Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.
Who will be affected?
Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central
Authors: No access to the submission form or your user account.
Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Monday, March 11, 2019 at 4:00 PM.
Editors: No access to your user account to assign reviewers or make decisions.
Copyeditors: No access to user account. Please download manuscripts you are copyediting before Monday, March 11, 2019 at 4:00 PM.
(Toronto, Aug 29, 2019) Clarivate Analytics has now confirmed that JMIR Mental Health has (finally) been admitted to SCIE/JCR, meaning that it will receive its first official 2019 Impact Factor, to be released by Clarivate in June 2020, reflecting the number of citations in 2019 to articles published in 2018 or 2017. We expect the IF to place the journal firmly in the Q1 (top 25% of journals).
JMIR Mental Health (JMH, ISSN 2368-7959, Editor-in-Chief: John Torous MD MBI) is a PubMed-indexed, peer-reviewed journal which has a unique focus on digital health and Internet/mobile interventions, technologies and electronic innovations (software and hardware) for mental health, addictions, online counselling and behaviour change. This includes formative evaluation and system descriptions, theoretical papers, review papers, viewpoint/vision papers, and rigorous evaluations related to digital psychiatry, e-mental health, and clinical informatics in psychiatry/psychology. The main themes/topics covered by this journal can be found here.
JMIR Mental Health has an international author- and readership and welcomes submissions from around the world.
JMIR Mental Health features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs.
The journal is indexed in PubMed, PubMed Central, and SCIE (Science Citation Index Expanded)/WoS/JCR (Journal Citation Reports).
Background: In the last decade, there has been a proliferation of mobile apps claiming to support the needs of people living with depression. However, it is unclear what functionality is actually provided by apps for depression, or for whom they are intended.
Objective: This paper aimed to explore the key features of top-rated apps for depression, including descriptive characteristics, functionality, and ethical concerns, to better inform the design of apps for depression.
Methods: We reviewed top-rated iPhone OS (iOS) and Android mobile apps for depression retrieved from app marketplaces in spring 2019. We applied a systematic analysis to review the selected apps, for which data were gathered from the 2 marketplaces and through direct use of the apps. We report an in-depth analysis of app functionality, namely, screening, tracking, and provision of interventions. Of the initially identified 482 apps, 29 apps met the criteria for inclusion in this review. Apps were included if they remained accessible at the moment of evaluation, were offered in mental health–relevant categories, received a review score greater than 4.0 out of 5.0 by more than 100 reviewers, and had depression as a primary target.
Results: The analysis revealed that a majority of apps specify the evidence base for their intervention (18/29, 62%), whereas a smaller proportion describes receiving clinical input into their design (12/29, 41%). All the selected apps are rated as suitable for children and adolescents on the marketplace, but 83% (24/29) do not provide a privacy policy consistent with their rating. The findings also show that most apps provide multiple functions. The most commonly implemented functions include provision of interventions (24/29, 83%) either as a digitalized therapeutic intervention or as support for mood expression; tracking (19/29, 66%) of moods, thoughts, or behaviors for supporting the intervention; and screening (9/29, 31%) to inform the decision to use the app and its intervention. Some apps include overtly negative content.
Conclusions: Currently available top-ranked apps for depression on the major marketplaces provide diverse functionality to benefit users across a range of age groups; however, guidelines and frameworks are still needed to ensure users’ privacy and safety while using them. Suggestions include clearly defining the age of the target population and explicit disclosure of the sharing of users’ sensitive data with third parties. In addition, we found an opportunity for apps to better leverage digital affordances for mitigating harm, for personalizing interventions, and for tracking multimodal content. The study further demonstrated the need to consider potential risks while using depression apps, including the use of nonvalidated screening tools, tracking negative moods or thinking patterns, and exposing users to negative emotional expression content.
Background: Depression carries significant financial, medical, and emotional burden on modern society. Various proof-of-concept studies have highlighted how apps can link dynamic mental health status changes to fluctuations in smartphone usage in adult patients with major depressive disorder (MDD). However, the use of such apps to monitor adolescents remains a challenge.
Objective: This study aimed to investigate whether smartphone apps are useful in evaluating and monitoring depression symptoms in a clinically depressed adolescent population compared with the following gold-standard clinical psychometric instruments: Patient Health Questionnaire (PHQ-9), Hamilton Rating Scale for Depression (HAM-D), and Hamilton Anxiety Rating Scale (HAM-A).
Methods: We recruited 13 families with adolescent patients diagnosed with MDD with or without comorbid anxiety disorder. Over an 8-week period, daily self-reported moods and smartphone sensor data were collected by using the Smartphone- and OnLine usage–based eValuation for Depression (SOLVD) app. The evaluations from teens’ parents were also collected. Baseline depression and anxiety symptoms were measured biweekly using PHQ-9, HAM-D, and HAM-A.
Results: We observed a significant correlation between the self-evaluated mood averaged over a 2-week period and the biweekly psychometric scores from PHQ-9, HAM-D, and HAM-A (0.45≤|r|≤0.63; P=.009, P=.01, and P=.003, respectively). The daily steps taken, SMS frequency, and average call duration were also highly correlated with clinical scores (0.44≤|r|≤0.72; all P<.05). By combining self-evaluations and smartphone sensor data of the teens, we could predict the PHQ-9 score with an accuracy of 88% (23.77/27). When adding the evaluations from the teens’ parents, the prediction accuracy was further increased to 90% (24.35/27).
Conclusions: Smartphone apps such as SOLVD represent a useful way to monitor depressive symptoms in clinically depressed adolescents, and these apps correlate well with current gold-standard psychometric instruments. This is a first study of its kind that was conducted on the adolescent population, and it included inputs from both teens and their parents as observers. The results are preliminary because of the small sample size, and we plan to expand the study to a larger population.
Background: Despite increasing evidence of the effectiveness of digital learning solutions in higher vocational education, including the training of allied health professionals, the impact of Web-based training on the development of practical skills in psychiatry and psychology, in general, and in suicide prevention, specifically, remains largely understudied.
Objective: This study aimed to determine the effectiveness of an electronic learning (e-learning) module on the adherence to suicide prevention guidelines, knowledge of practical skills, and provider’s confidence to have a conversation about suicidal behavior with undergraduate psychology students.
Methods: The e-learning module, comprising video recordings of therapist-patient interactions, was designed with the aim of transferring knowledge about suicide prevention guideline recommendations. The program’s effects on guideline adherence, self-evaluated knowledge, and provider’s confidence were assessed using online questionnaires before the program (baseline and at 1 month [T1] and 3 months after baseline). The eligible third- and fourth-year undergraduate psychology students were randomly allocated to the e-learning (n=211) or to a waitlist control condition (n=187), with access to the intervention after T1.
Results: Overall, the students evaluated e-learning in a fairly positive manner. The intention-to-treat analysis showed that the students in the intervention condition (n=211) reported higher levels of self-evaluated knowledge, provider’s confidence, and guideline adherence than those in the waitlist control condition (n=187) after receiving the e-learning module (all P values<.001). When comparing the scores at the 1- and 3-month follow-up, after both groups had received access to the e-learning module, the completers-only analysis showed that the levels of knowledge, guideline adherence, and confidence remained constant (all P values>.05) within the intervention group, whereas a significant improvement was observed in the waitlist control group (all P values<.05).
Conclusions: An e-learning intervention on suicide prevention could be an effective first step toward improved knowledge of clinical skills. The learning outcomes of a stand-alone module were found to be similar to those of a training that combined e-learning with a face-to-face training, with the advantages of flexibility and low costs.
Background: Videoconferencing psychotherapy (VCP) is a growing practice among mental health professionals. Early adopters have predominantly been in private practice settings, and more recent adoption has occurred in larger organizations, such as the military. The implementation of VCP into larger health service providers in the public sector is an important step in reaching and helping vulnerable and at-risk individuals; however, several additional implementation challenges exist for public sector organizations.
Objective: The aim of this study was to offer an implementation model for effectively introducing VCP into public sector organizations. This model will also provide practical guidelines for planning and executing an embedded service trial to assess the effectiveness of the VCP modality once implemented.
Methods: An iterative search strategy was employed, drawing on multiple fields of research across mental health, information technology, and organizational psychology. Previous VCP implementation papers were considered in detail to provide a synthesis of the barriers, facilitators, and lessons learned from the implementation attempts in the military and other public sector settings.
Results: A model was formulated, which draws on change management for technology integration and considers the specific needs for VCP integration in larger organizations. A total of 6 phases were formulated and were further broken down into practical and measurable steps. The model explicitly considers the barriers often encountered in large organizational settings and suggests steps to increase facilitating factors.
Conclusions: Although the model proposed is time and resource intensive, it draws on a comprehensive understanding of larger organizational needs and the unique challenge that the introduction of VCP presents to such organizations.
Background: Suicide prevention remains challenging among youth, as many do not disclose suicidal ideation. Nearly one-third of American Indian and Alaska Native (AI and AN, tribal, or native) youth see concerning messages on social media at least weekly.
Objective: To prepare adults to support AI and AN youth who post or view concerning messages, our team designed an hour-long training: Responding to Concerning Posts on Social Media. This study tested the usability, appeal, and impact of the training.
Methods: A purposive sample of 70 adults was recruited to participate in the pilot, which included 2 study arms. Arm 1 participants completed a 30-min training video and reviewed accompanying handouts, including the Viewer Care Plan (VCP). The VCP provided a 3-step planning and response tool: (1) Start the Conversation, (2) Listen, Gather Information, and Assess Viewer Experience, and (3) Plan and Act. The intent of the VCP was to support and connect AI and AN youth who either view or post concerning messages on social media to life-saving resources. Those enrolled in arm 2 participated in an additional interactive role-play scenario with a coach that took place after the training, via text message. Participants provided qualitative and quantitative feedback on the training’s relevance, appeal, and utility. Paired t tests were used to assess confidence in addressing concerning posts between pre- and postsurveys. Content analysis of the role-play transcripts was used to assess the quality and completion of the coached role-plays, in relation to the recommended VCP.
Results: Altogether, 35 participants finished the training and completed pre- and postsurveys; 22 participants completed the 6-month follow-up survey. Pre-post analyses of differences in means found significant improvement across several efficacy measures, including confidence starting a conversation about social media (P=.003), confidence contacting the person who posted something concerning (P<.001), and confidence recommending support services to youth who view (P=.001) or youth who post concerning messages (P<.001). Similarly, pre- to 6-month analyses found significant positive improvement across multiple measures, including confidence contacting the youth who posted (P<.001), confidence starting a conversation about social media with youth (P=.003), and an increase in the number of experiences recommending resources for youth who viewed concerning social media posts (P=.02). Of the 3 steps of the VCP, the least followed step in coached role-plays was sharing tools and resources, which is a part of the third Plan and Act step.
Conclusions: Findings indicate that the Responding to Concerning Posts on Social Media training is a promising tool to prepare adults to intervene and complete the VCP. Additional evaluation with a larger cohort of participants is needed to determine the unique impact of the role-play scenario and changes in mental health referral rates, behaviors, and skills.
Background: Identifying signs of Alzheimer disease (AD) through longitudinal and passive monitoring techniques has become increasingly important. Previous studies have succeeded in quantifying language dysfunctions and identifying AD from speech data collected during neuropsychological tests. However, whether and how we can quantify language dysfunction in daily conversation remains unexplored.
Objective: The objective of this study was to explore the linguistic features that can be used for differentiating AD patients from daily conversations.
Methods: We analyzed daily conversational data of seniors with and without AD obtained from longitudinal follow-up in a regular monitoring service (from n=15 individuals including 2 AD patients at an average follow-up period of 16.1 months; 1032 conversational data items obtained during phone calls and approximately 221 person-hours). In addition to the standard linguistic features used in previous studies on connected speech data during neuropsychological tests, we extracted novel features related to atypical repetition of words and topics reported by previous observational and descriptive studies as one of the prominent characteristics in everyday conversations of AD patients.
Results: When we compared the discriminative power of AD, we found that atypical repetition in two conversations on different days outperformed other linguistic features used in previous studies on speech data during neuropsychological tests. It was also a better indicator than atypical repetition in single conversations as well as that in two conversations separated by a specific number of conversations.
Conclusions: Our results show how linguistic features related to atypical repetition across days could be used for detecting AD from daily conversations in a passive manner by taking advantage of longitudinal data.
Background: Standardized mindfulness training courses involve significant at-home assignments of meditation practice. Participants’ self-reported completion of these assignments has been correlated with treatment outcomes, but self-reported data are often incomplete and potentially biased. In addition, mindfulness teachers typically suggest that participants set aside a regular practice time, preferably in the morning, but the extent to which participants do this has not been empirically examined.
Objective: This study aimed to analyze patterns of participant engagement with home practice in a mindfulness-based stress reduction course.
Methods: We used a novel smartphone app to provide 25 participants with access to their daily practice assignments during the 8-week course. We analyzed data collected through our smartphone app to determine usage and listening patterns and performed analyses of the regularity and frequency of participant behavior.
Results: We found that participants listened to a median of 3 of the 6 practice sessions per week, and they did not typically set aside a regular daily practice time. Across weekdays, participants practiced most frequently in the morning, but there was considerable variation in participants’ practice start times. On weekends, the peak practice time was in the evening.
Conclusions: We suggest that it is feasible to integrate a smartphone-monitoring approach into existing mindfulness interventions. High-frequency smartphone monitoring can provide insights into how and when participants complete their homework, information that is important in supporting treatment engagement.
Background: In the ever-growing and technologically advancing world, an increasing amount of social interaction takes place through the Web. With this change, loneliness is becoming an unprecedented societal issue, making youth more susceptible to various physical and mental health problems. This societal change also influences the dynamics of addiction.
Objective: Employing the cognitive discrepancy loneliness model, this study aimed to provide a social psychological perspective on youth addictions.
Methods: A comprehensive survey was used to collect data from American (N=1212; mean 20.05, SD 3.19; 608/1212, 50.17% women), South Korean (N=1192; mean 20.61, SD 3.24; 601/1192, 50.42% women), and Finnish (N=1200; mean 21.29, SD 2.85; 600/1200, 50.00% women) youths aged 15 to 25 years. Perceived loneliness was assessed with the 3-item Loneliness Scale. A total of 3 addictive behaviors were measured, including excessive alcohol use, compulsive internet use, and problem gambling. A total of 2 separate models using linear regression analyses were estimated for each country to examine the association between perceived loneliness and addiction.
Results: Loneliness was significantly related to only compulsive internet use among the youth in all 3 countries (P<.001 in the United States, South Korea, and Finland). In the South Korean sample, the association remained significant with excessive alcohol use (P<.001) and problem gambling (P<.001), even after controlling for potentially confounding psychological variables.
Conclusions: The findings reveal existing differences between youths who spend excessive amounts of time online and those who engage in other types of addictive behaviors. Experiencing loneliness is consistently linked to compulsive internet use across countries, although different underlying factors may explain other forms of addiction. These findings provide a deeper understanding in the mechanisms of youth addiction and can help improve prevention and intervention work, especially in terms of compulsive internet use.
Background: In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy.
Objective: This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD.
Methods: The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC).
Results: A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67.
Conclusions: The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings.
Clinical Trial: PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779
Background: Anxiety levels before cesarean delivery (CD) can lead to a negative birth experience, which may influence several aspects of the woman’s life in the long term. Improving preoperative information may lower preoperative anxiety and lead to a more positive birth experience.
Objective: This study aimed to determine whether a virtual reality (VR) video in addition to standard preoperative information decreases anxiety levels before a planned CD.
Methods: Women scheduled to undergo term elective CD were recruited from the outpatient clinic. They were randomized and stratified based on history of emergency CD (yes or no). All participants received standard preoperative information (folder leaflets and counseling by the obstetrician); the VR group additionally watched the VR video showing all aspects of CD such as the ward admission, operating theater, spinal analgesia, and moment of birth. The primary outcome measure was a change in score on the Visual Analogue Scale for Anxiety (ΔVAS-A) measured at admission for CD, compared with the baseline VAS-A score.
Results: A total of 97 women were included for analysis. The baseline characteristics were similar in both groups, except for a significantly higher level of education in the control group. There was no significant decrease in the VAS-A score of the women in the VR group (n=49) compared with those in the control group (n=48; ΔVAS-A=1.0; P=.08; 95% CI −0.1 to 2.0). Subgroup analysis for the group of women with a history of emergency CD showed a trend toward decreased preoperative anxiety, despite the small sample size of this subgroup (n=17; P=.06). Of the 26 participants who provided completed questionnaires, 22 (85%) in the VR group reported feeling more prepared after seeing the VR video; of the 24 participants’ partners who completed the questionnaires, 19 (79%) agreed with the participants. No discomfort or motion sickness was reported.
Conclusions: A VR video may help patients and their partners feel better prepared when planning a CD. This study showed that VR does not lead to a decrease in preoperative anxiety. However, subgroups such as women with a history of emergency CD may benefit from VR videos.
Clinical Trial: International Standard Randomised Controlled Trial Number (ISRCTN) 74794447; http://www.isrctn.com/ISRCTN74794447 (retrospectively registered)
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.
Background: Social media data can be explored as a tool to detect sleep deprivation. First-year undergraduate students in their first quarter were invited to wear sleep-tracking devices (Basis; Intel), allow us to follow them on Twitter, and complete weekly surveys regarding their sleep.
Objective: This study aimed to determine whether social media data can be used to monitor sleep deprivation.
Methods: The sleep data obtained from the device were utilized to create a tiredness model that aided in labeling the tweets as sleep deprived or not at the time of posting. Labeled data were used to train and test a gated recurrent unit (GRU) neural network as to whether or not study participants were sleep deprived at the time of posting.
Results: Results from the GRU neural network suggest that it is possible to classify the sleep-deprivation status of a tweet’s author with an average area under the curve of 0.68.
Conclusions: It is feasible to use social media to identify students’ sleep deprivation. The results add to the body of research suggesting that social media data should be further explored as a potential source for monitoring health.
