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: 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.
Background: Self-monitoring of symptoms is a cornerstone of psychological interventions in bipolar disorder (BD), but individuals with lived experience also value tracking holistic outcomes, such as quality of life (QoL). Importantly, self-monitoring is not always experienced positively by people with BD and may have lower than expected rates of engagement. Therefore, before progressing into QoL tracking tools, it is important to explore user perspectives to identify possible risks and benefits, optimal methods to support engagement, and possible avenues to integrate QoL self-monitoring practices into clinical work.
Objective: This study aimed to conduct a qualitative exploration of how individuals with BD engaged with a Web-based version of a BD-specific QoL self-monitoring instrument, the QoL tool.
Methods: A total of 43 individuals with BD engaged with a self-management intervention with an optional Web-based QoL self-assessment tool as part of an overarching mixed method study. Individuals were later interviewed about personal experiences of engagement with the intervention, including experiences of gauging their own QoL. A thematic analysis was used to identify salient aspects of the experience of QoL self-monitoring in BD.
Results: In total, 4 categories describing people’s experiences of QoL self-monitoring were identified: (1) breadth of QoL monitoring, (2) highlighting the positive, (3) connecting self-monitoring to action, and (4) self-directed patterns of use.
Conclusions: The findings of this research generate novel insights into ways in which individuals with BD experience the Web-based QoL self-assessment tool. The value of tracking the breadth of domains was an overarching aspect, facilitating the identification of both areas of strength and life domains in need of intervention. Importantly, monitoring QoL appeared to have an inherently therapeutic quality, through validating flourishing areas and reinforcing self-management efforts. This contrasts the evidence suggesting that symptom tracking may be distressing because of its focus on negative experiences and positions QoL as a valuable adjunctive target of observation in BD. Flexibility and personalization of use of the QoL tool were key to engagement, informing considerations for health care providers wishing to support self-monitoring and future research into Web- or mobile phone–based apps.
The modern omnipresence of social media and social networking sites (SNSs) brings with it a range of important research questions. One of these concerns the impact of SNS use on mental health and well-being, a question that has been pursued in depth by scholars in the psychological sciences and the field of human-computer interaction. Despite this attention, the design choices made in the development of SNSs and the notion of well-being employed to evaluate such systems require further scrutiny. In this viewpoint paper, we examine the strategic design choices made in our development of an enclosed SNS for young people experiencing mental ill-health in terms of ethical and persuasive design and in terms of how it fosters well-being. In doing so, we critique the understanding of well-being that is used in much of the existing literature to make claims about the impact of a given technology on well-being. We also demonstrate how the holistic concept of eudaimonic well-being and ethical design of SNSs can complement one another.
Background: The acceptability of electronic mental (e-mental) health apps has already been studied. However, the attitudes of medical experts, students, and patients taking into account their knowledge of and previous experiences with e-mental health apps have not been investigated.
Objective: The aim of this study was to explore the attitudes, expectations, and concerns of medical experts, including physicians, psychotherapists and nursing staff, students of medicine or psychology, and patients toward e-mental health apps when considering their knowledge of and former experiences with e-mental health apps.
Methods: This cross-sectional quantitative and qualitative survey was based on a self-developed questionnaire. A total of 269 participants were included (104 experts, 80 students, and 85 patients), and 124 eligible participants answered a paper version and 145 answered an identical online version of the questionnaire. The measures focused on existing knowledge of and experiences with e-mental health apps, followed by a question on whether electronic health development was generally accepted or disliked. Further, we asked about the expectations for an ideal e-mental health app and possible concerns felt by the participants. All items were either presented on a 5-point Likert scale or as multiple-choice questions. Additionally, 4 items were presented as open text fields.
Results: Although 33.7% (35/104) of the experts, 15.0% (12/80) of the students, and 41.2% (35/85) of the patients knew at least one e-mental health app, few had already tried one (9/104 experts [8.7%], 1/80 students [1.3%], 22/85 patients [25.9%]). There were more advocates than skeptics in each group (advocates: 71/104 experts [68.3%], 50/80 students [62.5%], 46/85 patients [54.1%]; skeptics: 31/104 experts [29.8%], 20/80 students [25.0%], 26/85 patients [30.6%]). The experts, in particular, believed, that e-mental health apps will gain importance in the future (mean 1.08, SD 0.68; 95% CI 0.94-1.21). When asked about potential risks, all groups reported slight concerns regarding data security (mean 0.85, SD 1.09; 95% CI 0.72-0.98). Patient age was associated with several attitudes toward e-mental health apps (future expectations: r=–0.31, P=.005; total risk score: r=0.22, P=.05). Attitudes toward e-mental health apps correlated negatively with the professional experience of the experts (rs(94)=–0.23, P=.03).
Conclusions: As opposed to patients, medical experts and students lack knowledge of and experience with e-mental health apps. If present, the experiences were assessed positively. However, experts show a more open-minded attitude with less fear of risks. Although some risks were perceived regarding data security, the attitudes and expectations of all groups were rather positive. Older patients and medical experts with long professional experience tend to express more skepticism.
Clinical Trial: German Clinical Trials Register DRKS00013095; https://www.drks.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00013095
Background: Disfigurement (visible difference) from wide-ranging congenital or acquired conditions, injuries, or treatments can negatively impact adolescents’ psychological well-being, education and health behaviours. Alongside medical interventions, appearance-specific cognitive behavioural and social skills training to manage stigma and appearance anxiety may improve psychosocial outcomes. YP Face IT (YPF), is a Web-based seven session self-help program plus booster quiz, utilising cognitive behavioural and social skills training for young people (YP) struggling with a visible difference. Co-designed by adolescents and psychologists, it includes interactive multimedia and automated reminders to complete sessions/homework. Adolescents access YPF via a health professional who determines its suitability and remotely monitors clients’ usage.
Objective: To establish the feasibility of evaluating YPF for 12-17 year olds self-reporting appearance-related distress and/or bullying associated with a visible difference.
Methods: Randomized controlled trial with nested qualitative and economic study evaluating YPF compared with usual care (UC). Feasibility outcomes included: viability of recruiting via general practitioner (GP) practices (face to face and via patient databases) and charity advertisements; intervention acceptability and adherence; feasibility of study and data collection methods; and health professionals’ ability to monitor users’ online data for safeguarding issues. Primary psychosocial self-reported outcomes collected online at baseline, 13, 26, and 52 weeks were as follows: appearance satisfaction (Appearance Subscale from Mendleson et al’s (2001) Body Esteem Scale); social anxiety (La Greca’s (1999) Social Anxiety Scale for Adolescents). Secondary outcomes were; self-esteem; romantic concerns; perceived stigmatization; social skills and healthcare usage. Participants were randomised using remote Web-based allocation.
Results: Thirteen charities advertised the study yielding 11 recruits, 13 primary care practices sent 687 invitations to patients on their databases with a known visible difference yielding 17 recruits (2.5% response rate), 4 recruits came from GP consultations. Recruitment was challenging, therefore four additional practices mass-mailed 3,306 generic invitations to all 12-17 year old patients yielding a further 15 participants (0.5% response rate). Forty-seven YP with a range of socioeconomic backgrounds and conditions were randomised (26% male, 91% white, mean age 14 years (SD 1.7)); 23 to YPF, 24 to UC). At 52 weeks, 16 (70%) in the intervention and 20 (83%) in UC groups completed assessments. There were no intervention-related adverse events; most found YPF acceptable with three withdrawing because they judged it was for higher-level concerns; 12 (52%) completed seven sessions. The study design was acceptable and feasible, with multiple recruitment strategies. Preliminary findings indicate no changes from baseline in outcome measures among the UC group and positive changes in appearance satisfaction and fear of negative evaluation among the YPF group when factoring in baseline scores and intervention adherence.
Conclusions: YPF is novel, safe and potentially helpful. Its full psychosocial benefits should be evaluated in a large-scale RCT, which would be feasible with wide-ranging recruitment strategies.
Clinical Trial: ISRCTN registry ISRCTN40650639; http://www.isrctn.com/ISRCTN40650639
