially available Meru Health Program may be feasible, acceptable, and potentially beneficial to middle-aged and older adults. Although larger controlled trials are needed to demonstrate efficacy, these findings suggest that digital health interventions may benefit adults of all ages. UR - https://formative.jmir.org/2021/6/e25808 UR - http://dx.doi.org/10.2196/25808 UR - http://www.ncbi.nlm.nih.gov/pubmed/34185000 ID - info:doi/10.2196/25808 ER - TY - JOUR AU - Kim, Jina AU - Lee, Daeun AU - Park, Eunil PY - 2021/6/17 TI - Authors? Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on ?Machine Learning for Mental Health in Social Media: Bibliometric Study? JO - J Med Internet Res SP - e29549 VL - 23 IS - 6 KW - bibliometric analysis KW - machine learning KW - mental health KW - social media KW - bibliometrics UR - https://www.jmir.org/2021/6/e29549 UR - http://dx.doi.org/10.2196/29549 UR - http://www.ncbi.nlm.nih.gov/pubmed/34137721 ID - info:doi/10.2196/29549 ER - TY - JOUR AU - Resnik, Philip AU - De Choudhury, Munmun AU - Musacchio Schafer, Katherine AU - Coppersmith, Glen PY - 2021/6/17 TI - Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on ?Machine Learning for Mental Health in Social Media: Bibliometric Study? JO - J Med Internet Res SP - e28990 VL - 23 IS - 6 KW - bibliometric analysis KW - machine learning KW - mental health KW - social media KW - bibliometrics UR - https://www.jmir.org/2021/6/e28990 UR - http://dx.doi.org/10.2196/28990 UR - http://www.ncbi.nlm.nih.gov/pubmed/34137722 ID - info:doi/10.2196/28990 ER - TY - JOUR AU - Williams, Anne AU - Fossey, Ellie AU - Farhall, John AU - Foley, Fiona AU - Thomas, Neil PY - 2021/6/16 TI - Impact of Jointly Using an e?Mental Health Resource (Self-Management And Recovery Technology) on Interactions Between Service Users Experiencing Severe Mental Illness and Community Mental Health Workers: Grounded Theory Study JO - JMIR Ment Health SP - e25998 VL - 8 IS - 6 KW - digital mental health KW - tablet computers KW - therapeutic relationship KW - recovery narratives KW - lived experience video KW - personal recovery KW - schizophrenia KW - mobile phone N2 - Background: e?Mental health resources are increasingly available for people who experience severe mental illness, including those who are users of community mental health services. However, the potential for service users (SUs) living with severe mental illness to use e?mental health resources together with their community mental health workers (MHWs) has received little attention. Objective: This study aims to identify how jointly using an interactive website called Self-Management And Recovery Technology (SMART) in a community mental health context influenced therapeutic processes and interactions between SUs and MHWs from their perspective. Methods: We conducted a qualitative study using a constructivist grounded theory methodology. Data were collected through individual semistructured interviews with 37 SUs and 15 MHWs who used the SMART website together for 2 to 6 months. Data analysis involved iterative phases of coding, constant comparison, memo writing, theoretical sampling, and consultation with stakeholders to support the study?s credibility. Results: A substantive grounded theory, discovering ways to keep life on track, was developed, which portrays a shared discovery process arising from the SU-worker-SMART website interactions. The discovery process included choosing to use the website, revealing SUs? experiences, exploring these experiences, and gaining new perspectives on how SUs did and could keep their lives on track. SUs and MHWs perceived that their three-way interactions were enjoyable, beneficial, and recovery focused when using the website together. They experienced the shared discovery process as relationship building?their interactions when using the website together were more engaging and equal. Conclusions: Jointly using an e?mental health resource elicited recovery-oriented interactions and processes between SUs and MHWs that strengthened their therapeutic relationship in real-world community mental health services. Further work to develop and integrate this novel use of e?mental health in community mental health practice is warranted. UR - https://mental.jmir.org/2021/6/e25998 UR - http://dx.doi.org/10.2196/25998 UR - http://www.ncbi.nlm.nih.gov/pubmed/34132647 ID - info:doi/10.2196/25998 ER - TY - JOUR AU - Alavi, Nazanin AU - Stephenson, Callum AU - Yang, Megan AU - Kumar, Anchan AU - Shao, Yijia AU - Miller, Shadé AU - Yee, S. Caitlin AU - Stefatos, Anthi AU - Gholamzadehmir, Maedeh AU - Abbaspour, Zara AU - Jagayat, Jasleen AU - Shirazi, Amirhossein AU - Omrani, Mohsen AU - Patel, Archana AU - Patel, Charmy AU - Groll, Dianne PY - 2021/6/16 TI - Feasibility and Efficacy of Delivering Cognitive Behavioral Therapy Through an Online Psychotherapy Tool for Depression: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e27489 VL - 10 IS - 6 KW - mental health KW - depression KW - psychotherapy KW - cognitive behavioral therapy KW - online KW - internet KW - electronic KW - virtual KW - mental health care N2 - Background: Major depressive disorder (MDD) is a prevalent and debilitating mental health disorder. Among different therapeutic approaches (eg, medication and psychotherapy), psychotherapy in the form of cognitive behavioral therapy (CBT) is considered the gold standard treatment for MDD. However, although efficacious, CBT is not readily accessible to many patients in need because of hurdles such as stigma, long wait times, high cost, the large time commitment for health care providers, and cultural or geographic barriers. Electronically delivered cognitive behavioral therapy (e-CBT) can effectively address many of these accessibility barriers. Objective: This study aims to investigate the efficacy and feasibility of implementing an e-CBT program compared with in-person treatment for MDD. It is hypothesized that the e-CBT program will offer results comparable with those of the in-person treatment program, regarding symptom reduction and quality of life improvement. Methods: This nonrandomized controlled trial intervention will provide e-CBT for MDD through the Online Psychotherapy Tool, a secure, cloud-based, digital mental health platform. Participants (aged 18-65 years) will be offered 12 weekly sessions of an e-CBT program tailored to MDD to address their depressive symptoms. Participants (n=55) will complete predesigned modules and homework assignments while receiving personalized feedback and interacting with a therapist through the platform. Using clinically validated symptomology questionnaires, the efficacy of the e-CBT program will be compared with that of a group (n=55) receiving in-person CBT. Questionnaires will be completed at baseline, at week 6 and week 12, and at a 6-month follow-up. Focus groups will be conducted to investigate personal, cultural, and social factors impacting the accessibility and feasibility of implementing a web-based psychotherapy tool from a patient and care provider perspective. Inclusion criteria include diagnosis of MDD, competence to consent to participate, ability to speak and read English, and consistent and reliable access to the internet. Exclusion criteria include active psychosis, acute mania, severe alcohol or substance use disorder, and active suicidal or homicidal ideation. Results: Ethics approval was obtained in January 2019, and recruitment of participants began in June 2019. Recruitment has been conducted via social media, web-based communities, and physician referrals. To date, 52 participants have been recruited to the e-CBT group, and 48 patients have been recruited to the in-person CBT group. Data collection is expected to be completed by March 2021, and analyses are expected to be completed by June 2021, as linear regression (for continuous outcomes) and binomial regression analysis (for categorical outcomes) are still being conducted. Conclusions: The results of this study can provide valuable information for the development of more accessible and scalable mental health interventions with increased care capacity for MDD, without sacrificing the quality of care. Trial Registration: ClinicalTrials.gov NCT04478058; http://clinicaltrials.gov/ct2/show/NCT04478058 International Registered Report Identifier (IRRID): DERR1-10.2196/27489 UR - https://www.researchprotocols.org/2021/6/e27489 UR - http://dx.doi.org/10.2196/27489 UR - http://www.ncbi.nlm.nih.gov/pubmed/33990076 ID - info:doi/10.2196/27489 ER - TY - JOUR AU - Dohnt, C. Henriette AU - Dowling, J. Mitchell AU - Davenport, A. Tracey AU - Lee, Grace AU - Cross, P. Shane AU - Scott, M. Elizabeth AU - Song, C. Yun Ju AU - Hamilton, Blake AU - Hockey, J. Samuel AU - Rohleder, Cathrin AU - LaMonica, M. Haley AU - Hickie, B. Ian PY - 2021/6/14 TI - Supporting Clinicians to Use Technology to Deliver Highly Personalized and Measurement-Based Mental Health Care to Young People: Protocol for an Evaluation Study JO - JMIR Res Protoc SP - e24697 VL - 10 IS - 6 KW - mental health service delivery KW - youth mental health KW - model of care coordination KW - transdiagnostic KW - health information technology KW - education KW - training KW - adoption into clinical practice KW - Kirkpatrick evaluation N2 - Background: Australia?s mental health care system has long been fragmented and under-resourced, with services falling well short of demand. In response, the World Economic Forum has recently called for the rapid deployment of smarter, digitally enhanced health services to facilitate effective care coordination and address issues of demand. The University of Sydney?s Brain and Mind Centre (BMC) has developed an innovative digital health solution that incorporates 2 components: a highly personalized and measurement-based (data-driven) model of youth mental health care and a health information technology (HIT) registered on the Australian Register of Therapeutic Goods. Importantly, research into implementation of such solutions considers education and training of clinicians to be essential to adoption and optimization of use in standard clinical practice. The BMC?s Youth Mental Health and Technology Program has subsequently developed a comprehensive education and training program to accompany implementation of the digital health solution. Objective: This paper describes the protocol for an evaluation study to assess the effectiveness of the education and training program on the adoption and optimization of use of the digital health solution in service delivery. It also describes the proposed tools to assess the impact of training on knowledge and skills of mental health clinicians. Methods: The evaluation study will use the Kirkpatrick Evaluation Model as a framework with 4 levels of analysis: Reaction (to education and training), Learning (knowledge acquired), Behavior (practice change), and Results (client outcomes). Quantitative and qualitative data will be collected using a variety of tools, including evaluation forms, pre- and postknowledge questionnaires, skill development and behavior change scales, as well as a real-time clinical practice audit. Results: This project is funded by philanthropic funding from Future Generation Global. Ethics approval has been granted via Sydney Local Health District?s Human Research Ethics Committee. At the time of this publication, clinicians and their services were being recruited to this study. The first results are expected to be submitted for publication in 2021. Conclusions: The education and training program teaches clinicians the necessary knowledge and skills to assess, monitor, and manage complex needs; mood and psychotic syndromes; and trajectories of youth mental ill-health using a HIT that facilitates a highly personalized and measurement-based model of care. The digital health solution may therefore guide clinicians to help young people recover low functioning associated with subthreshold diagnostic presentations and prevent progression to more serious mental ill-health. International Registered Report Identifier (IRRID): PRR1-10.2196/24697 UR - https://www.researchprotocols.org/2021/6/e24697 UR - http://dx.doi.org/10.2196/24697 UR - http://www.ncbi.nlm.nih.gov/pubmed/34125074 ID - info:doi/10.2196/24697 ER - TY - JOUR AU - Gooding, Piers AU - Kariotis, Timothy PY - 2021/6/10 TI - Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review JO - JMIR Ment Health SP - e24668 VL - 8 IS - 6 KW - digital psychiatry KW - digital mental health KW - machine learning KW - algorithmic technology KW - data-driven technology KW - artificial intelligence KW - ethics KW - regulation KW - law KW - mobile phone N2 - Background: Uncertainty surrounds the ethical and legal implications of algorithmic and data-driven technologies in the mental health context, including technologies characterized as artificial intelligence, machine learning, deep learning, and other forms of automation. Objective: This study aims to survey empirical scholarly literature on the application of algorithmic and data-driven technologies in mental health initiatives to identify the legal and ethical issues that have been raised. Methods: We searched for peer-reviewed empirical studies on the application of algorithmic technologies in mental health care in the Scopus, Embase, and Association for Computing Machinery databases. A total of 1078 relevant peer-reviewed applied studies were identified, which were narrowed to 132 empirical research papers for review based on selection criteria. Conventional content analysis was undertaken to address our aims, and this was supplemented by a keyword-in-context analysis. Results: We grouped the findings into the following five categories of technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), and miscellaneous (17/132, 12.9%). Most initiatives were directed toward detection and diagnosis. Most papers discussed privacy, mainly in terms of respecting the privacy of research participants. There was relatively little discussion of privacy in this context. A small number of studies discussed ethics directly (10/132, 7.6%) and indirectly (10/132, 7.6%). Legal issues were not substantively discussed in any studies, although some legal issues were discussed in passing (7/132, 5.3%), such as the rights of user subjects and privacy law compliance. Conclusions: Ethical and legal issues tend to not be explicitly addressed in empirical studies on algorithmic and data-driven technologies in mental health initiatives. Scholars may have considered ethical or legal matters at the ethics committee or institutional review board stage. If so, this consideration seldom appears in published materials in applied research in any detail. The form itself of peer-reviewed papers that detail applied research in this field may well preclude a substantial focus on ethics and law. Regardless, we identified several concerns, including the near-complete lack of involvement of mental health service users, the scant consideration of algorithmic accountability, and the potential for overmedicalization and techno-solutionism. Most papers were published in the computer science field at the pilot or exploratory stages. Thus, these technologies could be appropriated into practice in rarely acknowledged ways, with serious legal and ethical implications. UR - https://mental.jmir.org/2021/6/e24668 UR - http://dx.doi.org/10.2196/24668 UR - http://www.ncbi.nlm.nih.gov/pubmed/34110297 ID - info:doi/10.2196/24668 ER - TY - JOUR AU - Blitz, Rogério AU - Storck, Michael AU - Baune, T. Bernhard AU - Dugas, Martin AU - Opel, Nils PY - 2021/6/9 TI - Design and Implementation of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage, and Export in Psychiatric Clinical Routine: Feasibility Study JO - JMIR Ment Health SP - e26681 VL - 8 IS - 6 KW - medical informatics KW - digital mental health KW - digital data collection KW - psychiatry KW - single-source metadata architecture transformation KW - mental health KW - design KW - implementation KW - feasibility KW - informatics KW - infrastructure KW - data N2 - Background: Empirically driven personalized diagnostic applications and treatment stratification is widely perceived as a major hallmark in psychiatry. However, databased personalized decision making requires standardized data acquisition and data access, which are currently absent in psychiatric clinical routine. Objective: Here, we describe the informatics infrastructure implemented at the psychiatric Münster University Hospital, which allows standardized acquisition, transfer, storage, and export of clinical data for future real-time predictive modelling in psychiatric routine. Methods: We designed and implemented a technical architecture that includes an extension of the electronic health record (EHR) via scalable standardized data collection and data transfer between EHRs and research databases, thus allowing the pooling of EHRs and research data in a unified database and technical solutions for the visual presentation of collected data and analyses results in the EHR. The Single-source Metadata ARchitecture Transformation (SMA:T) was used as the software architecture. SMA:T is an extension of the EHR system and uses module-driven engineering to generate standardized applications and interfaces. The operational data model was used as the standard. Standardized data were entered on iPads via the Mobile Patient Survey (MoPat) and the web application Mopat@home, and the standardized transmission, processing, display, and export of data were realized via SMA:T. Results: The technical feasibility of the informatics infrastructure was demonstrated in the course of this study. We created 19 standardized documentation forms with 241 items. For 317 patients, 6451 instances were automatically transferred to the EHR system without errors. Moreover, 96,323 instances were automatically transferred from the EHR system to the research database for further analyses. Conclusions: In this study, we present the successful implementation of the informatics infrastructure enabling standardized data acquisition and data access for future real-time predictive modelling in clinical routine in psychiatry. The technical solution presented here might guide similar initiatives at other sites and thus help to pave the way toward future application of predictive models in psychiatric clinical routine. UR - https://mental.jmir.org/2021/6/e26681 UR - http://dx.doi.org/10.2196/26681 UR - http://www.ncbi.nlm.nih.gov/pubmed/34106072 ID - info:doi/10.2196/26681 ER - TY - JOUR AU - Yuan, Jing AU - Libon, J. David AU - Karjadi, Cody AU - Ang, A. Alvin F. AU - Devine, Sherral AU - Auerbach, H. Sanford AU - Au, Rhoda AU - Lin, Honghuang PY - 2021/6/8 TI - Association Between the Digital Clock Drawing Test and Neuropsychological Test Performance: Large Community-Based Prospective Cohort (Framingham Heart Study) JO - J Med Internet Res SP - e27407 VL - 23 IS - 6 KW - clock drawing test KW - neuropsychological test KW - cognition KW - technology KW - digital assessment KW - mild cognitive impairment KW - association KW - neurology KW - Framingham Heart Study N2 - Background: The Clock Drawing Test (CDT) has been widely used in clinic for cognitive assessment. Recently, a digital Clock Drawing Text (dCDT) that is able to capture the entire sequence of clock drawing behaviors was introduced. While a variety of domain-specific features can be derived from the dCDT, it has not yet been evaluated in a large community-based population whether the features derived from the dCDT correlate with cognitive function. Objective: We aimed to investigate the association between dCDT features and cognitive performance across multiple domains. Methods: Participants from the Framingham Heart Study, a large community-based cohort with longitudinal cognitive surveillance, who did not have dementia were included. Participants were administered both the dCDT and a standard protocol of neuropsychological tests that measured a wide range of cognitive functions. A total of 105 features were derived from the dCDT, and their associations with 18 neuropsychological tests were assessed with linear regression models adjusted for age and sex. Associations between a composite score from dCDT features were also assessed for associations with each neuropsychological test and cognitive status (clinically diagnosed mild cognitive impairment compared to normal cognition). Results: The study included 2062 participants (age: mean 62, SD 13 years, 51.6% women), among whom 36 were diagnosed with mild cognitive impairment. Each neuropsychological test was associated with an average of 50 dCDT features. The composite scores derived from dCDT features were significantly associated with both neuropsychological tests and mild cognitive impairment. Conclusions: The dCDT can potentially be used as a tool for cognitive assessment in large community-based populations. UR - https://www.jmir.org/2021/6/e27407 UR - http://dx.doi.org/10.2196/27407 UR - http://www.ncbi.nlm.nih.gov/pubmed/34100766 ID - info:doi/10.2196/27407 ER - TY - JOUR AU - Sanatkar, Samineh AU - Heinsch, Milena AU - Baldwin, Andrew Peter AU - Rubin, Mark AU - Geddes, Jenny AU - Hunt, Sally AU - Baker, L. Amanda AU - Woodcock, Kathryn AU - Lewin, J. Terry AU - Brady, Kathleen AU - Deady, Mark AU - Thornton, Louise AU - Teesson, Maree AU - Kay-Lambkin, Frances PY - 2021/6/7 TI - Factors Predicting Trial Engagement, Treatment Satisfaction, and Health-Related Quality of Life During a Web-Based Treatment and Social Networking Trial for Binge Drinking and Depression in Young Adults: Secondary Analysis of a Randomized Controlled Trial JO - JMIR Ment Health SP - e23986 VL - 8 IS - 6 KW - digital mental health KW - personality KW - negative affect KW - study engagement KW - life quality N2 - Background: Mental health and alcohol use problems are among the most common causes of disease burden in young Australians, frequently co-occur (comorbidity), and lead to significant lifetime burden. However, comorbidities remain significantly underdetected and undertreated in health settings. Digital mental health tools designed to identify at-risk individuals, encourage help-seeking, or deliver treatment for comorbidity have the potential to address this service gap. However, despite a strong body of evidence that digital mental health programs provide an effective treatment option for a range of mental health and alcohol use problems in young adults, research shows that uptake rates can be low. Thus, it is important to understand the factors that influence treatment satisfaction and quality-of-life outcomes for young adults who access e?mental health interventions for comorbidity. Objective: In this study, we seek to understand the factors that influence treatment satisfaction and quality-of-life outcomes for young adults who access e?mental health interventions for comorbid alcohol and mood disorders. The aim is to determine the importance of personality (ie, Big Five personality traits and intervention attitudes), affective factors (ie, depression, anxiety, and stress levels), and baseline alcohol consumption in predicting intervention trial engagement at sign-up, satisfaction with the online tool, and quality of life at the end of the iTreAD (Internet Treatment for Alcohol and Depression) trial. Methods: Australian adults (N=411) aged between 18 and 30 years who screened positive for depression and alcohol use problems signed up for the iTreAD project between August 2014 and October 2015. During registration, participants provided information about their personality, current affective state, alcohol use, treatment expectations, and basic demographic information. Subsequent follow-up surveys were used to gauge the ongoing trial engagement. The last follow-up questionnaire, completed at 64 weeks, assessed participants? satisfaction with web-based treatment and quality-of-life outcomes. Results: Multiple linear regression analyses were used to assess the relative influence of predictor variables on trial engagement, treatment satisfaction, and quality-of-life outcomes. The analyses revealed that the overall predictive effects of personality and affective factors were 20% or lower. Neuroticism constituted a unique predictor of engagement with the iTreAD study in that neuroticism facilitated the return of web-based self-assessments during the study. The return of incentivized follow-up assessments predicted treatment satisfaction, and state-based depression predicted variance in quality-of-life reports at study completion. Conclusions: Our findings suggest that traditional predictors of engagement observed in face-to-face research may not be easily transferable to digital health interventions, particularly those aimed at comorbid mental health concerns and alcohol misuse among young adults. More research is needed to identify what determines engagement in this population to optimally design and execute digital intervention studies with multiple treatment aims. Trial Registration: Australian New Zealand Clinical Trials Registry (ACTRN): 12614000310662; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=365137&isReview=true. International Registered Report Identifier (IRRID): RR2-10.1186/s12889-015-2365-2 UR - https://mental.jmir.org/2021/6/e23986 UR - http://dx.doi.org/10.2196/23986 UR - http://www.ncbi.nlm.nih.gov/pubmed/34096873 ID - info:doi/10.2196/23986 ER - TY - JOUR AU - Galatzer-Levy, Isaac AU - Abbas, Anzar AU - Ries, Anja AU - Homan, Stephanie AU - Sels, Laura AU - Koesmahargyo, Vidya AU - Yadav, Vijay AU - Colla, Michael AU - Scheerer, Hanne AU - Vetter, Stefan AU - Seifritz, Erich AU - Scholz, Urte AU - Kleim, Birgit PY - 2021/6/3 TI - Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric Inpatients: Proof-of-Concept Study JO - J Med Internet Res SP - e25199 VL - 23 IS - 6 KW - digital phenotyping KW - digital biomarkers KW - digital health KW - depression KW - suicidal ideation KW - digital markers KW - digital KW - facial KW - suicide KW - suicide risk KW - visual KW - auditory N2 - Background: Multiple symptoms of suicide risk have been assessed based on visual and auditory information, including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. Objective: We aimed to examine measurements extracted from video interviews using open-source deep learning algorithms to quantify facial, vocal, and movement behaviors in relation to suicide risk severity in recently admitted patients following a suicide attempt. Methods: We utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open-source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide risk with the Beck Scale for Suicide Ideation controlling for age and sex, using multiple linear regression. Results: Suicide severity was associated with multiple visual and auditory markers, including speech prevalence (?=?0.68, P=.02, r2=0.40), overall expressivity (?=?0.46, P=.10, r2=0.27), and head movement measured as head pitch variability (?=?1.24, P=.006, r2=0.48) and head yaw variability (?=?0.54, P=.06, r2=0.32). Conclusions: Digital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and linear associations with the severity of suicidal ideation. UR - https://www.jmir.org/2021/6/e25199 UR - http://dx.doi.org/10.2196/25199 UR - http://www.ncbi.nlm.nih.gov/pubmed/34081022 ID - info:doi/10.2196/25199 ER - TY - JOUR AU - Juarascio, Adrienne AU - Srivastava, Paakhi AU - Presseller, Emily AU - Clark, Kelsey AU - Manasse, Stephanie AU - Forman, Evan PY - 2021/5/31 TI - A Clinician-Controlled Just-in-time Adaptive Intervention System (CBT+) Designed to Promote Acquisition and Utilization of Cognitive Behavioral Therapy Skills in Bulimia Nervosa: Development and Preliminary Evaluation Study JO - JMIR Form Res SP - e18261 VL - 5 IS - 5 KW - eating disorders KW - telemedicine KW - mobile phone KW - smartphone KW - technology KW - cognitive behavioral therapy N2 - Background: Cognitive behavioral therapy (CBT) for bulimia nervosa (BN) is most effective when patients demonstrate adequate skill utilization (ie, the frequency with which a patient practices or uses therapeutic skills) and skill acquisition (ie, the ability to successfully perform a skill learned in treatment). However, rates of utilization and acquisition of key treatment skills (eg, regular eating, urge management skills, and mood management skills) by the end of the treatment are frequently low; as a result, outcomes from CBT for BN are affected. Just-in-time adaptive interventions (JITAIs) may improve skill acquisition and utilization by delivering real-time interventions during algorithm-identified opportunities for skill practice. Objective: In this manuscript, we describe a newly developed JITAI system called CBT+ that is designed to facilitate the acquisition and utilization of CBT for BN treatment skills when used as a treatment augmentation. We also present feasibility, acceptability, and preliminary outcomes data from a small proof-of-concept pilot trial (n=5 patients and n=3 clinicians) designed to identify opportunities for iterative development of CBT+ ahead of a larger ongoing randomized controlled trial. Methods: A total of 5 individuals with BN received 16 sessions of outpatient CBT for BN while using the CBT+ app. Data were collected from patients and clinicians to evaluate the feasibility (eg, app use and user adherence), acceptability (eg, qualitative patient and clinician feedback, including usefulness ratings of CBT+ on a 6-point Likert scale ranging from 1=extremely useless to 6=extremely useful), and preliminary outcomes (eg, improvements in skill utilization and acquisition and BN symptoms) of the CBT+ system. Results: Patients reported that CBT+ was a relatively low burden (eg, quick and easy-to-use self-monitoring interface), and adherence to in-app self-monitoring was high (mean entries per day 3.13, SD 1.03). JITAIs were perceived as useful by both patients (median rating 5/6) and clinicians (median rating 5/6) for encouraging the use of CBT skills. Large improvements in CBT skills and clinically significant reductions in BN symptoms were observed post treatment. Although preliminary findings indicated that the CBT+ system was acceptable to most patients and clinicians, the overall study dropout was relatively high (ie, 2/5, 40% patients), which could indicate some moderate concerns regarding feasibility. Conclusions: CBT+, the first-ever JITAI system designed to facilitate the acquisition and utilization of CBT for BN treatment skills when used as a treatment augmentation, was shown to be feasible and acceptable. The results indicate that the CBT+ system should be subjected to more rigorous evaluations with larger samples and should be considered for wider implementation if found effective. Areas for iterative improvement of the CBT+ system ahead of a randomized controlled trial are also discussed. UR - https://formative.jmir.org/2021/5/e18261 UR - http://dx.doi.org/10.2196/18261 UR - http://www.ncbi.nlm.nih.gov/pubmed/34057416 ID - info:doi/10.2196/18261 ER - TY - JOUR AU - Morgiève, Margot AU - Mesdjian, Pierre AU - Las Vergnas, Olivier AU - Bury, Patrick AU - Demassiet, Vincent AU - Roelandt, Jean-Luc AU - Sebbane, Déborah PY - 2021/5/27 TI - Social Representations of e-Mental Health Among the Actors of the Health Care System: Free-Association Study JO - JMIR Ment Health SP - e25708 VL - 8 IS - 5 KW - e-mental health KW - social representations KW - free association task KW - psychiatry KW - mental health KW - mental health service users KW - technology KW - digital health N2 - Background: Electronic mental (e-mental) health offers an opportunity to overcome many challenges such as cost, accessibility, and the stigma associated with mental health, and most people with lived experiences of mental problems are in favor of using applications and websites to manage their mental health problems. However, the use of these new technologies remains weak in the area of mental health and psychiatry. Objective: This study aimed to characterize the social representations associated with e-mental health by all actors to implement new technologies in the best possible way in the health system. Methods: A free-association task method was used. The data were subjected to a lexicometric analysis to qualify and quantify words by analyzing their statistical distribution, using the ALCESTE method with the IRaMuTeQ software. Results: In order of frequency, the terms most frequently used to describe e-mental health in the whole corpus are: ?care? (n=21), ?internet? (n=21), ?computing? (n=15), ?health? (n=14), ?information? (n=13), ?patient? (n=12), and ?tool? (n=12). The corpus of text is divided into 2 themes, with technological and computing terms on one side and medical and public health terms on the other. The largest family is focused on ?care,? ?advances,? ?research,? ?life,? ?quality,? and ?well-being,? which was significantly associated with users. The nursing group used very medical terms such as ?treatment,? ?diagnosis,? ?psychiatry?,? and ?patient? to define e-mental health. Conclusions: This study shows that there is a gap between the representations of users on e-mental health as a tool for improving their quality of life and those of health professionals (except nurses) that are more focused on the technological potential of these digital care tools. Developers, designers, clinicians, and users must be aware of the social representation of e-mental health conditions uses and intention of use. This understanding of everyone?s stakes will make it possible to redirect the development of tools to adapt them as much as possible to the needs and expectations of the actors of the mental health system. UR - https://mental.jmir.org/2021/5/e25708 UR - http://dx.doi.org/10.2196/25708 UR - http://www.ncbi.nlm.nih.gov/pubmed/34042591 ID - info:doi/10.2196/25708 ER - TY - JOUR AU - Charles, Ashleigh AU - Nixdorf, Rebecca AU - Ibrahim, Nashwa AU - Meir, Gai Lion AU - Mpango, S. Richard AU - Ngakongwa, Fileuka AU - Nudds, Hannah AU - Pathare, Soumitra AU - Ryan, Grace AU - Repper, Julie AU - Wharrad, Heather AU - Wolf, Philip AU - Slade, Mike AU - Mahlke, Candelaria PY - 2021/5/27 TI - Initial Training for Mental Health Peer Support Workers: Systematized Review and International Delphi Consultation JO - JMIR Ment Health SP - e25528 VL - 8 IS - 5 KW - peer support work KW - peer support worker training KW - Delphi consultation KW - mental health KW - mobile phone N2 - Background: Initial training is essential for the mental health peer support worker (PSW) role. Training needs to incorporate recent advances in digital peer support and the increase of peer support work roles internationally. There is a lack of evidence on training topics that are important for initial peer support work training and on which training topics can be provided on the internet. Objective: The objective of this study is to establish consensus levels about the content of initial training for mental health PSWs and the extent to which each identified topic can be delivered over the internet. Methods: A systematized review was conducted to identify a preliminary list of training topics from existing training manuals. Three rounds of Delphi consultation were then conducted to establish the importance and web-based deliverability of each topic. In round 1, participants were asked to rate the training topics for importance, and the topic list was refined. In rounds 2 and 3, participants were asked to rate each topic for importance and the extent to which they could be delivered over the internet. Results: The systematized review identified 32 training manuals from 14 countries: Argentina, Australia, Brazil, Canada, Chile, Germany, Ireland, the Netherlands, Norway, Scotland, Sweden, Uganda, the United Kingdom, and the United States. These were synthesized to develop a preliminary list of 18 topics. The Delphi consultation involved 110 participants (49 PSWs, 36 managers, and 25 researchers) from 21 countries (14 high-income, 5 middle-income, and 2 low-income countries). After the Delphi consultation (round 1: n=110; round 2: n=89; and round 3: n=82), 20 training topics (18 universal and 2 context-specific) were identified. There was a strong consensus about the importance of five topics: lived experience as an asset, ethics, PSW well-being, and PSW role focus on recovery and communication, with a moderate consensus for all other topics apart from the knowledge of mental health. There was no clear pattern of differences among PSW, manager, and researcher ratings of importance or between responses from participants in countries with different resource levels. All training topics were identified with a strong consensus as being deliverable through blended web-based and face-to-face training (rating 1) or fully deliverable on the internet with moderation (rating 2), with none identified as only deliverable through face-to-face teaching (rating 0) or deliverable fully on the web as a stand-alone course without moderation (rating 3). Conclusions: The 20 training topics identified can be recommended for inclusion in the curriculum of initial training programs for PSWs. Further research on web-based delivery of initial training is needed to understand the role of web-based moderation and whether web-based training better prepares recipients to deliver web-based peer support. UR - https://mental.jmir.org/2021/5/e25528 UR - http://dx.doi.org/10.2196/25528 UR - http://www.ncbi.nlm.nih.gov/pubmed/34042603 ID - info:doi/10.2196/25528 ER - TY - JOUR AU - Slade, Mike AU - Rennick-Egglestone, Stefan AU - Llewellyn-Beardsley, Joy AU - Yeo, Caroline AU - Roe, James AU - Bailey, Sylvia AU - Smith, Andrew Roger AU - Booth, Susie AU - Harrison, Julian AU - Bhogal, Adaresh AU - Penas Morán, Patricia AU - Hui, Ada AU - Quadri, Dania AU - Robinson, Clare AU - Smuk, Melanie AU - Farkas, Marianne AU - Davidson, Larry AU - van der Krieke, Lian AU - Slade, Emily AU - Bond, Carmel AU - Nicholson, Joe AU - Grundy, Andrew AU - Charles, Ashleigh AU - Hare-Duke, Laurie AU - Pollock, Kristian AU - Ng, Fiona PY - 2021/5/27 TI - Recorded Mental Health Recovery Narratives as a Resource for People Affected by Mental Health Problems: Development of the Narrative Experiences Online (NEON) Intervention JO - JMIR Form Res SP - e24417 VL - 5 IS - 5 KW - narratives KW - storytelling KW - intervention development KW - mental health KW - online intervention KW - patient involvement KW - narrative medicine KW - internet KW - recovery KW - mobile phone N2 - Background: The internet enables sharing of narratives about health concerns on a substantial scale, and some digital health narratives have been integrated into digital health interventions. Narratives describing recovery from health problems are a focus of research, including those presented in recorded (eg, invariant) form. No clinical trial has been conducted on a web-based intervention providing access to a collection of Recorded Recovery Narratives (RRNs). Objective: This study presents knowledge produced through the development of the Narrative Experiences Online (NEON) Intervention, a web-based intervention incorporating the algorithmic recommendation of RRNs. Methods: Knowledge was gathered through knowledge integration (KI) activities. KI1 synthesized previous studies to produce the NEON Impact Model describing how accessing RRNs produces health-related outcomes. KI2 developed curation principles for the NEON Collection of RRNs through consultation with the NEON Lived Experience Advisory Panel and the curation of a preliminary collection. KI3 identified harm minimization strategies for the NEON Intervention through consultation with the NEON International Advisory Board and Lived Experience Advisory Panel. The NEON Intervention was finalized through 2 research studies (RS). In RS1, mental health service users (N=40) rated the immediate impact of randomly presented narratives to validate narrative feedback questions used to inform the recommendation algorithm. In RS2, mental health service users (n=25) were interviewed about their immediate response to a prototype of the NEON Intervention and trial procedures and then were interviewed again after 1 month of use. The usability and acceptability of the prototype and trial procedures were evaluated and refinements were made. Results: KI1 produced the NEON Impact Model, which identifies moderators (recipient and context), mechanisms of connection (reflection, comparison, learning, and empathy), processes (identification of change from narrative structure or content and internalization of observed change), and outcomes (helpful and unhelpful). KI2 identified 22 curation principles, including a mission to build a large, heterogeneous collection to maximize opportunities for connection. KI3 identified seven harm minimization strategies, including content warnings, proactive and reactive blocking of narratives, and providing resources for the self-management of emotional distress. RS1 found variation in the impact of narratives on different participants, indicating that participant-level feedback on individual narratives is needed to inform a recommender system. The order of presentation did not predict narrative feedback. RS2 identified amendments to web-based trial procedures and the NEON Intervention. Participants accessed some narratives multiple times, use reduced over the 4-week period, and narrative feedback was provided for 31.8% (105/330) of narrative accesses. Conclusions: RRNs can be integrated into web-based interventions. Evaluating the NEON Intervention in a clinical trial is feasible. The mixed methods design for developing the NEON Intervention can guide its extension to other clinical populations, the design of other web-based mental health interventions, and the development of narrative-based interventions in mental health. UR - https://formative.jmir.org/2021/5/e24417 UR - http://dx.doi.org/10.2196/24417 UR - http://www.ncbi.nlm.nih.gov/pubmed/34042595 ID - info:doi/10.2196/24417 ER - TY - JOUR AU - Alavi, Nazanin AU - Stephenson, Callum AU - Yang, Megan AU - Shirazi, Amirhossein AU - Shao, Yijia AU - Kumar, Anchan AU - Yee, S. Caitlin AU - Miller, Shadé AU - Stefatos, Anthi AU - Gholamzadehmir, Maedeh AU - Abbaspour, Zara AU - Patel, Archana AU - Patel, Charmy AU - Reshetukha, Taras AU - Omrani, Mohsen AU - Groll, Dianne PY - 2021/5/27 TI - Determining the Efficacy of Electronic Cognitive Behavioral Therapy for Generalized Anxiety Disorder Compared to Pharmaceutical Interventions: Protocol for a Quasi-Experimental Study JO - JMIR Res Protoc SP - e27772 VL - 10 IS - 5 KW - eHealth KW - mental health KW - anxiety KW - generalized anxiety disorder KW - cognitive behavioral therapy KW - psychotherapy KW - online KW - internet KW - electronic KW - virtual KW - mental health care N2 - Background: Generalized anxiety disorder (GAD) is an extremely prevalent and debilitating mental health disorder. Currently, the gold standard treatment for GAD is cognitive behavioral therapy (CBT) and/or pharmacotherapy. The most common medications used to treat GAD are selective serotonin reuptake inhibitors and selective norepinephrine reuptake inhibitors. While CBT is the gold standard treatment for GAD, it is costly, time-consuming, and often inaccessible. Fortunately, the electronic delivery of CBT (e-CBT) has emerged as a promising solution to address these barriers. e-CBT has shown to offer comparable results to in-person CBT while improving accessibility for patients and time efficiency for clinicians. Objective: This study aims to investigate the treatment efficacy of e-CBT compared to and in conjunction with pharmacotherapy for GAD. Methods: This study will use a quasi-experimental design to allow patients the freedom to choose which treatment modality they would like to receive. Participants with a diagnosis of GAD will be enrolled in 1 of 3 possible treatment arms: (1) e-CBT, (2) medication, or (3) a combination of e-CBT and medication. The e-CBT program will include a 12-week psychotherapy program delivered through the Online Psychotherapy Tool?a secure, cloud-based, digital mental health platform. The treatment efficacy of e-CBT will be compared with that of medication alone and medication in combination with e-CBT. Results: The study received ethics approval in April 2019 and participant recruitment began in June 2019. Participant recruitment has been conducted through social media advertisements, physical advertisements, and physician referrals. To date, 146 participants (e-CBT: n=53; medication: n=49; combination: n=44) have been recruited. Data collection is expected to conclude by June 2021, and data analysis is expected to be completed by October 2021. Linear regression (for continuous outcomes) and binomial regression (for categorical outcomes) analysis will be conducted using interpretive qualitative methods. Conclusions: If either the efficacy of e-CBT is shown to be comparable to that of medication or the effects of both treatments are augmented when used in tandem, these findings could have major implications on the mental health care system. e-CBT is a more accessible and affordable treatment that could increase mental health care capacity 4-fold if proven viable. Trial Registration: ClinicalTrials.gov NCT04478526; https://clinicaltrials.gov/ct2/show/NCT04478526 International Registered Report Identifier (IRRID): DERR1-10.2196/27772 UR - https://www.researchprotocols.org/2021/5/e27772 UR - http://dx.doi.org/10.2196/27772 UR - http://www.ncbi.nlm.nih.gov/pubmed/33857917 ID - info:doi/10.2196/27772 ER - TY - JOUR AU - Venkatesan, Aarathi AU - Krymis, Holly AU - Scharff, Jenny AU - Waber, Art PY - 2021/5/25 TI - Changes in Perceived Stress Following a 10-Week Digital Mindfulness-Based Stress Reduction Program: Retrospective Study JO - JMIR Form Res SP - e25078 VL - 5 IS - 5 KW - perceived stress KW - health coaching KW - digital mental health intervention KW - digital therapeutics KW - mobile phone N2 - Background: As the need for effective scalable interventions for mental health conditions such as depression, anxiety, and stress has grown, the digital delivery of mindfulness-based stress reduction (MBSR) has gained interest as a promising intervention in this domain. Objective: This study aims to evaluate the changes in perceived stress following a 10-week digital MBSR program that combined an app-based digital program with weekly one-on-one remote sessions with a health coach. Methods: This study used a retrospective, observational design. A total of 229 participants with moderate-to-high perceived stress scores as assessed by the Perceived Stress Scale (PSS)-10 enrolled in the 10-week Vida Health MBSR program. The program included weekly remote sessions with a certified health coach and digital content based on concepts fundamental to mindfulness practice. The PSS-10 was used to evaluate perceived stress. Of the 229 participants, 131 (57.2%) were considered program completers and provided at least one follow-up PSS-10. A secondary analysis examined the changes in stress scores at 6 months. This analysis was restricted to participants who had been enrolled in the program for at least 6 months (n=121). To account for random and fixed effects, linear mixed effects modeling was used to assess changes in stress scores over time. An intention-to-treat approach was used to evaluate the changes in perceived stress across the entire study cohort, including those who were lost to follow-up. In addition, a reliable change index was computed to evaluate the changes in scores from the baseline. Results: The findings revealed a significant positive association between program time and stress reduction (B=?0.365; P<.001) at 12 weeks. We observed an average reduction in stress scores of 3.17 points (95% CI ?3.93 to ?2.44) by program week 6 and 4.86 points (95% CI ?5.86 to ?3.85) by program week 12. Overall, 83.2% (109/131) of participants showed a reduction in stress scores by week 12, with 40.5% (53/131) of participants showing reliable improvement at 12 weeks and 47.8% (56/131) of participants showing a shift to a lower stress level category (ie, moderate-to-low stress). The intention-to-treat analysis revealed a significant, although attenuated, reduction in stress scores at 12 weeks (B=?0.23; P<.001). Participants who completed more lessons had an increased likelihood of moving down at least one stress level category (odds ratio 1.512, 95% CI 1.056 to 2.166; P=.02). In assessing medium-term outcomes, among participants who had completed at least 6 months in the program, 48.8% (59/121) of members provided a 6-month assessment. We observed a significant reduction in stress scores at 6 months (t58=10.24; P<.001), with 61% (36/59) of participants showing reliable improvement. Conclusions: The findings of this retrospective, observational study suggest that a blended, digital mindfulness-based intervention may support program uptake and meaningful, sustained reduction in stress outcomes. UR - https://formative.jmir.org/2021/5/e25078 UR - http://dx.doi.org/10.2196/25078 UR - http://www.ncbi.nlm.nih.gov/pubmed/34032571 ID - info:doi/10.2196/25078 ER - TY - JOUR AU - Lai, Kaisheng AU - Li, Dan AU - Peng, Huijuan AU - Zhao, Jingyuan AU - He, Lingnan PY - 2021/5/13 TI - Assessing Suicide Reporting in Top Newspaper Social Media Accounts in China: Content Analysis Study JO - JMIR Ment Health SP - e26654 VL - 8 IS - 5 KW - suicide KW - suicide reporting KW - mainstream publishers KW - social media KW - WHO guidelines N2 - Background: Previous studies have shown that suicide reporting in mainstream media has a significant impact on suicidal behaviors (eg, irresponsible suicide reporting can trigger imitative suicide). Traditional mainstream media are increasingly using social media platforms to disseminate information on public-related topics, including health. However, there is little empirical research on how mainstream media portrays suicide on social media platforms and the quality of their coverage. Objective: This study aims to explore the characteristics and quality of suicide reporting by mainstream publishers via social media in China. Methods: Via the application programming interface of the social media accounts of the top 10 Chinese mainstream publishers (eg, People?s Daily and Beijing News), we obtained 2366 social media posts reporting suicide. This study conducted content analysis to demonstrate the characteristics and quality of the suicide reporting. According to the World Health Organization (WHO) guidelines, we assessed the quality of suicide reporting by indicators of harmful information and helpful information. Results: Chinese mainstream publishers most frequently reported on suicides stated to be associated with conflict on their social media (eg, 24.47% [446/1823] of family conflicts and 16.18% [295/1823] of emotional frustration). Compared with the suicides of youth (730/1446, 50.48%) and urban populations (1454/1588, 91.56%), social media underreported suicides in older adults (118/1446, 8.16%) and rural residents (134/1588, 8.44%). Harmful reporting practices were common (eg, 54.61% [1292/2366] of the reports contained suicide-related words in the headline and 49.54% [1172/2366] disclosed images of people who died by suicide). Helpful reporting practices were very limited (eg, 0.08% [2/2366] of reports provided direct information about support programs). Conclusions: The suicide reporting of mainstream publishers on social media in China broadly had low adherence to the WHO guidelines. Considering the tremendous information dissemination power of social media platforms, we suggest developing national suicide reporting guidelines that apply to social media. By effectively playing their separate roles, we believe that social media practitioners, health institutions, social organizations, and the general public can endeavor to promote responsible suicide reporting in the Chinese social media environment. UR - https://mental.jmir.org/2021/5/e26654 UR - http://dx.doi.org/10.2196/26654 UR - http://www.ncbi.nlm.nih.gov/pubmed/33983127 ID - info:doi/10.2196/26654 ER - TY - JOUR AU - Darcy, Alison AU - Daniels, Jade AU - Salinger, David AU - Wicks, Paul AU - Robinson, Athena PY - 2021/5/11 TI - Evidence of Human-Level Bonds Established With a Digital Conversational Agent: Cross-sectional, Retrospective Observational Study JO - JMIR Form Res SP - e27868 VL - 5 IS - 5 KW - conversational agents KW - mobile mental health KW - chatbots KW - depression KW - anxiety KW - digital health N2 - Background: There are far more patients in mental distress than there is time available for mental health professionals to support them. Although digital tools may help mitigate this issue, critics have suggested that technological solutions that lack human empathy will prevent a bond or therapeutic alliance from being formed, thereby narrowing these solutions? efficacy. Objective: We aimed to investigate whether users of a cognitive behavioral therapy (CBT)?based conversational agent would report therapeutic bond levels that are similar to those in literature about other CBT modalities, including face-to-face therapy, group CBT, and other digital interventions that do not use a conversational agent. Methods: A cross-sectional, retrospective study design was used to analyze aggregate, deidentified data from adult users who self-referred to a CBT-based, fully automated conversational agent (Woebot) between November 2019 and August 2020. Working alliance was measured with the Working Alliance Inventory-Short Revised (WAI-SR), and depression symptom status was assessed by using the 2-item Patient Health Questionnaire (PHQ-2). All measures were administered by the conversational agent in the mobile app. WAI-SR scores were compared to those in scientific literature abstracted from recent reviews. Results: Data from 36,070 Woebot users were included in the analysis. Participants ranged in age from 18 to 78 years, and 57.48% (20,734/36,070) of participants reported that they were female. The mean PHQ-2 score was 3.03 (SD 1.79), and 54.67% (19,719/36,070) of users scored over the cutoff score of 3 for depression screening. Within 5 days of initial app use, the mean WAI-SR score was 3.36 (SD 0.8) and the mean bond subscale score was 3.8 (SD 1.0), which was comparable to those in recent studies from the literature on traditional, outpatient, individual CBT and group CBT (mean bond subscale scores of 4 and 3.8, respectively). PHQ-2 scores at baseline weakly correlated with bond scores (r=?0.04; P<.001); however, users with depression and those without depression had high bond scores of 3.45. Conclusions: Although bonds are often presumed to be the exclusive domain of human therapeutic relationships, our findings challenge the notion that digital therapeutics are incapable of establishing a therapeutic bond with users. Future research might investigate the role of bonds as mediators of clinical outcomes, since boosting the engagement and efficacy of digital therapeutics could have major public health benefits. UR - https://formative.jmir.org/2021/5/e27868 UR - http://dx.doi.org/10.2196/27868 UR - http://www.ncbi.nlm.nih.gov/pubmed/33973854 ID - info:doi/10.2196/27868 ER - TY - JOUR AU - Wong, Pui-Hing Josephine AU - Jia, Cun-Xian AU - Vahabi, Mandana AU - Liu, Wen Jenny Jing AU - Li, Tai-Wai Alan AU - Cong, Xiaofeng AU - Poon, Kwong-Lai Maurice AU - Yamada, Janet AU - Ning, Xuan AU - Gao, Jianguo AU - Cheng, Shengli AU - Sun, Guoxiao AU - Wang, Xinting AU - Fung, Po-Lun Kenneth PY - 2021/5/11 TI - Promoting the Mental Health of University Students in China: Protocol for Contextual Assessment to Inform Intervention Design and Adaptation JO - JMIR Res Protoc SP - e25009 VL - 10 IS - 5 KW - mental health KW - mental illness KW - stigma KW - protocol KW - acceptance and commitment therapy KW - implementation science KW - student mental health N2 - Background: Chinese students are extremely vulnerable to developing mental illness. The stigma associated with mental illness presents a barrier to seeking help for their mental health. Objective: The Linking Hearts?Linking Youth and ?Xin? (hearts) project is an implementation science project that seeks to reduce mental illness stigma and promote the mental health of university students in Jinan, China. The Linking Hearts project consists of 3 components. In this paper, we outline the protocol for the first component, that is, the contextual assessment and analysis of the mental health needs of university students as the first step to inform the adaptation of an evidence-based intervention to be implemented in Jinan, China. Methods: Six local universities will participate in the Linking Hearts project. A total of 100 students from each university (n=600) will engage in the contextual assessment through self-report surveys on depression, anxiety, stress, mental health knowledge, and mental health stigma. Quantitative data will be analyzed using several descriptive and inferential analyses via SPSS. A small number of participants (144 students and 144 service providers) will also be engaged in focus groups to assess the socio-environmental contexts of university students? health and availability of mental health resources. Qualitative data will be transcribed verbatim and NVivo will be used for data management. Social network analysis will also be performed using EgoNet. Results: Linking Hearts was funded in January 2018 for 5 years. The protocol of Linking Hearts and its 3 components was approved by the research ethics boards of all participating institutions in China in November 2018. Canadian institutions that gave approval were Ryerson University (REB2018-455) in January 2019, University of Alberta (Pro00089364), York University (e2019-162) in May 2019, and University of Toronto (RIS37724) in August 2019. Data collection took place upon ethics approval and was completed in January 2020. A total of 600 students were surveyed. An additional 147 students and 138 service providers took part in focus groups. Data analysis is ongoing. Results will be published in 2021. Conclusions: Findings from this contextual assessment and analysis will generate new knowledge on university students? mental health status, mental health knowledge, and resources available for them. These findings will be used to adapt and refine the Acceptance and Commitment to Empowerment-Linking Youth N? Xin intervention model. The results of this contextual assessment will be used to inform the adaptation and refinement of the mental health intervention to promote the mental health of Chinese university students in Jinan. International Registered Report Identifier (IRRID): RR1-10.2196/25009 UR - https://www.researchprotocols.org/2021/5/e25009 UR - http://dx.doi.org/10.2196/25009 UR - http://www.ncbi.nlm.nih.gov/pubmed/33973869 ID - info:doi/10.2196/25009 ER - TY - JOUR AU - Onie, Sandersan AU - Li, Xun AU - Liang, Morgan AU - Sowmya, Arcot AU - Larsen, Erik Mark PY - 2021/5/7 TI - The Use of Closed-Circuit Television and Video in Suicide Prevention: Narrative Review and Future Directions JO - JMIR Ment Health SP - e27663 VL - 8 IS - 5 KW - suicide KW - suicide prevention KW - CCTV KW - video KW - computer vision KW - machine learning N2 - Background: Suicide is a recognized public health issue, with approximately 800,000 people dying by suicide each year. Among the different technologies used in suicide research, closed-circuit television (CCTV) and video have been used for a wide array of applications, including assessing crisis behaviors at metro stations, and using computer vision to identify a suicide attempt in progress. However, there has been no review of suicide research and interventions using CCTV and video. Objective: The objective of this study was to review the literature to understand how CCTV and video data have been used in understanding and preventing suicide. Furthermore, to more fully capture progress in the field, we report on an ongoing study to respond to an identified gap in the narrative review, by using a computer vision?based system to identify behaviors prior to a suicide attempt. Methods: We conducted a search using the keywords ?suicide,? ?cctv,? and ?video? on PubMed, Inspec, and Web of Science. We included any studies which used CCTV or video footage to understand or prevent suicide. If a study fell into our area of interest, we included it regardless of the quality as our goal was to understand the scope of how CCTV and video had been used rather than quantify any specific effect size, but we noted the shortcomings in their design and analyses when discussing the studies. Results: The review found that CCTV and video have primarily been used in 3 ways: (1) to identify risk factors for suicide (eg, inferring depression from facial expressions), (2) understanding suicide after an attempt (eg, forensic applications), and (3) as part of an intervention (eg, using computer vision and automated systems to identify if a suicide attempt is in progress). Furthermore, work in progress demonstrates how we can identify behaviors prior to an attempt at a hotspot, an important gap identified by papers in the literature. Conclusions: Thus far, CCTV and video have been used in a wide array of applications, most notably in designing automated detection systems, with the field heading toward an automated detection system for early intervention. Despite many challenges, we show promising progress in developing an automated detection system for preattempt behaviors, which may allow for early intervention. UR - https://mental.jmir.org/2021/5/e27663 UR - http://dx.doi.org/10.2196/27663 UR - http://www.ncbi.nlm.nih.gov/pubmed/33960952 ID - info:doi/10.2196/27663 ER - TY - JOUR AU - Areán, A. Patricia AU - Pratap, Abhishek AU - Hsin, Honor AU - Huppert, K. Tierney AU - Hendricks, E. Karin AU - Heagerty, J. Patrick AU - Cohen, Trevor AU - Bagge, Courtney AU - Comtois, Anne Katherine PY - 2021/5/6 TI - Perceived Utility and Characterization of Personal Google Search Histories to Detect Data Patterns Proximal to a Suicide Attempt in Individuals Who Previously Attempted Suicide: Pilot Cohort Study JO - J Med Internet Res SP - e27918 VL - 23 IS - 5 KW - real-world data KW - web searches KW - suicide risk factors KW - suicide detection KW - suicide KW - eHealth KW - internet KW - website KW - search history KW - risk KW - EHR KW - social media KW - behavior KW - mental health KW - personalized KW - online seeking behavior N2 - Background: Despite decades of research to better understand suicide risk and to develop detection and prevention methods, suicide is still one of the leading causes of death globally. While large-scale studies using real-world evidence from electronic health records can identify who is at risk, they have not been successful at pinpointing when someone is at risk. Personalized social media and online search history data, by contrast, could provide an ongoing real-world datastream revealing internal thoughts and personal states of mind. Objective: We conducted this study to determine the feasibility and acceptability of using personalized online information-seeking behavior in the identification of risk for suicide attempts. Methods: This was a cohort survey study to assess attitudes of participants with a prior suicide attempt about using web search data for suicide prevention purposes, dates of lifetime suicide attempts, and an optional one-time download of their past web searches on Google. The study was conducted at the University of Washington School of Medicine Psychiatry Research Offices. The main outcomes were participants? opinions on internet search data for suicide prediction and intervention and any potential change in online information-seeking behavior proximal to a suicide attempt. Individualized nonparametric association analysis was used to assess the magnitude of difference in web search data features derived from time periods proximal (7, 15, 30, and 60 days) to the suicide attempts versus the typical (baseline) search behavior of participants. Results: A total of 62 participants who had attempted suicide in the past agreed to participate in the study. Internet search activity varied from person to person (median 2-24 searches per day). Changes in online search behavior proximal to suicide attempts were evident up to 60 days before attempt. For a subset of attempts (7/30, 23%) search features showed associations from 2 months to a week before the attempt. The top 3 search constructs associated with attempts were online searching patterns (9/30 attempts, 30%), semantic relatedness of search queries to suicide methods (7/30 attempts, 23%), and anger (7/30 attempts, 23%). Participants (40/59, 68%) indicated that use of this personalized web search data for prevention purposes was acceptable with noninvasive potential interventions such as connection to a real person (eg, friend, family member, or counselor); however, concerns were raised about detection accuracy, privacy, and the potential for overly invasive intervention. Conclusions: Changes in online search behavior may be a useful and acceptable means of detecting suicide risk. Personalized analysis of online information-seeking behavior showed notable changes in search behavior and search terms that are tied to early warning signs of suicide and are evident 2 months to 7 days before a suicide attempt. UR - https://www.jmir.org/2021/5/e27918 UR - http://dx.doi.org/10.2196/27918 UR - http://www.ncbi.nlm.nih.gov/pubmed/33955838 ID - info:doi/10.2196/27918 ER - TY - JOUR AU - Kang, Myeong Jae AU - Kim, Nambeom AU - Lee, Young Sook AU - Woo, Kyun Soo AU - Park, Geumjin AU - Yeon, Kil Byeong AU - Park, Woon Jung AU - Youn, Jung-Hae AU - Ryu, Seung-Ho AU - Lee, Jun-Young AU - Cho, Seong-Jin PY - 2021/5/6 TI - Effect of Cognitive Training in Fully Immersive Virtual Reality on Visuospatial Function and Frontal-Occipital Functional Connectivity in Predementia: Randomized Controlled Trial JO - J Med Internet Res SP - e24526 VL - 23 IS - 5 KW - virtual reality KW - cognitive training KW - visuospatial function KW - fMRI KW - visual network KW - mild cognitive impairment N2 - Background: Cognitive training can potentially prevent cognitive decline. However, the results of recent studies using semi-immersive virtual reality (VR)-assisted cognitive training are inconsistent. Objective: We aimed to examine the hypothesis that cognitive training using fully immersive VR, which may facilitate visuospatial processes, could improve visuospatial functioning, comprehensive neuropsychological functioning, psychiatric symptoms, and functional connectivity in the visual brain network in predementia. Methods: Participants over 60 years old with subjective cognitive decline or mild cognitive impairment from a memory clinic were randomly allocated to the VR (n=23) or the control (n=18) group. The VR group participants received multidomain and neuropsychologist-assisted cognitive training in a fully immersive VR environment twice a week for 1 month. The control group participants did not undergo any additional intervention except for their usual therapy such as pharmacotherapy. Participants of both groups were evaluated for cognitive function using face-to-face comprehensive neuropsychological tests, including the Rey-Osterrieth Complex Figure Test (RCFT) copy task; for psychiatric symptoms such as depression, apathy, affect, and quality of life; as well as resting-state functional magnetic resonance imaging (rsfMRI) at baseline and after training. Repeated-measures analysis of variance was used to compare the effect of cognitive training between groups. Seed-to-voxel?based analyses were used to identify the cognitive improvement?related functional connectivity in the visual network of the brain. Results: After VR cognitive training, significant improvement was found in the total score (F1,39=14.69, P=.001) and basic components score of the RCFT copy task (F1,39=9.27, P=.005) compared with those of the control group. The VR group also showed improvements, albeit not significant, in naming ability (F1,39=3.55, P=.07), verbal memory delayed recall (F1,39=3.03, P=.09), and phonemic fluency (F1,39=3.08, P=.09). Improvements in psychiatric symptoms such as apathy (F1,39=7.02, P=.01), affect (F1,39=14.40, P=.001 for positive affect; F1,39=4.23, P=.047 for negative affect), and quality of life (F1,39=4.49, P=.04) were found in the VR group compared to the control group. Improvement in the RCFT copy task was associated with a frontal-occipital functional connectivity increase revealed by rsfMRI in the VR group compared to the control group. Conclusions: Fully immersive VR cognitive training had positive effects on the visuospatial function, apathy, affect, quality of life, and increased frontal-occipital functional connectivity in older people in a predementia state. Future trials using VR cognitive training with larger sample sizes and more sophisticated designs over a longer duration may reveal greater improvements in cognition, psychiatric symptoms, and brain functional connectivity. Trial Registration: Clinical Research Information Service KCT0005243; https://tinyurl.com/2a4kfasa UR - https://www.jmir.org/2021/5/e24526 UR - http://dx.doi.org/10.2196/24526 UR - http://www.ncbi.nlm.nih.gov/pubmed/33955835 ID - info:doi/10.2196/24526 ER - TY - JOUR AU - AlHadi, N. Ahmad AU - Alammari, A. Khawla AU - Alsiwat, J. Lojain AU - Alhaidri, E. Nojood AU - Alabdulkarim, H. Nouf AU - Altwaijri, A. Nouf AU - AlSohaili, A. Shamma PY - 2021/5/3 TI - Perception of Mental Health Care Professionals in Saudi Arabia on Computerized Cognitive Behavioral Therapy: Observational Cross-sectional Study JO - JMIR Form Res SP - e26294 VL - 5 IS - 5 KW - CBT KW - iCBT KW - cCBT KW - knowledge KW - attitude KW - mental health care professionals KW - computer usage KW - psychotherapy KW - therapy KW - cognitive behavioral therapy KW - health care worker KW - perception KW - Saudi Arabia KW - preference KW - mental health N2 - Background: Mental health disorders are common in Saudi Arabia with a 34% lifetime prevalence. Cognitive behavioral therapy (CBT), a type of psychotherapy, is an evidence-based intervention for the majority of mental disorders. Although the demand for CBT is increasing, unfortunately, there are few therapists available to meet this demand and the therapy is expensive. Computerized cognitive behavioral therapy (cCBT) is a new modality that can help fill this gap. Objective: We aimed to measure the knowledge of cCBT among mental health care professionals in Saudi Arabia, and to evaluate their attitudes and preferences toward cCBT. Methods: This quantitative observational cross-sectional study used a convenience sample, selecting mental health care professionals working in the tertiary hospitals of Saudi Arabia. The participants received a self-administered electronic questionnaire through data collectors measuring their demographics, knowledge, and attitudes about cCBT, and their beliefs about the efficacy of using computers in therapy. Results: Among the 121 participating mental health care professionals, the mean age was 36.55 years and 60.3% were women. Most of the participants expressed uncertainty and demonstrated a lack of knowledge regarding cCBT. However, the majority of participants indicated a positive attitude toward using computers in therapy. Participants agreed with the principles of cCBT, believed in its efficacy, and were generally confident in using computers. Among the notable results, participants having a clinical license and with cCBT experience had more knowledge of cCBT. The overall attitude toward cCBT was not affected by demographic or work-related factors. Conclusions: Mental health care professionals in Saudi Arabia need more education and training regarding cCBT; however, their attitude toward its use and their comfort in using computers in general show great promise. Further research is needed to assess the acceptance of cCBT by patients in Saudi Arabia, in addition to clinical trials measuring its effectiveness in the Saudi population. UR - https://formative.jmir.org/2021/5/e26294 UR - http://dx.doi.org/10.2196/26294 UR - http://www.ncbi.nlm.nih.gov/pubmed/33938810 ID - info:doi/10.2196/26294 ER - TY - JOUR AU - Owoyemi, Praise AU - Salcone, Sarah AU - King, Christopher AU - Kim, Julie Heejung AU - Ressler, James Kerry AU - Vahia, Vihang Ipsit PY - 2021/4/14 TI - Measuring and Quantifying Collateral Information in Psychiatry: Development and Preliminary Validation of the McLean Collateral Information and Clinical Actionability Scale JO - JMIR Ment Health SP - e25050 VL - 8 IS - 4 KW - electronic media KW - psychotherapy KW - text message KW - electronic mail KW - collateral information KW - telecommunication KW - communications media KW - digital N2 - Background: The review of collateral information is an essential component of patient care. Although this is standard practice, minimal research has been done to quantify collateral information collection and to understand how collateral information translates to clinical decision making. To address this, we developed and piloted a novel measure (the McLean Collateral Information and Clinical Actionability Scale [M-CICAS]) to evaluate the types and number of collateral sources viewed and the resulting actions made in a psychiatric setting. Objective: This study aims to test the feasibility of the M-CICAS, validate this measure against clinician notes via medical records, and evaluate whether reviewing a higher volume of collateral sources is associated with more clinical actions taken. Methods: For the M-CICAS, we developed a three-part instrument, focusing on measuring collateral sources reviewed, clinical actions taken, and shared decision making between the clinician and patient. To determine feasibility and preliminary validity, we piloted this measure among clinicians providing psychotherapy at McLean Hospital. These clinicians (n=7) completed the M-CICAS after individual clinical sessions with 89 distinct patient encounters. Scales were completed by clinicians only once for each patient during routine follow-up visits. After clinicians completed these scales, researchers conducted chart reviews by completing the M-CICAS using only the clinician?s corresponding note from that session. For the analyses, we generated summary scores for the number of collateral sources and clinical actions for each encounter. We examined Pearson correlation coefficients to assess interrater reliability between clinicians and chart reviewers, and simple univariate regression modeling followed by multilevel mixed effects regression modeling to test the relationship between collateral information accessed and clinical actions taken. Results: The study staff had high interrater reliability on the M-CICAS for the sources reviewed (r=0.98; P<.001) and actions taken (r=0.97; P<.001). Clinician and study staff ratings were moderately correlated and statistically significant on the M-CICAS summary scores for the sources viewed (r=0.24, P=.02 and r=0.25, P=.02, respectively). Univariate regression modeling with a two-tailed test demonstrated a significant association between collateral sources and clinical actions taken when clinicians completed the M-CICAS (?=.27; t87=2.47; P=.02). The multilevel fixed slopes random intercepts model confirmed a significant association even when accounting for clinician differences (?=.23; t57=2.13; P=.04). Conclusions: This pilot study established the feasibility and preliminary validity of the M-CICAS in assessing collateral sources and clinical decision making in psychiatry. This study also indicated that reviewing more collateral sources may lead to an increased number of clinical actions following a session. UR - https://mental.jmir.org/2021/4/e25050 UR - http://dx.doi.org/10.2196/25050 UR - http://www.ncbi.nlm.nih.gov/pubmed/33851928 ID - info:doi/10.2196/25050 ER - TY - JOUR AU - Fehribach, Rhiannon Jamie AU - Toffolo, Jolien Marieke Bianca AU - Cornelisz, Ilja AU - van Klaveren, Chris AU - van Straten, Annemieke AU - van Gelder, Jean-Louis AU - Donker, Tara PY - 2021/4/12 TI - Virtual Reality Self-help Treatment for Aviophobia: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e22008 VL - 10 IS - 4 KW - aviophobia KW - specific phobia KW - virtual reality KW - cognitive behavioral therapy KW - exposure therapy N2 - Background: Aviophobia (the fear of flying) can greatly impact the daily life functioning of people with the condition. Traditional exposure-based treatment is hampered by the limited availability of airplane practice situations, which is a result of economical and practical concerns. Easily accessible and low-cost virtual reality exposure therapy may address these challenges. Objective: The purpose of our study is to investigate the effectiveness of ZeroPhobia: Aviophobia (a self-help mobile app?based treatment) in reducing flight anxiety symptoms and depressive and anxiety symptoms. We will also investigate the effects of usage intensity, the sense of immersion, inherent absorption ability, and perceived user-friendliness on the treatment effect. Methods: Participants (N=114) who are aged 18-64 years and experience at least mild symptoms of aviophobia will be recruited from the general Dutch population and randomized into a treatment group or waitlist control group. By using their own phones and rudimentary mobile virtual reality headsets, participants will receive six modules of psychoeducation and cognitive behavioral therapy, which will include six levels of virtual reality exposure therapy over a period of 6 weeks. Assessments will be conducted at baseline, posttest (ie, after 6 weeks), and 3- and 12-month follow-ups. The primary outcome measure of our study is the Flight Anxiety Situations Questionnaire. The secondary outcome measures include anxiety and depression measures and additional covariates (including usage intensity, the degree of immersion, etc). We will test treatment effectiveness by conducting an intention-to-treat analysis and estimating average treatment effects on the treated. The mechanisms of treatment effect will also be explored. Results: The study was funded on September 25, 2018. Ethical approval was received on October 11, 2019. Recruitment closed on May 7, 2020. Conclusions: Our study will further the scientific understanding and clinical implications of technology?s current ability to aid in providing effective, accessible treatment for the fear of flying. Trial Registration: Netherlands Trial Registry NL70238.029.19; https://www.trialregister.nl/trial/8257. International Registered Report Identifier (IRRID): DERR1-10.2196/22008 UR - https://www.researchprotocols.org/2021/4/e22008 UR - http://dx.doi.org/10.2196/22008 UR - http://www.ncbi.nlm.nih.gov/pubmed/33843605 ID - info:doi/10.2196/22008 ER - TY - JOUR AU - Rodriguez-Jimenez, Roberto AU - Corripio, Iluminada AU - Campos, Ricardo AU - Páramo, Mario AU - Franco-Martin, Manuel AU - Segura, Estefanía AU - González, Sergio AU - Martínez-Raga, José PY - 2021/4/7 TI - Structure and Functioning of Acute Inpatient Psychiatric Units in Spain: Qualitative Study JO - JMIR Res Protoc SP - e26214 VL - 10 IS - 4 KW - acute inpatient psychiatric units KW - organization KW - resources KW - scorecard KW - Spain N2 - Background: As a consequence of the decentralization of health care provision to the different Regions (called Autonomous Communities) in Spain, different health care models and resources have been developed for psychiatric patients. It would be very useful to obtain comprehensive and comparative data on health care models, resources, and activity of acute inpatient psychiatric units (AIPUs) as a key part of mental health systems. Objective: The aim of this study was to determine the current state of AIPUs in Spain through a national scorecard that allows the current situation to be visualized in terms of resources, processes, and outputs. Methods: A 104-item online questionnaire was sent to all the AIPUs of the different Regions in Spain. It was divided into 11 sections, including data on the resources, processes, and outputs of the AIPUs plus general data, an indicator dashboard, and good practices. Results: The questionnaire was completed by 60.0% (117/195) of the AIPUs invited to participate. The information collected has allowed us to obtain a detailed snapshot of the current situation of AIPUs in Spain at the levels of infrastructure and material resources, staffing, organization and activity of the units, coordination with other units, guidelines, processes and protocols used, participation and communication with patients and their families, teaching activity, and research linked to the units. Conclusions: This project aimed to help understand the general situation of AIPUs in Spain and its different Regions, contribute to enhancing the benchmarking and harmonization among Spanish Regions, and provide data for future comparisons with other countries. International Registered Report Identifier (IRRID): RR1-10.2196/26214 UR - https://www.researchprotocols.org/2021/4/e26214 UR - http://dx.doi.org/10.2196/26214 UR - http://www.ncbi.nlm.nih.gov/pubmed/33729167 ID - info:doi/10.2196/26214 ER - TY - JOUR AU - Sükei, Emese AU - Norbury, Agnes AU - Perez-Rodriguez, Mercedes M. AU - Olmos, M. Pablo AU - Artés, Antonio PY - 2021/3/22 TI - Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach JO - JMIR Mhealth Uhealth SP - e24465 VL - 9 IS - 3 KW - mental health KW - affect KW - mobile health KW - mobile phone KW - digital phenotype KW - machine learning KW - Bayesian analysis KW - probabilistic models KW - personalized models N2 - Background: Mental health disorders affect multiple aspects of patients? lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient?s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning?based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days? worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals? overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days? data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients? mood states. UR - https://mhealth.jmir.org/2021/3/e24465 UR - http://dx.doi.org/10.2196/24465 UR - http://www.ncbi.nlm.nih.gov/pubmed/33749612 ID - info:doi/10.2196/24465 ER - TY - JOUR AU - Kim, Jina AU - Lee, Daeun AU - Park, Eunil PY - 2021/3/8 TI - Machine Learning for Mental Health in Social Media: Bibliometric Study JO - J Med Internet Res SP - e24870 VL - 23 IS - 3 KW - bibliometric analysis KW - machine learning KW - mental health KW - social media N2 - Background: Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. Objective: We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. Methods: Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. Results: We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. Conclusions: The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners. UR - https://www.jmir.org/2021/3/e24870 UR - http://dx.doi.org/10.2196/24870 UR - http://www.ncbi.nlm.nih.gov/pubmed/33683209 ID - info:doi/10.2196/24870 ER - TY - JOUR AU - Tacheva, Zhasmina AU - Ivanov, Anton PY - 2021/3/8 TI - Exploring the Association Between the ?Big Five? Personality Traits and Fatal Opioid Overdose: County-Level Empirical Analysis JO - JMIR Ment Health SP - e24939 VL - 8 IS - 3 KW - opioid addiction KW - personality traits KW - community health KW - text mining KW - opioid KW - addiction KW - psychological N2 - Background: Opioid-related deaths constitute a problem of pandemic proportions in the United States, with no clear solution in sight. Although addressing addiction?the heart of this problem?ought to remain a priority for health practitioners, examining the community-level psychological factors with a known impact on health behaviors may provide valuable insights for attenuating this health crisis by curbing risky behaviors before they evolve into addiction. Objective: The goal of this study is twofold: to demonstrate the relationship between community-level psychological traits and fatal opioid overdose both theoretically and empirically, and to provide a blueprint for using social media data to glean these psychological factors in a real-time, reliable, and scalable manner. Methods: We collected annual panel data from Twitter for 2891 counties in the United States between 2014-2016 and used a novel data mining technique to obtain average county-level ?Big Five? psychological trait scores. We then performed interval regression, using a control function to alleviate omitted variable bias, to empirically test the relationship between county-level psychological traits and the prevalence of fatal opioid overdoses in each county. Results: After controlling for a wide range of community-level biopsychosocial factors related to health outcomes, we found that three of the operationalizations of the five psychological traits examined at the community level in the study were significantly associated with fatal opioid overdoses: extraversion (?=.308, P<.001), neuroticism (?=.248, P<.001), and conscientiousness (?=.229, P<.001). Conclusions: Analyzing the psychological characteristics of a community can be a valuable tool in the local, state, and national fight against the opioid pandemic. Health providers and community health organizations can benefit from this research by evaluating the psychological profile of the communities they serve and assessing the projected risk of fatal opioid overdose based on the relationships our study predict when making decisions for the allocation of overdose-reversal medication and other vital resources. UR - https://mental.jmir.org/2021/3/e24939 UR - http://dx.doi.org/10.2196/24939 UR - http://www.ncbi.nlm.nih.gov/pubmed/33683210 ID - info:doi/10.2196/24939 ER - TY - JOUR AU - Bai, Ran AU - Xiao, Le AU - Guo, Yu AU - Zhu, Xuequan AU - Li, Nanxi AU - Wang, Yashen AU - Chen, Qinqin AU - Feng, Lei AU - Wang, Yinghua AU - Yu, Xiangyi AU - Wang, Chunxue AU - Hu, Yongdong AU - Liu, Zhandong AU - Xie, Haiyong AU - Wang, Gang PY - 2021/3/8 TI - Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study JO - JMIR Mhealth Uhealth SP - e24365 VL - 9 IS - 3 KW - digital phenotype KW - major depressive disorder KW - machine learning KW - mobile phone N2 - Background: Major depressive disorder (MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable devices to monitor the mental condition of patients with MDD has been examined in several studies. However, few studies have used passively collected data to monitor mood changes over time. Objective: The aim of this study is to examine the feasibility of monitoring mood status and stability of patients with MDD using machine learning models trained by passively collected data, including phone use data, sleep data, and step count data. Methods: We constructed 950 data samples representing time spans during three consecutive Patient Health Questionnaire-9 assessments. Each data sample was labeled as Steady or Mood Swing, with subgroups Steady-remission, Steady-depressed, Mood Swing-drastic, and Mood Swing-moderate based on patients? Patient Health Questionnaire-9 scores from three visits. A total of 252 features were extracted, and 4 feature selection models were applied; 6 different combinations of types of data were experimented with using 6 different machine learning models. Results: A total of 334 participants with MDD were enrolled in this study. The highest average accuracy of classification between Steady and Mood Swing was 76.67% (SD 8.47%) and that of recall was 90.44% (SD 6.93%), with features from all types of data being used. Among the 6 combinations of types of data we experimented with, the overall best combination was using call logs, sleep data, step count data, and heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, and Steady-depressed and Mood Swing-drastic were over 80%, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75%. Comparing all 6 aforementioned combinations, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate) are better than those between Steady-depressed and Mood Swing (drastic and moderate). Conclusions: Our proposed method could be used to monitor mood changes in patients with MDD with promising accuracy by using passively collected data, which can be used as a reference by doctors for adjusting treatment plans or for warning patients and their guardians of a relapse. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900021461; http://www.chictr.org.cn/showprojen.aspx?proj=36173 UR - https://mhealth.jmir.org/2021/3/e24365 UR - http://dx.doi.org/10.2196/24365 UR - http://www.ncbi.nlm.nih.gov/pubmed/33683207 ID - info:doi/10.2196/24365 ER - TY - JOUR AU - Carrotte, Rose Elise AU - Webb, Marianne AU - Flego, Anna AU - Vincent, Bonnie AU - Heath, Jack AU - Blanchard, Michelle PY - 2021/3/3 TI - Acceptability, Safety, and Resonance of the Pilot Digital Suicide Prevention Campaign ?Better Off With You?: Qualitative Study JO - JMIR Form Res SP - e23892 VL - 5 IS - 3 KW - suicide KW - interpersonal theory of suicide KW - social media KW - co-design KW - lived experience N2 - Background: The Interpersonal Theory of Suicide posits that there are three key elements of suicidal behavior: perceived burdensomeness, thwarted belongingness, and the acquired capability for suicide. The digital campaign Better Off With You was developed to directly challenge the idea of perceived burdensomeness among people who are contemplating suicide in 2 communities within Australia. Objective: The aim of this study is to explore the needs and preferences of people with lived experience of suicidal thoughts and actions to inform the development of Better Off With You. Methods: This study involved a series of focus groups that aimed to discuss campaign messaging, scope, and approach. People with lived experience of suicidal thoughts and actions attended the focus groups. After the completion of initial focus groups, the results informed the creation of campaign collateral by creative agencies. Early versions of the campaign collateral were then presented in the user testing sessions. Transcriptions were analyzed via thematic analysis. Results: In total, 13 participants attended the focus groups and 14 attended the user testing sessions. The following three overarching themes were presented: acceptability, safety, and resonance. Participants believed that suicide is a serious and ongoing issue in their communities and welcomed a localized suicide prevention focus via peer-to-peer storytelling. The idea of perceived burdensomeness required clarification but was perceived as acceptable and relevant. Participants seemed drawn toward peer narratives that they perceived to be authentic, genuine, and believable as given by real people with lived experience. Campaign messaging needs to be clear and empathetic while directly talking about suicide. Participants did not anticipate any significant negative or harmful impact from any campaign videos and highlighted the importance of providing appropriate help-seeking information. Conclusions: This iterative study provided important insights and knowledge about peer-to-peer storytelling in suicide prevention campaigns. Future campaigns should involve simple messaging, be validating and empathetic, and consider including a lived experience perspective. UR - https://formative.jmir.org/2021/3/e23892 UR - http://dx.doi.org/10.2196/23892 UR - http://www.ncbi.nlm.nih.gov/pubmed/33656441 ID - info:doi/10.2196/23892 ER - TY - JOUR AU - Stalujanis, Esther AU - Neufeld, Joel AU - Glaus Stalder, Martina AU - Belardi, Angelo AU - Tegethoff, Marion AU - Meinlschmidt, Gunther PY - 2021/2/17 TI - Induction of Efficacy Expectancies in an Ambulatory Smartphone-Based Digital Placebo Mental Health Intervention: Randomized Controlled Trial JO - JMIR Mhealth Uhealth SP - e20329 VL - 9 IS - 2 KW - digital placebo effect KW - efficacy expectancies KW - ecological momentary assessment KW - mHealth KW - mobile phone KW - placebo effect KW - randomized controlled trial KW - smartphone-based intervention N2 - Background: There is certain evidence on the efficacy of smartphone-based mental health interventions. However, the mechanisms of action remain unclear. Placebo effects contribute to the efficacy of face-to-face mental health interventions and may also be a potential mechanism of action in smartphone-based interventions. Objective: This study aimed to investigate whether different types of efficacy expectancies as potential factors underlying placebo effects could be successfully induced in a smartphone-based digital placebo mental health intervention, ostensibly targeting mood and stress. Methods: We conducted a randomized, controlled, single-blinded, superiority trial with a multi-arm parallel design. Participants underwent an Android smartphone-based digital placebo mental health intervention for 20 days. We induced prospective efficacy expectancies via initial instructions on the purpose of the intervention and retrospective efficacy expectancies via feedback on the success of the intervention at days 1, 4, 7, 10, and 13. A total of 132 healthy participants were randomized to a prospective expectancy?only condition (n=33), a retrospective expectancy?only condition (n=33), a combined expectancy condition (n=34), or a control condition (n=32). As the endpoint, we assessed changes in efficacy expectancies with the Credibility Expectancy Questionnaire, before the intervention and on days 1, 7, 14, and 20. For statistical analyses, we used a random effects model for the intention-to-treat sample, with intervention day as time variable and condition as two factors: prospective expectancy (yes vs no) and retrospective expectancy (yes vs no), allowed to vary over participant and intervention day. Results: Credibility (?=?1.63; 95% CI ?2.37 to ?0.89; P<.001) and expectancy (?=?0.77; 95% CI ?1.49 to ?0.05; P=.04) decreased across the intervention days. For credibility and expectancy, we found significant three-way interactions: intervention day×prospective expectancy×retrospective expectancy (credibility: ?=2.05; 95% CI 0.60-3.50; P=.006; expectancy: ?=1.55; 95% CI 0.14-2.95; P=.03), suggesting that efficacy expectancies decreased least in the combined expectancy condition and the control condition. Conclusions: To our knowledge, this is the first empirical study investigating whether efficacy expectancies can be successfully induced in a specifically designed placebo smartphone-based mental health intervention. Our findings may pave the way to diminish or exploit digital placebo effects and help to improve the efficacy of digital mental health interventions. Trial Registration: Clinicaltrials.gov NCT02365220; https://clinicaltrials.gov/ct2/show/NCT02365220. UR - http://mhealth.jmir.org/2021/2/e20329/ UR - http://dx.doi.org/10.2196/20329 UR - http://www.ncbi.nlm.nih.gov/pubmed/33594991 ID - info:doi/10.2196/20329 ER - TY - JOUR AU - Karin, Eyal AU - Crane, Frances Monique AU - Dear, Farran Blake AU - Nielssen, Olav AU - Heller, Ziona Gillian AU - Kayrouz, Rony AU - Titov, Nickolai PY - 2021/2/5 TI - Predictors, Outcomes, and Statistical Solutions of Missing Cases in Web-Based Psychotherapy: Methodological Replication and Elaboration Study JO - JMIR Ment Health SP - e22700 VL - 8 IS - 2 KW - psychotherapy KW - treatment adherence and compliance KW - missing data KW - treatment evaluation KW - statistical bias N2 - Background: Missing cases present a challenge to our ability to evaluate the effects of web-based psychotherapy trials. As missing cases are often lost to follow-up, less is known about their characteristics, their likely clinical outcomes, or the likely effect of the treatment being trialed. Objective: The aim of this study is to explore the characteristics of missing cases, their likely treatment outcomes, and the ability of different statistical models to approximate missing posttreatment data. Methods: A sample of internet-delivered cognitive behavioral therapy participants in routine care (n=6701, with 36.26% missing cases at posttreatment) was used to identify predictors of dropping out of treatment and predictors that moderated clinical outcomes, such as symptoms of psychological distress, anxiety, and depression. These variables were then incorporated into a range of statistical models that approximated replacement outcomes for missing cases, and the results were compared using sensitivity and cross-validation analyses. Results: Treatment adherence, as measured by the rate of progress of an individual through the treatment modules, and higher pretreatment symptom scores were identified as the dominant predictors of missing cases probability (Nagelkerke R2=60.8%) and the rate of symptom change. Low treatment adherence, in particular, was associated with increased odds of presenting as missing cases during posttreatment assessment (eg, odds ratio 161.1:1) and, at the same time, attenuated the rate of symptom change across anxiety (up to 28% of the total symptom with 48% reduction effect), depression (up to 41% of the total with 48% symptom reduction effect), and psychological distress symptom outcomes (up to 52% of the total with 37% symptom reduction effect) at the end of the 8-week window. Reflecting this pattern of results, statistical replacement methods that overlooked the features of treatment adherence and baseline severity underestimated missing case symptom outcomes by as much as 39% at posttreatment. Conclusions: The treatment outcomes of the cases that were missing at posttreatment were distinct from those of the remaining observed sample. Thus, overlooking the features of missing cases is likely to result in an inaccurate estimate of the effect of treatment. UR - https://mental.jmir.org/2021/2/e22700 UR - http://dx.doi.org/10.2196/22700 UR - http://www.ncbi.nlm.nih.gov/pubmed/33544080 ID - info:doi/10.2196/22700 ER - TY - JOUR AU - Stanyon, Miriam AU - Streater, Amy AU - Coleston-Shields, Maria Donna AU - Yates, Jennifer AU - Challis, David AU - Dening, Tom AU - Hoe, Juanita AU - Lloyd-Evans, Brynmor AU - Mitchell, Shirley AU - Moniz-Cook, Esme AU - Poland, Fiona AU - Prothero, David AU - Orrell, Martin PY - 2021/1/27 TI - Development of an Evidence-Based Best Practice Model for Teams Managing Crisis in Dementia: Protocol for a Qualitative Study JO - JMIR Res Protoc SP - e14781 VL - 10 IS - 1 KW - dementia KW - caregivers KW - crisis KW - mental health KW - home management N2 - Background: Teams working in the community to manage crisis in dementia currently exist, but with widely varying models of practice, it is difficult to determine the effectiveness of such teams. Objective: The aim of this study is to develop a ?best practice model? for dementia services managing crisis, as well as a set of resources to help teams implement this model to measure and improve practice delivery. These will be the best practice tool and toolkit to be utilized by teams to improve the effectiveness of crisis teams working with older people with dementia and their caregivers. This paper describes the protocol for a prospective study using qualitative methods to establish an understanding of the current practice to develop a ?best practice model.? Methods: Participants (people with dementia, caregivers, staff members, and stakeholders) from a variety of geographical areas, with a broad experience of crisis and noncrisis work, will be purposively selected to participate in qualitative approaches including interviews, focus groups, a consensus workshop, and development and field testing of both the best practice tool and toolkit. Results: Data were collected between October 2016 and August 2018. Thematic analysis will be utilized to establish the current working of teams managing crisis in dementia in order to draw together elements of the best practice. Conclusions: This is the first study to systematically explore the requirements needed to fulfill effective and appropriate home management for people with dementia and their caregivers at the time of mental health crisis, as delivered by teams managing crisis in dementia. This systematic approach to development will support greater acceptability and validity of the best practice tool and toolkit and lay the foundation for a large scale trial with teams managing crisis in dementia across England to investigate the effects on practice and impact on service provision, as well as the associated experiences of people with dementia and their caregivers. International Registered Report Identifier (IRRID): RR1-10.2196/14781 UR - http://www.researchprotocols.org/2021/1/e14781/ UR - http://dx.doi.org/10.2196/14781 UR - http://www.ncbi.nlm.nih.gov/pubmed/33502333 ID - info:doi/10.2196/14781 ER - TY - JOUR AU - Agley, Jon AU - Jun, Mikyoung AU - Eldridge, Lori AU - Agley, L. Daniel AU - Xiao, Yunyu AU - Sussman, Steve AU - Golzarri-Arroyo, Lilian AU - Dickinson, L. Stephanie AU - Jayawardene, Wasantha AU - Gassman, Ruth PY - 2021/1/6 TI - Effects of ACT Out! Social Issue Theater on Social-Emotional Competence and Bullying in Youth and Adolescents: Cluster Randomized Controlled Trial JO - JMIR Ment Health SP - e25860 VL - 8 IS - 1 KW - cyberbullying KW - bullying KW - social-emotional learning KW - SEL KW - social-emotional competence KW - RCT KW - randomized controlled trial KW - outcome KW - emotion KW - bully KW - prevention KW - school KW - intervention KW - assessment KW - effectiveness KW - implementation KW - fidelity KW - reception KW - children KW - young adults KW - adolescents N2 - Background: Schools increasingly prioritize social-emotional competence and bullying and cyberbullying prevention, so the development of novel, low-cost, and high-yield programs addressing these topics is important. Further, rigorous assessment of interventions prior to widespread dissemination is crucial. Objective: This study assesses the effectiveness and implementation fidelity of the ACT Out! Social Issue Theater program, a 1-hour psychodramatic intervention by professional actors; it also measures students? receptiveness to the intervention. Methods: This study is a 2-arm cluster randomized control trial with 1:1 allocation that randomized either to the ACT Out! intervention or control (treatment as usual) at the classroom level (n=76 classrooms in 12 schools across 5 counties in Indiana, comprised of 1571 students at pretest in fourth, seventh, and tenth grades). The primary outcomes were self-reported social-emotional competence, bullying perpetration, and bullying victimization; the secondary outcomes were receptiveness to the intervention, implementation fidelity (independent observer observation), and prespecified subanalyses of social-emotional competence for seventh- and tenth-grade students. All outcomes were collected at baseline and 2-week posttest, with planned 3-months posttest data collection prevented due to the COVID-19 pandemic. Results: Intervention fidelity was uniformly excellent (>96% adherence), and students were highly receptive to the program. However, trial results did not support the hypothesis that the intervention would increase participants? social-emotional competence. The intervention?s impact on bullying was complicated to interpret and included some evidence of small interaction effects (reduced cyberbullying victimization and increased physical bullying perpetration). Additionally, pooled within-group reductions were also observed and discussed but were not appropriate for causal attribution. Conclusions: This study found no superiority for a 1-hour ACT Out! intervention compared to treatment as usual for social-emotional competence or offline bullying, but some evidence of a small effect for cyberbullying. On the basis of these results and the within-group effects, as a next step, we encourage research into whether the ACT Out! intervention may engender a bystander effect not amenable to randomization by classroom. Therefore, we recommend a larger trial of the ACT Out! intervention that focuses specifically on cyberbullying, measures bystander behavior, is randomized by school, and is controlled for extant bullying prevention efforts at each school. Trial Registration: Clinicaltrials.gov NCT04097496; https://clinicaltrials.gov/ct2/show/NCT04097496 International Registered Report Identifier (IRRID): RR2-10.2196/17900 UR - http://mental.jmir.org/2021/1/e25860/ UR - http://dx.doi.org/10.2196/25860 UR - http://www.ncbi.nlm.nih.gov/pubmed/33338986 ID - info:doi/10.2196/25860 ER - TY - JOUR AU - Andalibi, Nazanin AU - Flood, K. Madison PY - 2021/1/4 TI - Considerations in Designing Digital Peer Support for Mental Health: Interview Study Among Users of a Digital Support System (Buddy Project) JO - JMIR Ment Health SP - e21819 VL - 8 IS - 1 KW - mental health KW - peer support KW - technology KW - design KW - digital peer support KW - mHealth KW - digital health KW - internet N2 - Background: Peer support is an approach to cope with mental illness, and technology provides a way to facilitate peer support. However, there are barriers to seeking support in offline and technology-mediated contexts. Objective: This study aims to uncover potential ways to design digital mental health peer support systems and to outline a set of principles for future designers to consider as they embark on designing these systems. By learning how existing systems are used by people in daily life and by centering their experiences, we can better understand how to design mental health peer support technologies that foreground people?s needs. One existing digital peer support system is Buddy Project, the case study in this paper. Methods: This paper reports on an interview study with Buddy Project users (N=13). Data were analyzed using the constant comparative approach. Results: Individuals matched through Buddy Project developed supportive friendships with one another, leading them to become each other?s peer supporters in their respective journeys. It was not only the mental health peer support that was important to participants but also being able to connect over other parts of their lives and identities. The design of Buddy Project provided a sense of anonymity and separation from pre-existing ties, making it easier for participants to disclose struggles; moreover, the pairs appreciated being able to browse each other?s social media pages before connecting. Buddy Project has an explicit mission to prevent suicide and demonstrates this mission across its online platforms, which helps reduce the stigma around mental health within the peer support space. Pairs were matched based on shared interests and identities. This choice aided the pairs in developing meaningful, compatible, and supportive relationships with each other, where they felt seen and understood. However, the pairs were concerned that matching based on a shared mental health diagnosis may lead to sharing unhealthy coping mechanisms or comparing themselves and the severity of their experiences with their peers. Conclusions: The results of this study shed light on desirable features of a digital mental health peer support system: matching peers based on interests and identities that they self-identify with; having an explicit mental health?related mission coupled with social media and other web-based presences to signal that discussing mental health is safe within the peer support ecosystem; and not matching peers based on a broad mental health diagnosis. However, if the diagnosis is important, this matching should account for illness severity and educate peers on how to provide support while avoiding suggesting unhelpful coping mechanisms; allowing for some degree of anonymity and control over how peers present themselves to each other; and providing relevant information and tools to potential peers to help them decide if they would like to embark on a relationship with their matched peer before connecting with them. UR - https://mental.jmir.org/2021/1/e21819 UR - http://dx.doi.org/10.2196/21819 UR - http://www.ncbi.nlm.nih.gov/pubmed/33393909 ID - info:doi/10.2196/21819 ER - TY - JOUR AU - D'Alfonso, Simon AU - Lederman, Reeva AU - Bucci, Sandra AU - Berry, Katherine PY - 2020/12/29 TI - The Digital Therapeutic Alliance and Human-Computer Interaction JO - JMIR Ment Health SP - e21895 VL - 7 IS - 12 KW - therapeutic alliance KW - digital mental health KW - affective computing KW - persuasive computing KW - positive computing KW - mobile phone KW - mHealth UR - http://mental.jmir.org/2020/12/e21895/ UR - http://dx.doi.org/10.2196/21895 UR - http://www.ncbi.nlm.nih.gov/pubmed/33372897 ID - info:doi/10.2196/21895 ER - TY - JOUR AU - Karystianis, George AU - Simpson, Annabeth AU - Adily, Armita AU - Schofield, Peter AU - Greenberg, David AU - Wand, Handan AU - Nenadic, Goran AU - Butler, Tony PY - 2020/12/24 TI - Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study JO - J Med Internet Res SP - e23725 VL - 22 IS - 12 KW - text mining KW - mental illnesses KW - domestic violence KW - police data KW - trend analysis N2 - Background: The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim?s and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes. Objective: The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. Methods: We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. Results: In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%). Conclusions: A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information. UR - http://www.jmir.org/2020/12/e23725/ UR - http://dx.doi.org/10.2196/23725 UR - http://www.ncbi.nlm.nih.gov/pubmed/33361056 ID - info:doi/10.2196/23725 ER - TY - JOUR AU - Richter, Frederike Maike AU - Storck, Michael AU - Blitz, Rogério AU - Goltermann, Janik AU - Seipp, Juliana AU - Dannlowski, Udo AU - Baune, T. Bernhard AU - Dugas, Martin AU - Opel, Nils PY - 2020/12/1 TI - Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study JO - JMIR Ment Health SP - e24066 VL - 7 IS - 12 KW - affective disorders KW - digital data collection KW - psychiatry KW - P4 medicine N2 - Background: Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. Objective: The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity. Methods: We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. Results: Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. Conclusions: Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care. UR - https://mental.jmir.org/2020/12/e24066 UR - http://dx.doi.org/10.2196/24066 UR - http://www.ncbi.nlm.nih.gov/pubmed/33258791 ID - info:doi/10.2196/24066 ER - TY - JOUR AU - Stratton, Elizabeth AU - Choi, Isabella AU - Peters, Dorian AU - Calvo, A. Rafael AU - Harvey, B. Samuel AU - Glozier, Nicholas PY - 2020/11/6 TI - Co-Designing a Web-Based Decision Aid Tool for Employees Disclosure of Mental Health Conditions: A Participatory Study Design Using Employee and Organizational Preferences JO - JMIR Form Res SP - e23337 VL - 4 IS - 11 KW - employee disclosure KW - decision aid tool KW - mental health KW - web-based N2 - Background: Decisions of whether to disclose mental health conditions are extremely personal and require the consideration of multiple factors associated with the disclosure process (eg, weighing the risks and benefits). Decision aid tools help people make these complex decisions. Such an aid needs to be confidential, easily accessible, and easy to use with the potential to access the tool on multiple occasions. Web programs are well suited to meet these requirements and, if properly developed, can provide feasible, accessible, affordable, and effective workplace interventions. Objective: This study aims to gain insights from potential end users, in this case both employees and organizations, into what type of components including language, style, and content would avoid potential stigma and ensure that elements of clear value for users would be built into a web-based decision aid tool that aims to assist employees in making decisions about the disclosure of their mental health condition at work. Methods: A participatory design approach was used to allow developers, researchers, experts, and end users to collaborate in co-designing the tool. During the user research phase of the development of the web-based tool, a participatory design workshop approach was selected as a part of a larger study of focus groups. Australian employees and managers in rural, suburban, and urban locations participated in an exploratory qualitative study involving participatory workshops designed to elicit their perspectives and preferences for a decision aid tool. Results: A total of 2 workshops were conducted with 13 participants. The majority were from a transport company (9/13, 69%), male (8/13, 62%), and employed full time (11/13, 85%). Six employees had previous experience disclosing their own mental health condition, and 7 were in a supervisory role and had previously been disclosed to. In any co-design development, there are certain trade-offs that need to be made between the views of experts, developers, end users, and the available budget. In this specific instance of a very delicate, personal decision, the end users provided valuable design insights into key areas such as language, and they were very antipathetic to a key feature, the avatar, which was thought to be desirable by experts and developers. Findings including aspects of the tool where all stakeholders were in agreement, aspects where some stakeholders disagreed and adaptations were implemented, where disagreements could not be implemented because of financial constraints, and misalignment between stakeholders and how to decide on a balance were shared. Conclusions: The co-design with a lived experience approach is useful for contributing much to the design, language, and features. The key in this study was balancing the needs of the workers and the potential impact for the managers and organizations, while ensuring legislation and regulation requirements were upheld. UR - https://formative.jmir.org/2020/11/e23337 UR - http://dx.doi.org/10.2196/23337 UR - http://www.ncbi.nlm.nih.gov/pubmed/33155982 ID - info:doi/10.2196/23337 ER - TY - JOUR AU - Utter, Kierstin AU - Waineo, Eva AU - Bell, M. Capricia AU - Quaal, L. Harrison AU - Levine, L. Diane PY - 2020/10/27 TI - Instagram as a Window to Societal Perspective on Mental Health, Gender, and Race: Observational Pilot Study JO - JMIR Ment Health SP - e19171 VL - 7 IS - 10 KW - mental health KW - Instagram KW - social media KW - stigma KW - gender KW - race KW - depression N2 - Background: Gender and race are known to impact attitudes toward mental health topics and help-seeking behavior. Men and minorities are more likely to cite stigma as a reason for not seeking help for mental health concerns, which is of particular relevance given the high rate of suicide in men and challenges of historic proportion currently facing minority communities. Instagram provides a platform to discuss mental health, though a lack of male and minority representation may further alienate these populations. Objective: We aimed to investigate whether men and nonwhite individuals are underrepresented in Instagram photos tagged with #mentalhealth (compared to photos tagged with #health) to better understand how gender and race-based representations are manifested on this popular social media platform and discuss the implications. Methods: Three investigators of different genders and racial backgrounds met on nine different days via teleconference to analyze a total of 215 publicly available Instagram photos tagged with #mentalhealth and 215 with #health. These photos were generated using Instagram?s search function, and search results were sorted by most recently published at the time of data collection. For each photo, the three investigators recorded their observations about the gender (male versus female) and race (white versus nonwhite versus racially unclassifiable) of subjects featured in the photo, which they did not discuss with other investigators. Chi-squared analysis was performed on each investigator?s data set to compare the frequency of male versus female and white versus nonwhite subjects identified in each hashtag category. Kappa interrater agreement was calculated for each investigator pair, category (gender or race), and hashtag. Results: All three investigators observed significantly more female as compared to male subjects in photos tagged with #mentalhealth (X2=14.4, P<.001 for all investigators) while observing no significant difference between numbers of male and female subjects in photos tagged with #health (X2=1.533, P=.22; X2=1.241, P=.27; X2=0.096, P=.76). All three investigators identified significantly more white than nonwhite subjects in photos tagged with both #health and #mentalhealth (X2 values range from 11.912 to 98.927, P<.001 for all). Kappa interrater agreement revealed almost perfect agreement for gender (kappa=0.908-0.992) with the agreement for race ranging from 0.614 to 0.822, depending on hashtag and rater pair. Conclusions: Women are featured more frequently than men in Instagram photos tagged with #mentalhealth. The topic of #health, meanwhile, is not gendered this way. Low visibility of mental health among men may both represent and exacerbate existing stigma and barriers to care. White subjects are featured significantly more frequently than nonwhite subjects in photos tagged with both #mentalhealth and #health. Directed interventions using the Instagram platform may be indicated to increase the visibility of underrepresented groups and break the cycle of stigma. UR - http://mental.jmir.org/2020/10/e19171/ UR - http://dx.doi.org/10.2196/19171 UR - http://www.ncbi.nlm.nih.gov/pubmed/33107831 ID - info:doi/10.2196/19171 ER - TY - JOUR AU - Knights, Jonathan AU - Heidary, Zahra AU - Cochran, M. Jeffrey PY - 2020/9/10 TI - Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System JO - JMIR Ment Health SP - e21378 VL - 7 IS - 9 KW - digital medicine KW - mobile phone KW - entropy rate KW - Markov chains KW - medication adherence KW - contextual anomaly KW - psychiatric disorders N2 - Background: Adherence to medication is often represented in the form of a success percentage over a period of time. Although noticeable changes to aggregate adherence levels may be indicative of unstable medication behavior, a lack of noticeable changes in aggregate levels over time does not necessarily indicate stability. The ability to detect developing changes in medication-taking behavior under such conditions in real time would allow patients and care teams to make more timely and informed decisions. Objective: This study aims to develop a method capable of identifying shifts in behavioral (medication) patterns at the individual level and subsequently assess the presence of such shifts in retrospective clinical trial data from patients with serious mental illness. Methods: We defined the term adherence volatility as ?the degree to which medication ingestion behavior fits expected behavior based on historically observed data? and defined a contextual anomaly system around this concept, leveraging the empirical entropy rate of a stochastic process as the basis for formulating anomaly detection. For the presented methodology, each patient?s evolving behavior is used to dynamically construct the expectation bounds for each future interval, eliminating the need to rely on model training or a static reference sequence. Results: Simulations demonstrated that the presented methodology identifies anomalous behavior patterns even when aggregate adherence levels remain constant and highlight the temporal dependence inherent in these anomalies. Although a given sequence of events may present as anomalous during one period, that sequence should subsequently contribute to future expectations and may not be considered anomalous at a later period?this feature was demonstrated in retrospective clinical trial data. In the same clinical trial data, anomalous behavioral shifts were identified at both high- and low-adherence levels and were spread across the whole treatment regimen, with 77.1% (81/105) of the population demonstrating at least one behavioral anomaly at some point in their treatment. Conclusions: Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of adherence volatility and develops a new type of contextual anomaly detection, which does not require an a priori definition of normal and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area. UR - https://mental.jmir.org/2020/9/e21378 UR - http://dx.doi.org/10.2196/21378 UR - http://www.ncbi.nlm.nih.gov/pubmed/32909950 ID - info:doi/10.2196/21378 ER - TY - JOUR AU - Obeid, S. Jihad AU - Dahne, Jennifer AU - Christensen, Sean AU - Howard, Samuel AU - Crawford, Tami AU - Frey, J. Lewis AU - Stecker, Tracy AU - Bunnell, E. Brian PY - 2020/7/30 TI - Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach JO - JMIR Med Inform SP - e17784 VL - 8 IS - 7 KW - machine learning KW - deep learning KW - suicide KW - suicide, attempted KW - electronic health records KW - natural language processing N2 - Background: Suicide is an important public health concern in the United States and around the world. There has been significant work examining machine learning approaches to identify and predict intentional self-harm and suicide using existing data sets. With recent advances in computing, deep learning applications in health care are gaining momentum. Objective: This study aimed to leverage the information in clinical notes using deep neural networks (DNNs) to (1) improve the identification of patients treated for intentional self-harm and (2) predict future self-harm events. Methods: We extracted clinical text notes from electronic health records (EHRs) of 835 patients with International Classification of Diseases (ICD) codes for intentional self-harm and 1670 matched controls who never had any intentional self-harm ICD codes. The data were divided into training and holdout test sets. We tested a number of algorithms on clinical notes associated with the intentional self-harm codes using the training set, including several traditional bag-of-words?based models and 2 DNN models: a convolutional neural network (CNN) and a long short-term memory model. We also evaluated the predictive performance of the DNNs on a subset of patients who had clinical notes 1 to 6 months before the first intentional self-harm event. Finally, we evaluated the impact of a pretrained model using Word2vec (W2V) on performance. Results: The area under the receiver operating characteristic curve (AUC) for the CNN on the phenotyping task, that is, the detection of intentional self-harm in clinical notes concurrent with the events was 0.999, with an F1 score of 0.985. In the predictive task, the CNN achieved the highest performance with an AUC of 0.882 and an F1 score of 0.769. Although pretraining with W2V shortened the DNN training time, it did not improve performance. Conclusions: The strong performance on the first task, namely, phenotyping based on clinical notes, suggests that such models could be used effectively for surveillance of intentional self-harm in clinical text in an EHR. The modest performance on the predictive task notwithstanding, the results using DNN models on clinical text alone are competitive with other reports in the literature using risk factors from structured EHR data. UR - https://medinform.jmir.org/2020/7/e17784 UR - http://dx.doi.org/10.2196/17784 UR - http://www.ncbi.nlm.nih.gov/pubmed/32729840 ID - info:doi/10.2196/17784 ER - TY - JOUR AU - Kumar, Shefali AU - Tran, A. Jennifer L. AU - Ramirez, Ernesto AU - Lee, Wei-Nchih AU - Foschini, Luca AU - Juusola, L. Jessie PY - 2020/7/23 TI - Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study JO - JMIR Ment Health SP - e17075 VL - 7 IS - 7 KW - mental health KW - anxiety KW - depression KW - behavioral data N2 - Background: Depression and anxiety greatly impact daily behaviors, such as sleep and activity levels. With the increasing use of activity tracking wearables among the general population, there has been a growing interest in how data collected from these devices can be used to further understand the severity and progression of mental health conditions. Objective: This virtual 1-year observational study was designed with the objective of creating a longitudinal data set combining self-reported health outcomes, health care utilization, and quality of life data with activity tracker and app-based behavioral data for individuals with depression and anxiety. We provide an overview of the study design, report on baseline health and behavioral characteristics of the study population, and provide initial insights into how behavioral characteristics differ between groups of individuals with varying levels of disease severity. Methods: Individuals who were existing members of an online health community (Achievement, Evidation Health Inc) and were 18 years or older who had self-reported a diagnosis of depression or anxiety were eligible to enroll in this virtual 1-year study. Participants agreed to connect wearable activity trackers that captured data related to physical activity and sleep behavior. Mental health outcomes such as the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder Questionnaire (GAD-7), mental health hospitalizations, and medication use were captured with surveys completed at baseline and months 3, 6, 9, and 12. In this analysis, we report on baseline characteristics of the sample, including mental health disease severity and health care utilization. Additionally, we explore the relationship between passively collected behavioral data and baseline mental health status and health care utilization. Results: Of the 1304 participants enrolled in the study, 1277 individuals completed the baseline survey and 1068 individuals had sufficient activity tracker data. Mean age was 33 (SD 9) years, and the majority of the study population was female (77.2%, 994/1288) and identified as Caucasian (88.3%, 1137/1288). At baseline, 94.8% (1211/1277) of study participants reported experiencing depression or anxiety symptoms in the last year. This baseline analysis found that some passively tracked behavioral traits are associated with more severe forms of anxiety or depression. Individuals with depressive symptoms were less active than those with minimal depressive symptoms. Severe forms of depression were also significantly associated with inconsistent sleep patterns and more disordered sleep. Conclusions: These initial findings suggest that longitudinal behavioral and health outcomes data may be useful for developing digital measures of health for mental health symptom severity and progression. UR - http://mental.jmir.org/2020/7/e17075/ UR - http://dx.doi.org/10.2196/17075 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706712 ID - info:doi/10.2196/17075 ER - TY - JOUR AU - Sadeghi, Mahnoosh AU - Sasangohar, Farzan AU - McDonald, D. Anthony PY - 2020/7/22 TI - Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework JO - JMIR Ment Health SP - e16654 VL - 7 IS - 7 KW - heart rate KW - statistics KW - PTSD KW - mental health KW - physiology N2 - Background: Posttraumatic stress disorder (PTSD) is a prevalent psychiatric condition that is associated with symptoms such as hyperarousal and overreactions. Treatments for PTSD are limited to medications and in-session therapies. Assessing the way the heart responds to PTSD has shown promise in detecting and understanding the onset of symptoms. Objective: This study aimed to extract statistical and mathematical approaches that researchers can use to analyze heart rate (HR) data to understand PTSD. Methods: A scoping literature review was conducted to extract HR models. A total of 5 databases including Medical Literature Analysis and Retrieval System Online (Medline) OVID, Medline EBSCO, Cumulative Index to Nursing and Allied Health Literature (CINAHL) EBSCO, Excerpta Medica Database (Embase) Ovid, and Google Scholar were searched. Non?English language studies, as well as studies that did not analyze human data, were excluded. A total of 54 studies that met the inclusion criteria were included in this review. Results: We identified 4 categories of models: descriptive time-independent output, descriptive and time-dependent output, predictive and time-independent output, and predictive and time-dependent output. Descriptive and time-independent output models include analysis of variance and first-order exponential; the descriptive time-dependent output model includes a classical time series analysis and mixed regression. Predictive time-independent output models include machine learning methods and analysis of the HR-based fluctuation-dissipation method. Finally, predictive time-dependent output models include the time-variant method and nonlinear dynamic modeling. Conclusions: All of the identified modeling categories have relevance in PTSD, although the modeling selection is dependent on the specific goals of the study. Descriptive models are well-founded for the inference of PTSD. However, there is a need for additional studies in this area that explore a broader set of predictive models and other factors (eg, activity level) that have not been analyzed with descriptive models. UR - https://mental.jmir.org/2020/7/e16654 UR - http://dx.doi.org/10.2196/16654 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706710 ID - info:doi/10.2196/16654 ER - TY - JOUR AU - Newson, Jane Jennifer AU - Thiagarajan, C. Tara PY - 2020/7/20 TI - Assessment of Population Well-Being With the Mental Health Quotient (MHQ): Development and Usability Study JO - JMIR Ment Health SP - e17935 VL - 7 IS - 7 KW - psychiatry KW - public health KW - methods KW - mental health KW - population health KW - social determinants of health KW - global health KW - behavioral symptoms KW - diagnosis KW - symptom assessment KW - psychopathology KW - mental disorders KW - mhealth KW - depression KW - anxiety KW - attention deficit disorder with hyperactivity KW - autistic disorder KW - internet N2 - Background: Existing mental health assessment tools provide an incomplete picture of symptom experience and create ambiguity, bias, and inconsistency in mental health outcomes. Furthermore, by focusing on disorders and dysfunction, they do not allow a view of mental health and well-being across a general population. Objective: This study aims to demonstrate the outcomes and validity of a new web-based assessment tool called the Mental Health Quotient (MHQ), which is designed for the general population. The MHQ covers the complete breadth of clinical mental health symptoms and also captures healthy mental functioning to provide a complete profile of an individual?s mental health from clinical to thriving. Methods: The MHQ was developed based on the coding of symptoms assessed in 126 existing Diagnostic and Statistical Manual of Mental Disorders (DSM)?based psychiatric assessment tools as well as neuroscientific criteria laid out by Research Domain Criteria to arrive at a comprehensive set of semantically distinct mental health symptoms and attributes. These were formulated into questions on a 9-point scale with both positive and negative dimensions and developed into a web-based tool that takes approximately 14 min to complete. As its output, the assessment provides overall MHQ scores as well as subscores for 6 categories of mental health that distinguish clinical and at-risk groups from healthy populations based on a nonlinear scoring algorithm. MHQ items were also mapped to the DSM fifth edition (DSM-5), and clinical diagnostic criteria for 10 disorders were applied to the MHQ outcomes to cross-validate scores labeled at-risk and clinical. Initial data were collected from 1665 adult respondents to test the tool. Results: Scores in the normal healthy range spanned from 0 to 200 for the overall MHQ, with an average score of approximately 100 (SD 45), and from 0 to 100 with average scores between 48 (SD 21) and 55 (SD 22) for subscores in each of the 6 mental health subcategories. Overall, 2.46% (41/1665) and 13.09% (218/1665) of respondents were classified as clinical and at-risk, respectively, with negative scores. Validation against DSM-5 diagnostic criteria showed that 95% (39/41) of those designated clinical were positive for at least one DSM-5?based disorder, whereas only 1.14% (16/1406) of those with a positive MHQ score met the diagnostic criteria for a mental health disorder. Conclusions: The MHQ provides a fast, easy, and comprehensive way to assess population mental health and well-being; identify at-risk individuals and subgroups; and provide diagnosis-relevant information across 10 disorders. UR - http://mental.jmir.org/2020/7/e17935/ UR - http://dx.doi.org/10.2196/17935 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706730 ID - info:doi/10.2196/17935 ER - TY - JOUR AU - Robinson, Heather AU - Appelbe, Duncan AU - Dodd, Susanna AU - Flowers, Susan AU - Johnson, Sonia AU - Jones, H. Steven AU - Mateus, Céu AU - Mezes, Barbara AU - Murray, Elizabeth AU - Rainford, Naomi AU - Rosala-Hallas, Anna AU - Walker, Andrew AU - Williamson, Paula AU - Lobban, Fiona PY - 2020/7/17 TI - Methodological Challenges in Web-Based Trials: Update and Insights From the Relatives Education and Coping Toolkit Trial JO - JMIR Ment Health SP - e15878 VL - 7 IS - 7 KW - randomized controlled trial KW - research design KW - methods KW - internet KW - web KW - mental health KW - relatives KW - carers N2 - International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2017-016965 UR - https://mental.jmir.org/2020/7/e15878 UR - http://dx.doi.org/10.2196/15878 UR - http://www.ncbi.nlm.nih.gov/pubmed/32497018 ID - info:doi/10.2196/15878 ER - TY - JOUR AU - Linden, Brooke AU - Tam-Seto, Linna AU - Stuart, Heather PY - 2020/6/17 TI - Adherence of the #Here4U App ? Military Version to Criteria for the Development of Rigorous Mental Health Apps JO - JMIR Form Res SP - e18890 VL - 4 IS - 6 KW - mental health services KW - telemedicine KW - mHealth KW - chatbot KW - e-solutions KW - Canadian Armed Forces KW - military health KW - mobile phone N2 - Background: Over the past several years, the emergence of mobile mental health apps has increased as a potential solution for populations who may face logistical and social barriers to traditional service delivery, including individuals connected to the military. Objective: The goal of the #Here4U App ? Military Version is to provide evidence-informed mental health support to members of Canada?s military community, leveraging artificial intelligence in the form of IBM Canada?s Watson Assistant to carry on unique text-based conversations with users, identify presenting mental health concerns, and refer users to self-help resources or recommend professional health care where appropriate. Methods: As the availability and use of mental health apps has increased, so too has the list of recommendations and guidelines for efficacious development. We describe the development and testing conducted between 2018 and 2020 and assess the quality of the #Here4U App against 16 criteria for rigorous mental health app development, as identified by Bakker and colleagues in 2016. Results: The #Here4U App ? Military Version met the majority of Bakker and colleagues? criteria, with those unmet considered not applicable to this particular product or out of scope for research conducted to date. Notably, a formal evaluation of the efficacy of the app is a major priority moving forward. Conclusions: The #Here4U App ? Military Version is a promising new mental health e-solution for members of the Canadian Armed Forces community, filling many of the gaps left by traditional service delivery. UR - https://formative.jmir.org/2020/6/e18890 UR - http://dx.doi.org/10.2196/18890 UR - http://www.ncbi.nlm.nih.gov/pubmed/32554374 ID - info:doi/10.2196/18890 ER - TY - JOUR AU - Fu, Weifeng PY - 2020/6/3 TI - Application of an Isolated Word Speech Recognition System in the Field of Mental Health Consultation: Development and Usability Study JO - JMIR Med Inform SP - e18677 VL - 8 IS - 6 KW - speech recognition KW - isolated words KW - mental health KW - small vocabulary KW - HMM KW - hidden Markov model KW - programming N2 - Background: Speech recognition is a technology that enables machines to understand human language. Objective: In this study, speech recognition of isolated words from a small vocabulary was applied to the field of mental health counseling. Methods: A software platform was used to establish a human-machine chat for psychological counselling. The software uses voice recognition technology to decode the user's voice information. The software system analyzes and processes the user's voice information according to many internal related databases, and then gives the user accurate feedback. For users who need psychological treatment, the system provides them with psychological education. Results: The speech recognition system included features such as speech extraction, endpoint detection, feature value extraction, training data, and speech recognition. Conclusions: The Hidden Markov Model was adopted, based on multithread programming under a VC2005 compilation environment, to realize the parallel operation of the algorithm and improve the efficiency of speech recognition. After the design was completed, simulation debugging was performed in the laboratory. The experimental results showed that the designed program met the basic requirements of a speech recognition system. UR - https://medinform.jmir.org/2020/6/e18677 UR - http://dx.doi.org/10.2196/18677 UR - http://www.ncbi.nlm.nih.gov/pubmed/32384054 ID - info:doi/10.2196/18677 ER - TY - JOUR AU - Kumar, Anupama AU - Wang, Michael AU - Riehm, Alison AU - Yu, Eileen AU - Smith, Ted AU - Kaplin, Adam PY - 2020/5/20 TI - An Automated Mobile Mood Tracking Technology (Mood 24/7): Validation Study JO - JMIR Ment Health SP - e16237 VL - 7 IS - 5 KW - depression KW - text messaging KW - patient monitoring KW - mobile phone KW - short message service KW - ecological momentary assessment KW - digital health N2 - Background: Electronic tracking has been utilized for a variety of health conditions. Previous studies have shown that there is higher adherence to electronic methods vs paper-and-pencil tracking modalities. Electronic tracking also ensures that there are no back-filled entries, where patients have?to appear compliant?entered their responses retrospectively just before their visits with their health care provider. On the basis of the recognition of an unmet need for a Web-based automated platform to track psychiatric outcomes, Johns Hopkins University partnered with Health Central (a subsidiary of Remedy Health Media LLC) to develop Mood 24/7, an electronic, mobile, automated, SMS-based mood tracker. This is a pilot study to validate the use of Mood 24/7 in anticipation of clinical trials to demonstrate the therapeutic benefit on patients? health outcomes of utilizing digital mood-tracking technology. Objective: Mood 24/7 is an electronic mood-monitoring platform developed to accurately and efficiently track mood over time through automated daily SMS texts or emails. This study was designed to assess the accuracy and validity of Mood 24/7 in an outpatient psychiatric setting. Methods: This pilot study involved a retrospective chart review for depressed outpatients (N=9) to compare their self-reported Mood 24/7 daily mood ratings with their psychiatrist?s independent clinical mood assessment at the time of the patient?s visit. Their mood ratings via Mood 24/7 were collected over 36 weeks. In addition, a mixed model analysis was applied to compare the weekly Montgomery-Åsberg Depression Rating Scale (MADRS) scores with Mood 24/7 scores over an average of 3 months. Results: A 97.2% (315/324) digital mood reporting adherence was found over 36 weeks, and a significant correlation (r=0.86, P<.001) was observed between patients? Mood 24/7 scores and their psychiatrist?s blinded clinical assessment of the patient?s mood when seen in the clinic. In addition, a significant concordance (intraclass correlation of 0.69, 95% CI 0.33-0.91, P<.001) was observed in the mixed model analysis of the clinician-administered MADRS vs Mood 24/7 scores over time. Conclusions: Our chart review and mixed model analyses demonstrate that Mood 24/7 is a valid instrument for convenient, simple, noninvasive, and accurate longitudinal mood assessment in the outpatient clinical setting. UR - https://mental.jmir.org/2020/5/e16237 UR - http://dx.doi.org/10.2196/16237 UR - http://www.ncbi.nlm.nih.gov/pubmed/32432558 ID - info:doi/10.2196/16237 ER - TY - JOUR AU - Gremyr, Andreas AU - Andersson Gäre, Boel AU - Greenhalgh, Trisha AU - Malm, Ulf AU - Thor, Johan AU - Andersson, Ann-Christine PY - 2020/4/23 TI - Using Complexity Assessment to Inform the Development and Deployment of a Digital Dashboard for Schizophrenia Care: Case Study JO - J Med Internet Res SP - e15521 VL - 22 IS - 4 KW - health care KW - complexity KW - schizophrenia KW - coproduction KW - learning health systems N2 - Background: Health care is becoming more complex. For an increasing number of individuals, interacting with health care means addressing more than just one illness or disorder, engaging in more than one treatment, and interacting with more than one care provider. Individuals with severe mental illnesses such as schizophrenia are disproportionately affected by this complexity. Characteristic symptoms can make it harder to establish and maintain relationships. Treatment failure is common even where there is access to effective treatments, increasing suicide risk. Knowledge of complex adaptive systems has been increasingly recognized as useful in understanding and developing health care. A complex adaptive system is a collection of interconnected agents with the freedom to act based on their own internalized rules, affecting each other. In a complex health care system, relevant feedback is crucial in enabling continuous learning and improvement on all levels. New technology has potential, but the failure rate of technology projects in health care is high, arguably due to complexity. The Nonadoption, Abandonment, and challenges to Scale-up, Spread, and Sustainability (NASSS) framework and complexity assessment tool (NASSS-CAT) have been developed specifically to help identify and manage complexity in technology-related development projects in health care. Objective: This study aimed to use a pilot version of the NASSS-CAT instrument to inform the development and deployment of a point-of-care dashboard supporting schizophrenia care in west Sweden. Specifically, we report on the complexity profile of the project, stakeholders? experiences with using NASSS-CAT, and practical implications. Methods: We used complexity assessment to structure data collection and feedback sessions with stakeholders, thereby informing an emergent approach to the development and deployment of the point-of-care dashboard. We also performed a thematic analysis, drawing on observations and documents related to stakeholders' use of the NASSS-CAT to describe their views on its usefulness. Results: Application of the NASSS framework revealed different types of complexity across multiple domains, including the condition, technology, value proposition, organizational tasks and pathways, and wider system. Stakeholders perceived the NASSS-CAT tool as useful in gaining perspective and new insights, covering areas that might otherwise have been neglected. Practical implications derived from feedback sessions with managers and developers are described. Conclusions: This case study shows how stakeholders can identify and plan to address complexities during the introduction of a technological solution. Our findings suggest that NASSS-CAT can bring participants a greater understanding of complexities in digitalization projects in general. UR - http://www.jmir.org/2020/4/e15521/ UR - http://dx.doi.org/10.2196/15521 UR - http://www.ncbi.nlm.nih.gov/pubmed/32324143 ID - info:doi/10.2196/15521 ER - TY - JOUR AU - Chmitorz, Andrea AU - Kurth, Karolina AU - Mey, K. Lara AU - Wenzel, Mario AU - Lieb, Klaus AU - Tüscher, Oliver AU - Kubiak, Thomas AU - Kalisch, Raffael PY - 2020/2/24 TI - Assessment of Microstressors in Adults: Questionnaire Development and Ecological Validation of the Mainz Inventory of Microstressors JO - JMIR Ment Health SP - e14566 VL - 7 IS - 2 KW - microstressor KW - daily hassles KW - validation KW - ecological momentary assessment N2 - Background: Many existing scales for microstressor assessment do not differentiate between objective (ie, observable) stressor events and stressful cognitions or concerns. They often mix items assessing objective stressor events with items measuring other aspects of stress, such as perceived stressor severity, the evoked stress reaction, or further consequences on health, which may result in spurious associations in studies that include other questionnaires that measure such constructs. Most scales were developed several decades ago; therefore, modern life stressors may not be represented. Ecological momentary assessment (EMA) allows for sampling of current behaviors and experiences in real time and in the natural habitat, thereby maximizing the generalization of the findings to real-life situations (ie, ecological validity) and minimizing recall bias. However, it has not been used for the validation of microstressor questionnaires so far. Objective: The aim is to develop a questionnaire that (1) allows for retrospective assessment of microstressors over one week, (2) focuses on objective (ie, observable) microstressors, (3) includes stressors of modern life, and (4) separates stressor occurrence from perceived stressor severity. Methods: Cross-sectional (N=108) and longitudinal studies (N=10 and N=70) were conducted to evaluate the Mainz Inventory of Microstressors (MIMIS). In the longitudinal studies, EMA was used to compare stressor data, which was collected five times per day for 7 or 30 days with retrospective reports (end-of-day, end-of-week). Pearson correlations and multilevel modeling were used in the analyses. Results: High correlations were found between end-of-week, end-of-day, and EMA data for microstressor occurrence (counts) (r?.69 for comparisons per week, r?.83 for cumulated data) and for mean perceived microstressor severity (r?.74 for comparisons per week, r?.85 for cumulated data). The end-of-week questionnaire predicted the EMA assessments sufficiently (counts: beta=.03, 95% CI .02-.03, P<.001; severity: beta=.73, 95% CI .59-.88, P<.001) and the association did not change significantly over four subsequent weeks. Conclusions: Our results provide evidence for the ecological validity of the MIMIS questionnaire. UR - http://mental.jmir.org/2020/2/e14566/ UR - http://dx.doi.org/10.2196/14566 UR - http://www.ncbi.nlm.nih.gov/pubmed/32130154 ID - info:doi/10.2196/14566 ER - TY - JOUR AU - Ospina-Pinillos, Laura AU - Davenport, A. Tracey AU - Navarro-Mancilla, Andres Alvaro AU - Cheng, Sze Vanessa Wan AU - Cardozo Alarcón, Camilo Andrés AU - Rangel, M. Andres AU - Rueda-Jaimes, Eduardo German AU - Gomez-Restrepo, Carlos AU - Hickie, B. Ian PY - 2020/2/6 TI - Involving End Users in Adapting a Spanish Version of a Web-Based Mental Health Clinic for Young People in Colombia: Exploratory Study Using Participatory Design Methodologies JO - JMIR Ment Health SP - e15914 VL - 7 IS - 2 KW - Colombia KW - telemedicine KW - medical informatics KW - eHealth KW - mental health KW - cultural characteristics KW - cultural competency KW - ethnic groups KW - quality of health care KW - community-based participatory research KW - primary health care KW - patient participation KW - patient preference KW - patient satisfaction KW - consumer health information KW - methods KW - research design N2 - Background: Health information technologies (HITs) hold enormous promise for improving access to and providing better quality of mental health care. However, despite the spread of such technologies in high-income countries, these technologies have not yet been commonly adopted in low- and middle-income countries. People living in these parts of the world are at risk of experiencing physical, technological, and social health inequalities. A possible solution is to utilize the currently available HITs developed in other counties. Objective: Using participatory design methodologies with Colombian end users (young people, their supportive others, and health professionals), this study aimed to conduct co-design workshops to culturally adapt a Web-based Mental Health eClinic (MHeC) for young people, perform one-on-one user-testing sessions to evaluate an alpha prototype of a Spanish version of the MHeC and adapt it to the Colombian context, and inform the development of a skeletal framework and alpha prototype for a Colombian version of the MHeC (MHeC-C). Methods: This study involved the utilization of a research and development (R&D) cycle including 4 iterative phases: co-design workshops; knowledge translation; tailoring to language, culture, and place (or context); and one-on-one user-testing sessions. Results: A total of 2 co-design workshops were held with 18 users?young people (n=7) and health professionals (n=11). Moreover, 10 users participated in one-on-one user-testing sessions?young people (n=5), supportive others (n=2), and health professionals (n=3). A total of 204 source documents were collected and 605 annotations were coded. A thematic analysis resulted in 6 themes (ie, opinions about the MHeC-C, Colombian context, functionality, content, user interface, and technology platforms). Participants liked the idea of having an MHeC designed and adapted for Colombian young people, and its 5 key elements were acceptable in this context (home page and triage system, self-report assessment, dashboard of results, booking and video-visit system, and personalized well-being plan). However, to be relevant in Colombia, participants stressed the need to develop additional functionality (eg, phone network backup; chat; geolocation; and integration with electronic medical records, apps, or electronic tools) as well as an adaptation of the self-report assessment. Importantly, the latter not only included language but also culture and context. Conclusions: The application of an R&D cycle that also included processes for adaptation to Colombia (language, culture, and context) resulted in the development of an evidence-based, language-appropriate, culturally sensitive, and context-adapted HIT that is relevant, applicable, engaging, and usable in both the short and long term. The resultant R&D cycle allowed for the adaptation of an already available HIT (ie, MHeC) to the MHeC-C?a low-cost and scalable technology solution for low- and middle-income countries like Colombia, which has the potential to provide young people with accessible, available, affordable, and integrated mental health care at the right time. UR - https://mental.jmir.org/2020/2/e15914 UR - http://dx.doi.org/10.2196/15914 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/15914 ER - TY - JOUR AU - Aupperle, Leora Robin AU - Paulus, P. Martin AU - Kuplicki, Rayus AU - Touthang, James AU - Victor, Teresa AU - Yeh, Hung-Wen AU - AU - Khalsa, S. Sahib PY - 2020/1/28 TI - Web-Based Graphic Representation of the Life Course of Mental Health: Cross-Sectional Study Across the Spectrum of Mood, Anxiety, Eating, and Substance Use Disorders JO - JMIR Ment Health SP - e16919 VL - 7 IS - 1 KW - mental health KW - life history KW - psychosocial factors KW - depression KW - anxiety KW - substance use disorders KW - eating disorders N2 - Background: Although patient history is essential for informing mental health assessment, diagnosis, and prognosis, there is a dearth of standardized instruments measuring time-dependent factors relevant to psychiatric disorders. Previous research has demonstrated the potential utility of graphical representations, termed life charts, for depicting the complexity of the course of mental illness. However, the implementation of these assessments is limited by the exclusive focus on specific mental illnesses (ie, bipolar disorder) and the lack of intuitive graphical interfaces for data collection and visualization. Objective: This study aimed to develop and test the utility of the Tulsa Life Chart (TLC) as a Web-based, structured approach for obtaining and graphically representing historical information on psychosocial and mental health events relevant across a spectrum of psychiatric disorders. Methods: The TLC interview was completed at baseline by 499 participants of the Tulsa 1000, a longitudinal study of individuals with depressive, anxiety, substance use, or eating disorders and healthy comparisons (HCs). All data were entered electronically, and a 1-page electronic and interactive graphical representation was developed using the Google Visualization Application Programming Interface. For 8 distinct life epochs (periods of approximately 5-10 years), the TLC assessed the following factors: school attendance, hobbies, jobs, social support, substance use, mental health treatment, family structure changes, negative and positive events, and epoch and event-related mood ratings. We used generalized linear mixed models (GLMMs) to evaluate trajectories of each domain over time and by sex, age, and diagnosis, using case examples and Web-based interactive graphs to visualize data. Results: GLMM analyses revealed main or interaction effects of epoch and diagnosis for all domains. Epoch by diagnosis interactions were identified for mood ratings and the number of negative-versus-positive events (all P values <.001), with all psychiatric groups reporting worse mood and greater negative-versus-positive events than HCs. These differences were most robust at different epochs, depending on diagnosis. There were also diagnosis and epoch main effects for substance use, mental health treatment received, social support, and hobbies (P<.001). User experience ratings (each on a 1-5 scale) revealed that participants found the TLC pleasant to complete (mean 3.07, SD 1.26) and useful for understanding their mental health (mean 3.07, SD 1.26), and that they were likely to recommend it to others (mean 3.42, SD 0.85). Conclusions: The TLC provides a structured, Web-based transdiagnostic assessment of psychosocial history relevant for the diagnosis and treatment of psychiatric disorders. Interactive, 1-page graphical representations of the TLC allow for the efficient communication of historical life information that would be useful for clinicians, patients, and family members. UR - http://mental.jmir.org/2020/1/e16919/ UR - http://dx.doi.org/10.2196/16919 UR - http://www.ncbi.nlm.nih.gov/pubmed/32012081 ID - info:doi/10.2196/16919 ER - TY - JOUR AU - Ferreri, Florian AU - Bourla, Alexis AU - Peretti, Charles-Siegfried AU - Segawa, Tomoyuki AU - Jaafari, Nemat AU - Mouchabac, Stéphane PY - 2019/12/10 TI - How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review JO - JMIR Ment Health SP - e11643 VL - 6 IS - 12 KW - obsessive-compulsive disorder KW - ecological momentary assessment KW - biofeedback KW - digital biomarkers KW - digital phenotyping KW - mobile health KW - virtual reality KW - machine learning N2 - Background: New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example. Objective: The impact of new technologies on health professionals? practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD. Methods: We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video]. Results: We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention. Conclusions: The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles. UR - https://mental.jmir.org/2019/12/e11643 UR - http://dx.doi.org/10.2196/11643 UR - http://www.ncbi.nlm.nih.gov/pubmed/31821153 ID - info:doi/10.2196/11643 ER - TY - JOUR AU - Tønning, Lindbjerg Morten AU - Kessing, Vedel Lars AU - Bardram, Eivind Jakob AU - Faurholt-Jepsen, Maria PY - 2019/10/27 TI - Methodological Challenges in Randomized Controlled Trials on Smartphone-Based Treatment in Psychiatry: Systematic Review JO - J Med Internet Res SP - e15362 VL - 21 IS - 10 KW - psychiatry KW - methodology KW - smartphone KW - mHealth KW - mobile Health KW - digital health KW - digital psychiatry KW - systematic review N2 - Background: Smartphone-based technology is developing at high speed, and many apps offer potential new ways of monitoring and treating a range of psychiatric disorders and symptoms. However, the effects of most available apps have not been scientifically investigated. Within medicine, randomized controlled trials (RCTs) are the standard method for providing the evidence of effects. However, their rigidity and long time frame may contrast with the field of information technology research. Therefore, a systematic review of methodological challenges in designing and conducting RCTs within mobile health is needed. Objective: This systematic review aimed to (1) identify and describe RCTs investigating the effect of smartphone-based treatment in adult patients with a psychiatric diagnosis, (2) discuss methodological challenges in designing and conducting individual trials, and (3) suggest recommendations for future trials. Methods: A systematic search in English was conducted in PubMed, PsycINFO, and EMBASE up to August 12, 2019. The search terms were (1) psychiatric disorders in broad term and for specific disorders AND (2) smartphone or app AND (3) RCT. The Consolidated Standards of Reporting Trials electronic health guidelines were used as a template for data extraction. The focus was on trial design, method, and reporting. Only trials having sufficient information on diagnosis and acceptable diagnostic procedures, having a smartphone as a central part of treatment, and using an RCT design were included. Results: A total of 27 trials comprising 3312 patients within a range of psychiatric diagnoses were included. Among them, 2 trials were concerning drug or alcohol abuse, 3 psychosis, 10 affective disorders, 9 anxiety and posttraumatic stress disorder, 1 eating disorder, and 1 attention-deficit/hyperactivity disorder. In addition, 1 trial used a cross-diagnostic design, 7 trials included patients with a clinical diagnosis that was subsequently assessed and validated by the researchers, and 11 trials had a sample size above 100. Generally, large between-trial heterogeneity and multiple approaches to patient recruitment, diagnostic procedures, trial design, comparator, outcome measures, and analyses were identified. Only 5 trials published a trial protocol. Furthermore, 1 trial provided information regarding technological updates, and only 18 trials reported on the conflicts of interest. No trial addressed the ethical aspects of using smartphones in treatment. Conclusions: This first systematic review of the methodological challenges in designing and conducting RCTs investigating smartphone-based treatment in psychiatric patients suggests an increasing number of trials but with a lower quality compared with classic medical RCTs. Heterogeneity and methodological issues in individual trials limit the evidence. Methodological recommendations are presented. UR - http://www.jmir.org/2019/10/e15362/ UR - http://dx.doi.org/10.2196/15362 UR - http://www.ncbi.nlm.nih.gov/pubmed/31663859 ID - info:doi/10.2196/15362 ER - TY - JOUR AU - Ralph-Nearman, Christina AU - Arevian, C. Armen AU - Puhl, Maria AU - Kumar, Rajay AU - Villaroman, Diane AU - Suthana, Nanthia AU - Feusner, D. Jamie AU - Khalsa, S. Sahib PY - 2019/10/29 TI - A Novel Mobile Tool (Somatomap) to Assess Body Image Perception Pilot Tested With Fashion Models and Nonmodels: Cross-Sectional Study JO - JMIR Ment Health SP - e14115 VL - 6 IS - 10 KW - body image KW - body perception KW - body representation KW - body image disorder KW - eating disorder KW - mobile health KW - mental health KW - mobile app KW - digital health N2 - Background: Distorted perception of one?s body and appearance, in general, is a core feature of several psychiatric disorders including anorexia nervosa and body dysmorphic disorder and is operative to varying degrees in nonclinical populations. Yet, body image perception is challenging to assess, given its subjective nature and variety of manifestations. The currently available methods have several limitations including restricted ability to assess perceptions of specific body areas. To address these limitations, we created Somatomap, a mobile tool that enables individuals to visually represent their perception of body-part sizes and shapes as well as areas of body concerns and record the emotional valence of concerns. Objective: This study aimed to develop and pilot test the feasibility of a novel mobile tool for assessing 2D and 3D body image perception. Methods: We developed a mobile 2D tool consisting of a manikin figure on which participants outline areas of body concern and indicate the nature, intensity, and emotional valence of the concern. We also developed a mobile 3D tool consisting of an avatar on which participants select individual body parts and use sliders to manipulate their size and shape. The tool was pilot tested on 103 women: 65 professional fashion models, a group disproportionately exposed to their own visual appearance, and 38 nonmodels from the general population. Acceptability was assessed via a usability rating scale. To identify areas of body concern in 2D, topographical body maps were created by combining assessments across individuals. Statistical body maps of group differences in body concern were subsequently calculated using the formula for proportional z-score. To identify areas of body concern in 3D, participants? subjective estimates from the 3D avatar were compared to corresponding measurements of their actual body parts. Discrepancy scores were calculated based on the difference between the perceived and actual body parts and evaluated using multivariate analysis of covariance. Results: Statistical body maps revealed different areas of body concern between models (more frequently about thighs and buttocks) and nonmodels (more frequently about abdomen/waist). Models were more accurate at estimating their overall body size, whereas nonmodels tended to underestimate the size of individual body parts, showing greater discrepancy scores for bust, biceps, waist, hips, and calves but not shoulders and thighs. Models and nonmodels reported high ease-of-use scores (8.4/10 and 8.5/10, respectively), and the resulting 3D avatar closely resembled their actual body (72.7% and 75.2%, respectively). Conclusions: These pilot results suggest that Somatomap is feasible to use and offers new opportunities for assessment of body image perception in mobile settings. Although further testing is needed to determine the applicability of this approach to other populations, Somatomap provides unique insight into how humans perceive and represent the visual characteristics of their body. UR - http://mental.jmir.org/2019/10/e14115/ UR - http://dx.doi.org/10.2196/14115 UR - http://www.ncbi.nlm.nih.gov/pubmed/31469647 ID - info:doi/10.2196/14115 ER - TY - JOUR AU - Han, Jin AU - Torok, Michelle AU - Gale, Nyree AU - Wong, JJ Quincy AU - Werner-Seidler, Aliza AU - Hetrick, E. Sarah AU - Christensen, Helen PY - 2019/10/4 TI - Use of Web Conferencing Technology for Conducting Online Focus Groups Among Young People With Lived Experience of Suicidal Thoughts: Mixed Methods Research JO - JMIR Ment Health SP - e14191 VL - 6 IS - 10 KW - online focus group KW - young people KW - suicide KW - qualitative methods N2 - Background: There is an increasing interest in engaging people with lived experience in suicide prevention research. However, young people with suicidal thoughts have been described as a ?hard-to-include? population due to time, distance, stigma, and social barriers. Objective: This study aims to investigate whether conducting synchronous Web conferencing technology?based online focus groups (W-OFGs) is a feasible method to engage young people with lived experience of suicidal thoughts in suicide prevention research. Methods: Young people aged between 16 and 25 years and living in Sydney, Australia, were recruited through flyers, emails, and social media advertisements. The W-OFGs were established using a Web conferencing technology called GoToMeeting. Participants? response rate, attendance, and feedback of the W-OFGs were analyzed to determine whether the W-OFG system is feasible for suicide prevention research. Researchers? reflections about how to effectively implement the W-OFGs were also reported as part of the results. Results: In the pre?W-OFG survey, 39 (97.5%) young people (n=40) chose to attend the online focus group. Among the 22 participants who responded to the W-OFG invitations, 15 confirmed that they would attend the W-OFGs, of which 11 participants attended the W-OFGs. Feedback collected from the participants in the W-OFG and the post?W-OFG survey suggested that online focus groups are acceptable to young people in suicide prevention research. Considerations for selecting the Web conferencing platform, conducting the mock W-OFGs, implementing the risk management procedure, inviting participants to the W-OFGs, and hosting and moderating the W-OFGs as well as a few potential ethical and pragmatic challenges in using this method are discussed in this study. Conclusions: The Web conferencing technology provides a feasible replacement for conventional methods, particularly for qualitative research involving vulnerable populations and stigmatized topics including suicide prevention. Our results indicate that this modality is an optimal alternative to engage young people in the focus group discussion. Future studies should compare the data collected from the Web conferencing technology and conventional face-to-face methods in suicide prevention research to determine if these two methods are equivalent in data quality from a quantitative approach. UR - https://mental.jmir.org/2019/10/e14191 UR - http://dx.doi.org/10.2196/14191 UR - http://www.ncbi.nlm.nih.gov/pubmed/31588913 ID - info:doi/10.2196/14191 ER - TY - JOUR AU - Chung, Kyungmi AU - Park, Young Jin AU - Joung, DaYoung AU - Jhung, Kyungun PY - 2019/09/13 TI - Response Time as an Implicit Self-Schema Indicator for Depression Among Undergraduate Students: Preliminary Findings From a Mobile App?Based Depression Assessment JO - JMIR Mhealth Uhealth SP - e14657 VL - 7 IS - 9 KW - depressive symptoms KW - response time KW - self-concept KW - mobile phone KW - mobile apps KW - diagnostic screening programs KW - self-assessment KW - treatment adherence KW - compliance N2 - Background: Response times to depressive symptom items in a mobile-based depression screening instrument has potential as an implicit self-schema indicator for depression but has yet to be determined; the instrument was designed to readily record depressive symptoms experienced on a daily basis. In this study, the well-validated Korean version of the Center for Epidemiologic Studies Depression Scale-Revised (K-CESD-R) was adopted. Objective: The purpose of this study was to investigate the relationship between depression severity (ie, explicit measure: total K-CESD-R Mobile scores) and the latent trait of interest in schematic self-referent processing of depressive symptom items (ie, implicit measure: response times to items in the K-CESD-R Mobile scale). The purpose was to investigate this relationship among undergraduate students who had never been diagnosed with, but were at risk for, major depressive disorder (MDD) or comorbid MDD with other neurological or psychiatric disorders. Methods: A total of 70 participants?36 males (51%) and 34 females (49%)?aged 19-29 years (mean 22.66, SD 2.11), were asked to complete both mobile and standard K-CESD-R assessments via their own mobile phones. The mobile K-CESD-R sessions (binary scale: yes or no) were administered on a daily basis for 2 weeks. The standard K-CESD-R assessment (5-point scale) was administered on the final day of the 2-week study period; the assessment was delivered via text message, including a link to the survey, directly to participants? mobile phones. Results: A total of 5 participants were excluded from data analysis. The result of polynomial regression analysis showed that the relationship between total K-CESD-R Mobile scores and the reaction times to the depressive symptom items was better explained by a quadratic trend?F (2, 62)=21.16, P<.001, R2=.41?than by a linear trend?F (1, 63)=25.43, P<.001, R2=.29. It was further revealed that the K-CESD-R Mobile app had excellent internal consistency (Cronbach alpha=.94); at least moderate concurrent validity with other depression scales, such as the Korean version of the Quick Inventory for Depressive Symptomatology-Self Report (?=.38, P=.002) and the Patient Health Questionnaire-9 (?=.48, P<.001); a high adherence rate for all participants (65/70, 93%); and a high follow-up rate for 10 participants whose mobile or standard K-CESD-R score was 13 or greater (8/10, 80%). Conclusions: As hypothesized, based on a self-schema model for depression that represented both item and person characteristics, the inverted U-shaped relationship between the explicit and implicit self-schema measures for depression showed the potential of an organizational breakdown; this also showed the potential for a subsequent return to efficient processing of schema-consistent information along a continuum, ranging from nondepression through mild depression to severe depression. Further, it is expected that the updated K-CESD-R Mobile app can play an important role in encouraging people at risk for depression to seek professional follow-up for mental health care. UR - https://mhealth.jmir.org/2019/9/e14657/ UR - http://dx.doi.org/10.2196/14657 UR - http://www.ncbi.nlm.nih.gov/pubmed/31586362 ID - info:doi/10.2196/14657 ER - TY - JOUR AU - Chua, Loon Sean Ing AU - Tan, Chuan Ngiap AU - Wong, Teen Wei AU - Allen Jr, Carson John AU - Quah, Min Joanne Hui AU - Malhotra, Rahul AU - Østbye, Truls PY - 2019/08/01 TI - Virtual Reality for Screening of Cognitive Function in Older Persons: Comparative Study JO - J Med Internet Res SP - e14821 VL - 21 IS - 8 KW - virtual reality KW - feasibility studies KW - mental status and dementia tests KW - technology KW - video games KW - dementia KW - cognitive dysfunction N2 - Background: The prevalence of dementia, which presents as cognitive decline in one or more cognitive domains affecting function, is increasing worldwide. Traditional cognitive screening tools for dementia have their limitations, with emphasis on memory and, to a lesser extent, on the cognitive domain of executive function. The use of virtual reality (VR) in screening for cognitive function in older persons is promising, but evidence for its use is sparse. Objective: The primary aim was to examine the feasibility and acceptability of using VR to screen for cognitive impairment in older persons in a primary care setting. The secondary aim was to assess the module?s ability to discriminate between cognitively intact and cognitively impaired participants. Methods: A comparative study was conducted at a public primary care clinic in Singapore, where persons aged 65-85 years were recruited based on a cut-off score of 26 on the Montreal Cognitive Assessment (MoCA) scale. They participated in a VR module for assessment of their learning and memory, perceptual-motor function, and executive function. Each participant was evaluated by the total performance score (range: 0-700) upon completion of the study. A questionnaire was also administered to assess their perception of and attitude toward VR. Results: A total of 37 participants in Group 1 (cognitively intact; MoCA score?26) and 23 participants in Group 2 (cognitively impaired; MoCA score<26) were assessed. The mean time to completion of the study was 19.1 (SD 3.6) minutes in Group 1 and 20.4 (3.4) minutes in Group 2. Mean feedback scores ranged from 3.80 to 4.48 (max=5) in favor of VR. The total performance score in Group 1 (552.0, SD 57.2) was higher than that in Group 2 (476.1, SD 61.9; P<.001) and exhibited a moderate positive correlation with scores from other cognitive screening tools: Abbreviated Mental Test (0.312), Mini-Mental State Examination (0.373), and MoCA (0.427). A receiver operating characteristic curve analysis for the relationship between the total performance score and the presence of cognitive impairment showed an area under curve of 0.821 (95% CI 0.714-0.928). Conclusions: We demonstrated the feasibility of using a VR-based screening tool for cognitive function in older persons in primary care, who were largely in favor of this tool. UR - https://www.jmir.org/2019/8/e14821/ UR - http://dx.doi.org/10.2196/14821 UR - http://www.ncbi.nlm.nih.gov/pubmed/31373274 ID - info:doi/10.2196/14821 ER - TY - JOUR AU - Davenport, A. Tracey AU - LaMonica, M. Haley AU - Whittle, Lisa AU - English, Amelia AU - Iorfino, Frank AU - Cross, Shane AU - Hickie, B. Ian PY - 2019/05/31 TI - Validation of the InnoWell Platform: Protocol for a Clinical Trial JO - JMIR Res Protoc SP - e13955 VL - 8 IS - 5 KW - clinical trial protocol KW - mental health KW - medical informatics KW - suicide N2 - Background: New electronic health technologies are being rapidly developed to improve the delivery of mental health care for both health professionals and consumers and better support self-management of care. We developed a Web-based platform (the InnoWell Platform) that supports the prevention, earlyintervention, treatment, and continuous monitoring of mental health and maintenance of well-being in people aged 2 years and older. The platform is a customizable digital tool kitthat operates through existing service providers who utilize thesystem to provide their consumers with access to evidence-basedassessments and feedback, intervention options, and outcomemonitoring. It does this by collecting, storing, and reportingpersonal and health information back to consumers and theirhealth professionals to promote collaborative care partnershipsthat aim to improve the management of mental ill health andmaintenance of well-being Objective: The aim of this study was to describe the research protocol for a naturalistic prospective clinical trial wherein all consumers presenting for care to a traditional face-to-face or Web-based mental health service in which the InnoWell Platform is being offered as part of standard clinical care will be given the opportunity to use the platform. Methods: The Web-based platform is a configurable and customizable digital tool that assists in the assessment, monitoring and management of mental ill health, and maintenance of well-being. It does this by collecting, storing, and reporting health information back to the person and his or her clinician to enable transformation to person-centered care. The clinical trial will be conducted with individuals aged 2 years and older presenting to participating services for care, including persons from the veteran community, Aboriginal and Torres Strait Islander people, people from culturally and linguistically diverse backgrounds, the lesbian, gay, bisexual, transgender, and intersex community, and those from broader education and workforce sectors, as well as people with disabilities, lived experience of comorbidity, complex disorders, and suicidality. Results: Project Synergy was funded in June 2017, and data collection began in November 2018 at a youth mental health service. At the time of this publication, 5 additional services have also begun recruitment, including 4 youth mental health services and a veteran?s service. The first results are expected to be submitted in 2020 for publication. Conclusions: This clinical trial will promote access to comprehensive, high-quality mental health care to improve outcomes for consumers and health professionals. The data collected will be used to validate a clinical staging algorithm designed to match consumers with the right level of care and reduce the rate of suicidal thoughts and/or behaviors and suicide by suggesting pathways to care that are appropriate for the identified level of need, while simultaneously enabling a timely service response. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12618001676202; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374632 (Archived by WebCite at http://www.webcitation.org/78TOi5jwl) International Registered Report Identifier (IRRID): DERR1-10.2196/13955 UR - http://www.researchprotocols.org/2019/5/e13955/ UR - http://dx.doi.org/10.2196/13955 UR - http://www.ncbi.nlm.nih.gov/pubmed/31152524 ID - info:doi/10.2196/13955 ER - TY - JOUR AU - Pereira-Sanchez, Victor AU - Alvarez-Mon, Angel Miguel AU - Asunsolo del Barco, Angel AU - Alvarez-Mon, Melchor AU - Teo, Alan PY - 2019/05/29 TI - Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets JO - J Med Internet Res SP - e14167 VL - 21 IS - 5 KW - social isolation KW - loneliness KW - hikikomori KW - hidden youth KW - social media KW - Twitter KW - social withdrawal N2 - Background: Hikikomori is a severe form of social withdrawal, originally described in Japan but recently reported in other countries. Debate exists as to what extent hikikomori is viewed as a problem outside of the Japanese context. Objective: We aimed to explore perceptions about hikikomori outside Japan by analyzing Western language content from the popular social media platform, Twitter. Methods: We conducted a mixed methods analysis of all publicly available tweets using the hashtag #hikikomori between February 1 and August 16, 2018, in 5 Western languages (Catalan, English, French, Italian, and Spanish). Tweets were first classified as to whether they described hikikomori as a problem or a nonproblematic phenomenon. Tweets regarding hikikomori as a problem were then subclassified in terms of the type of problem (medical, social, or anecdotal) they referred to, and we marked if they referenced scientific publications or the presence of hikikomori in countries other than Japan. We also examined measures of interest in content related to hikikomori, including retweets, likes, and associated hashtags. Results: A total of 1042 tweets used #hikikomori, and 656 (62.3%) were included in the content analysis. Most of the included tweets were written in English (44.20%) and Italian (34.16%), and a majority (56.70%) discussed hikikomori as a problem. Tweets referencing scientific publications (3.96%) and hikikomori as present in countries other than Japan (13.57%) were less common. Tweets mentioning hikikomori outside Japan were statistically more likely to be retweeted (P=.01) and liked (P=.01) than those not mentioning it, whereas tweets with explicit scientific references were statistically more retweeted (P=.01) but not liked (P=.10) than those without that reference. Retweet and like figures were not statistically significantly different among other categories and subcategories. The most associated hashtags included references to Japan, mental health, and the youth. Conclusions: Hikikomori is a repeated word in non-Japanese Western languages on Twitter, suggesting the presence of hikikomori in countries outside Japan. Most tweets treat hikikomori as a problem, but the ways they post about it are highly heterogeneous. UR - http://www.jmir.org/2019/5/e14167/ UR - http://dx.doi.org/10.2196/14167 UR - http://www.ncbi.nlm.nih.gov/pubmed/31144665 ID - info:doi/10.2196/14167 ER - TY - JOUR AU - Mofsen, M. Aaron AU - Rodebaugh, L. Thomas AU - Nicol, E. Ginger AU - Depp, A. Colin AU - Miller, Philip J. AU - Lenze, J. Eric PY - 2019/4/21 TI - When All Else Fails, Listen to the Patient: A Viewpoint on the Use of Ecological Momentary Assessment in Clinical Trials JO - JMIR Ment Health SP - e11845 VL - 6 IS - 5 KW - ecological momentary assessment KW - mental health KW - controlled clinical trial KW - psychiatry KW - health technology UR - https://mental.jmir.org/2019/5/e11845/ UR - http://dx.doi.org/10.2196/11845 UR - http://www.ncbi.nlm.nih.gov/pubmed/31066701 ID - info:doi/10.2196/11845 ER - TY - JOUR AU - Mandryk, Lee Regan AU - Birk, Valentin Max PY - 2019/04/23 TI - The Potential of Game-Based Digital Biomarkers for Modeling Mental Health JO - JMIR Ment Health SP - e13485 VL - 6 IS - 4 KW - digital games KW - digital phenotyping KW - mental health KW - computational modeling KW - big data KW - video games KW - biomarkers N2 - Background: Assessment for mental health is performed by experts using interview techniques, questionnaires, and test batteries and following standardized manuals; however, there would be myriad benefits if behavioral correlates could predict mental health and be used for population screening or prevalence estimations. A variety of digital sources of data (eg, online search data and social media posts) have been previously proposed as candidates for digital biomarkers in the context of mental health. Playing games on computers, gaming consoles, or mobile devices (ie, digital gaming) has become a leading leisure activity of choice and yields rich data from a variety of sources. Objective: In this paper, we argue that game-based data from commercial off-the-shelf games have the potential to be used as a digital biomarker to assess and model mental health and health decline. Although there is great potential in games developed specifically for mental health assessment (eg, Sea Hero Quest), we focus on data gathered ?in-the-wild? from playing commercial off-the-shelf games designed primarily for entertainment. Methods: We argue that the activity traces left behind by natural interactions with digital games can be modeled using computational approaches for big data. To support our argument, we present an investigation of existing data sources, a categorization of observable traits from game data, and examples of potentially useful game-based digital biomarkers derived from activity traces. Results: Our investigation reveals different types of data that are generated from play and the sources from which these data can be accessed. Based on these insights, we describe five categories of digital biomarkers that can be derived from game-based data, including behavior, cognitive performance, motor performance, social behavior, and affect. For each type of biomarker, we describe the data type, the game-based sources from which it can be derived, its importance for mental health modeling, and any existing statistical associations with mental health that have been demonstrated in prior work. We end with a discussion on the limitations and potential of data from commercial off-the-shelf games for use as a digital biomarker of mental health. Conclusions: When people play commercial digital games, they produce significant volumes of high-resolution data that are not only related to play frequency, but also include performance data reflecting low-level cognitive and motor processing; text-based data that are indicative of the affective state; social data that reveal networks of relationships; content choice data that imply preferred genres; and contextual data that divulge where, when, and with whom the players are playing. These data provide a source for digital biomarkers that may indicate mental health. Produced by engaged human behavior, game data have the potential to be leveraged for population screening or prevalence estimations, leading to at-scale, nonintrusive assessment of mental health. UR - http://mental.jmir.org/2019/4/e13485/ UR - http://dx.doi.org/10.2196/13485 UR - http://www.ncbi.nlm.nih.gov/pubmed/31012857 ID - info:doi/10.2196/13485 ER - TY - JOUR AU - McCaig, Duncan AU - Elliott, T. Mark AU - Siew, SQ Cynthia AU - Walasek, Lukasz AU - Meyer, Caroline PY - 2019/04/22 TI - Profiling Commenters on Mental Health?Related Online Forums: A Methodological Example Focusing on Eating Disorder?Related Commenters JO - JMIR Ment Health SP - e12555 VL - 6 IS - 4 KW - mental health KW - eating disorders KW - social media KW - social networks N2 - Background: Understanding the characteristics of commenters on mental health?related online forums is vital for the development of effective psychological interventions in these communities. The way in which commenters interact can enhance our understanding of their characteristics. Objective: Using eating disorder?related (EDR) forums as an example, this study detailed a methodology that aimed to determine subtypes of mental health?related forums and profile their commenters based on the other forums to which they contributed. Methods: The researchers identified all public EDR forums (with ?500 contributing commenters between March 2017 and February 2018) on a large Web-based discussion platform (Reddit). A mixed-methods approach comprising network analysis with community detection, text mining, and manual review identified subtypes of EDR forums. For each subtype, another network analysis with community detection was conducted using the EDR forum commenter overlap between 50 forums on which the commenters also commented. The topics of forums in each detected community were then manually reviewed to identify the shared interests of each subtype of EDR forum commenters. Results: Six subtypes of EDR forums were identified, to which 14,024 commenters had contributed. The results focus on 2 subtypes?proeating disorder and thinspiration?and communities of commenters within both subtypes. Within the proeating disorder subtype, 3 communities of commenters were detected that related to the body and eating, mental health, and women, appearance, and mixed topics. With regard to the thinspiration group, 78.17% (849/1086) of commenters had also commented on pornographic forums and 16.66% (181/1086) had contributed to proeating disorder forums. Conclusions: The article exemplifies a methodology that provides insight into subtypes of mental health?related forums and the characteristics of their commenters. The findings have implications for future research and Web-based psychological interventions. With the publicly available data and code provided, researchers can easily reproduce the analyses or utilize the methodology to investigate other mental health?related forums. UR - http://mental.jmir.org/2019/4/e12555/ UR - http://dx.doi.org/10.2196/12555 UR - http://www.ncbi.nlm.nih.gov/pubmed/31008715 ID - info:doi/10.2196/12555 ER - TY - JOUR AU - Lord, E. Sarah AU - Seavey, M. Katherine AU - Oren, D. Sonia AU - Budney, J. Alan AU - Marsch, A. Lisa PY - 2019/04/05 TI - Digital Presence of a Research Center as a Research Dissemination Platform: Reach and Resources JO - JMIR Ment Health SP - e11686 VL - 6 IS - 4 KW - telemedicine KW - internet KW - social media KW - behavioral sciences KW - implementation science KW - information dissemination N2 - Background: Web-based platforms can be powerful tools for research dissemination. By leveraging the advantages of mass media and interpersonal channels of communication, Web-based dissemination platforms may improve awareness about, and subsequent adoption of, evidence-based practices (EBPs). Digital dissemination strategies can augment traditional dissemination models, improving stakeholder access to digestible and actionable information and promoting translation of EBPs. Objective: This study aimed to describe the reach and content of the Web presence of a National Institute on Drug Abuse Center of Excellence and how it is used to disseminate research related to digital behavioral health approaches. Methods: The Center for Technology and Behavioral Health (CTBH) has a website and regularly updated Facebook and Twitter accounts. The website features include summaries of digital behavioral health approaches and related empirical literature, a blog feed focused on the state of the science and technology concerning digital health care approaches, and a newsletter about Center activities. We extracted website usage metrics from Google Analytics and follower counts from social media accounts for the period from March 1, 2013, to July 17, 2018. Results: Since the implementation of analytic tracking, 70,331 users have initiated 96,995 sessions on the CTBH website. The website includes summaries of 86 digital therapeutic programs, encompassing 447 empirical articles. There are 1160 posts in the CTBH blog feed, including 180 summaries of scholarly articles. The Twitter and Facebook accounts have 577 and 1500 followers, respectively. The newsletter has reached a growing subscriber network and has a high open rate relative to industry standards. Conclusions: The CTBH Web presence serves as a model for how to leverage accessible and easily updatable digital platforms as research dissemination channels. Digital dissemination tools can augment traditional dissemination strategies to promote awareness about evidence-based digital therapeutic approaches for behavioral health and health care more broadly. UR - https://mental.jmir.org/2019/4/e11686/ UR - http://dx.doi.org/10.2196/11686 UR - http://www.ncbi.nlm.nih.gov/pubmed/30950800 ID - info:doi/10.2196/11686 ER - TY - JOUR AU - Fortuna, Karen AU - Barr, Paul AU - Goldstein, Carly AU - Walker, Robert AU - Brewer, LaPrincess AU - Zagaria, Alexandra AU - Bartels, Stephen PY - 2019/03/19 TI - Application of Community-Engaged Research to Inform the Development and Implementation of a Peer-Delivered Mobile Health Intervention for Adults With Serious Mental Illness JO - J Participat Med SP - e12380 VL - 11 IS - 1 KW - stakeholder participation KW - mental health KW - patient participation KW - consumer advocacy KW - mobile health N2 - Background: Involving certified peer specialists in all phases of intervention development and research is a high priority to advance peer-delivered services. Certified peer specialists are individuals with a lived experience of a mental illness, and they are trained and accredited to provide Medicaid reimbursable mental health services. Community-engaged research can facilitate the development and implementation of peer-delivered interventions; however, little is known about the processes. We present our application of community-engaged research to inform the development and implementation of a peer-delivered mobile health (mHealth) intervention for adults with serious mental illness. Objective: The aim of this study was to present a framework that can be used as a guide for researchers and certified peer specialists to develop and implement peer-delivered mHealth interventions in community settings. Methods: Informed by principles of community-engaged research, we developed the Academic Researchers-Certified Peer Specialists mHealth Research Continuum. Principles of community-engaged research included in the Continuum include the following: (1) develop a clear understanding of the purpose, goal, and population involved in community change; (2) become knowledgeable about all aspects of the community; (3) interact and establish relationships with the community; (4) encourage community self-determination; (5) partner with the community; (6) respect community diversity and culture; (7) activate community assets and develop capacity; (8) maintain flexibility; and (9) commit to long-term collaboration. Results: Overall, 4 certified peer specialists participated in all phases of intervention development and research. Individuals who participated in the Academic Researchers-Certified Peer Specialists? mHealth Research Continuum collaborated on 5 studies advancing peers? roles in services delivery using mHealth and secured grant funding from a foundation to sustain their study. The Academic Researchers-Certified Peer Specialists? mHealth Research Continuum has created a rare environment of inclusion by combining scientific expertise and certified peer specialists? expertise to achieve a shared vision. Conclusions: This study delineates a process by which academic researchers and certified peer specialists participated in community-engaged research to develop and implement peer-delivered mHealth interventions in community settings. UR - http://jopm.jmir.org/2019/1/e12380/ UR - http://dx.doi.org/10.2196/12380 UR - http://www.ncbi.nlm.nih.gov/pubmed/32095314 ID - info:doi/10.2196/12380 ER - TY - JOUR AU - Nandy, R. Rajesh AU - Nandy, Karabi AU - Hébert, T. Emily AU - Businelle, S. Michael AU - Walters, T. Scott PY - 2019/02/07 TI - Identifying Behaviors Predicting Early Morning Emotions by Observing Permanent Supportive Housing Residents: An Ecological Momentary Assessment JO - JMIR Ment Health SP - e10186 VL - 6 IS - 2 KW - permanent supportive housing KW - circumplex model of affect KW - ecological momentary assessment KW - emotion KW - valence KW - arousal KW - hierarchical mixed effects model KW - mobile phone N2 - Background: Behavior and emotions are closely intertwined. The relationship between behavior and emotions might be particularly important in populations of underserved people, such as people with physical or mental health issues. We used ecological momentary assessment (EMA) to examine the relationship between emotional state and other characteristics among people with a history of chronic homelessness who were participating in a health coaching program. Objective: The goal of this study was to identify relationships between daily emotional states (valence and arousal) shortly after waking and behavioral variables such as physical activity, diet, social interaction, medication compliance, and tobacco usage the prior day, controlling for demographic characteristics. Methods: Participants in m.chat, a technology-assisted health coaching program, were recruited from housing agencies in Fort Worth, Texas, United States. All participants had a history of chronic homelessness and reported at least one mental health condition. We asked a subset of participants to complete daily EMAs of emotions and other behaviors. From the circumplex model of affect, the EMA included 9 questions related to the current emotional state of the participant (happy, frustrated, sad, worried, restless, excited, calm, bored, and sluggish). The responses were used to calculate two composite scores for valence and arousal. Results: Nonwhites reported higher scores for both valence and arousal, but not at a statistically significant level after correcting for multiple testing. Among momentary predictors, greater time spent in one-on-one interactions, greater time spent in physical activities, a greater number of servings of fruits and vegetables, greater time spent interacting in a one-on-one setting as well as adherence to prescribed medication the previous day were generally associated with higher scores for both valence and arousal, and statistical significance was achieved in most cases. Number of cigarettes smoked the previous day was generally associated with lower scores on both valence and arousal, although statistical significance was achieved for valence only when correcting for multiple testing. Conclusions: This study provides an important glimpse into factors that predict morning emotions among people with mental health issues and a history of chronic homelessness. Behaviors considered to be positive (eg, physical activity and consumption of fruits and vegetables) generally enhanced positive affect and restrained negative affect the following morning. The opposite was true for behaviors such as smoking, which are considered to be negative. UR - http://mental.jmir.org/2019/2/e10186/ UR - http://dx.doi.org/10.2196/10186 UR - http://www.ncbi.nlm.nih.gov/pubmed/30730296 ID - info:doi/10.2196/10186 ER - TY - JOUR AU - Birckhead, Brandon AU - Khalil, Carine AU - Liu, Xiaoyu AU - Conovitz, Samuel AU - Rizzo, Albert AU - Danovitch, Itai AU - Bullock, Kim AU - Spiegel, Brennan PY - 2019/01/31 TI - Recommendations for Methodology of Virtual Reality Clinical Trials in Health Care by an International Working Group: Iterative Study JO - JMIR Ment Health SP - e11973 VL - 6 IS - 1 KW - clinical trials KW - consensus KW - virtual reality N2 - Background: Therapeutic virtual reality (VR) has emerged as an efficacious treatment modality for a wide range of health conditions. However, despite encouraging outcomes from early stage research, a consensus for the best way to develop and evaluate VR treatments within a scientific framework is needed. Objective: We aimed to develop a methodological framework with input from an international working group in order to guide the design, implementation, analysis, interpretation, and communication of trials that develop and test VR treatments. Methods: A group of 21 international experts was recruited based on their contributions to the VR literature. The resulting Virtual Reality Clinical Outcomes Research Experts held iterative meetings to seek consensus on best practices for the development and testing of VR treatments. Results: The interactions were transcribed, and key themes were identified to develop a scientific framework in order to support best practices in methodology of clinical VR trials. Using the Food and Drug Administration Phase I-III pharmacotherapy model as guidance, a framework emerged to support three phases of VR clinical study designs?VR1, VR2, and VR3. VR1 studies focus on content development by working with patients and providers through the principles of human-centered design. VR2 trials conduct early testing with a focus on feasibility, acceptability, tolerability, and initial clinical efficacy. VR3 trials are randomized, controlled studies that evaluate efficacy against a control condition. Best practice recommendations for each trial were provided. Conclusions: Patients, providers, payers, and regulators should consider this best practice framework when assessing the validity of VR treatments. UR - https://mental.jmir.org/2019/1/e11973/ UR - http://dx.doi.org/10.2196/11973 UR - http://www.ncbi.nlm.nih.gov/pubmed/30702436 ID - info:doi/10.2196/11973 ER - TY - JOUR AU - Bifulco, Antonia AU - Spence, Ruth AU - Nunn, Stephen AU - Kagan, Lisa AU - Bailey-Rodriguez, Deborah AU - Hosang, M. Georgina AU - Taylor, Matthew AU - Fisher, L. Helen PY - 2019/01/08 TI - Web-Based Measure of Life Events Using Computerized Life Events and Assessment Record (CLEAR): Preliminary Cross-Sectional Study of Reliability, Validity, and Association With Depression JO - JMIR Ment Health SP - e10675 VL - 6 IS - 1 KW - depression KW - life change events KW - life stress KW - health technology KW - internet KW - psychometrics KW - psychological tests N2 - Background: Given the criticisms of life event checklists and the costs associated with interviews, life event research requires a sophisticated but easy-to-use measure for research and clinical practice. Therefore, the Computerized Life Events and Assessment Record (CLEAR), based on the Life Events and Difficulties Schedule (LEDS), was developed. Objective: The objective of our study was to test CLEAR?s reliability, validity, and association with depression. Methods: CLEAR, the General Health Questionnaire, and the List of Threatening Experiences Questionnaire (LTE-Q) were completed by 328 participants (126 students; 202 matched midlife sample: 127 unaffected controls, 75 recurrent depression cases). Test-retest reliability over 3-4 weeks was examined and validity determined by comparing CLEAR with LEDS and LTE-Q. Both CLEAR and LTE-Q were examined in relation to depression. Results: CLEAR demonstrated good test-retest reliability for the overall number of life events (0.89) and severe life events (.60). Long-term problems showed similar findings. In terms of validity, CLEAR severe life events had moderate sensitivity (59.1%) and specificity (65.4%) when compared with LEDS. CLEAR demonstrated moderate sensitivity (43.1%) and specificity (78.6%) when compared with LTE-Q. CLEAR severe life events and long-term problems were significantly associated with depression (odds ratio, OR 3.50, 95% CI 2.10 to 5.85, P<.001; OR 3.38, 95% CI 2.02 to 5.67, P<.001, respectively), whereas LTE-Q events were not (OR 1.06, 95% CI 0.43 to 2.60, P=.90). Conclusions: CLEAR has acceptable reliability and validity and predicts depression. It, therefore, has great potential for effective use in research and clinical practice identifying stress-related factors for the onset and maintenance of depression and related disorders. UR - https://mental.jmir.org/2019/1/e10675/ UR - http://dx.doi.org/10.2196/10675 UR - http://www.ncbi.nlm.nih.gov/pubmed/30622088 ID - info:doi/10.2196/10675 ER - TY - JOUR AU - Doherty, Kevin AU - Barry, Marguerite AU - Marcano-Belisario, José AU - Arnaud, Bérenger AU - Morrison, Cecily AU - Car, Josip AU - Doherty, Gavin PY - 2018/11/27 TI - A Mobile App for the Self-Report of Psychological Well-Being During Pregnancy (BrightSelf): Qualitative Design Study JO - JMIR Ment Health SP - e10007 VL - 5 IS - 4 KW - engagement KW - mental health KW - mHealth KW - midwifery KW - perinatal depression KW - pregnancy KW - self-report KW - well-being KW - mobile phone N2 - Background: Maternal mental health impacts both parental well-being and childhood development. In the United Kingdom, 15% of women are affected by depression during pregnancy or within 1 year of giving birth. Suicide is a leading cause of perinatal maternal mortality, and it is estimated that >50% of perinatal depression cases go undiagnosed. Mobile technologies are potentially valuable tools for the early recognition of depressive symptoms, but complex design challenges must be addressed to enable their use in public health screening. Objective: The aim of this study was to explore the issues and challenges surrounding the use of mobile phones for the self-report of psychological well-being during pregnancy. Methods: This paper presents design research carried out as part of the development of BrightSelf, a mobile app for the self-report of psychological well-being during pregnancy. Design sessions were carried out with 38 participants, including pregnant women, mothers, midwives, and other health professionals. Overall, 19 hours of audio were fully transcribed and used as the basis of thematic analysis. Results: The study highlighted anxieties concerning the pregnancy journey, challenges surrounding current approaches to the appraisal of well-being in perinatal care, and the midwife-patient relationship. Designers should consider the framing of perinatal mental health technologies, the experience of self-report, supporting self-awareness and disclosure, providing value to users through both self-report and supplementary features, and designing for longitudinal engagement. Conclusions: This study highlights the needs, motivations, and anxieties of women with respect to technology use in pregnancy and implications for the design of mobile health technologies. UR - http://mental.jmir.org/2018/4/e10007/ UR - http://dx.doi.org/10.2196/10007 UR - http://www.ncbi.nlm.nih.gov/pubmed/30482742 ID - info:doi/10.2196/10007 ER - TY - JOUR AU - Thornton, Louise AU - Kay-Lambkin, Frances AU - Tebbutt, Bree AU - Hanstock, L. Tanya AU - Baker, L. Amanda PY - 2018/10/1 TI - A Mobile Phone?Based Healthy Lifestyle Monitoring Tool for People With Mental Health Problems (MyHealthPA): Development and Pilot Testing JO - JMIR Cardio SP - e10228 VL - 2 IS - 2 KW - telemedicine KW - mental health KW - cardiovascular diseases KW - mhealth KW - smartphone KW - mobile phone N2 - Background: People with mental health disorders live, on average, 20 years less than those without, often because of poor physical health including cardiovascular disease (CVD). Evidence-based interventions are required to reduce this lifespan gap. Objective: This study aimed to develop, test, and evaluate a mobile phone?based lifestyle program (MyHealthPA) to help people with mental health problems improve key health risk behaviors and reduce their risk of CVD. Methods: The development of MyHealthPA occurred in 3 stages: (1) scoping of the literature, (2) a survey (n=251) among people with and without the experience of mental health problems, and (3) program development informed by stages 1 and 2. A small pilot trial among young people with and without mental health disorders was also conducted. Participants completed a baseline assessment and were given access to the MyHealthPA program for a period of 8 weeks. They were then asked to complete an end-of-treatment assessment and a follow-up assessment 1 month later. Results: In the study, 28 young people aged 19 to 25 years were recruited to the pilot trial. Of these, 12 (12/28, 43%) had been previously diagnosed with a mental illness. Overall, 12 participants (12/28, 43%) completed the end-of-treatment assessment and 6 (6/28, 21%) completed the follow-up assessment. Small improvements in fruit and vegetable consumption, level of physical activity, alcohol use, and mood were found between baseline and end of treatment and follow-up, particularly among people with experience of mental health issues. Most participants (history of mental illness: 4/7, 57%; no history of mental illness: 3/5, 60%) reported the program had above average usability; however, only 29% (2/7, no history of mental illness) to 40% (2/5, history of mental illness) of participants reported that they would like to use the program frequently and would recommend it to other young people. Participants also identified a number of ways in which the program could be improved. Conclusions: This study describes the formative research and process of planning that formed the development of MyHealthPA and the evidence base underpinning the approach. The MyHealthPA program represents an innovative approach to CVD risk reduction among people with mental health problems. MyHealthPA appears to be an acceptable, easy-to-use, and potentially effective mHealth intervention to assist young people with mental illness to monitor risk factors for CVD. However, ways in which the program could be improved for future testing and dissemination were identified and discussed. UR - http://cardio.jmir.org/2018/2/e10228/ UR - http://dx.doi.org/10.2196/10228 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758772 ID - info:doi/10.2196/10228 ER - TY - JOUR AU - Heraz, Alicia AU - Clynes, Manfred PY - 2018/08/30 TI - Recognition of Emotions Conveyed by Touch Through Force-Sensitive Screens: Observational Study of Humans and Machine Learning Techniques JO - JMIR Ment Health SP - e10104 VL - 5 IS - 3 KW - emotional artificial intelligence KW - human-computer interaction KW - smartphone KW - force-sensitive screens KW - mental health KW - positive computing KW - artificial intelligence KW - emotions KW - emotional intelligence N2 - Background: Emotions affect our mental health: they influence our perception, alter our physical strength, and interfere with our reason. Emotions modulate our face, voice, and movements. When emotions are expressed through the voice or face, they are difficult to measure because cameras and microphones are not often used in real life in the same laboratory conditions where emotion detection algorithms perform well. With the increasing use of smartphones, the fact that we touch our phones, on average, thousands of times a day, and that emotions modulate our movements, we have an opportunity to explore emotional patterns in passive expressive touches and detect emotions, enabling us to empower smartphone apps with emotional intelligence. Objective: In this study, we asked 2 questions. (1) As emotions modulate our finger movements, will humans be able to recognize emotions by only looking at passive expressive touches? (2) Can we teach machines how to accurately recognize emotions from passive expressive touches? Methods: We were interested in 8 emotions: anger, awe, desire, fear, hate, grief, laughter, love (and no emotion). We conducted 2 experiments with 2 groups of participants: good imagers and emotionally aware participants formed group A, with the remainder forming group B. In the first experiment, we video recorded, for a few seconds, the expressive touches of group A, and we asked group B to guess the emotion of every expressive touch. In the second experiment, we trained group A to express every emotion on a force-sensitive smartphone. We then collected hundreds of thousands of their touches, and applied feature selection and machine learning techniques to detect emotions from the coordinates of participant? finger touches, amount of force, and skin area, all as functions of time. Results: We recruited 117 volunteers: 15 were good imagers and emotionally aware (group A); the other 102 participants formed group B. In the first experiment, group B was able to successfully recognize all emotions (and no emotion) with a high 83.8% (769/918) accuracy: 49.0% (50/102) of them were 100% (450/450) correct and 25.5% (26/102) were 77.8% (182/234) correct. In the second experiment, we achieved a high 91.11% (2110/2316) classification accuracy in detecting all emotions (and no emotion) from 9 spatiotemporal features of group A touches. Conclusions: Emotions modulate our touches on force-sensitive screens, and humans have a natural ability to recognize other people?s emotions by watching prerecorded videos of their expressive touches. Machines can learn the same emotion recognition ability and do better than humans if they are allowed to continue learning on new data. It is possible to enable force-sensitive screens to recognize users? emotions and share this emotional insight with users, increasing users? emotional awareness and allowing researchers to design better technologies for well-being. UR - http://mental.jmir.org/2018/3/e10104/ UR - http://dx.doi.org/10.2196/10104 UR - http://www.ncbi.nlm.nih.gov/pubmed/30166276 ID - info:doi/10.2196/10104 ER - TY - JOUR AU - Di Matteo, Daniel AU - Fine, Alexa AU - Fotinos, Kathryn AU - Rose, Jonathan AU - Katzman, Martin PY - 2018/08/29 TI - Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire JO - JMIR Ment Health SP - e56 VL - 5 IS - 3 KW - passive sensing KW - mobile phone sensing KW - psychiatric assessment KW - mood and anxiety disorders KW - digital privacy KW - mobile apps KW - mobile phone KW - consent N2 - Background: It has become possible to use data from a patient?s mobile phone as an adjunct or alternative to the traditional self-report and interview methods of symptom assessment in psychiatry. Mobile data?based assessment is possible because of the large amounts of diverse information available from a modern mobile phone, including geolocation, screen activity, physical motion, and communication activity. This data may offer much more fine-grained insight into mental state than traditional methods, and so we are motivated to pursue research in this direction. However, passive data retrieval could be an unwelcome invasion of privacy, and some may not consent to such observation. It is therefore important to measure patients? willingness to consent to such observation if this approach is to be considered for general use. Objective: The aim of this study was to measure the ownership rates of mobile phones within the patient population, measure the patient population?s willingness to have their mobile phone used as an experimental assessment tool for their mental health disorder, and, finally, to determine how likely patients would be to provide consent for each individual source of mobile phone?collectible data across the variety of potential data sources. Methods: New patients referred to a tertiary care mood and anxiety disorder clinic from August 2016 to October 2017 completed a survey designed to measure their mobile phone ownership, use, and willingness to install a mental health monitoring app and provide relevant data through the app. Results: Of the 82 respondents, 70 (85%) reported owning an internet-connected mobile phone. When asked about installing a hypothetical mobile phone app to assess their mental health disorder, 41% (33/80) responded with complete willingness to install with another 43% (34/80) indicating potential willingness to install such an app. Willingness to give permissions for specific types of data varied by data source, with respondents least willing to consent to audio recording and analysis (19% [15/80] willing respondents, 31% [25/80] potentially willing) and most willing to consent to observation of the mobile phone screen being on or off (46% [36/79] willing respondents and 23% [18/79] potentially willing). Conclusions: The patients surveyed had a high incidence of ownership of internet-connected mobile phones, which suggests some plausibility for the general approach of mental health state inference through mobile phone data. Patients were also relatively willing to consent to data collection from sources that were less personal but expressed less willingness for the most personal communication and location data. UR - http://mental.jmir.org/2018/3/e56/ UR - http://dx.doi.org/10.2196/mental.9539 UR - http://www.ncbi.nlm.nih.gov/pubmed/30158102 ID - info:doi/10.2196/mental.9539 ER - TY - JOUR AU - Song, Jae Michael AU - Ward, John AU - Choi, Fiona AU - Nikoo, Mohammadali AU - Frank, Anastasia AU - Shams, Farhud AU - Tabi, Katarina AU - Vigo, Daniel AU - Krausz, Michael PY - 2018/08/20 TI - A Process Evaluation of a Web-Based Mental Health Portal (WalkAlong) Using Google Analytics JO - JMIR Ment Health SP - e50 VL - 5 IS - 3 KW - evaluation KW - Google Analytics KW - mental health KW - website N2 - Background: Despite the increasing amount of research on Web-based mental health interventions with proven efficacy, high attrition rates decrease their effectiveness. Continued process evaluations should be performed to maximize the target population?s engagement. Google Analytics has been used to evaluate various health-related Web-based programs and may also be useful for Web-based mental health programs. Objective: The objective of our study was to evaluate WalkAlong.ca, a youth-oriented mental health web-portal, using Google Analytics to inform the improvement strategy for the platform and to demonstrate the use of Google Analytics as a tool for process evaluation of Web-based mental health interventions. Methods: Google Analytics was used to monitor user activity during WalkAlong?s first year of operation (Nov 13, 2013-Nov 13, 2014). Selected Google Analytic variables were overall website engagement including pages visited per session, utilization rate of specific features, and user access mode and location. Results: The results included data from 3076 users viewing 29,299 pages. Users spent less average time on Mindsteps (0 minute 35 seconds) and self-exercises (1 minute 08 seconds), which are important self-help tools, compared with that on the Screener tool (3 minutes 4 seconds). Of all visitors, 82.3% (4378/5318) were desktop users, followed by 12.7 % (677/5318) mobile phone and 5.0% (263/5318) tablet users. Both direct traffic (access via URL) and referrals by email had more than 7 pages viewed per session and longer than average time of 6 minutes per session. The majority of users (67%) accessed the platform from Canada. Conclusions: Engagement and feature utilization rates are higher among people who receive personal invitations to visit the site. Low utilization rates with specific features offer a starting place for further exploration of users in order to identify the root cause. The data provided by Google Analytics, although informative, can be supplemented by other evaluation methods (ie, qualitative methods) in order to better determine the modifications required to improve user engagement. Google Analytics can play a vital role in highlighting the preferences of those using Web-based mental health tools. UR - http://mental.jmir.org/2018/3/e50/ UR - http://dx.doi.org/10.2196/mental.8594 UR - http://www.ncbi.nlm.nih.gov/pubmed/30126832 ID - info:doi/10.2196/mental.8594 ER - TY - JOUR AU - Quiroz, Carlos Juan AU - Geangu, Elena AU - Yong, Hooi Min PY - 2018/08/08 TI - Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study JO - JMIR Ment Health SP - e10153 VL - 5 IS - 3 KW - emotion recognition KW - accelerometer KW - supervised learning KW - psychology N2 - Background: Research in psychology has shown that the way a person walks reflects that person?s current mood (or emotional state). Recent studies have used mobile phones to detect emotional states from movement data. Objective: The objective of our study was to investigate the use of movement sensor data from a smart watch to infer an individual?s emotional state. We present our findings of a user study with 50 participants. Methods: The experimental design is a mixed-design study: within-subjects (emotions: happy, sad, and neutral) and between-subjects (stimulus type: audiovisual ?movie clips? and audio ?music clips?). Each participant experienced both emotions in a single stimulus type. All participants walked 250 m while wearing a smart watch on one wrist and a heart rate monitor strap on the chest. They also had to answer a short questionnaire (20 items; Positive Affect and Negative Affect Schedule, PANAS) before and after experiencing each emotion. The data obtained from the heart rate monitor served as supplementary information to our data. We performed time series analysis on data from the smart watch and a t test on questionnaire items to measure the change in emotional state. Heart rate data was analyzed using one-way analysis of variance. We extracted features from the time series using sliding windows and used features to train and validate classifiers that determined an individual?s emotion. Results: Overall, 50 young adults participated in our study; of them, 49 were included for the affective PANAS questionnaire and 44 for the feature extraction and building of personal models. Participants reported feeling less negative affect after watching sad videos or after listening to sad music, P<.006. For the task of emotion recognition using classifiers, our results showed that personal models outperformed personal baselines and achieved median accuracies higher than 78% for all conditions of the design study for binary classification of happiness versus sadness. Conclusions: Our findings show that we are able to detect changes in the emotional state as well as in behavioral responses with data obtained from the smartwatch. Together with high accuracies achieved across all users for classification of happy versus sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition. UR - http://mental.jmir.org/2018/3/e10153/ UR - http://dx.doi.org/10.2196/10153 UR - http://www.ncbi.nlm.nih.gov/pubmed/30089610 ID - info:doi/10.2196/10153 ER - TY - JOUR AU - Suganuma, Shinichiro AU - Sakamoto, Daisuke AU - Shimoyama, Haruhiko PY - 2018/07/31 TI - An Embodied Conversational Agent for Unguided Internet-Based Cognitive Behavior Therapy in Preventative Mental Health: Feasibility and Acceptability Pilot Trial JO - JMIR Ment Health SP - e10454 VL - 5 IS - 3 KW - embodied conversational agent KW - cognitive behavioral therapy KW - psychological distress KW - mental well?being KW - artificial intelligence technology N2 - Background: Recent years have seen an increase in the use of internet-based cognitive behavioral therapy in the area of mental health. Although lower effectiveness and higher dropout rates of unguided than those of guided internet-based cognitive behavioral therapy remain critical issues, not incurring ongoing human clinical resources makes it highly advantageous. Objective: Current research in psychotherapy, which acknowledges the importance of therapeutic alliance, aims to evaluate the feasibility and acceptability, in terms of mental health, of an application that is embodied with a conversational agent. This application was enabled for use as an internet-based cognitive behavioral therapy preventative mental health measure. Methods: Analysis of the data from the 191 participants of the experimental group with a mean age of 38.07 (SD 10.75) years and the 263 participants of the control group with a mean age of 38.05 (SD 13.45) years using a 2-way factorial analysis of variance (group × time) was performed. Results: There was a significant main effect (P=.02) and interaction for time on the variable of positive mental health (P=.02), and for the treatment group, a significant simple main effect was also found (P=.002). In addition, there was a significant main effect (P=.02) and interaction for time on the variable of negative mental health (P=.005), and for the treatment group, a significant simple main effect was also found (P=.001). Conclusions: This research can be seen to represent a certain level of evidence for the mental health application developed herein, indicating empirically that internet-based cognitive behavioral therapy with the embodied conversational agent can be used in mental health care. In the pilot trial, given the issues related to feasibility and acceptability, it is necessary to pursue higher quality evidence while continuing to further improve the application, based on the findings of the current research. UR - http://mental.jmir.org/2018/3/e10454/ UR - http://dx.doi.org/10.2196/10454 UR - http://www.ncbi.nlm.nih.gov/pubmed/30064969 ID - info:doi/10.2196/10454 ER - TY - JOUR AU - Zulueta, John AU - Piscitello, Andrea AU - Rasic, Mladen AU - Easter, Rebecca AU - Babu, Pallavi AU - Langenecker, A. Scott AU - McInnis, Melvin AU - Ajilore, Olusola AU - Nelson, C. Peter AU - Ryan, Kelly AU - Leow, Alex PY - 2018/07/20 TI - Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study JO - J Med Internet Res SP - e241 VL - 20 IS - 7 KW - digital phenotype KW - mHealth KW - ecological momentary assessment KW - keystroke dynamics KW - bipolar disorder KW - depression KW - mania KW - mobile phone N2 - Background: Mood disorders are common and associated with significant morbidity and mortality. Better tools are needed for their diagnosis and treatment. Deeper phenotypic understanding of these disorders is integral to the development of such tools. This study is the first effort to use passively collected mobile phone keyboard activity to build deep digital phenotypes of depression and mania. Objective: The objective of our study was to investigate the relationship between mobile phone keyboard activity and mood disturbance in subjects with bipolar disorders and to demonstrate the feasibility of using passively collected mobile phone keyboard metadata features to predict manic and depressive signs and symptoms as measured via clinician-administered rating scales. Methods: Using a within-subject design of 8 weeks, subjects were provided a mobile phone loaded with a customized keyboard that passively collected keystroke metadata. Subjects were administered the Hamilton Depression Rating Scale (HDRS) and Young Mania Rating Scale (YMRS) weekly. Linear mixed-effects models were created to predict HDRS and YMRS scores. The total number of keystrokes was 626,641, with a weekly average of 9791 (7861), and that of accelerometer readings was 6,660,890, with a weekly average 104,076 (68,912). Results: A statistically significant mixed-effects regression model for the prediction of HDRS-17 item scores was created: conditional R2=.63, P=.01. A mixed-effects regression model for YMRS scores showed the variance accounted for by random effect was zero, and so an ordinary least squares linear regression model was created: R2=.34, P=.001. Multiple significant variables were demonstrated for each measure. Conclusions: Mood states in bipolar disorder appear to correlate with specific changes in mobile phone usage. The creation of these models provides evidence for the feasibility of using passively collected keyboard metadata to detect and monitor mood disturbances. UR - http://www.jmir.org/2018/7/e241/ UR - http://dx.doi.org/10.2196/jmir.9775 UR - http://www.ncbi.nlm.nih.gov/pubmed/30030209 ID - info:doi/10.2196/jmir.9775 ER - TY - JOUR AU - Karin, Eyal AU - Dear, F. Blake AU - Heller, Z. Gillian AU - Gandy, Milena AU - Titov, Nickolai PY - 2018/07/12 TI - Measurement of Symptom Change Following Web-Based Psychotherapy: Statistical Characteristics and Analytical Methods for Measuring and Interpreting Change JO - JMIR Ment Health SP - e10200 VL - 5 IS - 3 KW - clinical measurement KW - treatment evaluation KW - symptom change KW - symptom scales KW - psychotherapeutic change N2 - Background: Accurate measurement of treatment-related change is a key part of psychotherapy research and the investigation of treatment efficacy. For this reason, the ability to measure change with accurate and valid methods is critical for psychotherapy. Objective: The aims of this study were to (1) explore the underlying characteristics of depressive symptom change, measured with the nine-item Patient Health Questionnaire (PHQ-9), following psychotherapy, and (2) compare the suitability of different ways to measure and interpret symptom change. A treatment sample of Web-based psychotherapy participants (n=1098) and a waitlist sample (n=96) were used to (1) explore the statistical characteristics of depressive symptom change, and (2) compare the suitability of two common types of change functions: linear and proportional change. Methods: These objectives were explored using hypotheses that tested (1) the relationship between baseline symptoms and the rate of change, (2) the shape of symptom score distribution following treatment, and (3) measurement error associated with linear and proportional measurement models. Results: Findings demonstrated that (1) individuals with severe depressive baseline symptoms had greater reductions in symptom scores than individuals with mild baseline symptoms (11.4 vs 3.7); however, as a percentage measurement, change remained similar across individuals with mild, moderate, or severe baseline symptoms (50%-55%); (2) positive skewness was observed in PHQ-9 score distributions following treatment; and (3) models that measured symptom change as a proportional function resulted in greater model fit and reduced measurement error (<30%). Conclusions: This study suggests that symptom scales, sharing an implicit feature of score bounding, are associated with a proportional function of change. Selecting statistics that overlook this proportional change (eg, Cohen d) is problematic and leads to (1) artificially increased estimates of change with higher baseline symptoms, (2) increased measurement error, and (3) confounded estimates of treatment efficacy and clinical change. Implications, limitations, and idiosyncrasies from these results are discussed. UR - http://mental.jmir.org/2018/3/e10200/ UR - http://dx.doi.org/10.2196/10200 UR - http://www.ncbi.nlm.nih.gov/pubmed/30001999 ID - info:doi/10.2196/10200 ER - TY - JOUR AU - Grigorash, Alexander AU - O'Neill, Siobhan AU - Bond, Raymond AU - Ramsey, Colette AU - Armour, Cherie AU - Mulvenna, D. Maurice PY - 2018/06/11 TI - Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data JO - JMIR Ment Health SP - e47 VL - 5 IS - 2 KW - data mining KW - machine learning KW - clustering KW - classification KW - mental health KW - suicide N2 - Background: This paper presents an analysis of call data records pertaining to a telephone helpline in Ireland among individuals seeking mental health and well-being support and among those who are in a suicidal crisis. Objective: The objective of our study was to examine whether rule sets generated from decision tree classification, trained using features derived from callers? several initial calls, could be used to predict what caller type they would become. Methods: Machine learning techniques were applied to the call log data, and five distinct patterns of caller behaviors were revealed, each impacting the helpline capacity in different ways. Results: The primary findings of this study indicate that a significant model (P<.001) for predicting caller type from call log data obtained from the first 8 calls is possible. This indicates an association between callers? behavior exhibited during initial calls and their behavior over the lifetime of using the service. Conclusions: These data-driven findings contribute to advanced workload forecasting for operational management of the telephone-based helpline and inform the literature on helpline caller behavior in general. UR - http://mental.jmir.org/2018/2/e47/ UR - http://dx.doi.org/10.2196/mental.9946 UR - http://www.ncbi.nlm.nih.gov/pubmed/29891472 ID - info:doi/10.2196/mental.9946 ER - TY - JOUR AU - Alvarez-Mon, Angel Miguel AU - Asunsolo del Barco, Angel AU - Lahera, Guillermo AU - Quintero, Javier AU - Ferre, Francisco AU - Pereira-Sanchez, Victor AU - Ortuño, Felipe AU - Alvarez-Mon, Melchor PY - 2018/05/28 TI - Increasing Interest of Mass Communication Media and the General Public in the Distribution of Tweets About Mental Disorders: Observational Study JO - J Med Internet Res SP - e205 VL - 20 IS - 5 KW - Twitter KW - social media KW - psychiatry KW - mental health N2 - Background: The contents of traditional communication media and new internet social media reflect the interests of society. However, certain barriers and a lack of attention towards mental disorders have been previously observed. Objective: The objective of this study is to measure the relevance of influential American mainstream media outlets for the distribution of psychiatric information and the interest generated in these topics among their Twitter followers. Methods: We investigated tweets generated about mental health conditions and diseases among 15 mainstream general communication media outlets in the United States of America between January 2007 and December 2016. Our study strategy focused on identifying several psychiatric terms of primary interest. The number of retweets generated from the selected tweets was also investigated. As a control, we examined tweets generated about the main causes of death in the United States of America, the main chronic neurological degenerative diseases, and HIV. Results: In total, 13,119 tweets about mental health disorders sent by the American mainstream media outlets were analyzed. The results showed a heterogeneous distribution but preferential accumulation for a select number of conditions. Suicide and gender dysphoria accounted for half of the number of tweets sent. Variability in the number of tweets related to each control disease was also found (5998). The number of tweets sent regarding each different psychiatric or organic disease analyzed was significantly correlated with the number of retweets generated by followers (1,030,974 and 424,813 responses to mental health disorders and organic diseases, respectively). However, the probability of a tweet being retweeted differed significantly among the conditions and diseases analyzed. Furthermore, the retweeted to tweet ratio was significantly higher for psychiatric diseases than for the control diseases (odds ratio 1.11, CI 1.07-1.14; P<.001). Conclusions: American mainstream media outlets and the general public demonstrate a preferential interest for psychiatric diseases on Twitter. The heterogeneous weights given by the media outlets analyzed to the different mental health disorders and conditions are reflected in the responses of Twitter followers. UR - http://www.jmir.org/2018/5/e205/ UR - http://dx.doi.org/10.2196/jmir.9582 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/jmir.9582 ER - TY - JOUR AU - Carotenuto, Anna AU - Rea, Raffaele AU - Traini, Enea AU - Ricci, Giovanna AU - Fasanaro, Maria Angiola AU - Amenta, Francesco PY - 2018/05/11 TI - Cognitive Assessment of Patients With Alzheimer's Disease by Telemedicine: Pilot Study JO - JMIR Ment Health SP - e31 VL - 5 IS - 2 KW - dementia KW - telemedicine KW - videoconference KW - telepsychology KW - MMSE by videoconference KW - ADAS-cog by videoconference N2 - Background: Approximately 46.8 million people are living with dementia worldwide and their number will grow in the next years. Any potential treatment should be administered as early as possible because it is important to provide an early cognitive assessment and to regularly monitor the mental function of patients. Information and communication technologies can be helpful to reach and follow patients without displacing them, but there may be doubts about the reliability of cognitive tests performed by telemedicine. Objective: The purpose of this study was to evaluate the reliability of the Mini Mental State Examination (MMSE) and the Alzheimer?s Disease Assessment Scale cognitive subscale (ADAS-cog) tests administered in hospital by videoconference to patients with mild to moderate Alzheimer's disease. Methods: The tests were administered to 28 Alzheimer's disease outpatients (8 male, mean age 73.88, SD 7.45 years; 20 female mean age 76.00, SD 5.40 years) recruited and followed in the Alzheimer?s Unit of the A Cardarelli National Hospital (Naples, Italy) at baseline and after 6, 12, 18, and 24 months of observation. Patients were evaluated first face-to-face by a psychologist and then, after 2 weeks, by another psychologist via videoconference in hospital. Results: This study showed no differences in the MMSE and ADAS-cog scores when the tests were administered face-to-face or by videoconference, except in patients with more pronounced cognitive deficits (MMSE<17), in which the assessment via videoconference overestimated the cognitive impairment (face to face, MMSE mean 13.9, SD 4.9 and ADAS-cog mean 9.0, SD 3.8; videoconference, MMSE mean 42.8, SD 12.5 and ADAS-cog mean 56.9, SD 5.5). Conclusions: We found that videoconferencing is a reliable approach to document cognitive stability or decline, and to measure treatment effects in patients with mild to moderate dementia. A more extended study is needed to confirm these results. UR - http://mental.jmir.org/2018/2/e31/ UR - http://dx.doi.org/10.2196/mental.8097 UR - http://www.ncbi.nlm.nih.gov/pubmed/29752254 ID - info:doi/10.2196/mental.8097 ER - TY - JOUR AU - Galvão Gomes da Silva, Joana AU - Kavanagh, J. David AU - Belpaeme, Tony AU - Taylor, Lloyd AU - Beeson, Konna AU - Andrade, Jackie PY - 2018/05/03 TI - Experiences of a Motivational Interview Delivered by a Robot: Qualitative Study JO - J Med Internet Res SP - e116 VL - 20 IS - 5 KW - robotics KW - counseling KW - motivational interviewing KW - motivation KW - exercise KW - qualitative research KW - computer-assisted therapy KW - person-centered therapy N2 - Background: Motivational interviewing is an effective intervention for supporting behavior change but traditionally depends on face-to-face dialogue with a human counselor. This study addressed a key challenge for the goal of developing social robotic motivational interviewers: creating an interview protocol, within the constraints of current artificial intelligence, which participants will find engaging and helpful. Objective: The aim of this study was to explore participants? qualitative experiences of a motivational interview delivered by a social robot, including their evaluation of usability of the robot during the interaction and its impact on their motivation. Methods: NAO robots are humanoid, child-sized social robots. We programmed a NAO robot with Choregraphe software to deliver a scripted motivational interview focused on increasing physical activity. The interview was designed to be comprehensible even without an empathetic response from the robot. Robot breathing and face-tracking functions were used to give an impression of attentiveness. A total of 20 participants took part in the robot-delivered motivational interview and evaluated it after 1 week by responding to a series of written open-ended questions. Each participant was left alone to speak aloud with the robot, advancing through a series of questions by tapping the robot?s head sensor. Evaluations were content-analyzed utilizing Boyatzis? steps: (1) sampling and design, (2) developing themes and codes, and (3) validating and applying the codes. Results: Themes focused on interaction with the robot, motivation, change in physical activity, and overall evaluation of the intervention. Participants found the instructions clear and the navigation easy to use. Most enjoyed the interaction but also found it was restricted by the lack of individualized response from the robot. Many positively appraised the nonjudgmental aspect of the interview and how it gave space to articulate their motivation for change. Some participants felt that the intervention increased their physical activity levels. Conclusions: Social robots can achieve a fundamental objective of motivational interviewing, encouraging participants to articulate their goals and dilemmas aloud. Because they are perceived as nonjudgmental, robots may have advantages over more humanoid avatars for delivering virtual support for behavioral change. UR - http://www.jmir.org/2018/5/e116/ UR - http://dx.doi.org/10.2196/jmir.7737 UR - http://www.ncbi.nlm.nih.gov/pubmed/29724701 ID - info:doi/10.2196/jmir.7737 ER - TY - JOUR AU - Berry, Katherine AU - Salter, Amy AU - Morris, Rohan AU - James, Susannah AU - Bucci, Sandra PY - 2018/04/19 TI - Assessing Therapeutic Alliance in the Context of mHealth Interventions for Mental Health Problems: Development of the Mobile Agnew Relationship Measure (mARM) Questionnaire JO - J Med Internet Res SP - e90 VL - 20 IS - 4 KW - mobile health KW - health care provider KW - digital interventions KW - therapeutic alliance KW - mental health KW - measure development N2 - Background: Digital health interventions in the form of smartphone apps aim to improve mental health and enable people access to support as and when needed without having to face the stigma they may experience in accessing services. If we are to evaluate mobile health (mHealth) apps and advance scientific understanding, we also need tools to help us understand in what ways mHealth interventions are effective or not. The concept of therapeutic alliance, a measure of the quality of the relationship between a health care provider and a service user, is a key factor in explaining the effects of mental health interventions. The Agnew Relationship Measure (ARM) is a well-validated measure of therapeutic alliance in face-to-face therapy. Objective: This study presented the first attempt to (1) explore service users? views of the concept of relationship within mHealth mental health interventions and (2) adapt a well-validated face-to-face measure of therapeutic alliance, the Agnew Relationship Measure (ARM), for use with mHealth interventions. Methods: In stage 1, we interviewed 9 mental health service users about the concept of therapeutic alliance in the context of a digital health intervention and derived key themes from interview transcripts using thematic analysis. In stage 2, we used rating scales and open-ended questions to elicit views from 14 service users and 10 mental health staff about the content and face validity of the scale, which replaced the word ?therapist? with the word ?app.? In stage 3, we used the findings from stages 1 and 2 to adapt the measure with the support of a decision-making algorithm about which items to drop, retain, or adapt. Results: Findings suggested that service users do identify relationship concepts when thinking about mHealth interventions, including forming a bond with an app and the ability to be open with an app. However, there were key differences between relationships with health professionals and relationships with apps. For example, apps were not as tailored and responsive to each person?s unique needs. Furthermore, apps were not capable of portraying uniquely human-like qualities such as friendliness, collaboration, and agreement. We made a number of changes to the ARM that included revising 16 items; removing 4 items due to lack of suitable alternatives; and adding 1 item to capture a key theme derived from stage 1 of the study (?The app is like having a member of my care team in my pocket?). Conclusions: This study introduces the mHealth version of the ARM, the mARM, that has good face and content validity. We encourage researchers to include this easy-to-use tool in digital health intervention studies to gather further data about its psychometric properties and advance our understanding of how therapeutic alliance influences the efficacy of mHealth interventions. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 34966555; http://www.isrctn.com/ISRCTN34966555 (Archived by WebCite at http://www.webcitation.org/6ymBVwKif) UR - http://www.jmir.org/2018/4/e90/ UR - http://dx.doi.org/10.2196/jmir.8252 UR - http://www.ncbi.nlm.nih.gov/pubmed/29674307 ID - info:doi/10.2196/jmir.8252 ER - TY - JOUR AU - Karin, Eyal AU - Dear, F. Blake AU - Heller, Z. Gillian AU - Crane, F. Monique AU - Titov, Nickolai PY - 2018/04/19 TI - ?Wish You Were Here?: Examining Characteristics, Outcomes, and Statistical Solutions for Missing Cases in Web-Based Psychotherapeutic Trials JO - JMIR Ment Health SP - e22 VL - 5 IS - 2 KW - psychotherapy KW - treatment adherence and compliance KW - missing data KW - treatment efficacy KW - statistical bias N2 - Background: Missing cases following treatment are common in Web-based psychotherapy trials. Without the ability to directly measure and evaluate the outcomes for missing cases, the ability to measure and evaluate the effects of treatment is challenging. Although common, little is known about the characteristics of Web-based psychotherapy participants who present as missing cases, their likely clinical outcomes, or the suitability of different statistical assumptions that can characterize missing cases. Objective: Using a large sample of individuals who underwent Web-based psychotherapy for depressive symptoms (n=820), the aim of this study was to explore the characteristics of cases who present as missing cases at posttreatment (n=138), their likely treatment outcomes, and compare between statistical methods for replacing their missing data. Methods: First, common participant and treatment features were tested through binary logistic regression models, evaluating the ability to predict missing cases. Second, the same variables were screened for their ability to increase or impede the rate symptom change that was observed following treatment. Third, using recontacted cases at 3-month follow-up to proximally represent missing cases outcomes following treatment, various simulated replacement scores were compared and evaluated against observed clinical follow-up scores. Results: Missing cases were dominantly predicted by lower treatment adherence and increased symptoms at pretreatment. Statistical methods that ignored these characteristics can overlook an important clinical phenomenon and consequently produce inaccurate replacement outcomes, with symptoms estimates that can swing from ?32% to 70% from the observed outcomes of recontacted cases. In contrast, longitudinal statistical methods that adjusted their estimates for missing cases outcomes by treatment adherence rates and baseline symptoms scores resulted in minimal measurement bias (<8%). Conclusions: Certain variables can characterize and predict missing cases likelihood and jointly predict lesser clinical improvement. Under such circumstances, individuals with potentially worst off treatment outcomes can become concealed, and failure to adjust for this can lead to substantial clinical measurement bias. Together, this preliminary research suggests that missing cases in Web-based psychotherapeutic interventions may not occur as random events and can be systematically predicted. Critically, at the same time, missing cases may experience outcomes that are distinct and important for a complete understanding of the treatment effect. UR - http://mental.jmir.org/2018/2/e22/ UR - http://dx.doi.org/10.2196/mental.8363 UR - http://www.ncbi.nlm.nih.gov/pubmed/29674311 ID - info:doi/10.2196/mental.8363 ER - TY - JOUR AU - Shen, Chen AU - Wang, Ping Man AU - Chu, TW Joanna AU - Wan, Alice AU - Viswanath, Kasisomayajula AU - Chan, Chee Sophia Siu AU - Lam, Hing Tai PY - 2017/11/23 TI - Sharing Family Life Information Through Video Calls and Other Information and Communication Technologies and the Association With Family Well-Being: Population-Based Survey JO - JMIR Ment Health SP - e57 VL - 4 IS - 4 KW - mobile phone, video call, Chinese N2 - Background: The use of information and communication technologies (ICTs) for information sharing among family members is increasing dramatically. However, little is known about the associated factors and the influence on family well-being. Objective: The authors investigated the pattern and social determinants of family life information sharing with family and the associations of different methods of sharing with perceived family health, happiness, and harmony (3Hs) in Hong Kong, where mobile phone ownership and Internet access are among the most prevalent, easiest, and fastest in the world. Methods: A territory-wide population-based telephone survey was conducted from January to August 2016 on different methods of family life information (ie, information related to family communication, relationships with family members, emotion and stress management) sharing with family members, including face-to-face, phone, instant messaging (IM), social media sites, video calls, and email. Family well-being was assessed by three single items on perceived family health, happiness, and harmony, with higher scores indicating better family well-being. Adjusted prevalence ratios were used to assess the associations of sociodemographic factors with family life information sharing, and adjusted beta coefficients for family well-being. Results: Of 2017 respondents, face-to-face was the most common method to share family life information (74.45%, 1502/2017), followed by IM (40.86%, 824/2017), phone (28.10%, 567/2017), social media sites (11.91%, 240/2017), video calls (5.89%, 119/2017), and email (5.48%, 111/2017). Younger age and higher education were associated with the use of any (at least one) method, face-to-face, IM, and social media sites for sharing family life information (all P for trend <.01). Higher education was most strongly associated with the use of video calls (adjusted prevalence ratio=5.61, 95% CI 2.29-13.74). Higher household income was significantly associated with the use of any method, face-to-face, and IM (all P for trend <.05). Sharing family life information was associated with a higher level of perceived family well-being (beta=0.56, 95% CI 0.37-0.75), especially by face-to-face (beta=0.62, 95% CI 0.45-0.80) and video calls (beta=0.34, 95% CI 0.04-0.65). The combination of face-to-face and video calls was most strongly associated with a higher level of perceived family well-being (beta=0.81, 95% CI 0.45-1.16). Conclusions: The differential use of ICTs to share family life information was observed. The prevalence of video calls was low, but associated with much better family well-being. The results need to be confirmed by prospective and intervention studies to promote the use of video calls to communicate and share information with family, particularly in disadvantaged groups. UR - http://mental.jmir.org/2017/4/e57/ UR - http://dx.doi.org/10.2196/mental.8139 UR - http://www.ncbi.nlm.nih.gov/pubmed/29170145 ID - info:doi/10.2196/mental.8139 ER - TY - JOUR AU - Lumsden, Jim AU - Skinner, Andy AU - Coyle, David AU - Lawrence, Natalia AU - Munafo, Marcus PY - 2017/11/22 TI - Attrition from Web-Based Cognitive Testing: A Repeated Measures Comparison of Gamification Techniques JO - J Med Internet Res SP - e395 VL - 19 IS - 11 KW - behavioral research/methods KW - games, experimental KW - computers KW - cognition KW - Internet KW - play and playthings/psychology KW - boredom KW - task performance and analysis KW - executive function KW - inhibition (psychology) N2 - Background: The prospect of assessing cognition longitudinally and remotely is attractive to researchers, health practitioners, and pharmaceutical companies alike. However, such repeated testing regimes place a considerable burden on participants, and with cognitive tasks typically being regarded as effortful and unengaging, these studies may experience high levels of participant attrition. One potential solution is to gamify these tasks to make them more engaging: increasing participant willingness to take part and reducing attrition. However, such an approach must balance task validity with the introduction of entertaining gamelike elements. Objective: This study aims to investigate the effects of gamelike features on participant attrition using a between-subjects, longitudinal Web-based testing study. Methods: We used three variants of a common cognitive task, the Stop Signal Task (SST), with a single gamelike feature in each: one variant where points were rewarded for performing optimally; another where the task was given a graphical theme; and a third variant, which was a standard SST and served as a control condition. Participants completed four compulsory test sessions over 4 consecutive days before entering a 6-day voluntary testing period where they faced a daily decision to either drop out or continue taking part. Participants were paid for each session they completed. Results: A total of 482 participants signed up to take part in the study, with 265 completing the requisite four consecutive test sessions. No evidence of an effect of gamification on attrition was observed. A log-rank test showed no evidence of a difference in dropout rates between task variants (?22=3.0, P=.22), and a one-way analysis of variance of the mean number of sessions completed per participant in each variant also showed no evidence of a difference (F2,262=1.534, P=.21, partial ?2=0.012). Conclusions: Our findings raise doubts about the ability of gamification to reduce attrition from longitudinal cognitive testing studies. UR - http://www.jmir.org/2017/11/e395/ UR - http://dx.doi.org/10.2196/jmir.8473 UR - http://www.ncbi.nlm.nih.gov/pubmed/29167090 ID - info:doi/10.2196/jmir.8473 ER - TY - JOUR AU - Tonn, Peter AU - Reuter, Christin Silja AU - Kuchler, Isabelle AU - Reinke, Britta AU - Hinkelmann, Lena AU - Stöckigt, Saskia AU - Siemoneit, Hanna AU - Schulze, Nina PY - 2017/10/03 TI - Development of a Questionnaire to Measure the Attitudes of Laypeople, Physicians, and Psychotherapists Toward Telemedicine in Mental Health JO - JMIR Ment Health SP - e39 VL - 4 IS - 4 KW - screening KW - questionnaire KW - e-mental health KW - remote consultation KW - attitude to computers KW - physician expectations KW - telemedicine KW - online-intervention N2 - Background: In the field of psychiatry and psychotherapy, there are now a growing number of Web-based interventions, mobile phone apps, or treatments that are available via remote transmission screen worldwide. Many of these interventions have been shown to be effective in studies but still find little use in everyday therapeutic work. However, it is important that attitude and expectation toward this treatment are generally examined, because these factors have an important effect on the efficacy of the treatment. To measure the general attitude of the users and prescribers toward telemedicine, which may include, for instance, Web-based interventions or interventions through mobile phone apps, there are a small number of extensive tests. The results of studies based on small groups of patients have been published too, but there is no useful short screening tool to give an insight into the general population?s attitude. We have developed a screening instrument that examines such attitude through a few graded questions. Objective: This study aimed to explore the Attitude toward Telemedicine in Psychiatry and Psychotherapy (ATiPP) and to evaluate the results of general population and some subgroups. Methods: In a three-step process, the questionnaire, which is available in three versions (laypeople, physicians, and psychologists), was developed. Afterwards, it was evaluated by four groups: population-representative laypeople, outpatients in different faculties, physicians, and psychotherapists. Results: The results were evaluated from a total of 1554 questionnaires. The sample population included 1000 laypeople, 455 outpatients, 62 physicians, and 37 psychotherapists. The reliability of all three versions of the questionnaire seemed good, as indicated by the Cronbach alpha values of .849 (the laypeople group), .80 (the outpatients? group), .827 (the physicians? group), and .855 (the psychotherapists? group). Conclusions: The ATiPP was found to be useful and reliable for measuring the attitudes toward the Web-based interventions in psychiatry and psychotherapy and should be used in different studies in this field in the future to evaluate and reflect the attitude of the participants. UR - https://mental.jmir.org/2017/4/e39/ UR - http://dx.doi.org/10.2196/mental.6802 UR - http://www.ncbi.nlm.nih.gov/pubmed/28974485 ID - info:doi/10.2196/mental.6802 ER - TY - JOUR AU - Milton, C. Alyssa AU - Ellis, A. Louise AU - Davenport, A. Tracey AU - Burns, M. Jane AU - Hickie, B. Ian PY - 2017/09/26 TI - Comparison of Self-Reported Telephone Interviewing and Web-Based Survey Responses: Findings From the Second Australian Young and Well National Survey JO - JMIR Ment Health SP - e37 VL - 4 IS - 3 KW - survey methods KW - youth KW - mental health KW - online behaviors KW - information disclosure N2 - Background: Web-based self-report surveying has increased in popularity, as it can rapidly yield large samples at a low cost. Despite this increase in popularity, in the area of youth mental health, there is a distinct lack of research comparing the results of Web-based self-report surveys with the more traditional and widely accepted computer-assisted telephone interviewing (CATI). Objective: The Second Australian Young and Well National Survey 2014 sought to compare differences in respondent response patterns using matched items on CATI versus a Web-based self-report survey. The aim of this study was to examine whether responses varied as a result of item sensitivity, that is, the item?s susceptibility to exaggeration on underreporting and to assess whether certain subgroups demonstrated this effect to a greater extent. Methods: A subsample of young people aged 16 to 25 years (N=101), recruited through the Second Australian Young and Well National Survey 2014, completed the identical items on two occasions: via CATI and via Web-based self-report survey. Respondents also rated perceived item sensitivity. Results: When comparing CATI with the Web-based self-report survey, a Wilcoxon signed-rank analysis showed that respondents answered 14 of the 42 matched items in a significantly different way. Significant variation in responses (CATI vs Web-based) was more frequent if the item was also rated by the respondents as highly sensitive in nature. Specifically, 63% (5/8) of the high sensitivity items, 43% (3/7) of the neutral sensitivity items, and 0% (0/4) of the low sensitivity items were answered in a significantly different manner by respondents when comparing their matched CATI and Web-based question responses. The items that were perceived as highly sensitive by respondents and demonstrated response variability included the following: sexting activities, body image concerns, experience of diagnosis, and suicidal ideation. For high sensitivity items, a regression analysis showed respondents who were male (beta=?.19, P=.048) or who were not in employment, education, or training (NEET; beta=?.32, P=.001) were significantly more likely to provide different responses on matched items when responding in the CATI as compared with the Web-based self-report survey. The Web-based self-report survey, however, demonstrated some evidence of avidity and attrition bias. Conclusions: Compared with CATI, Web-based self-report surveys are highly cost-effective and had higher rates of self-disclosure on sensitive items, particularly for respondents who identify as male and NEET. A drawback to Web-based surveying methodologies, however, includes the limited control over avidity bias and the greater incidence of attrition bias. These findings have important implications for further development of survey methods in the area of health and well-being, especially when considering research topics (in this case diagnosis, suicidal ideation, sexting, and body image) and groups that are being recruited (young people, males, and NEET). UR - http://mental.jmir.org/2017/3/e37/ UR - http://dx.doi.org/10.2196/mental.8222 UR - http://www.ncbi.nlm.nih.gov/pubmed/28951382 ID - info:doi/10.2196/mental.8222 ER - TY - JOUR AU - Brodey, B. Benjamin AU - Gonzalez, L. Nicole AU - Elkin, Ann Kathryn AU - Sasiela, Jordan W. AU - Brodey, S. Inger PY - 2017/09/06 TI - Assessing the Equivalence of Paper, Mobile Phone, and Tablet Survey Responses at a Community Mental Health Center Using Equivalent Halves of a ?Gold-Standard? Depression Item Bank JO - JMIR Ment Health SP - e36 VL - 4 IS - 3 KW - mobile phone KW - tablet KW - PROMIS KW - depression KW - item response theory KW - outcomes tracking KW - PORTAL KW - TeleSage KW - behavioral health KW - special issue on computing and mental health N2 - Background: The computerized administration of self-report psychiatric diagnostic and outcomes assessments has risen in popularity. If results are similar enough across different administration modalities, then new administration technologies can be used interchangeably and the choice of technology can be based on other factors, such as convenience in the study design. An assessment based on item response theory (IRT), such as the Patient-Reported Outcomes Measurement Information System (PROMIS) depression item bank, offers new possibilities for assessing the effect of technology choice upon results. Objective: To create equivalent halves of the PROMIS depression item bank and to use these halves to compare survey responses and user satisfaction among administration modalities?paper, mobile phone, or tablet?with a community mental health care population. Methods: The 28 PROMIS depression items were divided into 2 halves based on content and simulations with an established PROMIS response data set. A total of 129 participants were recruited from an outpatient public sector mental health clinic based in Memphis. All participants took both nonoverlapping halves of the PROMIS IRT-based depression items (Part A and Part B): once using paper and pencil, and once using either a mobile phone or tablet. An 8-cell randomization was done on technology used, order of technologies used, and order of PROMIS Parts A and B. Both Parts A and B were administered as fixed-length assessments and both were scored using published PROMIS IRT parameters and algorithms. Results: All 129 participants received either Part A or B via paper assessment. Participants were also administered the opposite assessment, 63 using a mobile phone and 66 using a tablet. There was no significant difference in item response scores for Part A versus B. All 3 of the technologies yielded essentially identical assessment results and equivalent satisfaction levels. Conclusions: Our findings show that the PROMIS depression assessment can be divided into 2 equivalent halves, with the potential to simplify future experimental methodologies. Among community mental health care recipients, the PROMIS items function similarly whether administered via paper, tablet, or mobile phone. User satisfaction across modalities was also similar. Because paper, tablet, and mobile phone administrations yielded similar results, the choice of technology should be based on factors such as convenience and can even be changed during a study without adversely affecting the comparability of results. UR - http://mental.jmir.org/2017/3/e36/ UR - http://dx.doi.org/10.2196/mental.6805 UR - http://www.ncbi.nlm.nih.gov/pubmed/28877861 ID - info:doi/10.2196/mental.6805 ER - TY - JOUR AU - Dogan, Ezgi AU - Sander, Christian AU - Wagner, Xenija AU - Hegerl, Ulrich AU - Kohls, Elisabeth PY - 2017/07/24 TI - Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review JO - J Med Internet Res SP - e262 VL - 19 IS - 7 KW - review KW - mood disorders KW - smartphone KW - ecological momentary assessment N2 - Background: Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones. Objective: We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes. Methods: We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols. Results: A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage. Conclusions: The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field. UR - http://www.jmir.org/2017/7/e262/ UR - http://dx.doi.org/10.2196/jmir.7006 UR - http://www.ncbi.nlm.nih.gov/pubmed/28739561 ID - info:doi/10.2196/jmir.7006 ER - TY - JOUR AU - Kaiser, Tim AU - Laireiter, Rupert Anton PY - 2017/07/20 TI - DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy JO - JMIR Med Inform SP - e20 VL - 5 IS - 3 KW - health information management KW - mental health KW - mental disorders KW - psychotherapeutic processes KW - algorithms N2 - Background: In recent years, the assessment of mental disorders has become more and more personalized. Modern advancements such as Internet-enabled mobile phones and increased computing capacity make it possible to tap sources of information that have long been unavailable to mental health practitioners. Objective: Software packages that combine algorithm-based treatment planning, process monitoring, and outcome monitoring are scarce. The objective of this study was to assess whether the DynAMo Web application can fill this gap by providing a software solution that can be used by both researchers to conduct state-of-the-art psychotherapy process research and clinicians to plan treatments and monitor psychotherapeutic processes. Methods: In this paper, we report on the current state of a Web application that can be used for assessing the temporal structure of mental disorders using information on their temporal and synchronous associations. A treatment planning algorithm automatically interprets the data and delivers priority scores of symptoms to practitioners. The application is also capable of monitoring psychotherapeutic processes during therapy and of monitoring treatment outcomes. This application was developed using the R programming language (R Core Team, Vienna) and the Shiny Web application framework (RStudio, Inc, Boston). It is made entirely from open-source software packages and thus is easily extensible. Results: The capabilities of the proposed application are demonstrated. Case illustrations are provided to exemplify its usefulness in clinical practice. Conclusions: With the broad availability of Internet-enabled mobile phones and similar devices, collecting data on psychopathology and psychotherapeutic processes has become easier than ever. The proposed application is a valuable tool for capturing, processing, and visualizing these data. The combination of dynamic assessment and process- and outcome monitoring has the potential to improve the efficacy and effectiveness of psychotherapy. UR - http://medinform.jmir.org/2017/3/e20/ UR - http://dx.doi.org/10.2196/medinform.6808 UR - http://www.ncbi.nlm.nih.gov/pubmed/28729233 ID - info:doi/10.2196/medinform.6808 ER - TY - JOUR AU - Wongkoblap, Akkapon AU - Vadillo, A. Miguel AU - Curcin, Vasa PY - 2017/06/29 TI - Researching Mental Health Disorders in the Era of Social Media: Systematic Review JO - J Med Internet Res SP - e228 VL - 19 IS - 6 KW - mental health KW - mental disorders KW - social networking KW - artificial intelligence KW - machine learning KW - public health informatics KW - depression KW - anxiety KW - infodemiology N2 - Background: Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. Objective: The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. Methods: We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. Results: The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Conclusions: Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. UR - http://www.jmir.org/2017/6/e228/ UR - http://dx.doi.org/10.2196/jmir.7215 UR - http://www.ncbi.nlm.nih.gov/pubmed/28663166 ID - info:doi/10.2196/jmir.7215 ER - TY - JOUR AU - Kahler, W. Christopher AU - Lechner, J. William AU - MacGlashan, James AU - Wray, B. Tyler AU - Littman, L. Michael PY - 2017/06/28 TI - Initial Progress Toward Development of a Voice-Based Computer-Delivered Motivational Intervention for Heavy Drinking College Students: An Experimental Study JO - JMIR Ment Health SP - e25 VL - 4 IS - 2 KW - Computer-delivered intervention KW - voice-based systems KW - alcohol intervention KW - heavy drinking N2 - Background: Computer-delivered interventions have been shown to be effective in reducing alcohol consumption in heavy drinking college students. However, these computer-delivered interventions rely on mouse, keyboard, or touchscreen responses for interactions between the users and the computer-delivered intervention. The principles of motivational interviewing suggest that in-person interventions may be effective, in part, because they encourage individuals to think through and speak aloud their motivations for changing a health behavior, which current computer-delivered interventions do not allow. Objective: The objective of this study was to take the initial steps toward development of a voice-based computer-delivered intervention that can ask open-ended questions and respond appropriately to users? verbal responses, more closely mirroring a human-delivered motivational intervention. Methods: We developed (1) a voice-based computer-delivered intervention that was run by a human controller and that allowed participants to speak their responses to scripted prompts delivered by speech generation software and (2) a text-based computer-delivered intervention that relied on the mouse, keyboard, and computer screen for all interactions. We randomized 60 heavy drinking college students to interact with the voice-based computer-delivered intervention and 30 to interact with the text-based computer-delivered intervention and compared their ratings of the systems as well as their motivation to change drinking and their drinking behavior at 1-month follow-up. Results: Participants reported that the voice-based computer-delivered intervention engaged positively with them in the session and delivered content in a manner consistent with motivational interviewing principles. At 1-month follow-up, participants in the voice-based computer-delivered intervention condition reported significant decreases in quantity, frequency, and problems associated with drinking, and increased perceived importance of changing drinking behaviors. In comparison to the text-based computer-delivered intervention condition, those assigned to voice-based computer-delivered intervention reported significantly fewer alcohol-related problems at the 1-month follow-up (incident rate ratio 0.60, 95% CI 0.44-0.83, P=.002). The conditions did not differ significantly on perceived importance of changing drinking or on measures of drinking quantity and frequency of heavy drinking. Conclusions: Results indicate that it is feasible to construct a series of open-ended questions and a bank of responses and follow-up prompts that can be used in a future fully automated voice-based computer-delivered intervention that may mirror more closely human-delivered motivational interventions to reduce drinking. Such efforts will require using advanced speech recognition capabilities and machine-learning approaches to train a program to mirror the decisions made by human controllers in the voice-based computer-delivered intervention used in this study. In addition, future studies should examine enhancements that can increase the perceived warmth and empathy of voice-based computer-delivered intervention, possibly through greater personalization, improvements in the speech generation software, and embodying the computer-delivered intervention in a physical form. UR - http://mental.jmir.org/2017/2/e25/ UR - http://dx.doi.org/10.2196/mental.7571 UR - http://www.ncbi.nlm.nih.gov/pubmed/28659259 ID - info:doi/10.2196/mental.7571 ER - TY - JOUR AU - Chen, Yuzen Robert AU - Feltes, Robert Jordan AU - Tzeng, Shun William AU - Lu, Yunzhu Zoe AU - Pan, Michael AU - Zhao, Nan AU - Talkin, Rebecca AU - Javaherian, Kavon AU - Glowinski, Anne AU - Ross, Will PY - 2017/06/16 TI - Phone-Based Interventions in Adolescent Psychiatry: A Perspective and Proof of Concept Pilot Study With a Focus on Depression and Autism JO - JMIR Res Protoc SP - e114 VL - 6 IS - 6 KW - telemedicine KW - depression KW - autistic disorder KW - mobile applications KW - text messaging KW - child KW - mental health N2 - Background: Telemedicine has emerged as an innovative platform to diagnose and treat psychiatric disorders in a cost-effective fashion. Previous studies have laid the functional framework for monitoring and treating child psychiatric disorders electronically using videoconferencing, mobile phones (smartphones), and Web-based apps. However, phone call and text message (short message service, SMS) interventions in adolescent psychiatry are less studied than other electronic platforms. Further investigations on the development of these interventions are needed. Objective: The aim of this paper was to explore the utility of text message interventions in adolescent psychiatry and describe a user feedback-driven iterative design process for text message systems. Methods: We developed automated text message interventions using a platform for both depression (EpxDepression) and autism spectrum disorder (ASD; EpxAutism) and conducted 2 pilot studies for each intervention (N=3 and N=6, respectively). The interventions were prescribed by and accessible to the patients? healthcare providers. EpxDepression and EpxAutism utilized an automated system to triage patients into 1 of 3 risk categories based on their text responses and alerted providers directly via phone and an online interface when patients met provider-specified risk criteria. Rapid text-based feedback from participants and interviews with providers allowed for quick iterative cycles to improve interventions. Results: Patients using EpxDepression had high weekly response rates (100% over 2 to 4 months), but exhibited message fatigue with daily prompts with mean (SD) overall response rates of 66.3% (21.6%) and 64.7% (8.2%) for mood and sleep questionnaires, respectively. In contrast, parents using EpxAutism displayed both high weekly and overall response rates (100% and 85%, respectively, over 1 to 4 months) that did not decay significantly with time. Monthly participant feedback surveys for EpxDepression (7 surveys) and EpxAutism (18 surveys) preliminarily indicated that for both interventions, daily messages constituted the ?perfect amount? of contact and that EpxAutism, but not EpxDepression, improved patient communication with providers. Notably, EpxDepression detected thoughts of self-harm in patients before their case managers or caregivers were aware of such ideation. Conclusions: Text-message interventions in adolescent psychiatry can provide a cost-effective and engaging method to track symptoms, behavior, and ideation over time. Following the collection of pilot data and feedback from providers and patients, larger studies are already underway to validate the clinical utility of EpxDepression and EpxAutism. Trial Registration: Clinicaltrials.gov NCT03002311; https://clinicaltrials.gov/ct2/show/NCT03002311 (Archived by WebCite at http://www.webcitation.org/6qQtlCIS0) UR - http://www.researchprotocols.org/2017/6/e114/ UR - http://dx.doi.org/10.2196/resprot.7245 UR - http://www.ncbi.nlm.nih.gov/pubmed/28623183 ID - info:doi/10.2196/resprot.7245 ER - TY - JOUR AU - Aledavood, Talayeh AU - Triana Hoyos, Maria Ana AU - Alakörkkö, Tuomas AU - Kaski, Kimmo AU - Saramäki, Jari AU - Isometsä, Erkki AU - Darst, K. Richard PY - 2017/06/09 TI - Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype JO - JMIR Res Protoc SP - e110 VL - 6 IS - 6 KW - data collection framework KW - mental health KW - digital phenotyping KW - big data N2 - Background: Mental and behavioral disorders are the main cause of disability worldwide. However, their diagnosis is challenging due to a lack of reliable biomarkers; current detection is based on structured clinical interviews which can be biased by the patient?s recall ability, affective state, changing in temporal frames, etc. While digital platforms have been introduced as a possible solution to this complex problem, there is little evidence on the extent of usability and usefulness of these platforms. Therefore, more studies where digital data is collected in larger scales are needed to collect scientific evidence on the capacities of these platforms. Most of the existing platforms for digital psychiatry studies are designed as monolithic systems for a certain type of study; publications from these studies focus on their results, rather than the design features of the data collection platform. Inevitably, more tools and platforms will emerge in the near future to fulfill the need for digital data collection for psychiatry. Currently little knowledge is available from existing digital platforms for future data collection platforms to build upon. Objective: The objective of this work was to identify the most important features for designing a digital platform for data collection for mental health studies, and to demonstrate a prototype platform that we built based on these design features. Methods: We worked closely in a multidisciplinary collaboration with psychiatrists, software developers, and data scientists and identified the key features which could guarantee short-term and long-term stability and usefulness of the platform from the designing stage to data collection and analysis of collected data. Results: The key design features that we identified were flexibility of access control, flexibility of data sources, and first-order privacy protection. We also designed the prototype platform Non-Intrusive Individual Monitoring Architecture (Niima), where we implemented these key design features. We described why each of these features are important for digital data collection for psychiatry, gave examples of projects where Niima was used or is going to be used in the future, and demonstrated how incorporating these design principles opens new possibilities for studies. Conclusions: The new methods of digital psychiatry are still immature and need further research. The design features we suggested are a first step to design platforms which can adapt to the upcoming requirements of digital psychiatry. UR - http://www.researchprotocols.org/2017/6/e110/ UR - http://dx.doi.org/10.2196/resprot.6919 UR - http://www.ncbi.nlm.nih.gov/pubmed/28600276 ID - info:doi/10.2196/resprot.6919 ER - TY - JOUR AU - Tsanas, Athanasios AU - Saunders, Kate AU - Bilderbeck, Amy AU - Palmius, Niclas AU - Goodwin, Guy AU - De Vos, Maarten PY - 2017/05/25 TI - Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment JO - JMIR Ment Health SP - e15 VL - 4 IS - 2 KW - bipolar disorder KW - borderline personality disorder KW - depression KW - mania KW - latent variable structure KW - mood monitoring KW - patient reported outcome measures KW - mhealth KW - mobile app N2 - Background: We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. Objective: The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. Methods: We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. Results: We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. Conclusions: This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice. UR - http://mental.jmir.org/2017/2/e15/ UR - http://dx.doi.org/10.2196/mental.6917 UR - http://www.ncbi.nlm.nih.gov/pubmed/28546141 ID - info:doi/10.2196/mental.6917 ER - TY - JOUR AU - Penders, M. Thomas AU - Wuensch, L. Karl AU - Ninan, T. Philip PY - 2017/05/24 TI - eMindLog: Self-Measurement of Anxiety and Depression Using Mobile Technology JO - JMIR Res Protoc SP - e98 VL - 6 IS - 5 KW - mobile KW - anxiety KW - depression KW - internet KW - measurement N2 - Background: Quantifying anxiety and depressive experiences permits individuals to calibrate where they are and monitor intervention-associated changes. eMindLog is a novel self-report measure for anxiety and depression that is grounded in psychology with an organizing structure based on neuroscience. Objective: Our aim was to explore the psychometric properties of eMindLog in a nonclinical sample of subjects. Methods: In a cross-sectional study of eMindLog, a convenience sample of 198 adults provided informed consent and completed eMindLog and the Hospital Anxiety and Depression Scale (HADS) as a reference. Brain systems (eg, negative and positive valence systems, cognitive systems) and their functional states that drive behavior are measured daily as emotions, thoughts, and behaviors. Associated symptoms, quality of life, and functioning are assessed weekly. eMindLog offers ease of use and expediency, using mobile technology across multiple platforms, with dashboard reporting of scores. It enhances precision by providing distinct, nonoverlapping description of terms, and accuracy through guidance for scoring severity. Results: eMindLog daily total score had a Cronbach alpha of .94. Pearson correlation coefficient for eMindLog indexes for anxiety and sadness/anhedonia were r=.66 (P<.001) and r=.62 (P<.001) contrasted with the HADS anxiety and depression subscales respectively. Of 195 subjects, 23 (11.8%) had cross-sectional symptoms above the threshold for Generalized Anxiety Disorder and 29 (29/195, 14.9%) for Major Depressive Disorder. Factor analysis supported the theoretically derived index derivatives for anxiety, anger, sadness, and anhedonia. Conclusions: eMindLog is a novel self-measurement tool to measure anxiety and depression, demonstrating excellent reliability and strong validity in a nonclinical population. Further studies in clinical populations are necessary for fuller validation of its psychometric properties. Self-measurement of anxiety and depressive symptoms with precision and accuracy has several potential benefits, including case detection, tracking change over time, efficacy assessment of interventions, and exploration of potential biomarkers. UR - http://www.researchprotocols.org/2017/5/e98/ UR - http://dx.doi.org/10.2196/resprot.7447 UR - http://www.ncbi.nlm.nih.gov/pubmed/28539304 ID - info:doi/10.2196/resprot.7447 ER - TY - JOUR AU - Mohr, C. David AU - Lyon, R. Aaron AU - Lattie, G. Emily AU - Reddy, Madhu AU - Schueller, M. Stephen PY - 2017/05/10 TI - Accelerating Digital Mental Health Research From Early Design and Creation to Successful Implementation and Sustainment JO - J Med Internet Res SP - e153 VL - 19 IS - 5 KW - eHealth KW - mHealth KW - methodology UR - http://www.jmir.org/2017/5/e153/ UR - http://dx.doi.org/10.2196/jmir.7725 UR - http://www.ncbi.nlm.nih.gov/pubmed/28490417 ID - info:doi/10.2196/jmir.7725 ER - TY - JOUR AU - Provoost, Simon AU - Lau, Ming Ho AU - Ruwaard, Jeroen AU - Riper, Heleen PY - 2017/05/09 TI - Embodied Conversational Agents in Clinical Psychology: A Scoping Review JO - J Med Internet Res SP - e151 VL - 19 IS - 5 KW - eHealth KW - review KW - embodied conversational agent KW - human computer interaction KW - clinical psychology KW - mental disorders KW - intelligent agent KW - health behavior N2 - Background: Embodied conversational agents (ECAs) are computer-generated characters that simulate key properties of human face-to-face conversation, such as verbal and nonverbal behavior. In Internet-based eHealth interventions, ECAs may be used for the delivery of automated human support factors. Objective: We aim to provide an overview of the technological and clinical possibilities, as well as the evidence base for ECA applications in clinical psychology, to inform health professionals about the activity in this field of research. Methods: Given the large variety of applied methodologies, types of applications, and scientific disciplines involved in ECA research, we conducted a systematic scoping review. Scoping reviews aim to map key concepts and types of evidence underlying an area of research, and answer less-specific questions than traditional systematic reviews. Systematic searches for ECA applications in the treatment of mood, anxiety, psychotic, autism spectrum, and substance use disorders were conducted in databases in the fields of psychology and computer science, as well as in interdisciplinary databases. Studies were included if they conveyed primary research findings on an ECA application that targeted one of the disorders. We mapped each study?s background information, how the different disorders were addressed, how ECAs and users could interact with one another, methodological aspects, and the study?s aims and outcomes. Results: This study included N=54 publications (N=49 studies). More than half of the studies (n=26) focused on autism treatment, and ECAs were used most often for social skills training (n=23). Applications ranged from simple reinforcement of social behaviors through emotional expressions to sophisticated multimodal conversational systems. Most applications (n=43) were still in the development and piloting phase, that is, not yet ready for routine practice evaluation or application. Few studies conducted controlled research into clinical effects of ECAs, such as a reduction in symptom severity. Conclusions: ECAs for mental disorders are emerging. State-of-the-art techniques, involving, for example, communication through natural language or nonverbal behavior, are increasingly being considered and adopted for psychotherapeutic interventions in ECA research with promising results. However, evidence on their clinical application remains scarce. At present, their value to clinical practice lies mostly in the experimental determination of critical human support factors. In the context of using ECAs as an adjunct to existing interventions with the aim of supporting users, important questions remain with regard to the personalization of ECAs? interaction with users, and the optimal timing and manner of providing support. To increase the evidence base with regard to Internet interventions, we propose an additional focus on low-tech ECA solutions that can be rapidly developed, tested, and applied in routine practice. UR - http://www.jmir.org/2017/5/e151/ UR - http://dx.doi.org/10.2196/jmir.6553 UR - http://www.ncbi.nlm.nih.gov/pubmed/28487267 ID - info:doi/10.2196/jmir.6553 ER - TY - JOUR AU - Berry, Natalie AU - Lobban, Fiona AU - Belousov, Maksim AU - Emsley, Richard AU - Nenadic, Goran AU - Bucci, Sandra PY - 2017/04/05 TI - #WhyWeTweetMH: Understanding Why People Use Twitter to Discuss Mental Health Problems JO - J Med Internet Res SP - e107 VL - 19 IS - 4 KW - mental health KW - Twitter KW - social media N2 - Background: Use of the social media website Twitter is highly prevalent and has led to a plethora of Web-based social and health-related data available for use by researchers. As such, researchers are increasingly using data from social media to retrieve and analyze mental health-related content. However, there is limited evidence regarding why people use this emerging platform to discuss mental health problems in the first place. Objectives: The aim of this study was to explore the reasons why individuals discuss mental health on the social media website Twitter. The study was the first of its kind to implement a study-specific hashtag for research; therefore, we also examined how feasible it was to circulate and analyze a study-specific hashtag for mental health research. Methods: Text mining methods using the Twitter Streaming Application Programming Interface (API) and Twitter Search API were used to collect and organize tweets from the hashtag #WhyWeTweetMH, circulated between September 2015 and November 2015. Tweets were analyzed thematically to understand the key reasons for discussing mental health using the Twitter platform. Results: Four overarching themes were derived from the 132 tweets collected: (1) sense of community; (2) raising awareness and combatting stigma; (3) safe space for expression; and (4) coping and empowerment. In addition, 11 associated subthemes were also identified. Conclusions: The themes derived from the content of the tweets highlight the perceived therapeutic benefits of Twitter through the provision of support and information and the potential for self-management strategies. The ability to use Twitter to combat stigma and raise awareness of mental health problems indicates the societal benefits that can be facilitated via the platform. The number of tweets and themes identified demonstrates the feasibility of implementing study-specific hashtags to explore research questions in the field of mental health and can be used as a basis for other health-related research. UR - http://www.jmir.org/2017/4/e107/ UR - http://dx.doi.org/10.2196/jmir.6173 UR - http://www.ncbi.nlm.nih.gov/pubmed/28381392 ID - info:doi/10.2196/jmir.6173 ER - TY - JOUR AU - Tashjian, C. Vartan AU - Mosadeghi, Sasan AU - Howard, R. Amber AU - Lopez, Mayra AU - Dupuy, Taylor AU - Reid, Mark AU - Martinez, Bibiana AU - Ahmed, Shahzad AU - Dailey, Francis AU - Robbins, Karen AU - Rosen, Bradley AU - Fuller, Garth AU - Danovitch, Itai AU - IsHak, Waguih AU - Spiegel, Brennan PY - 2017/03/29 TI - Virtual Reality for Management of Pain in Hospitalized Patients: Results of a Controlled Trial JO - JMIR Ment Health SP - e9 VL - 4 IS - 1 KW - pain KW - virtual reality KW - inpatients KW - hospitalization N2 - Background: Improvements in software and design and reduction in cost have made virtual reality (VR) a practical tool for immersive, three-dimensional (3D), multisensory experiences that distract patients from painful stimuli. Objective: The objective of the study was to measure the impact of a onetime 3D VR intervention versus a two-dimensional (2D) distraction video for pain in hospitalized patients. Methods: We conducted a comparative cohort study in a large, urban teaching hospital in medical inpatients with an average pain score of ?3/10 from any cause. Patients with nausea, vomiting, dementia, motion sickness, stroke, seizure, and epilepsy and those placed in isolation were excluded. Patients in the intervention cohort viewed a 3D VR experience designed to reduce pain using the Samsung Gear Oculus VR headset; control patients viewed a high-definition, 2D nature video on a 14-inch bedside screen. Pre- and postintervention pain scores were recorded. Difference-in-difference scores and the proportion achieving a half standard deviation pain response were compared between groups. Results: There were 50 subjects per cohort (N=100). The mean pain reduction in the VR cohort was greater than in controls (?1.3 vs ?0.6 points, respectively; P=.008). A total of 35 (65%) patients in the VR cohort achieved a pain response versus 40% of controls (P=.01; number needed to treat=4). No adverse events were reported from VR. Conclusions: Use of VR in hospitalized patients significantly reduces pain versus a control distraction condition. These results indicate that VR is an effective and safe adjunctive therapy for pain management in the acute inpatient setting; future randomized trials should confirm benefit with different visualizations and exposure periods. Trial Registration: Clinicaltrials.gov NCT02456987; https://clinicaltrials.gov/ct2/show/NCT02456987 (Archived by WebCite at http://www.webcitation.org/6pJ1P644S) UR - http://mental.jmir.org/2017/1/e9/ UR - http://dx.doi.org/10.2196/mental.7387 UR - http://www.ncbi.nlm.nih.gov/pubmed/28356241 ID - info:doi/10.2196/mental.7387 ER - TY - JOUR AU - Kenny, Rachel AU - Dooley, Barbara AU - Fitzgerald, Amanda PY - 2016/11/29 TI - Ecological Momentary Assessment of Adolescent Problems, Coping Efficacy, and Mood States Using a Mobile Phone App: An Exploratory Study JO - JMIR Ment Health SP - e51 VL - 3 IS - 4 KW - adolescent KW - affect KW - ecological momentary assessment KW - mobile apps N2 - Background: Mobile technologies have the potential to be used as innovative tools for conducting research on the mental health and well-being of young people. In particular, they have utility for carrying out ecological momentary assessment (EMA) research by capturing data from participants in real time as they go about their daily lives. Objective: The aim of this study was to explore the utility of a mobile phone app as a means of collecting EMA data pertaining to mood, problems, and coping efficacy in a school-based sample of Irish young people. Methods: The study included a total of 208 participants who were aged 15-18 years, 64% female (113/208), recruited from second-level schools in Ireland, and who downloaded the CopeSmart mobile phone app as part of a randomized controlled trial. On the app, participants initially responded to 5 single-item measures of key protective factors in youth mental health (formal help-seeking, informal help-seeking, sleep, exercise, and sense of belonging). They were then encouraged to use the app daily to input data relating to mood states (happiness, sadness, anger, stress, and worry), daily problems, and coping self-efficacy. The app automatically collected data pertaining to user engagement over the course of the 28-day intervention period. Students also completed pen and paper questionnaires containing standardized measures of emotional distress (Depression, Anxiety, and Stress Scale; DASS-21), well-being (World Health Organization Well-Being Index; WHO-5), and coping (Coping Strategies Inventory; CSI). Results: On average the participants completed 18% (5/28) of daily ratings, and engagement levels did not differ across gender, age, school, socioeconomic status, ethnicity, or nationality. On a scale of 1 to 10, happiness was consistently the highest rated mood state (overall mean 6.56), and anger was consistently the lowest (overall mean 2.11). Pearson correlations revealed that average daily ratings of emotional states were associated with standardized measures of emotional distress (rhappiness=?.45, rsadness=.51, ranger=.32, rstress=.41, rworry=.48) and well-being (rhappiness=.39, rsadness =?.43, ranger=?.27, rstress=?.35, rworry=?.33). Inferential statistics indicated that single-item indicators of key protective factors were related to emotional distress, well-being, and average daily mood states, as measured by EMA ratings. Hierarchical regressions revealed that greater daily problems were associated with more negative daily mood ratings (all at the P<.001 level); however, when coping efficacy was taken into account, the relationship between problems and happiness, sadness, and anger became negligible. Conclusions: While engagement with the app was low, overall the EMA data collected in this exploratory study appeared valid and provided useful insights into the relationships between daily problems, coping efficacy, and mood states. Future research should explore ways to increase engagement with EMA mobile phone apps in adolescent populations to maximize the amount of data captured by these tools. Trial Registration: Clinicaltrials.gov NCT02265978; http://clinicaltrials.gov/ct2/show/NCT02265978 (Archived by WebCite at http://www.webcitation.org/6mMeYqseA). UR - http://mental.jmir.org/2016/4/e51/ UR - http://dx.doi.org/10.2196/mental.6361 UR - http://www.ncbi.nlm.nih.gov/pubmed/27899340 ID - info:doi/10.2196/mental.6361 ER - TY - JOUR AU - Wilansky, Pamela AU - Eklund, Mikael J. AU - Milner, Tracy AU - Kreindler, David AU - Cheung, Amy AU - Kovacs, Tim AU - Shooshtari, Shahin AU - Astell, Arlene AU - Ohinmaa, Arto AU - Henderson, Joanna AU - Strauss, John AU - Mills, SL Rosemary PY - 2016/11/10 TI - Cognitive Behavior Therapy for Anxious and Depressed Youth: Improving Homework Adherence Through Mobile Technology JO - JMIR Res Protoc SP - e209 VL - 5 IS - 4 KW - mHealth KW - mobile app KW - youth KW - anxiety KW - depression KW - cognitive behavior therapy KW - homework N2 - Background: Anxiety and mood disorders are the most common mental illnesses, peaking during adolescence and affecting approximately 25% of Canadians aged 14-17 years. If not successfully treated at this age, they often persist into adulthood, exerting a great social and economic toll. Given the long-term impact, finding ways to increase the success and cost-effectiveness of mental health care is a pressing need. Cognitive behavior therapy (CBT) is an evidence-based treatment for mood and anxiety disorders throughout the lifespan. Mental health technologies can be used to make such treatments more successful by delivering them in a format that increases utilization. Young people embrace technologies, and many want to actively manage their mental health. Mobile software apps have the potential to improve youth adherence to CBT and, in turn, improve outcomes of treatment. Objective: The purpose of this project is to improve homework adherence in CBT for youth anxiety and/or depression. The objectives are to (1) design and optimize the usability of a mobile app for delivering the homework component of CBT for youth with anxiety and/or depression, (2) assess the app?s impact on homework completion, and (3) implement the app in CBT programs. We hypothesize that homework adherence will be greater in the app group than in the no-app group. Methods: Phase 1: exploratory interviews will be conducted with adolescents and therapists familiar with CBT to obtain views and perspectives on the requirements and features of a usable app and the challenges involved in implementation. The information obtained will guide the design of a prototype. The prototype will be optimized via think-aloud procedures involving an iterative process of evaluation, modification, and re-evaluation, culminating in a fully functional version of the prototype that is ready for optimization in a clinical context. Phase 2: a usability study will be conducted to optimize the prototype in the context of treatment at clinics that provide CBT treatment for youth with anxiety and/or depression. This phase will result in a usable app that is ready to be tested for its effectiveness in increasing homework adherence. Phase 3: a pragmatic clinical trial will be conducted at several clinics to evaluate the impact of the app on homework adherence. Participants in the app group are expected to show greater homework completion than those in the no-app group. Results: Phase 3 will be completed by September 2019. Conclusions: The app will be a unique adjunct to treatment for adolescents in CBT, focusing on both anxiety and depression, developed in partnership with end users at every stage from design to implementation, customizable for different cognitive profiles, and designed with depression symptom tracking measures for youth made interoperable with electronic medical records. UR - http://www.researchprotocols.org/2016/4/e209/ UR - http://dx.doi.org/10.2196/resprot.5841 UR - http://www.ncbi.nlm.nih.gov/pubmed/27833071 ID - info:doi/10.2196/resprot.5841 ER - TY - JOUR AU - Batink, Tim AU - Bakker, Jindra AU - Vaessen, Thomas AU - Kasanova, Zuzana AU - Collip, Dina AU - van Os, Jim AU - Wichers, Marieke AU - Germeys, Inez AU - Peeters, Frenk PY - 2016/09/15 TI - Acceptance and Commitment Therapy in Daily Life Training: A Feasibility Study of an mHealth Intervention JO - JMIR Mhealth Uhealth SP - e103 VL - 4 IS - 3 KW - mHealth KW - behavior change KW - daily life intervention KW - acceptance and commitment therapy KW - experience sampling N2 - Background: With the development of mHealth, it is possible to treat patients in their natural environment. Mobile technology helps to bridge the gap between the therapist?s office and the ?real world.? The ACT in Daily Life training (ACT-DL) was designed as an add-on intervention to help patients practice with acceptance and commitment therapy in their daily lives. The ACT-DL consists of two main components: daily monitoring using experience sampling and ACT training in daily life. Objectives: To assess the acceptability and feasibility of the ACT-DL in a general outpatient population. A secondary objective was to conduct a preliminary examination of the effectiveness of the ACT-DL. Methods: An observational comparative study was conducted. The experimental group consisted of 49 patients who volunteered for ACT-DL, and the control group consisted of 112 patients who did not volunteer. As part of an inpatient treatment program, both groups received a 6-week ACT training. Participants went home to continue their treatment on an outpatient basis, during which time the experimental group received the 4-week add-on ACT-DL. Acceptability and feasibility of the ACT-DL was assessed weekly by telephone survey. Effectiveness of the ACT-DL was evaluated with several self-report questionnaires ( Flexibility Index Test (FIT-60): psychological flexibility, Brief Symptom Inventory: symptoms, Utrechtse Coping List: coping, and Quality of life visual analog scale (QoL-VAS): quality of life). Results: More than three-quarters of the participants (76%) completed the full 4-week training. User evaluations showed that ACT-DL stimulated the use of ACT in daily life: participants practiced over an hour a week (mean 78.8 minutes, standard deviation 54.4), doing 10.4 exercises (standard deviation 6.0) on average. Both ACT exercises and metaphors were experienced as useful components of the training (rated 5 out of 7). Repeated measures ANCOVA did not show significant effects of the ACT-DL on psychological flexibility (P=.88), symptoms (P=.39), avoidant coping (P=.28), or quality of life (P=.15). Conclusions: This is the first study that uses experience sampling to foster awareness in daily life in combination with acceptance and commitment therapy to foster skill building. Adherence to the ACT-DL was high for an intensive mHealth intervention. ACT-DL appears to be an acceptable and feasible mHealth intervention, suitable for a broad range of mental health problems. However, short-term effectiveness could not be demonstrated. Additional clinical trials are needed to examine both short-term and long-term effects. UR - http://mhealth.jmir.org/2016/3/e103/ UR - http://dx.doi.org/10.2196/mhealth.5437 UR - http://www.ncbi.nlm.nih.gov/pubmed/27634747 ID - info:doi/10.2196/mhealth.5437 ER - TY - JOUR AU - Wiloth, Stefanie AU - Lemke, Nele AU - Werner, Christian AU - Hauer, Klaus PY - 2016/7/18 TI - Validation of a Computerized, Game-based Assessment Strategy to Measure Training Effects on Motor-Cognitive Functions in People With Dementia JO - JMIR Serious Games SP - e12 VL - 4 IS - 2 KW - serious games KW - computerized assessment KW - validation KW - motor-cognitive functions KW - elderly KW - older adults KW - cognitive impairment KW - dementia N2 - Background: Exergames often used for training purpose can also be applied to create assessments based on quantitative data derived from the game. A number of studies relate to these use functionalities developing specific assessment tasks by using the game software and provided good data on psychometric properties. However, (1) assessments often include tasks other than the original game task used for training and therefore relate to similar but not to identical or integrated performances trained, (2) people with diagnosed dementia have insufficiently been addressed in validation studies, and (3) studies did commonly not present validation data such as sensitivity to change, although this is a paramount objective for validation to evaluate responsiveness in intervention studies. Objective: Specific assessment parameters have been developed using quantitative data directly derived from the data stream during the game task of a training device (Physiomat). The aim of this study was to present data on construct validity, test?retest reliability, sensitivity to change, and feasibility of this internal assessment approach, which allows the quantification of Physiomat training effects on motor-cognitive functions in 105 multimorbid patients with mild-to-moderate dementia (mean age 82.7±5.9). Methods: Physiomat assessment includes various tasks at different complexity levels demanding balance and cognitive abilities. For construct validity, motor-cognitive Physiomat assessment tasks were compared with established motor and cognitive tests using Spearman?s rank correlations (rs). For test?retest reliability, we used intra-class correlations (ICC3,1) and focused on all Physiomat tasks. Sensitivity to change of trained Physiomat tasks was tested using Wilcoxon statistic and standardized response means (SRMs). Completion rate and time were calculated for feasibility. Results: Analyses have mostly shown moderate-to-high correlations between established motor as well as cognitive tests and simple (rs=?.22 to .68, P ?.001-.03), moderate (rs=?.33 to .71, P ?.001-.004), and complex motor-cognitive Physiomat tasks (rs=?.22 to .83, P ?.001-.30) indicating a good construct validity. Moderate-to-high correlations between test and retest assessments were found for simple, moderate, and complex motor-cognitive tasks (ICC=.47-.83, P ?.001) indicating good test?retest reliability. Sensitivity to change was good to excellent for Physiomat assessment as it reproduced significant improvements (P ?.001) with mostly moderate-to-large effect sizes (SRM=0.5-2.0) regarding all trained tasks. Completion time averaged 25.8 minutes. Completion rate was high for initial Physiomat measures. No adverse events occurred during assessment. Conclusions: Overall, Physiomat proved to have good psychometric qualities in people with mild-to-moderate dementia representing a reliable, valid, responsive, and feasible assessment strategy for multimorbid older adults with or without cognitive impairment, which relates to identical and integrated performances trained by using the game. UR - http://games.jmir.org/2016/2/e12/ UR - http://dx.doi.org/10.2196/games.5696 UR - http://www.ncbi.nlm.nih.gov/pubmed/27432746 ID - info:doi/10.2196/games.5696 ER - TY - JOUR AU - Lumsden, Jim AU - Edwards, A. Elizabeth AU - Lawrence, S. Natalia AU - Coyle, David AU - Munafò, R. Marcus PY - 2016/07/15 TI - Gamification of Cognitive Assessment and Cognitive Training: A Systematic Review of Applications and Efficacy JO - JMIR Serious Games SP - e11 VL - 4 IS - 2 KW - gamification KW - gamelike KW - cognition KW - computer games KW - review N2 - Background: Cognitive tasks are typically viewed as effortful, frustrating, and repetitive, which often leads to participant disengagement. This, in turn, may negatively impact data quality and/or reduce intervention effects. However, gamification may provide a possible solution. If game design features can be incorporated into cognitive tasks without undermining their scientific value, then data quality, intervention effects, and participant engagement may be improved. Objectives: This systematic review aims to explore and evaluate the ways in which gamification has already been used for cognitive training and assessment purposes. We hope to answer 3 questions: (1) Why have researchers opted to use gamification? (2) What domains has gamification been applied in? (3) How successful has gamification been in cognitive research thus far? Methods: We systematically searched several Web-based databases, searching the titles, abstracts, and keywords of database entries using the search strategy (gamif* OR game OR games) AND (cognit* OR engag* OR behavi* OR health* OR attention OR motiv*). Searches included papers published in English between January 2007 and October 2015. Results: Our review identified 33 relevant studies, covering 31 gamified cognitive tasks used across a range of disorders and cognitive domains. We identified 7 reasons for researchers opting to gamify their cognitive training and testing. We found that working memory and general executive functions were common targets for both gamified assessment and training. Gamified tests were typically validated successfully, although mixed-domain measurement was a problem. Gamified training appears to be highly engaging and does boost participant motivation, but mixed effects of gamification on task performance were reported. Conclusions: Heterogeneous study designs and typically small sample sizes highlight the need for further research in both gamified training and testing. Nevertheless, careful application of gamification can provide a way to develop engaging and yet scientifically valid cognitive assessments, and it is likely worthwhile to continue to develop gamified cognitive tasks in the future. UR - http://games.jmir.org/2016/2/e11/ UR - http://dx.doi.org/10.2196/games.5888 UR - http://www.ncbi.nlm.nih.gov/pubmed/27421244 ID - info:doi/10.2196/games.5888 ER - TY - JOUR AU - Hökby, Sebastian AU - Hadlaczky, Gergö AU - Westerlund, Joakim AU - Wasserman, Danuta AU - Balazs, Judit AU - Germanavicius, Arunas AU - Machín, Núria AU - Meszaros, Gergely AU - Sarchiapone, Marco AU - Värnik, Airi AU - Varnik, Peeter AU - Westerlund, Michael AU - Carli, Vladimir PY - 2016/07/13 TI - Are Mental Health Effects of Internet Use Attributable to the Web-Based Content or Perceived Consequences of Usage? A Longitudinal Study of European Adolescents JO - JMIR Ment Health SP - e31 VL - 3 IS - 3 KW - problematic Internet use KW - addictive behavior KW - Internet KW - mental health KW - adolescent health KW - longitudinal study N2 - Background: Adolescents and young adults are among the most frequent Internet users, and accumulating evidence suggests that their Internet behaviors might affect their mental health. Internet use may impact mental health because certain Web-based content could be distressing. It is also possible that excessive use, regardless of content, produces negative consequences, such as neglect of protective offline activities. Objective: The objective of this study was to assess how mental health is associated with (1) the time spent on the Internet, (2) the time spent on different Web-based activities (social media use, gaming, gambling, pornography use, school work, newsreading, and targeted information searches), and (3) the perceived consequences of engaging in those activities. Methods: A random sample of 2286 adolescents was recruited from state schools in Estonia, Hungary, Italy, Lithuania, Spain, Sweden, and the United Kingdom. Questionnaire data comprising Internet behaviors and mental health variables were collected and analyzed cross-sectionally and were followed up after 4 months. Results: Cross-sectionally, both the time spent on the Internet and the relative time spent on various activities predicted mental health (P<.001), explaining 1.4% and 2.8% variance, respectively. However, the consequences of engaging in those activities were more important predictors, explaining 11.1% variance. Only Web-based gaming, gambling, and targeted searches had mental health effects that were not fully accounted for by perceived consequences. The longitudinal analyses showed that sleep loss due to Internet use (ß=.12, 95% CI=0.05-0.19, P=.001) and withdrawal (negative mood) when Internet could not be accessed (ß=.09, 95% CI=0.03-0.16, P<.01) were the only consequences that had a direct effect on mental health in the long term. Perceived positive consequences of Internet use did not seem to be associated with mental health at all. Conclusions: The magnitude of Internet use is negatively associated with mental health in general, but specific Web-based activities differ in how consistently, how much, and in what direction they affect mental health. Consequences of Internet use (especially sleep loss and withdrawal when Internet cannot be accessed) seem to predict mental health outcomes to a greater extent than the specific activities themselves. Interventions aimed at reducing the negative mental health effects of Internet use could target its negative consequences instead of the Internet use itself. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): 65120704; http://www.isrctn.com/ISRCTN65120704?q=&filters=recruitmentCountry:Lithuania&sort=&offset= 5&totalResults=32&page=1&pageSize=10&searchType=basic-search (Archived by WebCite at http://www.webcitation/abcdefg) UR - http://mental.jmir.org/2016/3/e31/ UR - http://dx.doi.org/10.2196/mental.5925 UR - http://www.ncbi.nlm.nih.gov/pubmed/27417665 ID - info:doi/10.2196/mental.5925 ER - TY - JOUR AU - Mosadeghi, Sasan AU - Reid, William Mark AU - Martinez, Bibiana AU - Rosen, Todd Bradley AU - Spiegel, Ross Brennan Mason PY - 2016/06/27 TI - Feasibility of an Immersive Virtual Reality Intervention for Hospitalized Patients: An Observational Cohort Study JO - JMIR Ment Health SP - e28 VL - 3 IS - 2 KW - virtual reality therapy KW - hospitalization KW - feasibility studies N2 - Background: Virtual reality (VR) offers immersive, realistic, three-dimensional experiences that ?transport? users to novel environments. Because VR is effective for acute pain and anxiety, it may have benefits for hospitalized patients; however, there are few reports using VR in this setting. Objective: The aim was to evaluate the acceptability and feasibility of VR in a diverse cohort of hospitalized patients. Methods: We assessed the acceptability and feasibility of VR in a cohort of patients admitted to an inpatient hospitalist service over a 4-month period. We excluded patients with motion sickness, stroke, seizure, dementia, nausea, and in isolation. Eligible patients viewed VR experiences (eg, ocean exploration; Cirque du Soleil; tour of Iceland) with Samsung Gear VR goggles. We then conducted semistructured patient interview and performed statistical testing to compare patients willing versus unwilling to use VR. Results: We evaluated 510 patients; 423 were excluded and 57 refused to participate, leaving 30 participants. Patients willing versus unwilling to use VR were younger (mean 49.1, SD 17.4 years vs mean 60.2, SD 17.7 years; P=.01); there were no differences by sex, race, or ethnicity. Among users, most reported a positive experience and indicated that VR could improve pain and anxiety, although many felt the goggles were uncomfortable. Conclusions: Most inpatient users of VR described the experience as pleasant and capable of reducing pain and anxiety. However, few hospitalized patients in this ?real-world? series were both eligible and willing to use VR. Consistent with the ?digital divide? for emerging technologies, younger patients were more willing to participate. Future research should evaluate the impact of VR on clinical and resource outcomes. ClinicalTrial: Clinicaltrials.gov NCT02456987; https://clinicaltrials.gov/ct2/show/NCT02456987 (Archived by WebCite at http://www.webcitation.org/6iFIMRNh3) UR - http://mental.jmir.org/2016/2/e28/ UR - http://dx.doi.org/10.2196/mental.5801 UR - http://www.ncbi.nlm.nih.gov/pubmed/27349654 ID - info:doi/10.2196/mental.5801 ER - TY - JOUR AU - Hamann, Christoph AU - Schultze-Lutter, Frauke AU - Tarokh, Leila PY - 2016/06/15 TI - Web-Based Assessment of Mental Well-Being in Early Adolescence: A Reliability Study JO - J Med Internet Res SP - e138 VL - 18 IS - 6 KW - early adolescence KW - online assessment KW - reliability N2 - Background: The ever-increasing use of the Internet among adolescents represents an emerging opportunity for researchers to gain access to larger samples, which can be queried over several years longitudinally. Among adolescents, young adolescents (ages 11 to 13 years) are of particular interest to clinicians as this is a transitional stage, during which depressive and anxiety symptoms often emerge. However, it remains unclear whether these youngest adolescents can accurately answer questions about their mental well-being using a Web-based platform. Objective: The aim of the study was to examine the accuracy of responses obtained from Web-based questionnaires by comparing Web-based with paper-and-pencil versions of depression and anxiety questionnaires. Methods: The primary outcome was the score on the depression and anxiety questionnaires under two conditions: (1) paper-and-pencil and (2) Web-based versions. Twenty-eight adolescents (aged 11-13 years, mean age 12.78 years and SD 0.78; 18 females, 64%) were randomly assigned to complete either the paper-and-pencil or the Web-based questionnaire first. Intraclass correlation coefficients (ICCs) were calculated to measure intrarater reliability. Intraclass correlation coefficients were calculated separately for depression (Children?s Depression Inventory, CDI) and anxiety (Spence Children?s Anxiety Scale, SCAS) questionnaires. Results: On average, it took participants 17 minutes (SD 6) to answer 116 questions online. Intraclass correlation coefficient analysis revealed high intrarater reliability when comparing Web-based with paper-and-pencil responses for both CDI (ICC=.88; P<.001) and the SCAS (ICC=.95; P<.001). According to published criteria, both of these values are in the ?almost perfect? category indicating the highest degree of reliability. Conclusions: The results of the study show an excellent reliability of Web-based assessment in 11- to 13-year-old children as compared with the standard paper-pencil assessment. Furthermore, we found that Web-based assessments with young adolescents are highly feasible, with all enrolled participants completing the Web-based form. As early adolescence is a time of remarkable social and behavioral changes, these findings open up new avenues for researchers from diverse fields who are interested in studying large samples of young adolescents over time. UR - http://www.jmir.org/2016/6/e138/ UR - http://dx.doi.org/10.2196/jmir.5482 UR - http://www.ncbi.nlm.nih.gov/pubmed/27306932 ID - info:doi/10.2196/jmir.5482 ER - TY - JOUR AU - Tong, Tiffany AU - Chignell, Mark AU - Tierney, C. Mary AU - Lee, Jacques PY - 2016/05/27 TI - A Serious Game for Clinical Assessment of Cognitive Status: Validation Study JO - JMIR Serious Games SP - e7 VL - 4 IS - 1 KW - cognitive assessments KW - cognitive screening tools KW - computerized assessments KW - games KW - human computer interaction KW - human factors KW - neuropsychological tests KW - screening KW - serious games KW - tablet computers KW - technology assessment KW - usability KW - validation studies KW - video games N2 - Background: We propose the use of serious games to screen for abnormal cognitive status in situations where it may be too costly or impractical to use standard cognitive assessments (eg, emergency departments). If validated, serious games in health care could enable broader availability of efficient and engaging cognitive screening. Objective: The objective of this work is to demonstrate the feasibility of a game-based cognitive assessment delivered on tablet technology to a clinical sample and to conduct preliminary validation against standard mental status tools commonly used in elderly populations. Methods: We carried out a feasibility study in a hospital emergency department to evaluate the use of a serious game by elderly adults (N=146; age: mean 80.59, SD 6.00, range 70-94 years). We correlated game performance against a number of standard assessments, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Confusion Assessment Method (CAM). Results: After a series of modifications, the game could be used by a wide range of elderly patients in the emergency department demonstrating its feasibility for use with these users. Of 146 patients, 141 (96.6%) consented to participate and played our serious game. Refusals to play the game were typically due to concerns of family members rather than unwillingness of the patient to play the game. Performance on the serious game correlated significantly with the MoCA (r=?.339, P <.001) and MMSE (r=?.558, P <.001), and correlated (point-biserial correlation) with the CAM (r=.565, P <.001) and with other cognitive assessments. Conclusions: This research demonstrates the feasibility of using serious games in a clinical setting. Further research is required to demonstrate the validity and reliability of game-based assessments for clinical decision making. UR - http://games.jmir.org/2016/1/e7/ UR - http://dx.doi.org/10.2196/games.5006 UR - http://www.ncbi.nlm.nih.gov/pubmed/27234145 ID - info:doi/10.2196/games.5006 ER - TY - JOUR AU - Torous, John AU - Kiang, V. Mathew AU - Lorme, Jeanette AU - Onnela, Jukka-Pekka PY - 2016/05/05 TI - New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research JO - JMIR Mental Health SP - e16 VL - 3 IS - 2 KW - mental health KW - schizophrenia KW - evaluation KW - smartphone KW - informatics N2 - Background: A longstanding barrier to progress in psychiatry, both in clinical settings and research trials, has been the persistent difficulty of accurately and reliably quantifying disease phenotypes. Mobile phone technology combined with data science has the potential to offer medicine a wealth of additional information on disease phenotypes, but the large majority of existing smartphone apps are not intended for use as biomedical research platforms and, as such, do not generate research-quality data. Objective: Our aim is not the creation of yet another app per se but rather the establishment of a platform to collect research-quality smartphone raw sensor and usage pattern data. Our ultimate goal is to develop statistical, mathematical, and computational methodology to enable us and others to extract biomedical and clinical insights from smartphone data. Methods: We report on the development and early testing of Beiwe, a research platform featuring a study portal, smartphone app, database, and data modeling and analysis tools designed and developed specifically for transparent, customizable, and reproducible biomedical research use, in particular for the study of psychiatric and neurological disorders. We also outline a proposed study using the platform for patients with schizophrenia. Results: We demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities. Conclusions: Smartphone sensors and phone usage patterns, when coupled with appropriate statistical learning tools, are able to capture various social and behavioral manifestations of illnesses, in naturalistic settings, as lived and experienced by patients. The ubiquity of smartphones makes this type of moment-by-moment quantification of disease phenotypes highly scalable and, when integrated within a transparent research platform, presents tremendous opportunities for research, discovery, and patient health. UR - http://mental.jmir.org/2016/2/e16/ UR - http://dx.doi.org/10.2196/mental.5165 UR - http://www.ncbi.nlm.nih.gov/pubmed/27150677 ID - info:doi/10.2196/mental.5165 ER - TY - JOUR AU - Gamito, Pedro AU - Morais, Diogo AU - Oliveira, Jorge AU - Ferreira Lopes, Paulo AU - Picareli, Felipe Luís AU - Matias, Marcelo AU - Correia, Sara AU - Brito, Rodrigo PY - 2016/05/04 TI - Systemic Lisbon Battery: Normative Data for Memory and Attention Assessments JO - JMIR Rehabil Assist Technol SP - e5 VL - 3 IS - 1 KW - Systemic Lisbon Battery KW - attention KW - memory KW - cognitive assessment KW - virtual reality N2 - Background: Memory and attention are two cognitive domains pivotal for the performance of instrumental activities of daily living (IADLs). The assessment of these functions is still widely carried out with pencil-and-paper tests, which lack ecological validity. The evaluation of cognitive and memory functions while the patients are performing IADLs should contribute to the ecological validity of the evaluation process. Objective: The objective of this study is to establish normative data from virtual reality (VR) IADLs designed to activate memory and attention functions. Methods: A total of 243 non-clinical participants carried out a paper-and-pencil Mini-Mental State Examination (MMSE) and performed 3 VR activities: art gallery visual matching task, supermarket shopping task, and memory fruit matching game. The data (execution time and errors, and money spent in the case of the supermarket activity) was automatically generated from the app. Results: Outcomes were computed using non-parametric statistics, due to non-normality of distributions. Age, academic qualifications, and computer experience all had significant effects on most measures. Normative values for different levels of these measures were defined. Conclusions: Age, academic qualifications, and computer experience should be taken into account while using our VR-based platform for cognitive assessment purposes. UR - http://rehab.jmir.org/2016/1/e5/ UR - http://dx.doi.org/10.2196/rehab.4155 UR - http://www.ncbi.nlm.nih.gov/pubmed/28582246 ID - info:doi/10.2196/rehab.4155 ER - TY - JOUR AU - Jankowski, Wojciech AU - Hoffmann, Marcin PY - 2016/02/25 TI - Can Google Searches Predict the Popularity and Harm of Psychoactive Agents? JO - J Med Internet Res SP - e38 VL - 18 IS - 2 KW - drugs KW - narcotics KW - Internet KW - psychoactive agents KW - forecasting KW - trends N2 - Background: Predicting the popularity of and harm caused by psychoactive agents is a serious problem that would be difficult to do by a single simple method. However, because of the growing number of drugs it is very important to provide a simple and fast tool for predicting some characteristics of these substances. We were inspired by the Google Flu Trends study on the activity of the influenza virus, which showed that influenza virus activity worldwide can be monitored based on queries entered into the Google search engine. Objective: Our aim was to propose a fast method for ranking the most popular and most harmful drugs based on easily available data gathered from the Internet. Methods: We used the Google search engine to acquire data for the ranking lists. Subsequently, using the resulting list and the frequency of hits for the respective psychoactive drugs combined with the word ?harm? or ?harmful?, we estimated quickly how much harm is associated with each drug. Results: We ranked the most popular and harmful psychoactive drugs. As we conducted the research over a period of several months, we noted that the relative popularity indexes tended to change depending on when we obtained them. This suggests that the data may be useful in monitoring changes over time in the use of each of these psychoactive agents. Conclusions: Our data correlate well with the results from a multicriteria decision analysis of drug harms in the United Kingdom. We showed that Google search data can be a valuable source of information to assess the popularity of and harm caused by psychoactive agents and may help in monitoring drug use trends. UR - http://www.jmir.org/2016/2/e38/ UR - http://dx.doi.org/10.2196/jmir.4033 UR - http://www.ncbi.nlm.nih.gov/pubmed/26916984 ID - info:doi/10.2196/jmir.4033 ER - TY - JOUR AU - Orlowski, Simone AU - Matthews, Ben AU - Bidargaddi, Niranjan AU - Jones, Gabrielle AU - Lawn, Sharon AU - Venning, Anthony AU - Collin, Philippa PY - 2016/01/28 TI - Mental Health Technologies: Designing With Consumers JO - JMIR Human Factors SP - e4 VL - 3 IS - 1 KW - design thinking KW - participatory design KW - mental health KW - technology UR - http://humanfactors.jmir.org/2016/1/e4/ UR - http://dx.doi.org/10.2196/humanfactors.4336 UR - http://www.ncbi.nlm.nih.gov/pubmed/27026210 ID - info:doi/10.2196/humanfactors.4336 ER - TY - JOUR AU - Baumel, Amit AU - Muench, Fred PY - 2016/01/13 TI - Heuristic Evaluation of Ehealth Interventions: Establishing Standards That Relate to the Therapeutic Process Perspective JO - JMIR Mental Health SP - e5 VL - 3 IS - 1 KW - eHealth KW - mHealth KW - digital health KW - mobile health KW - heuristics KW - evaluation KW - principles KW - therapeutic process UR - http://mental.jmir.org/2016/1/e5/ UR - http://dx.doi.org/10.2196/mental.4563 UR - http://www.ncbi.nlm.nih.gov/pubmed/26764209 ID - info:doi/10.2196/mental.4563 ER - TY - JOUR AU - Mohr, C. David AU - Schueller, M. Stephen AU - Riley, T. William AU - Brown, Hendricks C. AU - Cuijpers, Pim AU - Duan, Naihua AU - Kwasny, J. Mary AU - Stiles-Shields, Colleen AU - Cheung, Ken PY - 2015/07/08 TI - Trials of Intervention Principles: Evaluation Methods for Evolving Behavioral Intervention Technologies JO - J Med Internet Res SP - e166 VL - 17 IS - 7 KW - mHealth KW - eHealth KW - clinical trials KW - methodology UR - http://www.jmir.org/2015/7/e166/ UR - http://dx.doi.org/10.2196/jmir.4391 UR - http://www.ncbi.nlm.nih.gov/pubmed/26155878 ID - info:doi/10.2196/jmir.4391 ER - TY - JOUR AU - Runge, K. Shannon AU - Craig, M. Benjamin AU - Jim, S. Heather PY - 2015/06/02 TI - Word Recall: Cognitive Performance Within Internet Surveys JO - JMIR Mental Health SP - e20 VL - 2 IS - 2 KW - cognition KW - online surveys KW - episodic memory KW - Health and Retirement Study KW - Women?s Health Valuation Study N2 - Background: The use of online surveys for data collection has increased exponentially, yet it is often unclear whether interview-based cognitive assessments (such as face-to-face or telephonic word recall tasks) can be adapted for use in application-based research settings. Objective: The objective of the current study was to compare and characterize the results of online word recall tasks to those of the Health and Retirement Study (HRS) and determine the feasibility and reliability of incorporating word recall tasks into application-based cognitive assessments. Methods: The results of the online immediate and delayed word recall assessment, included within the Women?s Health and Valuation (WHV) study, were compared to the results of the immediate and delayed recall tasks of Waves 5-11 (2000-2012) of the HRS. Results: Performance on the WHV immediate and delayed tasks demonstrated strong concordance with performance on the HRS tasks (?c=.79, 95% CI 0.67-0.91), despite significant differences between study populations (P<.001) and study design. Sociodemographic characteristics and self-reported memory demonstrated similar relationships with performance on both the HRS and WHV tasks. Conclusions: The key finding of this study is that the HRS word recall tasks performed similarly when used as an online cognitive assessment in the WHV. Online administration of cognitive tests, which has the potential to significantly reduce participant and administrative burden, should be considered in future research studies and health assessments. UR - http://mental.jmir.org/2015/2/e20/ UR - http://dx.doi.org/10.2196/mental.3969 UR - http://www.ncbi.nlm.nih.gov/pubmed/26543924 ID - info:doi/10.2196/mental.3969 ER - TY - JOUR AU - Guan, Li AU - Hao, Bibo AU - Cheng, Qijin AU - Yip, SF Paul AU - Zhu, Tingshao PY - 2015/05/12 TI - Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model JO - JMIR Mental Health SP - e17 VL - 2 IS - 2 KW - suicide probability KW - microblog KW - Chinese KW - classification model N2 - Background: Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field. Objective: The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data. Methods: There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric ?Screening Efficiency? that were adopted to evaluate model effectiveness. Results: Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%. Conclusions: Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media. UR - http://mental.jmir.org/2015/2/e17/ UR - http://dx.doi.org/10.2196/mental.4227 UR - http://www.ncbi.nlm.nih.gov/pubmed/26543921 ID - info:doi/10.2196/mental.4227 ER - TY - JOUR AU - AL-Asadi, M. Ali AU - Klein, Britt AU - Meyer, Denny PY - 2015/02/26 TI - Multiple Comorbidities of 21 Psychological Disorders and Relationships With Psychosocial Variables: A Study of the Online Assessment and Diagnostic System Within a Web-Based Population JO - J Med Internet Res SP - e55 VL - 17 IS - 3 KW - comorbidity KW - multiple comorbidities KW - co-occurrences KW - e-mental health KW - online KW - fully automated KW - generalized anxiety disorder KW - obsessive-compulsive disorder KW - social anxiety disorder KW - posttraumatic stress disorder KW - panic disorder, major depressive episode KW - insomnia, hypersomnia, dependency KW - alcohol KW - drug KW - suicidal ideation KW - social support KW - quality of life KW - sex KW - age N2 - Background: While research in the area of e-mental health has received considerable attention over the last decade, there are still many areas that have not been addressed. One such area is the comorbidity of psychological disorders in a Web-based sample using online assessment and diagnostic tools, and the relationships between comorbidities and psychosocial variables. Objective: We aimed to identify comorbidities of psychological disorders of an online sample using an online diagnostic tool. Based on diagnoses made by an automated online assessment and diagnostic system administered to a large group of online participants, multiple comorbidities (co-occurrences) of 21 psychological disorders for males and females were identified. We examined the relationships between dyadic comorbidities of anxiety and depressive disorders and the psychosocial variables sex, age, suicidal ideation, social support, and quality of life. Methods: An online complex algorithm based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Text Revision, was used to assign primary and secondary diagnoses of 21 psychological disorders to 12,665 online participants. The frequency of co-occurrences of psychological disorders for males and females were calculated for all disorders. A series of hierarchical loglinear analyses were performed to examine the relationships between the dyadic comorbidities of depression and various anxiety disorders and the variables suicidal ideation, social support, quality of life, sex, and age. Results: A 21-by-21 frequency of co-occurrences of psychological disorders matrix revealed the presence of multiple significant dyadic comorbidities for males and females. Also, for those with some of the dyadic depression and the anxiety disorders, the odds for having suicidal ideation, reporting inadequate social support, and poorer quality of life increased for those with two-disorder comorbidity than for those with only one of the same two disorders. Conclusions: Comorbidities of several psychological disorders using an online assessment tool within a Web-based population were similar to those found in face-to-face clinics using traditional assessment tools. Results provided support for the transdiagnostic approaches and confirmed the positive relationship between comorbidity and suicidal ideation, the negative relationship between comorbidity and social support, and the negative relationship comorbidity and quality of life. Trial Registration: Australian and New Zealand Clinical Trials Registry ACTRN121611000704998; http://www.anzctr.org.au/trial_view.aspx?ID=336143 (Archived by WebCite at http://www.webcitation.org/618r3wvOG) UR - http://www.jmir.org/2015/3/e55/ UR - http://dx.doi.org/10.2196/jmir.4143 UR - http://www.ncbi.nlm.nih.gov/pubmed/25803420 ID - info:doi/10.2196/jmir.4143 ER - TY - JOUR AU - Clemente, Miriam AU - Rey, Beatriz AU - Rodriguez-Pujadas, Aina AU - Breton-Lopez, Juani AU - Barros-Loscertales, Alfonso AU - Baños, M. Rosa AU - Botella, Cristina AU - Alcañiz, Mariano AU - Avila, Cesar PY - 2014/06/27 TI - A Functional Magnetic Resonance Imaging Assessment of Small Animals? Phobia Using Virtual Reality as a Stimulus JO - JMIR Serious Games SP - e6 VL - 2 IS - 1 KW - neuroimaging KW - patient assessment KW - virtual reality KW - phobia N2 - Background: To date, still images or videos of real animals have been used in functional magnetic resonance imaging protocols to evaluate the brain activations associated with small animals? phobia. Objective: The objective of our study was to evaluate the brain activations associated with small animals? phobia through the use of virtual environments. This context will have the added benefit of allowing the subject to move and interact with the environment, giving the subject the illusion of being there. Methods: We have analyzed the brain activation in a group of phobic people while they navigated in a virtual environment that included the small animals that were the object of their phobia. Results: We have found brain activation mainly in the left occipital inferior lobe (P<.05 corrected, cluster size=36), related to the enhanced visual attention to the phobic stimuli; and in the superior frontal gyrus (P<.005 uncorrected, cluster size=13), which is an area that has been previously related to the feeling of self-awareness. Conclusions: In our opinion, these results demonstrate that virtual stimulus can enhance brain activations consistent with previous studies with still images, but in an environment closer to the real situation the subject would face in their daily lives. UR - http://games.jmir.org/2014/1/e6/ UR - http://dx.doi.org/10.2196/games.2836 UR - http://www.ncbi.nlm.nih.gov/pubmed/25654753 ID - info:doi/10.2196/games.2836 ER -