%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e67129 %T A Social Media Study of Portrayals of Bipolar Disorders on YouTube: Content and Thematic Analyses %A Mayor,Eric %A Bietti,Lucas M %+ Department of Psychology, Norwegian University of Science and Technology, Dragvoll Campus, Edvard Bulls veg 1, Trondheim, 7491, Norway, 47 73 59 19 60, lucas.bietti@ntnu.no %K bipolar disorder %K YouTube %K social media %K content analysis %K thematic analysis %D 2025 %7 25.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Individuals with mental disorders frequently use YouTube to express themselves, reach an audience, or as a means of understanding their condition. Testimonies posted on YouTube provide longer and richer perspectives than the short posts found on other social media platforms. Research focusing on the depiction of mental disorders on YouTube is blossoming. Bipolar disorders (BDs) are disabling mood disorders. The diagnosis of any mental disorder, and more so BD, is often a life-changing event. However, no published study has investigated the portrayal of diagnoses of BD on YouTube. Objective: This study aims to investigate the portrayals of BDs on YouTube, focusing on the diagnosis narratives and their accompanying narrative context, in particular, reports of personal experiences and reactions. Methods: We performed a manual content analysis of 39 testimonies (women: n=24, 62%) depicting BDs and their diagnosis by individuals with BD. We also performed a thematic analysis of the corpus relying upon a deductive and inductive approach. Results: Our manual content analysis revealed that portrayals included the disclosure of diagnoses of BD-I (as per both coders’ agreement: 10 testimonies) and BD-II (11 testimonies) to a similar extent. The reactions to the diagnosis were mostly negative (8 testimonies), followed by positive (5 testimonies), while fewer portrayals indicated a denial of the condition (4 testimonies). Several portrayals made mention of issues in the areas of money and accommodation (15 testimonies), profession and education (13 testimonies), and relationships (20 testimonies). Medication (31 testimonies) and psychotherapy (23 testimonies) were often mentioned as part of treatment for BD, most generally in positive terms. The 8 themes emerging from the thematic analysis were: “reactions on diagnosis, treatment, and health care professionals’ expertise,” “trial and error in medication,” “positive effects of BD,” “disability, stigma, and shame,” “loss,” “family planning and genetics,” “identity change (psychological and physical),” and “human social relationships.” Conclusions: Overall, our results underline the complexity and richness of the depiction of the diagnosis of BD and its narrative context, and highlight the importance of the moment of the diagnosis, medication, and psychotherapy. Our study emphasizes the need for further exploration of the impact of social media on mental health awareness. %M 40279634 %R 10.2196/67129 %U https://www.jmir.org/2025/1/e67129 %U https://doi.org/10.2196/67129 %U http://www.ncbi.nlm.nih.gov/pubmed/40279634 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e65555 %T Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study %A Crocamo,Cristina %A Cioni,Riccardo Matteo %A Canestro,Aurelia %A Nasti,Christian %A Palpella,Dario %A Piacenti,Susanna %A Bartoccetti,Alessandra %A Re,Martina %A Simonetti,Valentina %A Barattieri di San Pietro,Chiara %A Bulgheroni,Maria %A Bartoli,Francesco %A Carrà,Giuseppe %K digital mental health %K remote assessment %K mHealth %K speech %K NLP %K natural language processing %K acoustic %K symptom severity %K machine learning %K markers %K mental health %K bipolar disorders %K app %K applications %K multimodal %K mobile health %K voice %K vocal %K bipolar %K verbal %K emotion %K emotional %K psychiatry %K psychiatric %K mental illness %D 2025 %7 16.4.2025 %9 %J JMIR Form Res %G English %X Background: Monitoring symptoms of bipolar disorder (BD) is a challenge faced by mental health services. Speech patterns are crucial in assessing the current experiences, emotions, and thought patterns of people with BD. Natural language processing (NLP) and acoustic signal processing may support ongoing BD assessment within a mobile health (mHealth) framework. Objective: Using both acoustic and NLP-based features from the speech of people with BD, we built an app-based tool and tested its feasibility and performance to remotely assess the individual clinical status. Methods: We carried out a pilot, observational study, sampling adults diagnosed with BD from the caseload of the Nord Milano Mental Health Trust (Italy) to explore the relationship between selected speech features and symptom severity and to test their potential to remotely assess mental health status. Symptom severity assessment was based on clinician ratings, using the Young Mania Rating Scale (YMRS) and Montgomery-Åsberg Depression Rating Scale (MADRS) for manic and depressive symptoms, respectively. Leveraging a digital health tool embedded in a mobile app, which records and processes speech, participants self-administered verbal performance tasks. Both NLP-based and acoustic features were extracted, testing associations with mood states and exploiting machine learning approaches based on random forest models. Results: We included 32 subjects (mean [SD] age 49.6 [14.3] years; 50% [16/32] females) with a MADRS median (IQR) score of 13 (21) and a YMRS median (IQR) score of 5 (16). Participants freely managed the digital environment of the app, without perceiving it as intrusive and reporting an acceptable system usability level (average score 73.5, SD 19.7). Small-to-moderate correlations between speech features and symptom severity were uncovered, with sex-based differences in predictive capability. Higher latency time (ρ=0.152), increased silences (ρ=0.416), and vocal perturbations correlated with depressive symptomatology. Pressure of speech based on the mean intraword time (ρ=–0.343) and lower voice instability based on jitter-related parameters (ρ ranging from –0.19 to –0.27) were detected for manic symptoms. However, a higher contribution of NLP-based and conversational features, rather than acoustic features, was uncovered, especially for predictive models for depressive symptom severity (NLP-based: R2=0.25, mean squared error [MSE]=110.07, mean absolute error [MAE]=8.17; acoustics: R2=0.11, MSE=133.75, MAE=8.86; combined: R2=0.16; MSE=118.53, MAE=8.68). Conclusions: Remotely collected speech patterns, including both linguistic and acoustic features, are associated with symptom severity levels and may help differentiate clinical conditions in individuals with BD during their mood state assessments. In the future, multimodal, smartphone-integrated digital ecological momentary assessments could serve as a powerful tool for clinical purposes, remotely complementing standard, in-person mental health evaluations. %R 10.2196/65555 %U https://formative.jmir.org/2025/1/e65555 %U https://doi.org/10.2196/65555 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 5 %N %P e65632 %T Using Natural Language Processing Methods to Build the Hypersexuality in Bipolar Reddit Corpus: Infodemiology Study of Reddit %A Harvey,Daisy %A Rayson,Paul %A Lobban,Fiona %A Palmier-Claus,Jasper %A Dolman,Clare %A Chataigné,Anne %A Jones,Steven %+ Spectrum Centre for Mental Health Research, Division of Health Research, Lancaster University, Bailrigg, Lancaster, LA1 4YW, United Kingdom, 44 1524 65201, d.harvey4@lancaster.ac.uk %K bipolar %K hypersexuality %K natural language processing %K Linguistic Inquiry and Word Count %K LIWC %K BERTopic %K topic modeling %K computational linguistics %D 2025 %7 6.3.2025 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Bipolar is a severe mental health condition affecting at least 2% of the global population, with clinical observations suggesting that individuals experiencing elevated mood states, such as mania or hypomania, may have an increased propensity for engaging in risk-taking behaviors, including hypersexuality. Hypersexuality has historically been stigmatized in society and in health care provision, which makes it more difficult for service users to talk about their behaviors. There is a need for greater understanding of hypersexuality to develop better evidence-based treatment, support, and training for health professionals. Objective: This study aimed to develop and assess effective methodologies for identifying posts on Reddit related to hypersexuality posted by people with a self-reported bipolar diagnosis. Using natural language processing techniques, this research presents a specialized dataset, the Talking About Bipolar on Reddit Corpus (TABoRC). We used various computational tools to filter and categorize posts that mentioned hypersexuality, forming the Hypersexuality in Bipolar Reddit Corpus (HiB-RC). This paper introduces a novel methodology for detecting hypersexuality-related conversations on Reddit and offers both methodological insights and preliminary findings, laying the groundwork for further research in this emerging field. Methods: A toolbox of computational linguistic methods was used to create the corpora and infer demographic variables for the Redditors in the dataset. The key psychological domains in the corpus were measured using Linguistic Inquiry and Word Count, and a topic model was built using BERTopic to identify salient language clusters. This paper also discusses ethical considerations associated with this type of analysis. Results: The TABoRC is a corpus of 6,679,485 posts from 5177 Redditors, and the HiB-RC is a corpus totaling 2146 posts from 816 Redditors. The results demonstrate that, between 2012 and 2021, there was a 91.65% average yearly increase in posts in the HiB-RC (SD 119.6%) compared to 48.14% in the TABoRC (SD 51.2%) and an 86.97% average yearly increase in users (SD 93.8%) compared to 27.17% in the TABoRC (SD 38.7%). These statistics suggest that there was an increase in posting activity related to hypersexuality that exceeded the increase in general Reddit use over the same period. Several key psychological domains were identified as significant in the HiB-RC (P<.001), including more negative tone, more discussion of sex, and less discussion of wellness compared to the TABoRC. Finally, BERTopic was used to identify 9 key topics from the dataset. Conclusions: Hypersexuality is an important symptom that is discussed by people with bipolar on Reddit and needs to be systematically recognized as a symptom of this illness. This research demonstrates the utility of a computational linguistic framework and offers a high-level overview of hypersexuality in bipolar, providing empirical evidence that paves the way for a deeper understanding of hypersexuality from a lived experience perspective. %M 40053804 %R 10.2196/65632 %U https://infodemiology.jmir.org/2025/1/e65632 %U https://doi.org/10.2196/65632 %U http://www.ncbi.nlm.nih.gov/pubmed/40053804 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e65140 %T Improving the Utility, Safety, and Ethical Use of a Passive Mood-Tracking App for People With Bipolar Disorder Using Coproduction: Qualitative Focus Group Study %A Astill Wright,Laurence %A Moore,Matthew %A Reeves,Stuart %A Vallejos,Elvira Perez %A Morriss,Richard %+ Institute of Mental Health, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham, NG7 2TU, United Kingdom, 44 115 823 1294, laurence.astillwright@nottingham.ac.uk %K mood monitoring %K ecological momentary assessment %K EMA %K passive ecological momentary assessment %K passive EMA %K bipolar disorder %K implementation %K qualitative %K mobile phone %D 2025 %7 7.2.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Coproduction with users of new digital technology, such as passive mood monitoring, is likely to improve its utility, safety, and successful implementation via improved design and consideration of how such technology fits with their daily lives. Mood-monitoring interventions are commonly used by people with bipolar disorder (BD) and have promising potential for digitization using novel technological methods. Objective: This study aims to explore how a passive behavioral monitoring platform, Remote Assessment of Disease and Relapse, would meet the needs of people with BD by specifically considering purpose and function, diversity of need, personal preference, essential components and potential risks, and harms and mitigation strategies through an iterative coproduction process. Methods: A total of 17 people with BD were recruited via national charities. We conducted 3 web-based focus groups as a part of an iterative coproduction process in line with responsible research and innovation principles and with consideration of clinical challenges associated with BD. Data were analyzed thematically. Results were cross-checked by someone with lived experience of BD. Results: Focus groups were transcribed and analyzed using thematic analysis. Six themes were identified as follows: (1) the purpose of using the app, (2) desired features, (3) when to use the app, (4) risks of using the app, (5) sharing with family and friends, and (6) sharing with health care professionals. Conclusions: People with BD who are interested in using passive technology to monitor their mood wish to do so for a wide variety of purposes, identifying several preferences and potential risks. Principally, people with BD wished to use this novel technology to aid them in self-managing their BD with greater insight and a better understanding of potential triggers. We discuss key features that may aid this functionality and purpose, including crisis plans and sharing with others. Future development of passive mood-monitoring technologies should not assume that the involvement of formal mental health services is desired. %M 39918865 %R 10.2196/65140 %U https://formative.jmir.org/2025/1/e65140 %U https://doi.org/10.2196/65140 %U http://www.ncbi.nlm.nih.gov/pubmed/39918865 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 12 %N %P e65246 %T Barriers and Facilitators of User Engagement With Digital Mental Health Interventions for People With Psychosis or Bipolar Disorder: Systematic Review and Best-Fit Framework Synthesis %A Eisner,Emily %A Faulkner,Sophie %A Allan,Stephanie %A Ball,Hannah %A Di Basilio,Daniela %A Nicholas,Jennifer %A Priyam,Aansha %A Wilson,Paul %A Zhang,Xiaolong %A Bucci,Sandra %+ Division of Psychology and Mental Health, University of Manchester, Jean McFarlane Building, Manchester, M13 9PL, United Kingdom, 44 1613066000, emily.eisner@manchester.ac.uk %K psychosis %K bipolar %K schizophrenia %K smartphone %K digital %K wearable %K mobile phone %K PRISMA %D 2025 %7 20.1.2025 %9 Review %J JMIR Ment Health %G English %X Background: Digital mental health interventions (DMHIs) to monitor and improve the health of people with psychosis or bipolar disorder show promise; however, user engagement is variable, and integrated clinical use is low. Objective: This prospectively registered systematic review examined barriers and facilitators of clinician and patient engagement with DMHIs, to inform implementation within real-world settings. Methods: A systematic search of 7 databases identified empirical studies reporting qualitative or quantitative data about factors affecting staff or patient engagement with DMHIs aiming to monitor or improve the mental or physical health of people with psychosis or bipolar disorder. The Consolidated Framework for Implementation Research was used to synthesize data on barriers and facilitators, following a best-fit framework synthesis approach. Results: The review included 175 papers (150 studies; 11,446 participants) describing randomized controlled trials; surveys; qualitative interviews; and usability, cohort, and case studies. Samples included people with schizophrenia spectrum psychosis (98/150, 65.3% of studies), bipolar disorder (62/150, 41.3% of studies), and clinicians (26/150, 17.3% of studies). Key facilitators were a strong recognition of DMHIs’ relative advantages, a clear link between intervention focus and specific patient needs, a simple, low-effort digital interface, human-supported delivery, and device provision where needed. Although staff thought patients would lose, damage, or sell devices, reviewed studies found only 11% device loss. Barriers included intervention complexity, perceived risks, user motivation, discomfort with self-reflection, digital poverty, symptoms of psychosis, poor compatibility with existing clinical workflows, staff and patient fears that DMHIs would replace traditional face-to-face care, infrastructure limitations, and limited financial support for delivery. Conclusions: Identified barriers and facilitators highlight key considerations for DMHI development and implementation. As to broader implications, sustainable business models are needed to ensure that evidence-based DMHIs are maintained and deployed. Trial Registration: PROSPERO CRD42021282871; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=282871 %M 39832352 %R 10.2196/65246 %U https://mental.jmir.org/2025/1/e65246 %U https://doi.org/10.2196/65246 %U http://www.ncbi.nlm.nih.gov/pubmed/39832352 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e57624 %T The Trifecta of Industry, Academic, and Health System Partnership to Improve Mental Health Care Through Smartphone-Based Remote Patient Monitoring: Development and Usability Study %A Epperson,C Neill %A Davis,Rachel %A Dempsey,Allison %A Haller,Heinrich C %A Kupfer,David J %A Love,Tiffany %A Villarreal,Pamela M %A Matthews,Mark %A Moore,Susan L %A Muller,Kimberly %A Schneck,Christopher D %A Scott,Jessica L %A Zane,Richard D %A Frank,Ellen %+ Department of Psychiatry, School of Medicine, University of Colorado Anschutz Medical Campus, 1890 N Revere Ct, Suite 4003, Mail Stop F546, Aurora, CO, 80045, United States, 1 303 724 4940, neill.epperson@cuanschutz.edu %K digital health %K mobile intervention %K telepsychiatry %K artificial intelligence %K psychiatry %K mental health %K depression %K mood %K bipolar %K monitor %K diagnostic tool %K diagnosis %K electronic health record %K EHR %K alert %K notification %K prediction %K mHealth %K mobile health %K smartphone %K passive %K self-reported %K patient generated %D 2025 %7 7.1.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Mental health treatment is hindered by the limited number of mental health care providers and the infrequency of care. Digital mental health technology can help supplement treatment by remotely monitoring patient symptoms and predicting mental health crises in between clinical visits. However, the feasibility of digital mental health technologies has not yet been sufficiently explored. Rhythms, from the company Health Rhythms, is a smartphone platform that uses passively acquired smartphone data with artificial intelligence and predictive analytics to alert patients and providers to an emerging mental health crisis. Objective: The objective of this study was to test the feasibility and acceptability of Rhythms among patients attending an academic psychiatric outpatient clinic. Methods: Our group embedded Rhythms into the electronic health record of a large health system. Patients with a diagnosis of major depressive disorder, bipolar disorder, or other mood disorder were contacted online and enrolled for a 6-week trial of Rhythms. Participants provided data by completing electronic surveys as well as by active and passive use of Rhythms. Emergent and urgent alerts were monitored and managed according to passively collected data and patient self-ratings. A purposively sampled group of participants also participated in qualitative interviews about their experience with Rhythms at the end of the study. Results: Of the 104 participants, 89 (85.6%) completed 6 weeks of monitoring. The majority of the participants were women (72/104, 69.2%), White (84/104, 80.8%), and non-Hispanic (100/104, 96.2%) and had a diagnosis of major depressive disorder (71/104, 68.3%). Two emergent alerts and 19 urgent alerts were received and managed according to protocol over 16 weeks. More than two-thirds (63/87, 72%) of those participating continued to use Rhythms after study completion. Comments from participants indicated appreciation for greater self-awareness and provider connection, while providers reported that Rhythms provided a more nuanced understanding of patient experience between clinical visits. Conclusions: Rhythms is a user-friendly, electronic health record–adaptable, smartphone-based tool that provides patients and providers with a greater understanding of patient mental health status. Integration of Rhythms into health systems has the potential to facilitate mental health care and improve the experience of both patients and providers. %M 39773396 %R 10.2196/57624 %U https://formative.jmir.org/2025/1/e57624 %U https://doi.org/10.2196/57624 %U http://www.ncbi.nlm.nih.gov/pubmed/39773396 %0 Journal Article %@ 2152-7202 %I JMIR Publications %V 16 %N %P e56970 %T Self-Induced Mania Methods and Motivations Reported in Online Forums: Observational Qualitative Study %A Bostock,Emmanuelle CS %A Nevarez-Flores,Adriana G %A Neil,Amanda L %A Pontes,Halley M %A Kirkby,Kenneth C %+ Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, 7000, Australia, 61 3 6226 7700, ebostock@utas.edu.au %K bipolar disorder %K mania %K hypomania %K self-induced %K online forums %K consumer reports %D 2024 %7 6.12.2024 %9 Original Paper %J J Particip Med %G English %X Background: In bipolar disorder (BD), mania may be self-induced by manipulation of specific precipitants, as reported in case studies. Another potential source of information on the self-induction of mania is the online postings of users with lived experience of mania. Objective: The primary aim of this study is to examine the range of methods used to self-induce mania or hypomania described by users of online forums with self-reported BD. Second, we summarize the motivations of users to engage in these behaviors. Methods: We conducted an observational study of online forum posts that discussed self-induction of mania or hypomania, either in the posters themselves or observed firsthand in others. Posts were identified using Google advanced search operators, then extracted and coded for content in NVivo (version 12 for Mac; QSR International). A total of 44 online forum threads were identified discussing self-induced mania (n=25) or hypomania (n=19). These forums contained 585 posts by 405 usernames, of which 126 usernames discussed methods for self-induction across 327 posts (number of methods per username: median 2, IQR 1-4; range 1-11). Results: In total, 36 methods were grouped by the authors. The most frequently reported were sleep reduction (n=50), caffeine (n=37), and cessation of medication (n=27). Twenty-six usernames reported their motivation to self-induce mania or hypomania; almost three-quarters (n=19) reported a desire to end a depressive episode. Almost a third of usernames (118/405) explicitly discouraged other forum users from self-inducing mania or hypomania. Conclusions: Online forums provide an additional and valuable source of information about triggers for mania that may inform relapse prevention in BD. The online forum conversations investigated were generally responsible and included cautionary advice not to pursue these methods. %M 39642359 %R 10.2196/56970 %U https://jopm.jmir.org/2024/1/e56970 %U https://doi.org/10.2196/56970 %U http://www.ncbi.nlm.nih.gov/pubmed/39642359 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e45640 %T Examining the Use of Virtual Reality to Support Mindfulness Skills Practice in Mood and Anxiety Disorders: Mixed Methods Study %A Blackmore,Rebecca %A Giles,Claudia %A Tremain,Hailey %A Kelly,Ryan %A Foley,Fiona %A Fletcher,Kathryn %A Nedeljkovic,Maja %A Wadley,Greg %A Seabrook,Elizabeth %A Thomas,Neil %+ Centre for Mental Health and Brain Sciences, Swinburne University of Technology, John Street, Hawthorn, VIC, 3122, Australia, 61 392148444, neilthomas@swin.edu.au %K virtual reality %K mindfulness %K mood disorders %K anxiety disorders %K depression %K bipolar disorder %D 2024 %7 6.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Virtual reality (VR) has been proposed as a technology to support mindfulness practice through promoting increased engagement and presence. The proposed benefits of this technology have been largely unexamined with clinical populations. Further research is required to understand its clinical potential and utility in improving and managing mental health symptoms. Objective: This study aims to investigate the proximal impacts of a single, brief, VR-supported mindfulness practice for individuals with a mood or anxiety disorder and to understand user experiences, which may affect the acceptability and efficacy of VR mindfulness for this population. Methods: This mixed methods study recruited 28 participants with a primary diagnosis of major depressive disorder, bipolar disorder, or anxiety disorder. Participants completed a mindfulness practice wearing a VR headset that was presenting an omnidirectional video of a forest scene, which was overlaid with a guided audio voiceover. Before and after the practice, measures were completed assessing state mindfulness (Toronto Mindfulness Scale), affect (Positive and Negative Affect Schedule), and anxiety (State-Trait Anxiety Inventory Y-1; n=27). Semistructured interviews were then held inquiring about the user experience and were analyzed using thematic analysis (n=24). Results: After completing the VR-supported mindfulness practice, both measures of state mindfulness on the Toronto Mindfulness Scale, mean curiosity and decentering, increased significantly (Cohen d=1.3 and 1.51, respectively; P<.001). Negative affect on the Positive and Negative Affect Schedule (Cohen d=0.62; P=.003) and State-Trait Anxiety Inventory Y-1 state anxiety (Cohen d=0.84; P<.001) significantly reduced. There was no significant change in positive affect (Cohen d=0.29; P=.08). Qualitative analysis of interviews identified 14 themes across 5 primary theme categories. The results suggested that being mindful during the use of the app was experienced as relatively effortless because of the visual and immersive elements. It was also experienced as convenient and safe, including when compared with prior traditional experiences of mindfulness. Participants also identified the uses for VR-supported mindfulness in managing emotions and symptoms of mental illness. Conclusions: The results provide preliminary evidence that VR-supported mindfulness can improve emotional states and manage mental health symptoms for those with mood or anxiety disorders. It offers some potential clinical applications for those with mood or anxiety disorders for exploration within future research. %M 39641990 %R 10.2196/45640 %U https://www.jmir.org/2024/1/e45640 %U https://doi.org/10.2196/45640 %U http://www.ncbi.nlm.nih.gov/pubmed/39641990 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55965 %T How Does an Online Mental Health Community on Twitter Empower Diverse Population Levels and Groups? A Qualitative Analysis of #BipolarClub %A AbouWarda,Horeya %A Dolata,Mateusz %A Schwabe,Gerhard %+ Department of Informatics, Faculty of Business, Economics and Informatics, University of Zurich, Binzmuehlestrasse 14, Zurich, 8050, Switzerland, 41 44 635 75 83, abouwarda@ifi.uzh.ch %K social media %K Twitter %K online mental health community %K OMHC %K empowerment processes %K diverse population levels and groups %K World Health Organization %K WHO %K Integrated People-Centred Health Services %K IPCHS framework (Strategy 1) %D 2024 %7 19.8.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Social media, including online health communities (OHCs), are widely used among both healthy people and those with health conditions. Platforms like Twitter (recently renamed X) have become powerful tools for online mental health communities (OMHCs), enabling users to exchange information, express feelings, and socialize. Recognized as empowering processes, these activities could empower mental health consumers, their families and friends, and society. However, it remains unclear how OMHCs empower diverse population levels and groups. Objective: This study aimed to develop an understanding of how empowerment processes are conducted within OMHCs on Twitter by identifying members who shape these communities, detecting the types of empowerment processes aligned with the population levels and groups outlined in Strategy 1 of the Integrated People-Centred Health Services (IPCHS) framework by the World Health Organization (WHO), and clarifying members’ involvement tendencies in these processes. Methods: We conducted our analysis on a Twitter OMHC called #bipolarclub. We captured 2068 original tweets using its hashtag #bipolarclub between December 19, 2022, and January 15, 2023. After screening, 547 eligible tweets by 182 authors were analyzed. Using qualitative content analysis, community members were classified by examining the 182 authors’ Twitter profiles, and empowerment processes were identified by analyzing the 547 tweets and categorized according to the WHO’s Strategy 1. Members’ tendencies of involvement were examined through their contributions to the identified processes. Results: The analysis of #bipolarclub community members unveiled 5 main classifications among the 182 members, with the majority classified as individual members (n=138, 75.8%), followed by health care–related members (n=39, 21.4%). All members declared that they experience mental health conditions, including mental health and general practitioner members, who used the community as consumers and peers rather than for professional services. The analysis of 547 tweets for empowerment processes revealed 3 categories: individual-level processes (6 processes and 2 subprocesses), informal carer processes (1 process for families and 1 process for friends), and society-level processes (1 process and 2 subprocesses). The analysis also demonstrated distinct involvement tendencies among members, influenced by their identities, with individual members engaging in self-expression and family awareness support and health care–related members supporting societal awareness. Conclusions: The examination of the #bipolarclub community highlights the capability of Twitter-based OMHCs to empower mental health consumers (including those from underserved and marginalized populations), their families and friends, and society, aligning with the WHO’s empowerment agenda. This underscores the potential benefits of leveraging Twitter for such objectives. This pioneering study is the very first to analyze how a single OMHC can empower diverse populations, offering various health care stakeholders valuable guidance and aiding them in developing consumer-oriented empowerment programs using such OMHCs. We also propose a structured framework that classifies empowerment processes in OMHCs, inspired by the WHO’s Strategy 1 (IPCHS framework). %M 39158945 %R 10.2196/55965 %U https://www.jmir.org/2024/1/e55965 %U https://doi.org/10.2196/55965 %U http://www.ncbi.nlm.nih.gov/pubmed/39158945 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e58631 %T Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions %A de Azevedo Cardoso,Taiane %A Kochhar,Shruti %A Torous,John %A Morton,Emma %+ JMIR Publications, 130 Queens Quay E Suite 1100, Toronto, ON, M5A 0P6, Canada, 1 2899411482, taiane.cardoso@jmir.org %K bipolar disorder %K digital phenotyping %K machine learning %K mobile health interventions %K mobile health %K mHealth %K apps %D 2024 %7 1.4.2024 %9 Editorial %J JMIR Ment Health %G English %X Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions. %M 38557724 %R 10.2196/58631 %U https://mental.jmir.org/2024/1/e58631 %U https://doi.org/10.2196/58631 %U http://www.ncbi.nlm.nih.gov/pubmed/38557724 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e50738 %T Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach %A Benacek,Jiri %A Lawal,Nimotalai %A Ong,Tommy %A Tomasik,Jakub %A Martin-Key,Nayra A %A Funnell,Erin L %A Barton-Owen,Giles %A Olmert,Tony %A Cowell,Dan %A Bahn,Sabine %+ Department of Chemical Engineering and Biotechnology, Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, United Kingdom, 44 1223334151, sb209@cam.ac.uk %K misdiagnosis %K help-seeking %K gradient-boosted trees %K machine learning %K depression %K bipolar disorder %K diagnose %K diagnosis %K mood %K mental health %K mental disorder %K mental disorders %K depression %K depressive %K predict %K predictive %K prediction %K depressed %K algorithm %K algorithms %D 2024 %7 11.1.2024 %9 Original Paper %J JMIR Ment Health %G English %X Background: Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder and bipolar disorder. Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with major depressive disorder and bipolar disorder due to the overlap of symptoms with other mental and physical health conditions, as well as, stigma and insufficient understanding of these disorders. Objective: In this study, we aimed to identify factors that may contribute to mood disorder misdiagnosis and delayed help-seeking. Methods: Participants with current depressive symptoms were recruited online and data were collected using an extensive digital mental health questionnaire, with the World Health Organization World Mental Health Composite International Diagnostic Interview delivered via telephone. A series of predictive gradient-boosted tree algorithms were trained and validated to identify the most important predictors of misdiagnosis and subsequent help-seeking in misdiagnosed individuals. Results: The analysis included data from 924 symptomatic individuals for predicting misdiagnosis and from a subset of 379 misdiagnosed participants who provided follow-up information when predicting help-seeking. Models achieved good predictive power, with area under the receiver operating characteristic curve of 0.75 and 0.71 for misdiagnosis and help-seeking, respectively. The most predictive features with respect to misdiagnosis were high severity of depressed mood, instability of self-image, the involvement of a psychiatrist in diagnosing depression, higher age at depression diagnosis, and reckless spending. Regarding help-seeking behavior, the strongest predictors included shorter time elapsed since last speaking to a general practitioner about mental health, sleep problems disrupting daily tasks, taking antidepressant medication, and being diagnosed with depression at younger ages. Conclusions: This study provides a novel, machine learning–based approach to understand the interplay of factors that may contribute to the misdiagnosis and subsequent help-seeking in patients experiencing low mood. The present findings can inform the development of targeted interventions to improve early detection and appropriate treatment of individuals with mood disorders. %M 38206660 %R 10.2196/50738 %U https://mental.jmir.org/2024/1/e50738 %U https://doi.org/10.2196/50738 %U http://www.ncbi.nlm.nih.gov/pubmed/38206660 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e46165 %T Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study %A Lee,Dong Yun %A Choi,Byungjin %A Kim,Chungsoo %A Fridgeirsson,Egill %A Reps,Jenna %A Kim,Myoungsuk %A Kim,Jihyeong %A Jang,Jae-Won %A Rhee,Sang Youl %A Seo,Won-Woo %A Lee,Seunghoon %A Son,Sang Joon %A Park,Rae Woong %+ Department of Biomedical Informatics, Ajou University School of Medicine, 164, World cup-ro, Yeongtong-gu, Suwon-si, 16499, Republic of Korea, 82 2194471, rwpark99@gmail.com %K federated learning %K depression %K bipolar disorder %K data standardization %K differential privacy %D 2023 %7 20.7.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing. Objective: This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model. Methods: This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets. Results: In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months). Conclusions: We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only. %M 37471130 %R 10.2196/46165 %U https://www.jmir.org/2023/1/e46165 %U https://doi.org/10.2196/46165 %U http://www.ncbi.nlm.nih.gov/pubmed/37471130 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42365 %T Physical Versus Virtual Reality–Based Calm Rooms for Psychiatric Inpatients: Quasi-Randomized Trial %A Ilioudi,Maria %A Lindner,Philip %A Ali,Lilas %A Wallström,Sara %A Thunström,Almira Osmanovic %A Ioannou,Michael %A Anving,Nicole %A Johansson,Viktor %A Hamilton,William %A Falk,Örjan %A Steingrimsson,Steinn %+ Psychiatric Department, Sahlgrenska University Hospital, Region Västra Götaland, Journalvägen 5, Gothenburg, 41650, Sweden, 46 722448372, steinn.steingrimsson@vgregion.se %K psychiatry %K psychiatric inpatient care %K relaxation %K sensory room %K virtual reality %D 2023 %7 19.5.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Interest in sensory rooms or so-called “calm rooms” in psychiatric inpatient care has increased significantly. In a hospital setting, their purpose is to introduce a relaxing environment to increase well-being as well as to decrease anxiety and aggressive behaviors. Calm rooms can also be used as a tool to provide self-help through a convenient environment for the patients and, at the same time, strengthen the therapeutic relationship between the patient and the professional. Recent developments in virtual reality (VR) have made virtual calm rooms possible, but these have not yet been evaluated in psychiatric inpatient care. Objective: This study aimed to compare the effects of VR and physical calm rooms on self-reported well-being and physiological markers of arousal. Methods: The study was conducted in 2 inpatient psychiatric wards specializing in bipolar disorder from March 2019 to February 2021. Patients who were already admitted were asked if they were interested in using a calm room and willing to provide ratings. This study relied on the quasi-randomized allocation of patients to the wards, which either had a physical or VR calm room. Self-assessment scales (Montgomery-Åsberg Depression Rating Scale-Self Assessment [MADRS-S], Beck Anxiety Scale, and Clinical Global Impression) were used to determine the participants' baseline level of depressive and anxiety symptoms before their use of the physical or VR calm room. The study determined the state of well-being measured using an 11-point visual analog scale (VAS) as well as arousal measured by blood pressure (systolic and diastolic) and heart rate before and after the use of the calm rooms. The primary end point was self-reported well-being using the VAS. Results: A total of 60 participants were included—40 used the VR calm room and 20 used the physical calm room. The mean age of participants was 39 years and the majority were women (35/60, 58%). Analysis of VAS measurement showed improved well-being at the group level from before to after the intervention (P<.05), with no statistically significant difference in effects between the 2 different interventions. Effects were not moderated by baseline depression levels (dichotomized as MADRS-S >20 or ≤20) despite an overall difference in reported well-being between subgroups. Conclusions: Although the power in this study was low, the findings of this first study indicate comparable effects with respect to well-being and arousal of a VR calm room and a physical calm room. This suggests that a VR calm room can be a viable alternative when the use of a physical calm room is not an option for logistic or other reasons. Trial Registration: ClinicalTrials.gov NCT03918954; https://clinicaltrials.gov/ct2/show/NCT03918954 %M 37204858 %R 10.2196/42365 %U https://www.jmir.org/2023/1/e42365 %U https://doi.org/10.2196/42365 %U http://www.ncbi.nlm.nih.gov/pubmed/37204858 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e46157 %T Evaluating the Efficacy of Web-Based Cognitive Behavioral Therapy for the Treatment of Patients With Bipolar II Disorder and Residual Depressive Symptoms: Protocol for a Randomized Controlled Trial %A Gutierrez,Gilmar %A Stephenson,Callum %A Eadie,Jazmin %A Moghimi,Elnaz %A Omrani,Mohsen %A Groll,Dianne %A Soares,Claudio N %A Milev,Roumen %A Vazquez,Gustavo %A Yang,Megan %A Alavi,Nazanin %+ Department of Psychiatry, Queen's University, 166 Brock St, Kingston, ON, K7L5G2, Canada, 1 6135443310, nazanin.alavitabari@kingstonhsc.ca %K bipolar disorder %K cognitive behavioral therapy %K depression %K eHealth %K electronic care %K internet %K mental health %K psychotherapy %K treatment %K web-based therapy %D 2023 %7 19.5.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Bipolar disorder (BD) is a highly prevalent psychiatric condition that can significantly impact every aspect of a person’s life if left untreated. A subtype of BD, bipolar disorder type II (BD-II), is characterized by long depressive episodes and residual depression symptoms, with short-lived hypomanic episodes. Medication and psychotherapy, such as cognitive behavioral therapy (CBT), are the main treatment options for BD-II. CBT specific for BD-II involves the recognition of warning signs, potentially triggering stimuli, and the development of coping skills to increase euthymic periods and improve global functioning. However, access to in-person CBT may be limited by several barriers, including low availability, high costs, and geographical limitations. Thus, web-based adaptations of CBT (e-CBT) have become a promising solution to address these treatment barriers. Nevertheless, e-CBT for the treatment of BD-II remains understudied. Objective: The proposed study aims to establish the first e-CBT program specific for the treatment of BD-II with residual depressive symptoms. The primary objective of this study will be to determine the effect of e-CBT in managing BD symptomatology. The secondary objective will be to assess the effects of this e-CBT program on quality of life and resilience. The tertiary objective will involve gathering user feedback using a posttreatment survey to support the continuous improvement and optimization of the proposed program. Methods: Adult participants (N=170) with a confirmed diagnosis of BD-II experiencing residual depressive symptoms will be randomly assigned to either the e-CBT and treatment as usual (TAU; n=85) group or the TAU (n=85) control group. Participants in the control group will be able to participate in the web-based program after the first 13 weeks. The e-CBT program will consist of 13 weekly web-based modules designed following a validated CBT framework. Participants will complete module-related homework and receive asynchronous personalized feedback from a therapist. TAU will consist of standard treatment services conducted outside of this research study. Depression and manic symptoms, quality of life, and resiliency will be assessed using clinically validated symptomatology questionnaires at baseline, week 6, and week 13. Results: The study received ethics approval in March 2020, and participant recruitment is expected to begin in February 2023 through targeted advertisements and physician referrals. Data collection and analysis are expected to conclude by December 2024. Linear and binomial regression (continuous and categorical outcomes, respectively) will be conducted along with qualitative interpretive methods. Conclusions: The findings will be the first on the effectiveness of delivering e-CBT for patients with BD-II with residual depressive symptoms. This approach can provide an innovative method to address barriers to in-person psychotherapy by increasing accessibility and decreasing costs. Trial Registration: ClinicalTrials.gov NCT04664257; https://clinicaltrials.gov/ct2/show/NCT04664257 International Registered Report Identifier (IRRID): PRR1-10.2196/46157 %M 37140460 %R 10.2196/46157 %U https://www.researchprotocols.org/2023/1/e46157 %U https://doi.org/10.2196/46157 %U http://www.ncbi.nlm.nih.gov/pubmed/37140460 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 10 %N %P e43164 %T Mobile Acceptance and Commitment Therapy in Bipolar Disorder: Microrandomized Trial %A Cochran,Amy %A Maronge,Jacob M %A Victory,Amanda %A Hoel,Sydney %A McInnis,Melvin G %A Thomas,Emily BK %+ Department of Population Health Sciences, University of Wisconsin Madison, 610 Walnut Street, Madison, WI, 53726, United States, 1 608 262 0772, cochran4@wisc.edu %K acceptance and commitment therapy %K bipolar disorder %K mobile applications %K randomized controlled trials %K micro-randomized trial %K precision medicine %K mindfulness %D 2023 %7 20.4.2023 %9 Original Paper %J JMIR Ment Health %G English %X Background: Mobile interventions promise to fill in gaps in care with their broad reach and flexible delivery. Objective: Our goal was to investigate delivery of a mobile version of acceptance and commitment therapy (ACT) for individuals with bipolar disorder (BP). Methods: Individuals with BP (n=30) participated in a 6-week microrandomized trial. Twice daily, participants logged symptoms in the app and were repeatedly randomized (or not) to receive an ACT intervention. Self-reported behavior and mood were measured as the energy devoted to moving toward valued domains or away from difficult emotions and with depressive d and manic m scores from the digital survey of mood in BP survey (digiBP). Results: Participants completed an average of 66% of in-app assessments. Interventions did not significantly impact the average toward energy or away energy but did significantly increase the average manic score m (P=.008) and depressive score d (P=.02). This was driven by increased fidgeting and irritability and interventions focused on increasing awareness of internal experiences. Conclusions: The findings of the study do not support a larger study on the mobile ACT in BP but have significant implications for future studies seeking mobile therapy for individuals with BP. Trial Registration: ClinicalTrials.gov NCT04098497; https://clinicaltrials.gov/ct2/show/NCT04098497 %M 37079363 %R 10.2196/43164 %U https://mental.jmir.org/2023/1/e43164 %U https://doi.org/10.2196/43164 %U http://www.ncbi.nlm.nih.gov/pubmed/37079363 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e44059 %T Direct and Indirect Predictors of Medication Adherence With Bipolar Disorder: Path Analysis %A Cohen,Bar %A Sixsmith,Andrew %A Pollock Star,Ariel %A Haglili,Ophir %A O'Rourke,Norm %+ Department of Epidemiology, Biostatistics and Community Health Sciences, Ben-Gurion University of the Negev, Building M6, Room #308, P.O. Box 653, Be'er Sheva, 8421637, Israel, 972 8 6477301, ORourke@bgu.ac.il %K alcohol misuse %K bipolar disorder %K cognitive loss %K depression %K hypo/mania %K mania %K medication adherence %K mental health %K path analysis %K perceived cognitive failures %K polypharmacy %K psychiatric disorder %K psychosocial %D 2023 %7 7.2.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Despite the efficacy of treatment and severity of symptoms, medication adherence by many with bipolar disorder (BD) is variable at best. This poses a significant challenge for BD care management. Objective: For this study, we set out to identify psychosocial and psychiatric predictors of medication adherence with BD. Methods: Using microtargeted social media advertising, we recruited an international sample of young and older adults with BD living in North America (Canada and the United States), Western Europe (eg, United Kingdom and Ireland), Australia and New Zealand (N=92). On average, participants were 55.35 (SD 9.65; range 22-73) years of age, had been diagnosed with BD 14.25 (SD 11.14; range 1-46) years ago, and were currently prescribed 2.40 (SD 1.28; range 0-6) psychotropic medications. Participants completed questionnaires online including the Morisky Medication Adherence Scale. Results: Medication adherence did not significantly differ across BD subtypes, country of residence, or prescription of lithium versus other mood stabilizers (eg, anticonvulsants). Path analyses indicate that alcohol misuse and subjective or perceived cognitive failures are direct predictors of medication adherence. BD symptoms, psychological well-being, and the number of comorbid psychiatric conditions emerged as indirect predictors of medication adherence via perceived cognitive failures. Conclusions: Alcohol misuse did not predict perceived cognitive failures. Nor did age predict medication adherence or cognitive failures. This is noteworthy given the 51-year age range of participants. That is, persons in their 20s with BD reported similar levels of medication adherence and perceived cognitive failures as those in their 60s. This suggests that perceived cognitive loss is a facet of adult life with BD, in contrast to the assumption that accelerated cognitive aging with BD begins in midlife. %M 36749623 %R 10.2196/44059 %U https://formative.jmir.org/2023/1/e44059 %U https://doi.org/10.2196/44059 %U http://www.ncbi.nlm.nih.gov/pubmed/36749623 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e41013 %T Neural Correlates of the DEEPP (Anti-suicidal Response to Ketamine in Treatment-Resistant Bipolar Depression) Study: Protocol for a Pilot, Open-Label Clinical Trial %A Knyahnytska,Yuliya %A Zomorrodi,Reza %A Kaster,Tyler %A Voineskos,Daphne %A Trevizol,Alisson %A Blumberger,Daniel %+ Centre for Addiction and Mental Health, 1025 Queen Street West, Toronto, ON, Canada, 1 4165358501, Yuliya.Knyahnytska@camh.ca %K bipolar depression %K suicidality %K ketamine intervention %K neurophysiological markers of response %D 2023 %7 27.1.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Suicide is among the top 10 leading causes of death worldwide. Of people who died by suicide, the majority are diagnosed with depression. It is estimated that 25%-60% of people with bipolar depression (BD) will attempt suicide at least once, and 10%-15% will die by suicide. Several treatments, such as lithium, clozapine, electroconvulsive therapy, and cognitive behavioral therapy, have been shown to be effective in treating suicidality. However, these treatments can be difficult to tolerate or may take months to take effect. Ketamine, a glutamate N-methyl-D-aspartate antagonist, has been shown to have rapid antisuicidal effect and antidepressant qualities, and is thus a promising intervention to target acute suicidality in patients with BD. However, the biological mechanism underlying its therapeutic action remains poorly understood. Enhancing our understanding of underlying mechanisms of action for ketamine’s effectiveness in reducing suicidality is critical to establishing biological markers of treatment response and developing tailored, personalized interventions for patients with BD. Objective: This is an open-label clinical trial to test the safety and feasibility of repeated ketamine infusions to treat acute suicidality. The primary objective is to test the safety and feasibility of ketamine intervention. The secondary objective is to examine ketamine’s potential neurophysiological mechanisms of action by assessing cortical excitation and inhibition to determine potential biomarkers of clinical response. Other objectives are to evaluate the effect of ketamine on acute suicidality and other clinical outcomes, such as depressive symptoms and quality of life, to inform a future larger trial. Methods: This open-label clinical trial aims to test the safety and feasibility of repeated ketamine infusions in patients with BD for suicidality and to assess ketamine’s neurophysiological effects. A sterile form of racemic ketamine hydrochloride will be administered over a 40-minute intravenous infusion 2 times per week on nonconsecutive days for 4 weeks (8 sessions). We will recruit 30 adults (24-65 year olds) over 2 years from an academic psychiatric hospital in Toronto, Canada. Results: This study is currently ongoing and actively recruiting participants. So far, 5 participants have completed the trial, 1 is currently in active treatment, and 8 participants are on the waitlist to be screened. We anticipate initial results being available in the fall of 2023. This proposal was presented as a poster presentation at the Research to Reality Global Summit on Psychedelic-Assisted Therapies and Medicine, held in May 2022 in Toronto, Canada. Conclusions: Developing effective interventions for acute suicidality in high-risk populations such as those with BD remains a major therapeutic challenge. Ketamine is a promising treatment due to its rapid antidepressant and antisuicidal effects, but its underlying neurophysiological mechanisms of action remain unknown. Trial Registration: ClinicalTrials.gov NCT05177146; https://clinicaltrials.gov/ct2/show/NCT05177146 International Registered Report Identifier (IRRID): DERR1-10.2196/41013 %M 36573651 %R 10.2196/41013 %U https://www.researchprotocols.org/2023/1/e41013 %U https://doi.org/10.2196/41013 %U http://www.ncbi.nlm.nih.gov/pubmed/36573651 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 10 %N %P e37225 %T Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review %A Ettore,Eric %A Müller,Philipp %A Hinze,Jonas %A Riemenschneider,Matthias %A Benoit,Michel %A Giordana,Bruno %A Postin,Danilo %A Hurlemann,Rene %A Lecomte,Amandine %A Musiol,Michel %A Lindsay,Hali %A Robert,Philippe %A König,Alexandra %+ Department of Psychiatry and Memory Clinic, University Hospital of Nice, 30 Voie Romaine, Nice, 06000, France, 33 633306394, ettore.e@chu-nice.fr %K depression %K bipolar disorder %K posttraumatic stress disorder %K differential diagnosis %K digital phenotyping %K speech analysis %K nonverbal behavior %K physiological measures %K posttraumatic stress disorder %K mental health %K clinical interview %K diagnosis %K mental disorder %K interview %K digital health %K psychotrauma %K digital %K information %D 2023 %7 23.1.2023 %9 Review %J JMIR Ment Health %G English %X Background: Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful. Objective: We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common. Methods: We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma. Results: A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes. Conclusions: Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed. %M 36689265 %R 10.2196/37225 %U https://mental.jmir.org/2023/1/e37225 %U https://doi.org/10.2196/37225 %U http://www.ncbi.nlm.nih.gov/pubmed/36689265 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 11 %P e36496 %T Cost-Utility and Cost-effectiveness of MoodSwings 2.0, an Internet-Based Self-management Program for Bipolar Disorder: Economic Evaluation Alongside a Randomized Controlled Trial %A Chatterton,Mary Lou %A Lee,Yong Yi %A Berk,Lesley %A Mohebbi,Mohammadreza %A Berk,Michael %A Suppes,Trisha %A Lauder,Sue %A Mihalopoulos,Cathrine %+ Institute for Health Transformation, Deakin University, Locked Bag 20001, Geelong, 3220, Australia, 61 03 52278409, mchatter@deakin.edu.au %K economic evaluation %K cost-effectiveness %K cost-utility %K clinical trial %K bipolar disorder %K psychoeducation %K cognitive behavioral therapy %K internet intervention %K mania %K depression %K psychiatry %K neuroscience %K mental disorders %D 2022 %7 1.11.2022 %9 Original Paper %J JMIR Ment Health %G English %X Background: Internet-delivered psychosocial interventions can overcome barriers to face-to-face psychosocial care, but limited evidence supports their cost-effectiveness for people with bipolar disorders (BDs). Objective: This study aimed to conduct within-trial cost-effectiveness and cost-utility analyses of an internet-based intervention for people with BD, MoodSwings 2.0, from an Australian health sector perspective. Methods: MoodSwings 2.0 included an economic evaluation alongside an international, parallel, and individually stratified randomized controlled trial comparing an internet-based discussion forum (control; group 1), a discussion forum plus internet-based psychoeducation (group 2), and a discussion forum plus psychoeducation and cognitive behavioral tools (group 3). The trial enrolled adults (aged 21 to 65 years) with a diagnosis of BD assessed by telephone using a structured clinical interview. Health sector costs included intervention delivery and additional health care resources used by participants over the 12-month trial follow-up. Outcomes included depression symptoms measured by the Montgomery-Åsberg Depression Rating Scale (MADRS; the trial primary outcome) and quality-adjusted life years (QALYs) calculated using the short-form 6-dimension instrument derived from the 12-item version of the short-form health survey. Average incremental cost-effectiveness (cost per MADRS score) and cost-utility (cost per QALY) ratios were calculated using estimated mean differences between intervention and control groups from linear mixed effects models in the base case. Results: In total, 304 participants were randomized. Average health sector cost was lowest for group 2 (Aus $9431, SD Aus $8540; Aus $1=US $0.7058) compared with the control group (Aus $15,175, SD Aus $17,206) and group 3 (Aus $15,518, SD Aus $30,523), but none was statistically significantly different. The average QALYs were not significantly different among the groups (group 1: 0.627, SD 0.062; group 2: 0.618, SD 0.094; and group 3: 0.622, SD 0.087). The MADRS scores were previously shown to differ significantly between group 2 and the control group at all follow-up time points (P<.05). Group 2 was dominant (lower costs and greater effects) compared with the control group for average incremental cost per point decrease in MADRS score over 12 months (95% CI dominated to Aus $331). Average cost per point change in MADRS score for group 3 versus the control group was dominant (95% CI dominant to Aus $22,585). Group 2 was dominant (95% CI Aus $43,000 to dominant) over the control group based on lower average health sector cost and average QALY benefit of 0.012 (95% CI –0.009 to 0.033). Group 3, compared with the control group, had an average incremental cost-effectiveness ratio of dominant (95% CI dominated to Aus $19,978). Conclusions: Web-based psychoeducation through MoodSwings 2.0 has the potential to be a cost-effective intervention for people with BD. Additional research is needed to understand the lack of effectiveness for the addition of cognitive behavioral tools with the group 3 intervention. %M 36318243 %R 10.2196/36496 %U https://mental.jmir.org/2022/11/e36496 %U https://doi.org/10.2196/36496 %U http://www.ncbi.nlm.nih.gov/pubmed/36318243 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 10 %P e35837 %T Use of an Online Forum for Relatives of People With Psychosis and Bipolar Disorder: Mixed Methods Study %A Jones,Steven %A Atanasova,Dimitrinka %A Dodd,Susanna %A Flowers,Susan %A Rosala-Hallas,Anna %A Robinson,Heather %A Semino,Elena %A Lobban,Fiona %+ Spectrum Centre for Mental Health Research, Division of Health Research, Lancaster University, Health Innovation 1, Sir John Fisher Drive, Lancaster, LA1 4AT, United Kingdom, 44 1525 593382, s.jones7@lancaster.ac.uk %K psychosis %K bipolar disorder %K relative %K carer %K mental health %K forum %K online %K digital health %K Relatives Education and Coping Toolkit %K REACT %K trial %D 2022 %7 20.10.2022 %9 Original Paper %J JMIR Ment Health %G English %X Background: Relatives of people with psychosis or bipolar disorder experience high levels of distress but are typically not offered the support they need. Online peer forums may offer a solution, but knowledge about who uses them, how, and why is limited. This study reported on online forum use during the Relatives Education and Coping Toolkit (REACT) trial. Objective: We aimed to report who used the forum and why; how sociodemographic factors are associated with participation; the relationship among frequency, type of use, and outcomes; and how the forum was used. Methods: The relationships between key sociodemographic characteristics, levels of forum use, and distress were statistically analyzed. We used thematic and semantic analyses to understand the reasons for relatives joining the forum and the key topics initiated by them. We also used the University Centre for Computer Corpus Research on Language Semantic Analysis System to compare how relatives and REACT supporters (moderators) used the forum. Results: A total of 348 participants with full forum use data from REACT were included in this study. The forum was accessed by 59.4% (207/348) of the relatives across the entire age range, with no significant associations between sociodemographic factors and forum participation, or between level or type of use and relatives’ distress levels. Relatives joined the forum primarily to find people in similar circumstances, express concerns, and talk about stressful events. Relatives were most concerned about recent events, negative emotions linked to caring, experiences of conflict or threat, and concerns about suicide. These posts underscored both the challenges the relatives were facing and the fact that they felt safe sharing them in this context. Conclusions: Although only a proportion of REACT participants engaged actively with its forum, they were widely distributed across age and other sociodemographic groupings. Relatives used the forum for information, support, and guidance and to offer detailed information about their experiences. The topics raised highlighted the burden carried by relatives and the potential value of easy-access, moderated, peer-supported forums in helping relatives to manage the challenges they faced. %M 36264621 %R 10.2196/35837 %U https://mental.jmir.org/2022/10/e35837 %U https://doi.org/10.2196/35837 %U http://www.ncbi.nlm.nih.gov/pubmed/36264621 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e39476 %T A Web-Based Positive Psychology App for Patients With Bipolar Disorder: Development Study %A Geerling,Bart %A Kelders,Saskia M %A Stevens,Anja W M M %A Kupka,Ralph W %A Bohlmeijer,Ernst T %+ Department of Psychology, Health and Technology, Centre for eHealth and Wellbeing Research, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands, 31 0622274351, b.geerling@dimence.nl %K bipolar disorder %K positive psychology %K cocreation %K mobile health %K mHealth %K web-based %K psychology %K bipolar %K intervention %K quality of life %K mental illness %K pilot %K self-esteem %K acceptance %K social isolation %K manic episode %K manic %K self-help %K positive %K mobile phone %D 2022 %7 19.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Patients with bipolar disorder (BD) report lower quality of life and lower levels of well-being than the general population. Despite the growing availability of psychotherapeutic and self-management interventions, important unmet needs remain. These unmet needs are closely linked to positive psychology domains. Although a growing number of studies have evaluated the impact of positive psychology interventions (PPIs) on patients with severe mental illness in general, only few have addressed the application of positive psychology for BD. Objective: This study aimed to gain insight into the opinions of patients with BD and health care professionals about (web-based) PPIs for BD and to develop and pilot-test an app containing PPIs specifically designed for patients with BD. Methods: The study was conducted in accordance with the Center for eHealth and Disease Management road map principles and incorporated cocreation and designing for implementation. Data were collected using focus group discussions, questionnaires, rapid prototyping, and web-based feedback on a prototype from the participants. In total, 3 focus groups were conducted with 62% (8/13) of patients with BD and 38% (5/13) of professionals. The collected data were used to develop a smartphone app containing short PPIs. The content was based on PPIs for which a solid base of evidence is available. Finally, a pilot test was conducted to test the app. Results: Focus groups revealed that PPIs as part of the current BD treatment can potentially meet the following needs: offering hope, increasing self-esteem, expressing feelings, acceptance, and preventing social isolation. Some patients expressed concern that PPIs may provoke a manic or hypomanic episode by increasing positive affect. The pilot of the app showed that the PPIs are moderately to highly valued by the participants. There were no adverse effects such as increase in manic or hypomanic symptoms. Conclusions: With the systematic use of user involvement (patients and professionals) in all steps of the development process, we were able to create an app that can potentially fulfill some of the current unmet needs in the treatment of BD. We reached consensus among consumers and professionals about the potential benefits of PPIs to address the unmet needs of patients with BD. The use of PPI for BD is intriguing and can be usefully explored in further studies. We emphasize that more evaluation studies (quantitative and qualitative) that are focused on the effect of PPIs in the treatment of BD should be conducted. In addition, to establish the working mechanisms in BD, explorative, qualitative, designed studies are required to reveal whether PPIs can address unmet needs in BD. %M 35946327 %R 10.2196/39476 %U https://formative.jmir.org/2022/9/e39476 %U https://doi.org/10.2196/39476 %U http://www.ncbi.nlm.nih.gov/pubmed/35946327 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 8 %P e39519 %T Social Media Use and Well-being With Bipolar Disorder During the COVID-19 Pandemic: Path Analysis %A Pollock Star,Ariel %A Bachner,Yaacov G %A Cohen,Bar %A Haglili,Ophir %A O'Rourke,Norm %+ Department of Epidemiology, Biostatistics and Community Health Sciences, School of Public Health, Ben-Gurion University of the Negev, P.O. Box 653, Be'er Sheva, Israel, 972 08 647 7301, ORourke@bgu.ac.il %K bipolar disorder %K COVID-19 %K life satisfaction %K loneliness %K social media use %K social media %K Facebook %K social support %K mental health %K mental illness %K mental disorder %K social media advertising %K advertising %K advertisement %K mania %K hypo/mania %K manic %K depressive %K depression %D 2022 %7 18.8.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Reliable and consistent social support is associated with the mental health and well-being of persons with severe mental illness, including bipolar disorder (BD). Yet the COVID-19 pandemic and associated social distancing measures (eg, shelter in place) reduced access to regular social contacts, while social media use (SMU) increased concomitantly. Little is currently known about associations between the well-being of adults with BD and different types of SMU (eg, passive and active). Objective: For this study, we had two goals. First, we report descriptive information regarding SMU by persons with BD during COVID-19 (all platforms). Specific to Facebook, we next developed and tested a hypothesized model to identify direct and indirect associations between BD symptoms, social support, loneliness, life satisfaction, and SMU. Responses were collected during the global spread of the Delta variant and prior/concurrent with the Omicron variant, 20 months after the World Health Organization declared COVID-19 a global pandemic. Methods: Over 8 weeks, we obtained responses from an international sample of 102 adults with BD using the Qualtrics online platform. Most had previously participated in the BADAS (Bipolar Affective Disorders and older Adults) Study (n=89, 87.3%); the remainder were recruited specifically for this research (n=13, 2.7%). The subsamples did not differ in age (t100=1.64; P=.10), gender (χ22=0.2; P=.90), socioeconomic status (χ26=9.9; P=.13), or time since BD diagnosis (t97=1.27; P=.21). Both were recruited using social media advertising micro-targeted to adults with BD. On average, participants were 53.96 (SD 13.22, range 20-77) years of age, they had completed 15.4 (SD 4.28) years of education, and were diagnosed with BD 19.6 (SD 10.31) years ago. Path analyses were performed to develop and test our hypothesized model. Results: Almost all participants (n=95, 93.1%) reported having both Facebook and LinkedIn accounts; 91.2% (n=93) reported regular use of either or both. During the pandemic, most (n=62, 60.8%) reported accessing social media several times a day; 36.3% (n=37) reported using social media more often since the emergence of COVID-19. Specific to Facebook, the model we hypothesized differed somewhat from what emerged. The resulting model suggests that symptoms of depression predict loneliness and, inversely, social support and life satisfaction. Social support predicts social Facebook use, whereas passive Facebook use predicts life satisfaction. Symptoms of depression emerged as indirect predictors of SMU via social support. Conclusions: Our findings suggest that the operational definition of passive-active SMU requires further analysis and refinement. In contrast to theory, passive Facebook use appears positively associated with well-being among certain populations. Longitudinal data collection over multiple points is required to identify associations between BD symptoms, SMU, and well-being over time. %M 35980726 %R 10.2196/39519 %U https://formative.jmir.org/2022/8/e39519 %U https://doi.org/10.2196/39519 %U http://www.ncbi.nlm.nih.gov/pubmed/35980726 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 8 %P e36213 %T Supporting Self-management and Quality of Life in Bipolar Disorder With the PolarUs App (Alpha): Protocol for a Mixed Methods Study %A Michalak,Erin E %A Barnes,Steven J %A Morton,Emma %A O'Brien,Heather L %A Murray,Greg %A Hole,Rachelle %A Meyer,Denny %+ Department of Psychiatry, University of British Columbia, 420-5950 University Blvd, Vancouver, BC, V6T1Z3, Canada, 1 604 827 3393, erin.michalak@ubc.ca %K eHealth %K mobile health %K mHealth %K bipolar disorder %K self-management %K engagement %K mobile phone %D 2022 %7 4.8.2022 %9 Protocol %J JMIR Res Protoc %G English %X 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 %M 35925666 %R 10.2196/36213 %U https://www.researchprotocols.org/2022/8/e36213 %U https://doi.org/10.2196/36213 %U http://www.ncbi.nlm.nih.gov/pubmed/35925666 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 7 %P e36665 %T Initial Psychometric Properties of 7 NeuroUX Remote Ecological Momentary Cognitive Tests Among People With Bipolar Disorder: Validation Study %A Moore,Raeanne C %A Parrish,Emma M %A Van Patten,Ryan %A Paolillo,Emily %A Filip,Tess F %A Bomyea,Jessica %A Lomas,Derek %A Twamley,Elizabeth W %A Eyler,Lisa T %A Depp,Colin A %+ Department of Psychiatry, University of California San Diego, 220 Dickinson St. Ste B (8231), San Diego, CA, 92103-8231, United States, 1 949 933 8063, r6moore@health.ucsd.edu %K neuropsychology %K mobile health %K ambulatory assessment %K ecological momentary assessment %K practice effects %K validity %K testing %K serious mental illness %K mobile phone %D 2022 %7 29.7.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: As smartphone technology has become nearly ubiquitous, there is a growing body of literature suggesting that ecological momentary cognitive testing (EMCT) offers advantages over traditional pen-and-paper psychological assessment. We introduce a newly developed platform for the self-administration of cognitive tests in ecologically valid ways. Objective: The aim of this study is to develop a Health Insurance Portability and Accountability Act–compliant EMCT smartphone-based platform for the frequent and repeated testing of cognitive abilities in everyday life. This study examines the psychometric properties of 7 mobile cognitive tests covering domains of processing speed, visual working memory, recognition memory, and response inhibition within our platform among persons with and without bipolar disorder (BD). Ultimately, if shown to have adequate psychometric properties, EMCTs may be useful in research on BD and other neurological and psychiatric illnesses. Methods: A total of 45 persons with BD and 21 demographically comparable healthy volunteer participants (aged 18-65 years) completed smartphone-based EMCTs 3 times daily for 14 days. Each EMCT session lasted approximately 1.5 minutes. Only 2 to 3 tests were administered in any given session, no test was administered more than once per day, and alternate test versions were administered in each session. Results: The mean adherence to the EMCT protocol was 69.7% (SD 20.5%), resulting in 3965 valid and complete tests across the full sample. Participants were significantly more likely to miss tests on later versus earlier study days. Adherence did not differ by diagnostic status, suggesting that BD does not interfere with EMCT participation. In most tests, age and education were related to EMCT performance in expected directions. The average performances on most EMCTs were moderately to strongly correlated with the National Institutes of Health Toolbox Cognition Battery. Practice effects were observed in 5 tests, with significant differences in practice effects by BD status in 3 tests. Conclusions: Although additional reliability and validity data are needed, this study provides initial psychometric support for EMCTs in the assessment of cognitive performance in real-world contexts in BD. %M 35904876 %R 10.2196/36665 %U https://www.jmir.org/2022/7/e36665 %U https://doi.org/10.2196/36665 %U http://www.ncbi.nlm.nih.gov/pubmed/35904876 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 4 %P e35928 %T Natural Language Processing Methods and Bipolar Disorder: Scoping Review %A Harvey,Daisy %A Lobban,Fiona %A Rayson,Paul %A Warner,Aaron %A Jones,Steven %+ Spectrum Centre for Mental Health Research, Division of Health Research, School of Health and Medicine, Lancaster University, Health Innovation One, Sir John Fisher Drive, Lancaster, LA1 4YG, United Kingdom, 44 152465201, d.harvey4@lancaster.ac.uk %K bipolar disorder %K mental health %K mental illness %K natural language processing %K computational linguistics %D 2022 %7 22.4.2022 %9 Review %J JMIR Ment Health %G English %X 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. %M 35451984 %R 10.2196/35928 %U https://mental.jmir.org/2022/4/e35928 %U https://doi.org/10.2196/35928 %U http://www.ncbi.nlm.nih.gov/pubmed/35451984 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 3 %P e29114 %T Process and Outcome Evaluations of Smartphone Apps for Bipolar Disorder: Scoping Review %A Tatham,Iona %A Clarke,Ellisiv %A Grieve,Kelly Ann %A Kaushal,Pulkit %A Smeddinck,Jan %A Millar,Evelyn Barron %A Sharma,Aditya Narain %+ Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Academic Psychiatry, Wolfson Research Centre, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, United Kingdom, 44 1912875262, aditya.sharma@ncl.ac.uk %K child and adolescent mental health %K scoping review %K bipolar disorder %K mental health %D 2022 %7 23.3.2022 %9 Review %J J Med Internet Res %G English %X Background: Mental health apps (MHAs) provide opportunities for accessible, immediate, and innovative approaches to better understand and support the treatment of mental health disorders, especially those with a high burden, such as bipolar disorder (BD). Many MHAs have been developed, but few have had their effectiveness evaluated. Objective: This systematic scoping review explores current process and outcome measures of MHAs for BD with the aim to provide a comprehensive overview of current research. This will identify the best practice for evaluating MHAs for BD and inform future studies. Methods: A systematic literature search of the health science databases PsycINFO, MEDLINE, Embase, EBSCO, Scopus, and Web of Science was undertaken up to January 2021 (with no start date) to narratively assess how studies had evaluated MHAs for BD. Results: Of 4051 original search results, 12 articles were included. These 12 studies included 435 participants, and of these, 343 had BD type I or II. Moreover, 11 of the 12 studies provided the ages (mean 37 years) of the participants. One study did not report age data. The male to female ratio of the 343 participants was 137:206. The most widely employed validated outcome measure was the Young Mania Rating Scale, being used 8 times. The Hamilton Depression Rating Scale-17/Hamilton Depression Rating Scale was used thrice; the Altman Self-Rating Mania Scale, Quick Inventory of Depressive Symptomatology, and Functional Assessment Staging Test were used twice; and the Coping Inventory for Stressful Situations, EuroQoL 5-Dimension Health Questionnaire, Generalized Anxiety Disorder Scale-7, Inventory of Depressive Symptomatology, Mindfulness Attention Awareness Scale, Major Depression Index, Morisky-Green 8-item, Perceived Stress Scale, and World Health Organization Quality of Life-BREF were used once. Self-report measures were captured in 9 different studies, 6 of which used MONARCA. Mood and energy levels were the most commonly used self-report measures, being used 4 times each. Furthermore, 11 of the 12 studies discussed the various confounding factors and barriers to the use of MHAs for BD. Conclusions: Reported low adherence rates, usability challenges, and privacy concerns act as barriers to the use of MHAs for BD. Moreover, as MHA evaluation is itself developing, guidance for clinicians in how to aid patient choices in mobile health needs to develop. These obstacles could be ameliorated by incorporating co-production and co-design using participatory patient approaches during the development and evaluation stages of MHAs for BD. Further, including qualitative aspects in trials that examine patient experience of both mental ill health and the MHA itself could result in a more patient-friendly fit-for-purpose MHA for BD. %M 35319470 %R 10.2196/29114 %U https://www.jmir.org/2022/3/e29114 %U https://doi.org/10.2196/29114 %U http://www.ncbi.nlm.nih.gov/pubmed/35319470 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 2 %P e30710 %T A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Empirical and Theoretical Framework, Intervention Design, and Study Protocol for a Randomized Controlled Trial %A Goulding,Evan H %A Dopke,Cynthia A %A Rossom,Rebecca C %A Michaels,Tania %A Martin,Clair R %A Ryan,Chloe %A Jonathan,Geneva %A McBride,Alyssa %A Babington,Pamela %A Bernstein,Mary %A Bank,Andrew %A Garborg,C Spencer %A Dinh,Jennifer M %A Begale,Mark %A Kwasny,Mary J %A Mohr,David C %+ Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, 303 E Chicago Ave., Chicago, IL, 60611, United States, 1 3125031189, e-goulding@fsm.northwestern.edu %K bipolar disorder %K self-management %K mHealth %K eHealth %K smartphone %K mobile phone %K mental health %K mobile health %D 2022 %7 21.2.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Bipolar disorder is a severe mental illness with high morbidity and mortality rates. Even with pharmacological treatment, frequent recurrence of episodes, long episode durations, and persistent interepisode symptoms are common and disruptive. Combining psychotherapy with pharmacotherapy improves outcomes; however, many individuals with bipolar disorder do not receive psychotherapy. Mental health technologies can increase access to self-management strategies derived from empirically supported bipolar disorder psychotherapies while also enhancing treatment by delivering real-time assessments, personalized feedback, and provider alerts. In addition, mental health technologies provide a platform for self-report, app use, and behavioral data collection to advance understanding of the longitudinal course of bipolar disorder, which can then be used to support ongoing improvement of treatment. Objective: A description of the theoretical and empirically supported framework, design, and protocol for a randomized controlled trial (RCT) of LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder, is provided to facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar disorder. The goal of the trial is to determine the effectiveness of LiveWell for reducing relapse risk and symptom burden as well as improving quality of life (QOL) while simultaneously clarifying behavioral targets involved in staying well and better characterizing the course of bipolar disorder and treatment response. Methods: The study is a single-blind RCT (n=205; 2:3 ratio of usual care vs usual care plus LiveWell). The primary outcome is the time to relapse. Secondary outcomes are percentage time symptomatic, symptom severity, and QOL. Longitudinal changes in target behaviors proposed to mediate the primary and secondary outcomes will also be determined, and their relationships with the outcomes will be assessed. A database of clinical status, symptom severity, real-time self-report, behavioral sensor, app use, and personalized content will be created to better predict treatment response and relapse risk. Results: Recruitment and screening began in March 2017 and ended in April 2019. Follow-up ended in April 2020. The results of this study are expected to be published in 2022. Conclusions: This study will examine whether LiveWell reduces relapse risk and symptom burden and improves QOL for individuals with bipolar disorder by increasing access to empirically supported self-management strategies. The role of selected target behaviors (medication adherence, sleep duration, routine, and management of signs and symptoms) in these outcomes will also be examined. Simultaneously, a database will be created to initiate the development of algorithms to personalize and improve treatment for bipolar disorder. In addition, we hope that this description of the theoretical and empirically supported framework, intervention design, and study protocol for the RCT of LiveWell will facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar and other mental health disorders. Trial Registration: ClinicalTrials.gov NCT03088462; https://www.clinicaltrials.gov/ct2/show/NCT03088462 International Registered Report Identifier (IRRID): DERR1-10.2196/30710 %M 35188473 %R 10.2196/30710 %U https://www.researchprotocols.org/2022/2/e30710 %U https://doi.org/10.2196/30710 %U http://www.ncbi.nlm.nih.gov/pubmed/35188473 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 2 %P e31565 %T Real-world Implementation of a Smartphone-Based Psychoeducation Program for Bipolar Disorder: Observational Ecological Study %A García-Estela,Aitana %A Cantillo,Jordi %A Angarita-Osorio,Natalia %A Mur-Milà,Estanislao %A Anmella,Gerard %A Pérez,Víctor %A Vieta,Eduard %A Hidalgo-Mazzei,Diego %A Colom,Francesc %+ Mental Health Research Group, Hospital del Mar Medical Research Institute (IMIM), Office 202, PRBB Building, Doctor Aiguader, 88, Barcelona, 08003, Spain, 34 933160400 ext 1493, fcolom@imim.es %K bipolar disorder %K psychoeducation %K smartphone %K app %K SIMPLe %K Intervention %K mobile phone %D 2022 %7 2.2.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: SIMPLe is an internet‐delivered self‐management mobile app for bipolar disorder (BD) designed to combine technology with evidence-based interventions and facilitate access to psychoeducational content. The SIMPLe app was launched to the real world to make it available worldwide within the context of BD treatment. Objective: The main aims of this study are as follows: to describe app use, engagement, and retention rates based on server data; to identify patterns of user retention over the first 6-month follow-up of use; and to explore potential factors contributing to discontinuation of app use. Methods: This was an observational ecological study in which we pooled available data from a real-world implementation of the SIMPLe app. Participation was open on the project website, and the data-collection sources were a web-based questionnaire on clinical data and treatment history administered at inclusion and at 6 months, subjective data gathered through continuous app use, and the use patterns captured by the app server. Characteristics and engagement of regular users, occasional users, and no users were compared using 2-tailed t tests or analysis of variance or their nonparametric equivalent. Survival analysis and risk functions were applied to regular users’ data to examine and compare use and user retention. In addition, a user evaluation analysis was performed based on satisfaction, perceived usefulness, and reasons to discontinue app use. Results: We included 503 participants with data collected between 2016 and 2018, of whom 77.5% (n=390) used the app. Among the app users, 44.4% (173/390) completed the follow-up assessment, and data from these participants were used in our analyses. Engagement declined gradually over the first 6 months of use. The probability of retention of the regular users after 1 month of app use was 67.4% (263/390; 95% CI 62.7%-72.4%). Age (P=.002), time passed since illness onset (P<.001), and years since diagnosis of BD (P=.048) correlate with retention duration. In addition, participants who had been diagnosed with BD for longer used the app on more days (mean 97.73, SD 69.15 days; P=.002) than those who had had a more recent onset (mean 66.49, SD 66.18 days; P=.002) or those who had been diagnosed more recently (mean 73.45, SD 66 days; P=.01). Conclusions: The user retention rate of the app decreased rapidly after each month until reaching only one-third of the users at 6 months. There exists a strong association between age and app engagement of individuals with BD. Other variables such as years lived with BD, diagnosis of an anxiety disorder, and taking antipsychotics seem relevant as well. Understanding these associations can help in the definition of the most suitable user profiles for predicting trends of engagement, optimization of app prescription, and management. %M 35107440 %R 10.2196/31565 %U https://www.jmir.org/2022/2/e31565 %U https://doi.org/10.2196/31565 %U http://www.ncbi.nlm.nih.gov/pubmed/35107440 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 1 %P e24699 %T Acoustic and Facial Features From Clinical Interviews for Machine Learning–Based Psychiatric Diagnosis: Algorithm Development %A Birnbaum,Michael L %A Abrami,Avner %A Heisig,Stephen %A Ali,Asra %A Arenare,Elizabeth %A Agurto,Carla %A Lu,Nathaniel %A Kane,John M %A Cecchi,Guillermo %+ Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, 75-59 263rd St, Glen Oaks, NY, 11004, United States, 1 7184708305, mbirnbaum@northwell.edu %K audiovisual patterns %K speech analysis %K facial analysis %K psychiatry %K schizophrenia spectrum disorders %K bipolar disorder %K symptom prediction %K diagnostic prediction %K machine learning %K audiovisual %K speech %K schizophrenia %K spectrum disorders %D 2022 %7 24.1.2022 %9 Original Paper %J JMIR Ment Health %G English %X 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. %M 35072648 %R 10.2196/24699 %U https://mental.jmir.org/2022/1/e24699 %U https://doi.org/10.2196/24699 %U http://www.ncbi.nlm.nih.gov/pubmed/35072648 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e28647 %T Behavioral and Self-reported Data Collected From Smartphones for the Assessment of Depressive and Manic Symptoms in Patients With Bipolar Disorder: Prospective Observational Study %A Dominiak,Monika %A Kaczmarek-Majer,Katarzyna %A Antosik-Wójcińska,Anna Z %A Opara,Karol R %A Olwert,Anna %A Radziszewska,Weronika %A Hryniewicz,Olgierd %A Święcicki,Łukasz %A Wojnar,Marcin %A Mierzejewski,Paweł %+ Department of Pharmacology and Physiology of the Nervous System, Institute of Psychiatry and Neurology, Sobieskiego 9, Warsaw, 02-957, Poland, 48 507183375, mdominia@wp.pl %K bipolar disorder %K generalized linear model %K mixed-effects regression %K classification %K manic episodes %K depressive episodes %K smartphone %K behavioral markers %K mHealth %K remote monitoring %D 2022 %7 19.1.2022 %9 Original Paper %J J Med Internet Res %G English %X 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 (β=−.064, P=.01); (2) the number of incoming answered calls during depression was lower than that during euthymia (β=−.15, P=.01) and, concurrently, missed incoming calls were more frequent and increased as depressive symptoms intensified (β=4.431, P<.001; β=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (β=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to that in the euthymic state (β=3.53, P=.01) and positively correlated to the severity of symptoms (β=2.991, P=.02); (5) the variability of the duration of the outgoing calls was higher in manic/mixed states (β=.0012, P=.045) and positively correlated to the severity of symptoms (β=.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 (β=.031, P=.01; β=.015, P=.01; respectively) and positively correlated to the severity of manic symptoms (β=.116, P<.001; β=.022, P<.001; respectively). We also observed that self-assessment of mood was lower in depressive (β=−1.452, P<.001) and higher in manic states (β=.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. %M 34874015 %R 10.2196/28647 %U https://www.jmir.org/2022/1/e28647 %U https://doi.org/10.2196/28647 %U http://www.ncbi.nlm.nih.gov/pubmed/34874015 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 12 %P e32932 %T A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Protocol Development for an Expert System to Provide Adaptive User Feedback %A Goulding,Evan H %A Dopke,Cynthia A %A Michaels,Tania %A Martin,Clair R %A Khiani,Monika A %A Garborg,Christopher %A Karr,Chris %A Begale,Mark %+ Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Suite 7-102, 303 East Chicago Ave, Chicago, IL, 60611, United States, 1 312 503 1189, e-goulding@fsm.northwestern.edu %K adaptive %K personalized %K self-management %K smartphone %K behavioral intervention technology %K mHealth %K bipolar disorder %K depression %K mania %D 2021 %7 24.12.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Bipolar disorder is a severe mental illness that results in significant morbidity and mortality. While pharmacotherapy is the primary treatment, adjunctive psychotherapy can improve outcomes. However, access to therapy is limited. Smartphones and other technologies can increase access to therapeutic strategies that enhance self-management while simultaneously augmenting care by providing adaptive delivery of content to users as well as alerts to providers to facilitate clinical care communication. Unfortunately, while adaptive interventions are being developed and tested to improve care, information describing the components of adaptive interventions is often not published in sufficient detail to facilitate replication and improvement of these interventions. Objective: To contribute to and support the improvement and dissemination of technology-based mental health interventions, we provide a detailed description of the expert system for adaptively delivering content and facilitating clinical care communication for LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder. Methods: Information from empirically supported psychotherapies for bipolar disorder, health psychology behavior change theories, and chronic disease self-management models was combined with user-centered design data and psychiatrist feedback to guide the development of the expert system. Results: Decision points determining the timing of intervention option adaptation were selected to occur daily and weekly based on self-report data for medication adherence, sleep duration, routine, and wellness levels. These data were selected for use as the tailoring variables determining which intervention options to deliver when and to whom. Decision rules linking delivery of options and tailoring variable thresholds were developed based on existing literature regarding bipolar disorder clinical status and psychiatrist feedback. To address the need for treatment adaptation with varying clinical statuses, decision rules for a clinical status state machine were developed using self-reported wellness rating data. Clinical status from this state machine was incorporated into hierarchal decision tables that select content for delivery to users and alerts to providers. The majority of the adaptive content addresses sleep duration, medication adherence, managing signs and symptoms, building and utilizing support, and keeping a regular routine, as well as determinants underlying engagement in these target behaviors as follows: attitudes and perceptions, knowledge, support, evaluation, and planning. However, when problems with early warning signs, symptoms, and transitions to more acute clinical states are detected, the decision rules shift the adaptive content to focus on managing signs and symptoms, and engaging with psychiatric providers. Conclusions: Adaptive mental health technologies have the potential to enhance the self-management of mental health disorders. The need for individuals with bipolar disorder to engage in the management of multiple target behaviors and to address changes in clinical status highlights the importance of detailed reporting of adaptive intervention components to allow replication and improvement of adaptive mental health technologies for complex mental health problems. %M 34951598 %R 10.2196/32932 %U https://formative.jmir.org/2021/12/e32932 %U https://doi.org/10.2196/32932 %U http://www.ncbi.nlm.nih.gov/pubmed/34951598 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 12 %P e27991 %T The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review %A Majid,Shazmin %A Reeves,Stuart %A Figueredo,Grazziela %A Brown,Susan %A Lang,Alexandra %A Moore,Matthew %A Morriss,Richard %+ School of Computer Science, Horizon Centre for Doctoral Training, University of Nottingham, Computer Science Building, Wollaton Rd, Nottingham, NG8 1BB, United Kingdom, 44 7713508981, shazmin.majid1@nottingham.ac.uk %K user-centered design %K participatory design %K human-computer interaction %K patient and public involvement %K self-monitoring technology %K bipolar disorder %K mobile phone %D 2021 %7 20.12.2021 %9 Review %J JMIR Ment Health %G English %X 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. %M 34931992 %R 10.2196/27991 %U https://mental.jmir.org/2021/12/e27991 %U https://doi.org/10.2196/27991 %U http://www.ncbi.nlm.nih.gov/pubmed/34931992 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 12 %P e32450 %T The Life Goals Self-Management Mobile App for Bipolar Disorder: Consumer Feasibility, Usability, and Acceptability Study %A Ryan,Kelly A %A Smith,Shawna N %A Yocum,Anastasia K %A Carley,Isabel %A Liebrecht,Celeste %A Navis,Bethany %A Vest,Erica %A Bertram,Holli %A McInnis,Melvin G %A Kilbourne,Amy M %+ Department of Psychiatry, University of Michigan, 2101 Commonwealth Blvd, Suite C, Ann Arbor, MI, 48105, United States, 1 734 936 5524, karyan@umich.edu %K self-management %K app %K bipolar disorder %K symptom management %K mental health %K feasibility %K usability %K acceptability %K intervention %K bipolar %K coping %K survey %K engagement %D 2021 %7 13.12.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Life Goals is an evidence-based self-management intervention that assists individuals with bipolar disorder (BD) by aligning BD symptom coping strategies with their personal goals. The intervention can be availed via in-person and telephonic sessions, and it has been recently developed as an individualized, customizable mobile app. Objective: We examined the feasibility, usability, and acceptability of the Life Goals self-management app among individuals diagnosed with BD who used the app for up to 6 months. Methods: A total of 28 individuals with BD used the Life Goals app on their personal smartphone for 6 months. They completed key clinical outcome measurements of functioning, disability, and psychiatric symptoms at baseline, 3 months, and 6 months, in addition to a poststudy survey about usability and satisfaction. Results: Participants used the app for a median of 25 times (IQR 13-65.75), and for a longer time during the first 3 months of the study. The modules on depression and anxiety were the most frequently used, accounting for 35% and 22% of total usage, respectively. Overall, the study participants found the app useful (15/25, 60%) and easy to use (18/25, 72%), and they reported that the screen displayed the material adequately (22/25, 88%). However, less than half of the participants found the app helpful in managing their health (10/25, 40%) or in making progress on their wellness goals (9/25, 36%). Clinical outcomes showed a trend for improvements in mental and physical health and mania-related well-being. Conclusions: The Life Goals app showed feasibility of use among individuals with BD. Higher user engagement was observed in the initial 3 months with users interested more frequently in the mood modules than other wellness modules. Participants reported acceptability with the ease of app use and satisfaction with the app user interface, but the app showed low success in encouraging self-management within this small sample. The Life Goals app is a mobile health technology that can provide individuals with serious mental illness with more flexible access to evidence-based treatments. %M 34898452 %R 10.2196/32450 %U https://formative.jmir.org/2021/12/e32450 %U https://doi.org/10.2196/32450 %U http://www.ncbi.nlm.nih.gov/pubmed/34898452 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 11 %P e32306 %T A Smartphone-Based Self-Management Intervention for Individuals with Bipolar Disorder (LiveWell): Qualitative Study on User Experiences of the Behavior Change Process %A Jonathan,Geneva K %A Dopke,Cynthia A %A Michaels,Tania %A Martin,Clair R %A Ryan,Chloe %A McBride,Alyssa %A Babington,Pamela %A Goulding,Evan H %+ Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Dr Suite 1520, Chicago, IL, 60660, United States, 1 312 503 1189, e-goulding@fsm.northwestern.edu %K behavioral intervention technology %K mHealth %K bipolar disorder %K depression %K illness management %K smartphone %K behavior change %K early warning signs %K self-management %K qualitative %K behavior %K intervention %K management %K user experience %K perception %K utilization %D 2021 %7 22.11.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Bipolar disorder is a severe mental illness characterized by recurrent episodes of depressed, elevated, and mixed mood states. The addition of psychotherapy to pharmacological management can decrease symptoms, lower relapse rates, and improve quality of life; however, access to psychotherapy is limited. Mental health technologies such as smartphone apps are being studied as a means to increase access to and enhance the effectiveness of adjunctive psychotherapies for bipolar disorder. Individuals with bipolar disorder find this intervention format acceptable, but our understanding of how people utilize and integrate these tools into their behavior change and maintenance processes remains limited. Objective: The objective of this study was to explore how individuals with bipolar disorder perceive and utilize a smartphone intervention for health behavior change and maintenance. Methods: Individuals with bipolar disorder were recruited via flyers placed at university-affiliated and private outpatient mental health practices to participate in a pilot study of LiveWell, a smartphone-based self-management intervention. At the end of the study, all participants completed in-depth qualitative exit interviews. The behavior change framework developed to organize the intervention design was used to deductively code behavioral targets and determinants involved in target engagement. Inductive coding was used to identify themes not captured by this framework. Results: In terms of behavioral targets, participants emphasized the importance of managing mood episode–related signs and symptoms. They also discussed the importance of maintaining regular routines, sleep duration, and medication adherence. Participants emphasized that receiving support from a coach as well as seeking and receiving assistance from family, friends, and providers were important for managing behavioral targets and staying well. In terms of determinants, participants stressed the important role of monitoring for their behavior change and maintenance efforts. Monitoring facilitated self-awareness and reflection, which was considered valuable for staying well. Some participants also felt that the intervention facilitated learning information necessary for managing bipolar disorder but others felt that the information provided was too basic. Conclusions: In addition to addressing acceptability, satisfaction, and engagement, a person-based design of mental health technologies can be used to understand how people experience the impact of these technologies on their behavior change and maintenance efforts. This understanding may then be used to guide ongoing intervention development. The participants’ perceptions aligned with the intervention’s primary behavioral targets and use of a monitoring tool as a core intervention feature. Participant feedback further indicates that developing additional content and tools to address building and engaging social support may be an important avenue for improving LiveWell. A comprehensive behavior change framework to understand participant perceptions of their behavior change and maintenance efforts may help facilitate ongoing intervention development. %M 34813488 %R 10.2196/32306 %U https://mental.jmir.org/2021/11/e32306 %U https://doi.org/10.2196/32306 %U http://www.ncbi.nlm.nih.gov/pubmed/34813488 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e29749 %T The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review %A Jan,Zainab %A AI-Ansari,Noor %A Mousa,Osama %A Abd-alrazaq,Alaa %A Ahmed,Arfan %A Alam,Tanvir %A Househ,Mowafa %+ Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Education City, AI Luqta St AI-Rayyan, Doha, 5825, Qatar, 974 55708549, mhouseh@hbku.edu.qa %K machine learning %K bipolar disorder %K diagnosis %K support vector machine %K clinical data %K mental health %K scoping review %D 2021 %7 19.11.2021 %9 Review %J J Med Internet Res %G English %X Background: Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD. Objective: This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes. Methods: The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed. Results: We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%. Conclusions: This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry. %M 34806996 %R 10.2196/29749 %U https://www.jmir.org/2021/11/e29749 %U https://doi.org/10.2196/29749 %U http://www.ncbi.nlm.nih.gov/pubmed/34806996 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e29201 %T A Smartphone Intervention for People With Serious Mental Illness: Fully Remote Randomized Controlled Trial of CORE %A Ben-Zeev,Dror %A Chander,Ayesha %A Tauscher,Justin %A Buck,Benjamin %A Nepal,Subigya %A Campbell,Andrew %A Doron,Guy %+ Behavioral Research in Technology and Engineering Center, Department of Psychiatry and Behavioral Sciences, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, United States, 1 206 685 9655, dbenzeev@uw.edu %K mobile health %K schizophrenia %K bipolar disorder %K depression %K mobile phone %D 2021 %7 12.11.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: People with serious mental illness (SMI) have significant unmet mental health needs. Development and testing of digital interventions that can alleviate the suffering of people with SMI is a public health priority. Objective: The aim of this study is to conduct a fully remote randomized waitlist-controlled trial of CORE, a smartphone intervention that comprises daily exercises designed to promote reassessment of dysfunctional beliefs in multiple domains. Methods: Individuals were recruited via the web using Google and Facebook advertisements. Enrolled participants were randomized into either active intervention or waitlist control groups. Participants completed the Beck Depression Inventory-Second Edition (BDI-II), Generalized Anxiety Disorder-7 (GAD-7), Hamilton Program for Schizophrenia Voices, Green Paranoid Thought Scale, Recovery Assessment Scale (RAS), Rosenberg Self-Esteem Scale (RSES), Friendship Scale, and Sheehan Disability Scale (SDS) at baseline (T1), 30-day (T2), and 60-day (T3) assessment points. Participants in the active group used CORE from T1 to T2, and participants in the waitlist group used CORE from T2 to T3. Both groups completed usability and accessibility measures after they concluded their intervention periods. Results: Overall, 315 individuals from 45 states participated in this study. The sample comprised individuals with self-reported bipolar disorder (111/315, 35.2%), major depressive disorder (136/315, 43.2%), and schizophrenia or schizoaffective disorder (68/315, 21.6%) who displayed moderate to severe symptoms and disability levels at baseline. Participants rated CORE as highly usable and acceptable. Intent-to-treat analyses showed significant treatment×time interactions for the BDI-II (F1,313=13.38; P<.001), GAD-7 (F1,313=5.87; P=.01), RAS (F1,313=23.42; P<.001), RSES (F1,313=19.28; P<.001), and SDS (F1,313=10.73; P=.001). Large effects were observed for the BDI-II (d=0.58), RAS (d=0.61), and RSES (d=0.64); a moderate effect size was observed for the SDS (d=0.44), and a small effect size was observed for the GAD-7 (d=0.20). Similar changes in outcome measures were later observed in the waitlist control group participants following crossover after they received CORE (T2 to T3). Approximately 41.5% (64/154) of participants in the active group and 60.2% (97/161) of participants in the waitlist group were retained at T2, and 33.1% (51/154) of participants in the active group and 40.3% (65/161) of participants in the waitlist group were retained at T3. Conclusions: We successfully recruited, screened, randomized, treated, and assessed a geographically dispersed sample of participants with SMI entirely via the web, demonstrating that fully remote clinical trials are feasible in this population; however, study retention remains challenging. CORE showed promise as a usable, acceptable, and effective tool for reducing the severity of psychiatric symptoms and disability while improving recovery and self-esteem. Rapid adoption and real-world dissemination of evidence-based mobile health interventions such as CORE are needed if we are to shorten the science-to-service gap and address the significant unmet mental health needs of people with SMI during the COVID-19 pandemic and beyond. Trial Registration: ClinicalTrials.gov NCT04068467; https://clinicaltrials.gov/ct2/show/NCT04068467 %M 34766913 %R 10.2196/29201 %U https://www.jmir.org/2021/11/e29201 %U https://doi.org/10.2196/29201 %U http://www.ncbi.nlm.nih.gov/pubmed/34766913 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 10 %P e29764 %T Digital Health Literacy in Bipolar Disorder: International Web-Based Survey %A Morton,Emma %A Ho,Kendall %A Barnes,Steven J %A Michalak,Erin E %+ Department of Psychiatry, University of British Columbia, 420-5950 University Blvd, Vancouver, BC, V6T 1Z3, Canada, 1 604 827 3393, erin.michalak@ubc.ca %K eHealth %K health literacy %K bipolar disorder %K self-management %D 2021 %7 19.10.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Web-based resources can support people with bipolar disorder (BD) to improve their knowledge and self-management. However, publicly available resources are heterogeneous in terms of their quality and ease of use. Characterizing digital health literacy (the skillset that enable people to navigate and make use of health information in a web-based context) in BD will support the development of educational resources. Objective: The aim of this study was to develop understanding of digital health literacy and its predictors in people with BD. Methods: A web-based survey was used to explore self-reported digital health literacy (as measured by the e-Health Literacy Scale [eHEALS]) in people with BD. Multiple regression analysis was used to evaluate potential predictors, including demographic/clinical characteristics and technology use. Results: A total of 919 respondents (77.9% female; mean age 36.9 years) completed the survey. Older age (β=0.09; P=.01), postgraduate education (β=0.11; P=.01), and current use of self-management apps related to BD (β=0.13; P<.001) were associated with higher eHEALS ratings. Conclusions: Levels of self-reported digital health literacy were comparable or higher than other studies in the general population and specific physical/mental health conditions. However, individuals with BD who are younger, have completed less education, or are less familiar with mental health apps may require extra support to safely and productively navigate web-based health resources. Relevant educational initiatives are discussed. Future studies should evaluate skill development interventions for less digitally literate groups. %M 34665143 %R 10.2196/29764 %U https://mental.jmir.org/2021/10/e29764 %U https://doi.org/10.2196/29764 %U http://www.ncbi.nlm.nih.gov/pubmed/34665143 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 9 %P e25168 %T Combined Use of Web-Based and In-Person Education on Ill Health Self-management Skills in Adults With Bipolar Disorder: Protocol for a Mixed Methods Study %A Hatzioannou,Anna %A Chatzittofis,Andreas %A Koutroubas,Virginia Sunday %A Papastavrou,Evridiki %A Karanikola,Maria %+ Nursing Department, School of Health Sciences, Cyprus University of Technology, 15 Vragadinou Street, Limassol, 3041, Cyprus, 357 99786069, maria.karanikola@cut.ac.cy %K education %K empowerment %K bipolar disorders %K self-management %K bipolar %K mental health %D 2021 %7 8.9.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Addressing the enhancement of ill health self-management skills in adults diagnosed with bipolar disorder may be considered an important intervention for health care systems worldwide. Objective: This protocol describes the study “Management of my Bipolarity” (MoB), which aims to develop an educational intervention for adults with bipolar disorder and assess its effectiveness. The objectives include (a) a literature review on bipolar disorder educational interventions; (b) a qualitative exploration of the educational needs of people with bipolar disorder; (c) development of an educational intervention based on objectives (a) and (b) (ie, the MoB educational intervention); and (d) exploration of the effectiveness of the intervention regarding participants’ knowledge of their mental health condition and enhancement of their ill health self-management skills. The MoB educational intervention will consist of an in-person and a web-based intervention in the form of a digital platform. Methods: The proposed interventional study is a combination of a qualitative and a quantitative design (mixed methods study). A focus group and content analysis will be implemented for the qualitative assessment of the educational needs of adults with bipolar disorder. The intervention will be developed based on the qualitative data of the study and relevant literature. The effectiveness of the acquired knowledge and self-management skills will be assessed according to (a) substance use behavior, (b) health locus of control, (c) impulse control, (d) adherence to pharmacotherapy, (e) relapse prevention, (f) improvement of quality of life, and (g) bipolar disorder knowledge level via structured instruments in the quantitative part of the study using descriptive and inferential statistics (SPSS version 24.0). Results: A total of 13 patients with bipolar disorder have been interviewed (8 women, 5 men) to identify educational needs to be covered through the intervention. Moreover, a literature review on bipolar disorder educational interventions has been completed. These data have been incorporated in the design of the MoB in-person intervention and the digital platform. The digital platform is live, and the development of the MoB in-person intervention was completed at the end of 2020. The recruitment of the participants for the intervention (40 patients) and the control group (40 patients) began during the first semester of 2021. Moreover, by tracking the platform for 1.5 years, we have recorded that 2180 users have visited the platform with an average session duration of almost 2 minutes. Mobile and tablet devices are being used by 70% of the visitors. Conclusions: Since new parameters regarding educational interventions will be explored, these findings are expected to provide evidence that participation in structured educational interventions offers patients the opportunity to improve adherence to pharmacotherapy and increase their quality of life. Trial Registration: ClinicalTrials.gov NCT04643210; https://clinicaltrials.gov/ct2/show/NCT04643210 International Registered Report Identifier (IRRID): DERR1-10.2196/25168 %M 34494969 %R 10.2196/25168 %U https://www.researchprotocols.org/2021/9/e25168 %U https://doi.org/10.2196/25168 %U http://www.ncbi.nlm.nih.gov/pubmed/34494969 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 9 %P e30472 %T Ecological Momentary Assessment of Bipolar Disorder Symptoms and Partner Affect: Longitudinal Pilot Study %A Yerushalmi,Mor %A Sixsmith,Andrew %A Pollock Star,Ariel %A King,David B %A O'Rourke,Norm %+ Department of Public Health and Multidisciplinary Center for Research on Aging, Ben-Gurion University of the Negev, P O Box 653, Be'er Sheva, 8410501, Israel, 972 549901808, ORourke@bgu.ac.il %K bipolar disorder %K couples %K dyadic analyses %K ecological momentary assessment %K EMA %K bipolar disorder %K partner %K relationships %K mHealth %K mobile apps %K mental health %K depression %K BPD %K mood %D 2021 %7 2.9.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The World Health Organization ranks bipolar disorder (BD) as the 7th leading cause of disability. Although the effects on those with BD are well described, less is reported on the impact of BD on cohabiting partners or any interactions between the two; this requires in vivo data collection measured each day over several months. Objective: We set out to demonstrate the utility of ecological momentary assessment with BD couples measured using yoked smartphone apps. When randomly prompted over time, we assumed distinct patterns of association would emerge between BD symptoms (both depression and hypo/mania) and partner mood (positive and negative affect). Methods: For this pilot study, we recruited an international sample of young and older adults with BD and their cohabiting partners where available. Both participants and partners downloaded separate apps onto their respective smartphones. Within self-specified “windows of general availability,” participants with BD were randomly prompted to briefly report symptoms of depression and hypo/mania (ie, BDSx), positive and negative mood (ie, POMS-15; partners), and any important events of the day (both). The partner app was yoked to the participant app so that the former was prompted roughly 30 minutes after the participant with BD or the next morning if outside the partner’s specified availability. Results: Four couples provided 312 matched BD symptom and partner mood responses over an average of 123 days (range 65-221 days). Both were GPS- and time-stamped (mean 3:11 hrs between questionnaires, SD 4:51 hrs). Total depression had a small but significant association with positive (r=–.14; P=.02) and negative partner affect (r=.15; P=.01]. Yet total hypo/mania appeared to have no association with positive partner affect (r=–.01; P=.87); instead, negative partner affect was significantly correlated with total hypo/mania (r=.26; P=.01). However, when we look specifically at BD factors, we see that negative partner affect is associated only with affrontive symptoms of hypo/mania (r=.38; P=.01); elation or loss of insight appears unrelated to either positive (r=.10; P=.09) or negative partner affect (r=.02; P=.71). Yet affrontive symptoms of hypo/mania were significantly correlated with negative affect, but only when couples were together (r=.41; P=.01), not when apart (r=.22; P=.12). That is, these angry interpersonal symptoms of hypo/mania appear to be experienced most negatively by spouses when couples are together. Conclusions: These initial findings demonstrate the utility of in vivo ambulatory data collection in longitudinal mental health research. Preliminary analyses suggest different BD symptoms are associated with negative and positive partner mood. These negative effects appear greater for hypo/mania than depressive symptoms, but proximity to the person with BD is important. %M 34473069 %R 10.2196/30472 %U https://formative.jmir.org/2021/9/e30472 %U https://doi.org/10.2196/30472 %U http://www.ncbi.nlm.nih.gov/pubmed/34473069 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 8 %P e23360 %T Development and Evaluation of Acceptability and Feasibility of a Web-Based Intervention for Patients With Bipolar Disorder in Iran: Implementation Study %A Ashrafi,Agaah %A Tabatabaee,Maryam %A Sharifi,Vandad %+ Department of Psychiatry, Roozbeh Hospital, Tehran University of Medical Sciences, South Kargar Avenue, Roozbeh Hospital, Tehran, Iran, 98 9123875869, vsharifi@tums.ac.ir %K bipolar disorder %K psychoeducation %K web-based intervention %K feasibility %K acceptability %D 2021 %7 17.8.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Psychoeducation for bipolar disorder has a significant impact on symptoms and treatment adherence. In Iran, as a low-resource setting, infrastructural barriers, such as inadequate mental health professionals, difficulties in transportation, and costs of care, may hinder optimum delivery of this evidence-based intervention to patients. Objective: This study sought to explore the acceptability and feasibility of a web-based intervention for bipolar patients in Iran. Methods: A website has been developed as a platform for providing psychoeducational content about bipolar disorder. Patients were chosen via a convenient sampling method in 2018-2019. The main component of the intervention included streaming 7 weekly video clips after attending a single in-person meeting, as well as a medication self-monitoring application. Information was collected about the feasibility and acceptability of the intervention. Results: We invited 45 patients from the day center and the outpatient clinic of Roozbeh psychiatric hospital and some private clinics in Tehran. Of the 23 patients (51%) who attended the first in-person session and provided informed consent, 14 patients dropped out during the study. While 9 patients completed the course (attended 4 or more online sessions), only 5 watched all the video sessions. The rate of adherence to the intervention and frequency of exposure to the website were much higher for those recruited from the private and outpatient clinics. Conclusions: This web-based intervention can be feasible and acceptable only for a subgroup of patients with specific educational status and socioeconomic level. %M 34402794 %R 10.2196/23360 %U https://formative.jmir.org/2021/8/e23360 %U https://doi.org/10.2196/23360 %U http://www.ncbi.nlm.nih.gov/pubmed/34402794 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 4 %P e24522 %T Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study %A Nestsiarovich,Anastasiya %A Kumar,Praveen %A Lauve,Nicolas Raymond %A Hurwitz,Nathaniel G %A Mazurie,Aurélien J %A Cannon,Daniel C %A Zhu,Yiliang %A Nelson,Stuart James %A Crisanti,Annette S %A Kerner,Berit %A Tohen,Mauricio %A Perkins,Douglas J %A Lambert,Christophe Gerard %+ Center for Global Health, Department of Internal Medicine, University of New Mexico Health Sciences Center, 915 Camino de Salud NE, Albuquerque, NM, United States, 1 5052729709, cglambert@unm.edu %K bipolar %K mood %K mania %K depression %K pharmacotherapy %K self-harm %K suicide %K machine learning %K psychotherapy %D 2021 %7 21.4.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Incomplete suicidality coding in administrative claims data is a known obstacle for observational studies. With most of the negative outcomes missing from the data, it is challenging to assess the evidence on treatment strategies for the prevention of self-harm in bipolar disorder (BD), including pharmacotherapy and psychotherapy. There are conflicting data from studies on the drug-dependent risk of self-harm, and there is major uncertainty regarding the preventive effect of monotherapy and drug combinations. Objective: The aim of this study was to compare all commonly used BD pharmacotherapies, as well as psychotherapy for the risk of self-harm, in a large population of commercially insured individuals, using self-harm imputation to overcome the known limitations of this outcome being underrecorded within US electronic health care records. Methods: The IBM MarketScan administrative claims database was used to compare self-harm risk in patients with BD following 65 drug regimens and drug-free periods. Probable but uncoded self-harm events were imputed via machine learning, with different probability thresholds examined in a sensitivity analysis. Comparators included lithium, mood-stabilizing anticonvulsants (MSAs), second-generation antipsychotics (SGAs), first-generation antipsychotics (FGAs), and five classes of antidepressants. Cox regression models with time-varying covariates were built for individual treatment regimens and for any pharmacotherapy with or without psychosocial interventions (“psychotherapy”). Results: Among 529,359 patients, 1.66% (n=8813 events) had imputed and/or coded self-harm following the exposure of interest. A higher self-harm risk was observed during adolescence. After multiple testing adjustment (P≤.012), the following six regimens had higher risk of self-harm than lithium: tri/tetracyclic antidepressants + SGA, FGA + MSA, FGA, serotonin-norepinephrine reuptake inhibitor (SNRI) + SGA, lithium + MSA, and lithium + SGA (hazard ratios [HRs] 1.44-2.29), and the following nine had lower risk: lamotrigine, valproate, risperidone, aripiprazole, SNRI, selective serotonin reuptake inhibitor (SSRI), “no drug,” bupropion, and bupropion + SSRI (HRs 0.28-0.74). Psychotherapy alone (without medication) had a lower self-harm risk than no treatment (HR 0.56, 95% CI 0.52-0.60; P=8.76×10-58). The sensitivity analysis showed that the direction of drug-outcome associations did not change as a function of the self-harm probability threshold. Conclusions: Our data support evidence on the effectiveness of antidepressants, MSAs, and psychotherapy for self-harm prevention in BD. Trial Registration: ClinicalTrials.gov NCT02893371; https://clinicaltrials.gov/ct2/show/NCT02893371 %M 33688834 %R 10.2196/24522 %U https://mental.jmir.org/2021/4/e24522 %U https://doi.org/10.2196/24522 %U http://www.ncbi.nlm.nih.gov/pubmed/33688834 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 4 %P e20424 %T A Smartphone-Based Self-management Intervention for Bipolar Disorder (LiveWell): User-Centered Development Approach %A Jonathan,Geneva K %A Dopke,Cynthia A %A Michaels,Tania %A Bank,Andrew %A Martin,Clair R %A Adhikari,Krina %A Krakauer,Rachel L %A Ryan,Chloe %A McBride,Alyssa %A Babington,Pamela %A Frauenhofer,Ella %A Silver,Jamilah %A Capra,Courtney %A Simon,Melanie %A Begale,Mark %A Mohr,David C %A Goulding,Evan H %+ Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States, 1 3125031189, e-goulding@fsm.northwestern.edu %K behavioral intervention technology %K mHealth %K bipolar disorder %K depression %K illness management %K smartphone %K behavior change %K early warning signs %K self-management %K qualitative %D 2021 %7 12.4.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Bipolar disorder is a serious mental illness that results in significant morbidity and mortality. Pharmacotherapy is the primary treatment for bipolar disorder; however, adjunctive psychotherapy can help individuals use self-management strategies to improve outcomes. Yet access to this therapy is limited. Smartphones and other technologies have the potential to increase access to therapeutic strategies that enhance self-management while simultaneously providing real-time user feedback and provider alerts to augment care. Objective: This paper describes the user-centered development of LiveWell, a smartphone-based self-management intervention for bipolar disorder, to contribute to and support the ongoing improvement and dissemination of technology-based mental health interventions. Methods: Individuals with bipolar disorder first participated in a field trial of a simple smartphone app for self-monitoring of behavioral targets. To develop a complete technology-based intervention for bipolar disorder, this field trial was followed by design sessions, usability testing, and a pilot study of a smartphone-based self-management intervention for bipolar disorder. Throughout all phases of development, intervention revisions were made based on user feedback. Results: The core of the LiveWell intervention consists of a daily self-monitoring tool, the Daily Check-in. This self-monitoring tool underwent multiple revisions during the user-centered development process. Daily Check-in mood and thought rating scales were collapsed into a single wellness rating scale to accommodate user development of personalized scale anchors. These anchors are meant to assist users in identifying early warning signs and symptoms of impending episodes to take action based on personalized plans. When users identified personal anchors for the wellness scale, the anchors most commonly reflected behavioral signs and symptoms (40%), followed by cognitive (25%), mood (15%), physical (10%), and motivational (7%) signs and symptoms. Changes to the Daily Check-in were also made to help users distinguish between getting adequate sleep and keeping a regular routine. At the end of the pilot study, users reported that the Daily Check-in made them more aware of early warning signs and symptoms and how much they were sleeping. Users also reported that they liked personalizing their anchors and plans and felt this process was useful. Users experienced some difficulties with developing, tracking, and achieving target goals. Users also did not consistently follow up with app recommendations to contact providers when Daily Check-in data suggested they needed additional assistance. As a result, the human support roles for the technology were expanded beyond app use support to include support for self-management and clinical care communication. The development of these human support roles was aided by feedback on the technology's usability from the users and the coaches who provided the human support. Conclusions: User input guided the development of intervention content, technology, and coaching support for LiveWell. Users valued the provision of monitoring tools and the ability to personalize plans for staying well, supporting the role of monitoring and personalization as important features of digital mental health technologies. Users also valued human support of the technology in the form of a coach, and user difficulties with aspects of self-management and care-provider communication led to an expansion of the coach's support roles. Obtaining feedback from both users and coaches played an important role in the development of both the LiveWell technology and human support. Attention to all stakeholders involved in the use of mental health technologies is essential for optimizing intervention development. %M 33843607 %R 10.2196/20424 %U https://mental.jmir.org/2021/4/e20424 %U https://doi.org/10.2196/20424 %U http://www.ncbi.nlm.nih.gov/pubmed/33843607 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 3 %P e25810 %T Development of Coaching Support for LiveWell: A Smartphone-Based Self-Management Intervention for Bipolar Disorder %A Dopke,Cynthia A %A McBride,Alyssa %A Babington,Pamela %A Jonathan,Geneva K %A Michaels,Tania %A Ryan,Chloe %A Duffecy,Jennifer %A Mohr,David C %A Goulding,Evan H %+ Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, 303 E Chicago Ave., Suite 7-102, Chicago, IL, 60611, United States, 1 3125031189, e-goulding@fsm.northwestern.edu %K human support %K adherence %K self-management %K behavior change %K mHealth %K bipolar disorder %D 2021 %7 24.3.2021 %9 Viewpoint %J JMIR Form Res %G English %X Despite effective pharmacological treatment, bipolar disorder is a leading cause of disability due to recurrence of episodes, long episode durations, and persistence of interepisode symptoms. While adding psychotherapy to pharmacotherapy improves outcomes, the availability of adjunctive psychotherapy is limited. To extend the accessibility and functionality of psychotherapy for bipolar disorder, we developed LiveWell, a smartphone-based self-management intervention. Unfortunately, many mental health technology interventions suffer from high attrition rates, with users rapidly failing to maintain engagement with the intervention technology. Human support reduces this commonly observed engagement problem but does not consistently improve clinical and recovery outcomes. To facilitate ongoing efforts to develop human support for digital mental health technologies, this paper describes the design decisions, theoretical framework, content, mode, timing of delivery, and the training and supervision for coaching support of the LiveWell technology. This support includes clearly defined and structured roles that aim to encourage the use of the technology, self-management strategies, and communication with care providers. A clear division of labor is established between the coaching support roles and the intervention technology to allow lay personnel to serve as coaches and thereby maximize accessibility to the LiveWell intervention. %M 33759798 %R 10.2196/25810 %U https://formative.jmir.org/2021/3/e25810 %U https://doi.org/10.2196/25810 %U http://www.ncbi.nlm.nih.gov/pubmed/33759798 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 2 %P e14378 %T A Brief Mobile-Augmented Suicide Prevention Intervention for People With Psychotic Disorders in Transition From Acute to Ongoing Care: Protocol for a Pilot Trial %A Depp,Colin %A Ehret,Blaire %A Villa,Jennifer %A Perivoliotis,Dimitri %A Granholm,Eric %+ Department of Psychiatry, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093-0603, United States, 1 858 822 4251, cdepp@ucsd.edu %K prevention %K mental health services %K psychosis %K technology %D 2021 %7 8.2.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: People with serious mental illnesses (SMIs) are at exceptionally high risk for lifetime suicidal ideation and behavior compared with the general population. The transition period between urgent evaluation and ongoing care could provide an important setting for brief suicide-specific interventions for SMIs. To address this concern, this trial, SafeTy and Recovery Therapy (START), involves a brief suicide-specific cognitive behavioral intervention for SMIs that is augmented with mobile phone interactions. Objective: The primary aim of this pilot trial is to evaluate the feasibility, acceptability, and preliminary effectiveness of the intervention. Methods: A 6-month pilot trial with 70 participants with a diagnosis of bipolar disorder, schizophrenia or schizoaffective disorder, and current active suicidal ideation were randomized to START or START with mobile augmentation. START consists of 4 weekly sessions addressing early warning signs and triggers, symptoms influencing suicidal thinking, and social relationships. Recovery planning is followed by biweekly telephone coaching. START with mobile augmentation includes personalized automated cognitive behavioral therapy scripts that build from in-person content. Participants were evaluated at baseline, 4 weeks (end of in-person sessions), 12 weeks (end of telephone coaching), and 24 weeks. In addition to providing point estimates of feasibility and acceptability, the primary outcome of the trial was the change in severity of suicidal ideation as measured with the Scale for Suicide Ideation (SSI) and secondary outcome included the rate of outpatient engagement. Results: The trial is ongoing. Feasibility and acceptability across conditions will be assessed using t tests or Mann-Whitney tests or chi-square tests. The reduction of SSI over time will be assessed using hierarchical linear models. Conclusions: The design considerations and results of this trial may be informative for adapted suicide prevention in psychotic disorders in applied community settings. Trial Registration: ClinicalTrials.gov NCT03198364; http://clinicaltrials.gov/ct2/show/NCT03198364 International Registered Report Identifier (IRRID): DERR1-10.2196/14378 %M 33555265 %R 10.2196/14378 %U https://www.researchprotocols.org/2021/2/e14378 %U https://doi.org/10.2196/14378 %U http://www.ncbi.nlm.nih.gov/pubmed/33555265 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 1 %P e13770 %T Mobile App–Based Self-Report Questionnaires for the Assessment and Monitoring of Bipolar Disorder: Systematic Review %A Chan,Eric C %A Sun,Yuting %A Aitchison,Katherine J %A Sivapalan,Sudhakar %+ Department of Psychiatry, University of Alberta, 1E1 Walter Mackenzie Health Sciences Center, 8440 112 St NW, Edmonton, AB, T6G 2B7, Canada, 1 7802332709, echantai@ualberta.ca %K mobile apps %K mental health %K bipolar disorder %K smartphone %K cell phone %D 2021 %7 8.1.2021 %9 Review %J JMIR Form Res %G English %X Background: Bipolar disorder is a chronic, progressive illness characterized by recurrent episodes of mania and depression. Self-report scales have historically played a significant role in the monitoring of bipolar symptoms. However, these tools rely on episodic memory, which can be unreliable and do not allow the clinician to monitor brief episodic symptoms or the course of symptoms over shorter periods of time. Mobile app–based questionnaires have been suggested as a tool to improve monitoring of patients with bipolar disorder. Objective: This paper aims to determine the feasibility and validity of mobile app–based self-report questionnaires. Methods: We performed a systematic review of the literature according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PubMed, PsycInfo, Web of Science, Ovid MEDLINE, and EMBASE databases were searched for papers published in English that assessed adherence to and the validity of mobile app–based self-report questionnaires. Relevant studies published from database creation to May 22, 2020, were identified, and results examining the validity of and rates of adherence to app-based self-report questionnaires are reported. Results: A total of 13 records were identified for inclusion in this review. Of these studies, 4 assessed the concurrent validity of mobile app–based self-report tools, with the majority of findings indicating significant associations between data collected using these tools and the Young Mania Rating Scale, Hamilton Depression Rating Scale-17, or Montgomery-Åsberg Depression Rating Scale (P<.001 to P=.24). Three studies comparing the variability or range of symptoms between patients with bipolar disorder and healthy controls suggested that these data are capable of differentiating between known groups. Two studies demonstrated statistically significant associations between data collected via mobile app–based self-report tools and instruments assessing other clinically important factors. Adherence rates varied across the studies examined. However, good adherence rates (>70%) were observed in all but 1 study using a once-daily assessment. There was a wide range of adherence rates observed in studies using twice-daily assessments (42%-95%). Conclusions: These findings suggest that mobile app–based self-report tools are valid in the assessment of symptoms of mania and depression in euthymic patients with bipolar disorder. Data collected using these tools appear to differ between patients with bipolar disorder and healthy controls and are significantly associated with other clinically important measures. It is unclear at this time whether these tools can be used to detect acute episodes of mania or depression in patients with bipolar disorder. Adherence data indicate that patients with bipolar disorder show good adherence to self-report assessments administered daily for the duration of the study periods evaluated. %M 33416510 %R 10.2196/13770 %U https://formative.jmir.org/2021/1/e13770 %U https://doi.org/10.2196/13770 %U http://www.ncbi.nlm.nih.gov/pubmed/33416510 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 9 %P e19476 %T A Smartphone App to Monitor Mood Symptoms in Bipolar Disorder: Development and Usability Study %A Ryan,Kelly Ann %A Babu,Pallavi %A Easter,Rebecca %A Saunders,Erika %A Lee,Andy Jinseok %A Klasnja,Predrag %A Verchinina,Lilia %A Micol,Valerie %A Doil,Brent %A McInnis,Melvin G %A Kilbourne,Amy M %+ Department of Psychiatry, University of Michigan, 2101 Commonwealth Blvd, Suite C, Ann Arbor, MI, 49105, United States, 1 734 936 5524, karyan@umich.edu %K bipolar disorder %K momentary assessment %K mood %K mobile phone %K mobile app %D 2020 %7 22.9.2020 %9 Original Paper %J JMIR Ment Health %G English %X Background: There is considerable scientific interest in finding new and innovative ways to capture rapid fluctuations in functioning within individuals with bipolar disorder (BD), a severe, recurrent mental disorder associated with frequent shifts in symptoms and functioning. The use of smartphones can provide valid and real-world tools for use in measurement-based care and could be used to inform more personalized treatment options for this group, which can improve standard of care. Objective: We examined the feasibility and usability of a smartphone to capture daily fluctuations in mood within BD and to relate daily self-rated mood to smartphone use behaviors indicative of psychomotor activity or symptoms of the illness. Methods: Participants were 26 individuals with BD and 12 healthy control individuals who were recruited from the Prechter Longitudinal Study of BD. All were given a smartphone with a custom-built app and prompted twice a day to complete questions of mood for 28 days. The app automatically and unobtrusively collected phone usage data. A poststudy satisfaction survey was also completed. Results: Our sample showed a very high adherence rate to the daily momentary assessments (91% of the 58 prompts completed). Multivariate mixed effect models showed that an increase in rapid thoughts over time was associated with a decrease in outgoing text messages (β=–.02; P=.04), and an increase in impulsivity self-ratings was related to a decrease in total call duration (β=–.29; P=.02). Participants generally reported positive experiences using the smartphone and completing daily prompts. Conclusions: Use of mobile technology shows promise as a way to collect important clinical information that can be used to inform treatment decision making and monitor outcomes in a manner that is not overly burdensome to the patient or providers, highlighting its potential use in measurement-based care. %M 32960185 %R 10.2196/19476 %U http://mental.jmir.org/2020/9/e19476/ %U https://doi.org/10.2196/19476 %U http://www.ncbi.nlm.nih.gov/pubmed/32960185 %0 Journal Article %@ 2152-7202 %I JMIR Publications %V 12 %N 3 %P e19475 %T Engaging Youth in the Bipolar Youth Action Project: Community-Based Participatory Research %A Lapadat,Laura %A Balram,Anusha %A Cheek,Joanna %A Canas,Eugenia %A Paquette,Andrea %A , %A Michalak,Erin E %+ Department of Psychiatry, University of British Columbia, 420-5950 University Blvd, Vancouver, BC, V6T 1Z3, Canada, 1 604 827 3393, erin.michalak@ubc.ca %K community-based participatory research %K bipolar disorder %K adolescent %K young adult %K youth %K participatory research %D 2020 %7 10.9.2020 %9 Original Paper %J J Participat Med %G English %X Background: We describe the methodological dimensions of community-based participatory research through a description of study design, youth engagement, and methods/processes in the cocreation of knowledge within a Canadian study, the Bipolar Youth Action Project. This collaborative partnership—carried out by a team composed of academic, community, and youth partners—was designed to investigate self-management and wellness strategies for young adults living with bipolar disorder. Objective: The aim is to describe the opportunities and challenges of this collaboration and to reflect upon the process of involving youth with bipolar disorder in health research that concerns them, and share lessons learned. Methods: The project was conducted in multiple phases over 2 years: (1) grant-writing, with youth contributing to the process; (2) recruitment, in which 12 youth were selected and trained to help shape and conduct two research forums; (3) the first research forum, where more youth were consulted about the strategies they apply to stay well (self-management strategies); (4) data analysis of Forum I findings; (5) research Forum II, which consulted youth with bipolar disorder about knowledge translation of Forum I findings; and (6) data analysis of Forum II findings. Youth peer researchers with bipolar disorder were involved in a significant capacity at every stage in the process. Results: Of the initial 12 youth peer researchers, 7 remained on the project from the recruitment phase until the project ended. They collaborated in the creation of two youth research forums that consulted youth with bipolar disorder on their self-management strategies. Conclusions: This article shares what was learned from the process of partnering with youth with bipolar disorder in a community-based participatory research study. %M 33044943 %R 10.2196/19475 %U http://jopm.jmir.org/2020/3/e19475/ %U https://doi.org/10.2196/19475 %U http://www.ncbi.nlm.nih.gov/pubmed/33044943 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 9 %N 8 %P e18818 %T Toi Même, a Mobile Health Platform for Measuring Bipolar Illness Activity: Protocol for a Feasibility Study %A Dargél,Aroldo A %A Mosconi,Elise %A Masson,Marc %A Plaze,Marion %A Taieb,Fabien %A Von Platen,Cassandra %A Buivan,Tan Phuc %A Pouleriguen,Guillaume %A Sanchez,Marie %A Fournier,Stéphane %A Lledo,Pierre-Marie %A Henry,Chantal %+ Perception and Memory Unit, Neuroscience Department, Pasteur Institute, 25 rue du Docteur Roux, Paris, 75015, France, 33 6 46 92 42 06, aroldo.dargel@pasteur.fr %K bipolar disorder %K digital phenotyping, smartphone app %K ecological momentary assessment %K mHealth %K mood instability %K cognitive speed %K affective response %K big data, machine learning %D 2020 %7 18.8.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: The diagnosis and management of bipolar disorder are limited by the absence of available biomarkers. Patients with bipolar disorder frequently present with mood instability even during remission, which is likely associated with the risk of relapse, impaired functioning, and suicidal behavior, indicating that the illness is active. Objective: This research protocol aimed to investigate the correlations between clinically rated mood symptoms and mood/behavioral data automatically collected using the Toi Même app in patients with bipolar disorder presenting with different mood episodes. This study also aimed to assess the feasibility of this app for self-monitoring subjective and objective mood/behavior parameters in those patients. Methods: This open-label, nonrandomized trial will enroll 93 (31 depressive, 31 euthymic, and 31 hypomanic) adults diagnosed with bipolar disorder type I/II (Diagnostic and Statistical Manual of Mental Disorders, 5th edition criteria) and owning an iPhone. Clinical evaluations will be performed by psychiatrists at the baseline and after 2 weeks, 1 month, 2 months, and 3 months during the follow-up. Rather than only accessing the daily mood symptoms, the Toi Même app also integrates ecological momentary assessments through 2 gamified tests to assess cognition speed (QUiCKBRAIN) and affective responses (PLAYiMOTIONS) in real-life contexts, continuously measures daily motor activities (eg, number of steps, distance) using the smartphone’s motion sensors, and performs a comprehensive weekly assessment. Results: Recruitment began in April 2018 and the completion of the study is estimated to be in December 2021. As of April 2019, 25 participants were enrolled in the study. The first results are expected to be submitted for publication in 2020. This project has been funded by the Perception and Memory Unit of the Pasteur Institute (Paris) and it has received the final ethical/research approvals in April 2018 (ID-RCB: 2017-A02450-53). Conclusions: Our results will add to the evidence of exploring other alternatives toward a more integrated approach in the management of bipolar disorder, including digital phenotyping, to develop an ethical and clinically meaningful framework for investigating, diagnosing, and treating individuals at risk of developing bipolar disorder or currently experiencing bipolar disorder. Further prospective studies on the validity of automatically generated smartphone data are needed for better understanding the longitudinal pattern of mood instability in bipolar disorder as well as to establish the reliability, efficacy, and cost-effectiveness of such an app intervention for patients with bipolar disorder. Trial Registration: ClinicalTrials.gov NCT03508427; https://clinicaltrials.gov/ct2/show/NCT03508427 International Registered Report Identifier (IRRID): DERR1-10.2196/18818 %M 32638703 %R 10.2196/18818 %U http://www.researchprotocols.org/2020/8/e18818/ %U https://doi.org/10.2196/18818 %U http://www.ncbi.nlm.nih.gov/pubmed/32638703 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 8 %P e17204 %T The Therapeutic Alliance in Digital Mental Health Interventions for Serious Mental Illnesses: Narrative Review %A Tremain,Hailey %A McEnery,Carla %A Fletcher,Kathryn %A Murray,Greg %+ Centre for Mental Health, Faculty of Health, Arts and Design, Swinburne University of Technology, John St, Hawthorn, 3122, Australia, 61 4039966 9100, htremain@swin.edu.au %K mental health %K mHealth %K eHealth %K telehealth %K psychosis %K bipolar disorder %K mobile phone %D 2020 %7 7.8.2020 %9 Review %J JMIR Ment Health %G English %X Background: Digital mental health interventions offer unique advantages, and research indicates that these interventions are effective for a range of mental health concerns. Although these interventions are less established for individuals with serious mental illnesses, they demonstrate significant promise. A central consideration in traditional face-to-face therapies is the therapeutic alliance, whereas the nature of a digital therapeutic alliance and its relationship with outcomes requires further attention, particularly for individuals with serious mental illnesses. Objective: This narrative review aims to encourage further consideration and critical evaluation of the therapeutic alliance in digital mental health, specifically for individuals with serious mental illnesses. Methods: A narrative review was conducted by combining 3 main areas of the literature: the first examining the evidence for digital mental health interventions for serious mental illnesses, the second illuminating the nature and role of the therapeutic alliance in digital interventions, and the third surrounding practical considerations to enhance a digital therapeutic alliance. Results: Results indicated that a therapeutic alliance can be cultivated in digital interventions for those with serious mental illnesses, but that it may have unique, yet-to-be-confirmed characteristics in digital contexts. In addition, a therapeutic alliance appears to be less directly associated with outcomes in digital interventions than with those in face-to-face therapies. One possibility is that the digital therapeutic alliance is associated with increased engagement and adherence to digital interventions, through which it appears to influence outcomes. A number of design and implementation considerations may enhance the digital therapeutic alliance, including human support and technological features. Conclusions: More research is required to further understand the nature and specific role of a therapeutic alliance in digital interventions for serious mental illnesses, particularly in informing their design. This review revealed several key research priorities to advance the therapeutic alliance in digital interventions. %M 32763881 %R 10.2196/17204 %U https://mental.jmir.org/2020/8/e17204 %U https://doi.org/10.2196/17204 %U http://www.ncbi.nlm.nih.gov/pubmed/32763881 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 7 %P e15878 %T Methodological Challenges in Web-Based Trials: Update and Insights From the Relatives Education and Coping Toolkit Trial %A Robinson,Heather %A Appelbe,Duncan %A Dodd,Susanna %A Flowers,Susan %A Johnson,Sonia %A Jones,Steven H %A Mateus,Céu %A Mezes,Barbara %A Murray,Elizabeth %A Rainford,Naomi %A Rosala-Hallas,Anna %A Walker,Andrew %A Williamson,Paula %A Lobban,Fiona %+ Division of Health Research, Spectrum Centre for Mental Health Research, Lancaster University, Bailrigg, Lancaster LA1 4YW, Lancaster, , United Kingdom, 44 (0)1524 593, s.jones7@lancaster.ac.uk %K randomized controlled trial %K research design %K methods %K internet %K web %K mental health %K relatives %K carers %D 2020 %7 17.7.2020 %9 Tutorial %J JMIR Ment Health %G English %X There has been a growth in the number of web-based trials of web-based interventions, adding to an increasing evidence base for their feasibility and effectiveness. However, there are challenges associated with such trials, which researchers must address. This discussion paper follows the structure of the Down Your Drink trial methodology paper, providing an update from the literature for each key trial parameter (recruitment, registration eligibility checks, consent and participant withdrawal, randomization, engagement with a web-based intervention, retention, data quality and analysis, spamming, cybersquatting, patient and public involvement, and risk management and adverse events), along with our own recommendations based on designing the Relatives Education and Coping Toolkit randomized controlled trial for relatives of people with psychosis or bipolar disorder. The key recommendations outlined here are relevant for future web-based and hybrid trials and studies using iterative development and test models such as the Accelerated Creation-to-Sustainment model, both within general health research and specifically within mental health research for relatives. Researchers should continue to share lessons learned from conducting web-based trials of web-based interventions to benefit future studies.International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2017-016965 %M 32497018 %R 10.2196/15878 %U https://mental.jmir.org/2020/7/e15878 %U https://doi.org/10.2196/15878 %U http://www.ncbi.nlm.nih.gov/pubmed/32497018 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 7 %P e14267 %T Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder %A Daus,Henning %A Bloecher,Timon %A Egeler,Ronny %A De Klerk,Richard %A Stork,Wilhelm %A Backenstrass,Matthias %+ Institute of Clinical Psychology, Centre for Mental Health, Klinikum Stuttgart, Prießnitzweg 24, Stuttgart, 70374, Germany, 49 711 278 22901, m.backenstrass@klinikum-stuttgart.de %K bipolar disorder %K mood recognition %K emotion recognition %K monitoring %K mobile apps %K assistance system %K mHealth %D 2020 %7 3.7.2020 %9 Viewpoint %J JMIR Ment Health %G English %X Internet- and mobile-based approaches have become increasingly significant to psychological research in the field of bipolar disorders. While research suggests that emotional aspects of bipolar disorders are substantially related to the social and global functioning or the suicidality of patients, these aspects have so far not sufficiently been considered within the context of mobile-based disease management approaches. As a multiprofessional research team, we have developed a new and emotion-sensitive assistance system, which we have adapted to the needs of patients with bipolar disorder. Next to the analysis of self-assessments, third-party assessments, and sensor data, the new assistance system analyzes audio and video data of these patients regarding their emotional content or the presence of emotional cues. In this viewpoint, we describe the theoretical and technological basis of our emotion-sensitive approach and do not present empirical data or a proof of concept. To our knowledge, the new assistance system incorporates the first mobile-based approach to analyze emotional expressions of patients with bipolar disorder. As a next step, the validity and feasibility of our emotion-sensitive approach must be evaluated. In the future, it might benefit diagnostic, prognostic, or even therapeutic purposes and complement existing systems with the help of new and intuitive interaction models. %M 32618577 %R 10.2196/14267 %U https://mental.jmir.org/2020/7/e14267 %U https://doi.org/10.2196/14267 %U http://www.ncbi.nlm.nih.gov/pubmed/32618577 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 4 %P e17497 %T A Web-Based Adaptation of the Quality of Life in Bipolar Disorder Questionnaire: Psychometric Evaluation Study %A Morton,Emma %A Hou,Sharon HJ %A Fogarty,Oonagh %A Murray,Greg %A Barnes,Steven %A Depp,Colin %A , %A Michalak,Erin %+ Department of Psychiatry, University of British Columbia, 420-5950 University Blvd, Vancouver, BC, V6T 1Z3, Canada, 1 604 827 3393, emma.morton@ubc.ca %K bipolar disorder %K survey methodology %K patient reported outcomes %K psychometrics %K questionnaire design %K quality of life %K validation studies %D 2020 %7 27.4.2020 %9 Original Paper %J JMIR Ment Health %G English %X Background: Quality of life (QoL) is considered a key treatment outcome in bipolar disorder (BD) across research, clinical, and self-management contexts. Web-based assessment of patient-reported outcomes offer numerous pragmatic benefits but require validation to ensure measurement equivalency. A web-based version of the Quality of Life in Bipolar Disorder (QoL.BD) questionnaire was developed (QoL Tool). Objective: This study aimed to evaluate the psychometric properties of a web-based QoL self-report questionnaire for BD (QoL Tool). Key aims were to (1) characterize the QoL of the sample using the QoL Tool, (2) evaluate the internal consistency of the web-based measure, and (3) determine whether the factor structure of the original version of the QoL.BD instrument was replicated in the web-based instrument. Methods: Community-based participatory research methods were used to inform the development of a web-based adaptation of the QoL.BD instrument. Individuals with BD who registered for an account with the QoL Tool were able to opt in to sharing their data for research purposes. The distribution of scores and internal consistency estimates, as indicated by Cronbach alpha, were inspected. An exploratory factor analysis using maximum likelihood and oblique rotation was conducted. Inspection of the scree plot, eigenvalues, and minimum average partial correlation were used to determine the optimal factor structure to extract. Results: A total of 498 people with BD (349/498, 70.1% female; mean age 39.64, SD 12.54 years; 181/498, 36.3% BD type I; 195/498, 39.2% BD type II) consented to sharing their QoL Tool data for the present study. Mean scores across the 14 QoL Tool domains were, in general, significantly lower than that of the original QoL.BD validation sample. Reliability estimates for QoL Tool domains were comparable with that observed for the QoL.BD instrument (Cronbach alpha=.70-.93). Exploratory factor analysis supported the extraction of an 11-factor model, with item loadings consistent with the factor structure suggested by the original study. Findings for the sleep and physical domains differed from the original study, with this analysis suggesting one shared latent construct. Conclusions: The psychometric properties of the web-based QoL Tool are largely concordant with the original pen-and-paper QoL.BD, although some minor differences in the structure of the sleep and physical domains were observed. Despite this small variation from the factor structure identified in the QoL.BD instrument, the latent factor structure of the QoL Tool largely reproduced the original findings and theoretical structure of QoL areas relevant to people with BD. These findings underscore the research and clinical utility of this instrument, but further comparison of the psychometric properties of the QoL Tool relative to the QoL.BD instrument is warranted. Future adaptations of the QoL Tool, including the production of an app-based version of the QoL Tool, are also discussed. %M 32338620 %R 10.2196/17497 %U http://mental.jmir.org/2020/4/e17497/ %U https://doi.org/10.2196/17497 %U http://www.ncbi.nlm.nih.gov/pubmed/32338620 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 4 %P e14278 %T Critical Predictors for the Early Detection of Conversion From Unipolar Major Depressive Disorder to Bipolar Disorder: Nationwide Population-Based Retrospective Cohort Study %A Hu,Ya-Han %A Chen,Kuanchin %A Chang,I-Chiu %A Shen,Cheng-Che %+ Department of Psychiatry, Chiayi Branch, Taichung Veterans General Hospital, No. 600, Sec 2, Shixian Road, West District, Chiayi City, 60090, Taiwan, 886 52359630, pures1000@yahoo.com.tw %K major depressive disorder %K bipolar disorder %K National Health Insurance Database %K data mining %K classification and regression tree %D 2020 %7 3.4.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Unipolar major depressive disorder (MDD) and bipolar disorder are two major mood disorders. The two disorders have different treatment strategies and prognoses. However, bipolar disorder may begin with depression and could be diagnosed as MDD in the initial stage, which may later contribute to treatment failure. Previous studies indicated that a high proportion of patients diagnosed with MDD will develop bipolar disorder over time. This kind of hidden bipolar disorder may contribute to the treatment resistance observed in patients with MDD. Objective: In this population-based study, our aim was to investigate the rate and risk factors of a diagnostic change from unipolar MDD to bipolar disorder during a 10-year follow-up. Furthermore, a risk stratification model was developed for MDD-to-bipolar disorder conversion. Methods: We conducted a retrospective cohort study involving patients who were newly diagnosed with MDD between January 1, 2000, and December 31, 2004, by using the Taiwan National Health Insurance Research Database. All patients with depression were observed until (1) diagnosis of bipolar disorder by a psychiatrist, (2) death, or (3) December 31, 2013. All patients with depression were divided into the following two groups, according to whether bipolar disorder was diagnosed during the follow-up period: converted group and nonconverted group. Six groups of variables within the first 6 months of enrollment, including personal characteristics, physical comorbidities, psychiatric comorbidities, health care usage behaviors, disorder severity, and psychotropic use, were extracted and were included in a classification and regression tree (CART) analysis to generate a risk stratification model for MDD-to-bipolar disorder conversion. Results: Our study enrolled 2820 patients with MDD. During the follow-up period, 536 patients were diagnosed with bipolar disorder (conversion rate=19.0%). The CART method identified five variables (kinds of antipsychotics used within the first 6 months of enrollment, kinds of antidepressants used within the first 6 months of enrollment, total psychiatric outpatient visits, kinds of benzodiazepines used within one visit, and use of mood stabilizers) as significant predictors of the risk of bipolar disorder conversion. This risk CART was able to stratify patients into high-, medium-, and low-risk groups with regard to bipolar disorder conversion. In the high-risk group, 61.5%-100% of patients with depression eventually developed bipolar disorder. On the other hand, in the low-risk group, only 6.4%-14.3% of patients with depression developed bipolar disorder. Conclusions: The CART method identified five variables as significant predictors of bipolar disorder conversion. In a simple two- to four-step process, these variables permit the identification of patients with low, intermediate, or high risk of bipolar disorder conversion. The developed model can be applied to routine clinical practice for the early diagnosis of bipolar disorder. %M 32242821 %R 10.2196/14278 %U https://medinform.jmir.org/2020/4/e14278 %U https://doi.org/10.2196/14278 %U http://www.ncbi.nlm.nih.gov/pubmed/32242821 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 12 %P e16121 %T Experiences of a Web-Based Quality of Life Self-Monitoring Tool for Individuals With Bipolar Disorder: A Qualitative Exploration %A Morton,Emma %A Hole,Rachelle %A Murray,Greg %A Buzwell,Simone %A Michalak,Erin %+ Department of Psychiatry, University of British Columbia, 420-5950 University Blvd, Vancouver, BC, V6T 1Z3, Canada, 1 604 827 3393, erin.michalak@ubc.ca %K bipolar disorder %K self-monitoring %K self-management %K qualitative %K recovery %K quality of life %K eHealth %D 2019 %7 4.12.2019 %9 Original Paper %J JMIR Ment Health %G English %X 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. %M 31799936 %R 10.2196/16121 %U https://mental.jmir.org/2019/12/e16121 %U https://doi.org/10.2196/16121 %U http://www.ncbi.nlm.nih.gov/pubmed/31799936 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 6 %P e12848 %T Preferences of Information Dissemination on Treatment for Bipolar Disorder: Patient-Centered Focus Group Study %A Kerner,Berit %A Crisanti,Annette S %A DeShaw,Jason L %A Ho,Janika-Marie G %A Jordan,Kimmie %A Krall,Ronald L %A Kuntz,Matt J %A Mazurie,Aurélien J %A Nestsiarovich,Anastasiya %A Perkins,Douglas J %A Schroeter,Quentin L %A Smith,Alicia N %A Tohen,Mauricio %A Volesky,Emma %A Zhu,Yiliang %A Lambert,Christophe G %+ Division of Translational Informatics, Center for Global Health, Department of Internal Medicine, University of New Mexico Health Sciences Center, MSC10-5550, Albuquerque, NM, 87131, United States, 1 505 272 9709, CGLambert@unm.edu %K internet %K information seeking %K psychiatry %K bipolar disorder %K patient-physician relationship %K decision-making %K patient education %K therapeutics %D 2019 %7 25.06.2019 %9 Original Paper %J JMIR Ment Health %G English %X Background: Patient education has taken center stage in successfully shared decision making between patients and health care providers. However, little is known about how patients with bipolar disorder typically obtain information on their illness and the treatment options available to them. Objective: This study aimed to obtain the perspectives of patients with bipolar disorder and their family members on the preferred and most effectively used information channels on bipolar disorder and the available treatment options. Methods: We conducted nine focus groups in Montana, New Mexico, and California, in which we surveyed 84 individuals including patients with bipolar disorder and family members of patients with bipolar disorder. The participants were recruited using National Alliance on Mental Illness mailing lists and websites. Written verbatim responses to semistructured questionnaires were analyzed using summative content analysis based on grounded theory. Two annotators coded and analyzed the data on the sentence or phrase level to create themes. Relationships between demographics and information channel were also examined using the Chi-square and Fisher exact tests. Results: The focus group participants mentioned a broad range of information channels that were successfully used in the past and could be recommended for future information dissemination. The majority of participants used providers (74%) and internet-based resources (75%) as their main information sources. There was no association between internet use and basic demographics such as age or geographical region of the focus groups. Patients considered time constraints and the fast pace in which an overwhelming amount of information is often presented by the provider as major barriers to successful provider-patient interactions. If Web-based channels were used, the participants perceived information obtained through Web-based channels as more helpful than information received in the provider’s office (P<.05). Conclusions: Web-based resources are increasingly used by patients with bipolar disorder and their family members to educate themselves about the disease and its treatment. Although provider-patient interactions are frequently perceived to be burdened with time constraints, Web-based information sources are considered reliable and helpful. Future research should explore how high-quality websites could be used to empower patients and improve provider-patient interactions with the goal of enhancing shared decision making between patients and providers. %M 31237566 %R 10.2196/12848 %U http://mental.jmir.org/2019/6/e12848/ %U https://doi.org/10.2196/12848 %U http://www.ncbi.nlm.nih.gov/pubmed/31237566 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 4 %P e13493 %T Supporting Self-Management in Bipolar Disorder: Mixed-Methods Knowledge Translation Study %A Michalak,Erin E %A Morton,Emma %A Barnes,Steven J %A Hole,Rachelle %A , %A Murray,Greg %+ Department of Psychiatry, University of British Columbia, 420-5950 University Boulevard, Vancouver, BC, V6T 1Z3, Canada, 1 604 827 3393, erin.michalak@ubc.ca %K bipolar disorder %K eHealth %K self-management %K knowledge translation %K community-based participatory research %K mHealth %D 2019 %7 15.04.2019 %9 Original Paper %J JMIR Ment Health %G English %X Background: Self-management is increasingly recognized as an important method through which individuals with bipolar disorder (BD) may cope with symptoms and improve quality of life. Digital health technologies have strong potential as a method to support the application of evidence-informed self-management strategies in BD. Little is known, however, about how to most effectively maximize user engagement with digital platforms. Objective: The aims of this study were (1) to create an innovative Web-based Bipolar Wellness Centre, (2) to conduct a mixed-methods (ie, quantitative and qualitative) evaluation to assess the impact of different sorts of engagement (ie, knowledge translation [KT]), and (3) to support engagement with the self-management information in the Bipolar Wellness Centre. Methods: The project was implemented in 2 phases. In phase 1, community-based participatory research and user-centered design methods were used to develop a website (Bipolar Wellness Centre) housing evidence-informed tools and strategies for self-management of BD. In phase 2, a mixed-methods evaluation was conducted to explore the potential impact of 4 KT strategies (Web-based webinars, Web-based videos, Web-based one-to-one Living Library peer support, and in-person workshops). Quantitative assessments occurred at 2 time points—preintervention and 3 weeks postintervention. Purposive sampling was used to recruit a subsample of participants for the qualitative interviews, ensuring each KT modality was represented, and interviews occurred approximately 3 weeks postintervention. Results: A total of 94 participants were included in the quantitative analysis. Responses to evaluative questions about engagement were broadly positive. When averaged across the 4 KT strategies, significant improvements were observed on the Bipolar Recovery Questionnaire (F1,77=5.887; P=.02) and Quality of Life in Bipolar Disorder (F1,77=8.212; P=.005). Nonsignificant improvements in positive affect and negative affect were also observed. The sole difference that emerged between KT strategies related to the Chronic Disease Self-Efficacy measure, which decreased after participation in the webinar and video arms but increased after the Living Library and workshop arms. A subsample of 43 participants was included in the qualitative analyses, with the majority of participants describing positive experiences with the 4 KT strategies; peer contact was emphasized as a benefit across all strategies. Infrequent negative experiences were reported in relation to the webinar and video strategies, and included technical difficulties, the academic tone of webinars, and feeling unable to relate to the actor in the videos. Conclusions: This study adds incremental evidence to a growing literature that suggests digital health technologies can provide effective support for self-management for people with BD. The finding that KT strategies could differentially impact chronic disease self-efficacy (hypothesized as being a product of differences in degree of peer contact) warrants further exploration. Implications of the findings for the development of evidence-informed apps for BD are discussed in this paper. %M 30985287 %R 10.2196/13493 %U http://mental.jmir.org/2019/4/e13493/ %U https://doi.org/10.2196/13493 %U http://www.ncbi.nlm.nih.gov/pubmed/30985287 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 5 %N 4 %P e63 %T Lamotrigine Therapy for Bipolar Depression: Analysis of Self-Reported Patient Data %A Nzeyimana,Antoine %A Saunders,Kate EA %A Geddes,John R %A McSharry,Patrick E %+ Department of Geography, University of Oregon, 1251 University of Oregon, Eugene, OR, 97403-1251, United States, 1 541 346 0785, anzeyima@uoregon.edu %K bipolar disorder %K CEQUEL study %K data analysis %K depressive symptoms %K lamotrigine %K time series %D 2018 %7 22.11.2018 %9 Original Paper %J JMIR Ment Health %G English %X Background: Depression in people with bipolar disorder is a major cause of long-term disability, possibly leading to early mortality and currently, limited safe and effective therapies exist. Although existing monotherapies such as quetiapine have limited proven efficacy and practical tolerability, treatment combinations may lead to improved outcomes. Lamotrigine is an anticonvulsant currently licensed for the prevention of depressive relapses in individuals with bipolar disorder. A double-blinded randomized placebo-controlled trial (comparative evaluation of Quetiapine-Lamotrigine [CEQUEL] study) was conducted to evaluate the efficacy of lamotrigine plus quetiapine versus quetiapine monotherapy in patients with bipolar type I or type II disorders. Objective: Because the original CEQUEL study found significant depressive symptom improvements, the objective of this study was to reanalyze CEQUEL data and determine an unbiased classification accuracy for active lamotrigine versus placebo. We also wanted to establish the time it took for the drug to provide statistically significant outcomes. Methods: Between October 21, 2008 and April 27, 2012, 202 participants from 27 sites in United Kingdom were randomly assigned to two treatments; 101: lamotrigine, 101: placebo. The primary variable used for estimating depressive symptoms was based on the Quick Inventory of Depressive Symptomatology—self report version 16 (QIDS-SR16). The original CEQUEL study findings were confirmed by performing t test and linear regression. Multiple features were computed from the QIDS-SR16 time series; different linear and nonlinear binary classifiers were trained to distinguish between the two groups. Various feature-selection techniques were used to select a feature set with the greatest explanatory power; a 10-fold cross-validation was used. Results: From weeks 10 to 14, the mean difference in QIDS-SR16 ratings between the groups was −1.6317 (P=.09; sample size=81, 77; 95% CI −0.2403 to 3.5036). From weeks 48 to 52, the mean difference was −2.0032 (P=.09; sample size=54, 48; 95% CI −0.3433 to 4.3497). The coefficient of variation (σ/μ) and detrended fluctuation analysis (DFA) exponent alpha had the greatest explanatory power. The out-of-sample classification accuracy for the 138 participants who reported more than 10 times after week 12 was 62%. A consistent classification accuracy higher than the no-information benchmark was obtained in week 44. Conclusions: Adding lamotrigine to quetiapine treatment decreased depressive symptoms in patients with bipolar disorder. Our classification model suggested that lamotrigine increased the coefficient of variation in the QIDS-SR16 scores. The lamotrigine group also tended to have a lower DFA exponent, implying a substantial temporal instability in the time series. The performance of the model over time suggested that a trial of at least 44 weeks was required to achieve consistent results. The selected model confirmed the original CEQUEL study findings and helped in understanding the temporal dynamics of bipolar depression during treatment. Trial Registration: EudraCT Number 2007-004513-33; https://www.clinicaltrialsregister.eu/ctr-search/trial/2007-004513-33/GB (Archived by WebCite at http://www.webcitation.org/73sNaI29O). %M 30467104 %R 10.2196/mental.9026 %U http://mental.jmir.org/2018/4/e63/ %U https://doi.org/10.2196/mental.9026 %U http://www.ncbi.nlm.nih.gov/pubmed/30467104 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 3 %N 3 %P e42 %T Technology-Based Early Warning Systems for Bipolar Disorder: A Conceptual Framework %A Depp,Colin %A Torous,John %A Thompson,Wesley %+ VA San Diego Healthcare System, 3350 La Jolla Village Drive (0603), San Diego, CA,, United States, 1 8588224251, cdepp@ucsd.edu %K psychiatry %K mHealth %K prevention %K technology %K psychotherapy %D 2016 %7 07.09.2016 %9 Viewpoint %J JMIR Ment Health %G English %X Recognition and timely action around “warning signs” of illness exacerbation is central to the self-management of bipolar disorder. Due to its heterogeneity and fluctuating course, passive and active mobile technologies have been increasingly evaluated as adjunctive or standalone tools to predict and prevent risk of worsening of course in bipolar disorder. As predictive analytics approaches to big data from mobile health (mHealth) applications and ancillary sensors advance, it is likely that early warning systems will increasingly become available to patients. Such systems could reduce the amount of time spent experiencing symptoms and diminish the immense disability experienced by people with bipolar disorder. However, in addition to the challenges in validating such systems, we argue that early warning systems may not be without harms. Probabilistic warnings may be delivered to individuals who may not be able to interpret the warning, have limited information about what behaviors to change, or are unprepared to or cannot feasibly act due to time or logistic constraints. We propose five essential elements for early warning systems and provide a conceptual framework for designing, incorporating stakeholder input, and validating early warning systems for bipolar disorder with a focus on pragmatic considerations. %M 27604265 %R 10.2196/mental.5798 %U http://mental.jmir.org/2016/3/e42/ %U https://doi.org/10.2196/mental.5798 %U http://www.ncbi.nlm.nih.gov/pubmed/27604265 %0 Journal Article %@ 2368-7959 %I JMIR Publications Inc. %V 3 %N 1 %P e2 %T Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study %A Beiwinkel,Till %A Kindermann,Sally %A Maier,Andreas %A Kerl,Christopher %A Moock,Jörn %A Barbian,Guido %A Rössler,Wulf %+ Innovation Incubator, Competence Tandem Integrated Care, Leuphana University of Lüneburg, Scharnhorststr. 1, Lüneburg, 21335, Germany, 49 4131677 ext 7826, till.beiwinkel@leuphana.de %K smartphone %K sensor technology %K bipolar disorder %K monitoring %K phase transitions %K communication patterns %K activity patterns %D 2016 %7 06.01.2016 %9 Original Paper %J JMIR Mental Health %G English %X Background: Relapse prevention in bipolar disorder can be improved by monitoring symptoms in patients' daily life. Smartphone apps are easy-to-use, low-cost tools that can be used to assess this information. To date, few studies have examined the usefulness of smartphone data for monitoring symptoms in bipolar disorder. Objective: We present results from a pilot test of a smartphone-based monitoring system, Social Information Monitoring for Patients with Bipolar Affective Disorder (SIMBA), that tracked daily mood, physical activity, and social communication in 13 patients. The objective of this study was to investigate whether smartphone measurements predicted clinical symptoms levels and clinical symptom change. The hypotheses that smartphone measurements are (1) negatively related to clinical depressive symptoms and (2) positively related to clinical manic symptoms were tested. Methods: Clinical rating scales were administered to assess clinical depressive and manic symptoms. Patients used a smartphone with the monitoring app for up to 12 months. Random-coefficient multilevel models were computed to analyze the relationship between smartphone data and externally rated manic and depressive symptoms. Overall clinical symptom levels and clinical symptom changes were predicted by separating between-patient and within-patient effects. Using established clinical thresholds from the literature, marginal effect plots displayed clinical relevance of smartphone data. Results: Overall symptom levels and change in clinical symptoms were related to smartphone measures. Higher overall levels of clinical depressive symptoms were predicted by lower self-reported mood measured by the smartphone (beta=-.56, P<.001). An increase in clinical depressive symptoms was predicted by a decline in social communication (ie, outgoing text messages: beta=-.28, P<.001) and a decline in physical activity as measured by the smartphone (ie, cell tower movements: beta=-.11, P=.03). Higher overall levels of clinical manic symptoms were predicted by lower physical activity on the smartphone (ie, distance travelled: beta=-.37, P<.001), and higher social communication (beta=.48, P=.03). An increase in clinical manic symptoms was predicted by a decrease in physical activity on the smartphone (beta=-.17, P<.001). Conclusions: Clinical symptoms were related to some objective and subjective smartphone measurements, but not all smartphone measures predicted the occurrence of bipolar symptoms above clinical thresholds. Thus, smartphones have the potential to monitor bipolar disorder symptoms in patients’ daily life. Further validation of monitoring tools in a larger sample is needed. Conclusions are limited by the low prevalence of manic and depressive symptoms in the study sample. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): 05663421; http://www.controlled-trials.com/ISRCTN05663421 (Archived by WebCite at http://www.webcitation.org/6d9wsibJB) %M 26740354 %R 10.2196/mental.4560 %U http://mental.jmir.org/2016/1/e2/ %U https://doi.org/10.2196/mental.4560 %U http://www.ncbi.nlm.nih.gov/pubmed/26740354 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 17 %N 8 %P e198 %T Mobile Apps for Bipolar Disorder: A Systematic Review of Features and Content Quality %A Nicholas,Jennifer %A Larsen,Mark Erik %A Proudfoot,Judith %A Christensen,Helen %+ Black Dog Institute, University of New South Wales, Hospital Road, Prince of Wales Hospital, Randwick, Sydney, 2031, Australia, 61 293829260, j.nicholas@blackdog.org.au %K mobile applications %K bipolar disorder %K review %K telemedicine %D 2015 %7 17.08.2015 %9 Review %J J Med Internet Res %G English %X Background: With continued increases in smartphone ownership, researchers and clinicians are investigating the use of this technology to enhance the management of chronic illnesses such as bipolar disorder (BD). Smartphones can be used to deliver interventions and psychoeducation, supplement treatment, and enhance therapeutic reach in BD, as apps are cost-effective, accessible, anonymous, and convenient. While the evidence-based development of BD apps is in its infancy, there has been an explosion of publicly available apps. However, the opportunity for mHealth to assist in the self-management of BD is only feasible if apps are of appropriate quality. Objective: Our aim was to identify the types of apps currently available for BD in the Google Play and iOS stores and to assess their features and the quality of their content. Methods: A systematic review framework was applied to the search, screening, and assessment of apps. We searched the Australian Google Play and iOS stores for English-language apps developed for people with BD. The comprehensiveness and quality of information was assessed against core psychoeducation principles and current BD treatment guidelines. Management tools were evaluated with reference to the best-practice resources for the specific area. General app features, and privacy and security were also assessed. Results: Of the 571 apps identified, 82 were included in the review. Of these, 32 apps provided information and the remaining 50 were management tools including screening and assessment (n=10), symptom monitoring (n=35), community support (n=4), and treatment (n=1). Not even a quarter of apps (18/82, 22%) addressed privacy and security by providing a privacy policy. Overall, apps providing information covered a third (4/11, 36%) of the core psychoeducation principles and even fewer (2/13, 15%) best-practice guidelines. Only a third (10/32, 31%) cited their information source. Neither comprehensiveness of psychoeducation information (r=-.11, P=.80) nor adherence to best-practice guidelines (r=-.02, P=.96) were significantly correlated with average user ratings. Symptom monitoring apps generally failed to monitor critical information such as medication (20/35, 57%) and sleep (18/35, 51%), and the majority of self-assessment apps did not use validated screening measures (6/10, 60%). Conclusions: In general, the content of currently available apps for BD is not in line with practice guidelines or established self-management principles. Apps also fail to provide important information to help users assess their quality, with most lacking source citation and a privacy policy. Therefore, both consumers and clinicians should exercise caution with app selection. While mHealth offers great opportunities for the development of quality evidence-based mobile interventions, new frameworks for mobile mental health research are needed to ensure the timely availability of evidence-based apps to the public. %M 26283290 %R 10.2196/jmir.4581 %U http://www.jmir.org/2015/8/e198/ %U https://doi.org/10.2196/jmir.4581 %U http://www.ncbi.nlm.nih.gov/pubmed/26283290 %0 Journal Article %@ 2368-7959 %I JMIR Publications Inc. %V 2 %N 3 %P e21 %T How Patients Contribute to an Online Psychoeducation Forum for Bipolar Disorder: A Virtual Participant Observation Study %A Poole,Ria %A Smith,Daniel %A Simpson,Sharon %+ Centre for the Development and Evaluation of Complex Interventions for Public Health Improvement (DECIPHer), School of Social Sciences, Cardiff University, 1-3 Museum Place, Cardiff, CF10 3BD, United Kingdom, 44 29 2087 9609 ext 9609, PooleR3@Cardiff.ac.uk %K bipolar disorder %K psychoeducation %K Internet %K forum %K qualitative %D 2015 %7 08.07.2015 %9 Original Paper %J JMIR Mental Health %G English %X Background: In a recent exploratory randomized controlled trial, an online psychoeducation intervention for bipolar disorder has been found to be feasible and acceptable to patients and may positively impact on their self-management behaviors and quality of life. Objective: The objective of the study was to investigate how these patients contribute to an online forum for bipolar disorder and the issues relevant for them. Methods: Participants in the intervention arm of the Bipolar Interactive PsychoEDucation (“BIPED”) trial were invited to contribute to the Beating Bipolar forum alongside receiving interactive online psychoeducation modules. Within this virtual participant observation study, forum posts were analyzed using thematic analysis, incorporating aspects of discourse analysis. Results: The key themes which arose from the forum posts included: medication, employment, stigma, social support, coping strategies, insight and acceptance, the life chart, and negative experiences of health care. Participants frequently provided personal narratives relating to their history of bipolar disorder, life experiences, and backgrounds, which often contained emotive language and humor. They regularly sought and offered advice, and expressed encouragement and empathy. The forum would have benefitted from more users to offer a greater support network with more diverse views and experiences. Conclusions: Online forums are inexpensive to provide and may offer peer support and the opportunity for patients to share their experiences and explore issues related to their illness anonymously. Future research should focus on how to enhance patient engagement with online health care forums. Trial Registration: ISRCTN81375447; http://www.isrctn.com/ISRCTN81375447 (Archived by WebCite at http://www.webcitation.org/6YzWtHUqu). %M 26543925 %R 10.2196/mental.4123 %U http://mental.jmir.org/2015/3/e21/ %U https://doi.org/10.2196/mental.4123 %U http://www.ncbi.nlm.nih.gov/pubmed/26543925 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 4 %N 1 %P e16 %T Auditory Brainstem Response as a Diagnostic Tool for Patients Suffering From Schizophrenia, Attention Deficit Hyperactivity Disorder, and Bipolar Disorder: Protocol %A Wahlström,Viktor %A Åhlander,Fredrik %A Wynn,Rolf %+ Department of Clinical Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, N-9037 Tromsø, Tromsø, N-9037, Norway, 47 77620888, rolf.wynn@gmail.com %K brainstem audiometry %K diagnosis %K schizophrenia %K ADHD %K bipolar disorder %D 2015 %7 12.02.2015 %9 Protocol %J JMIR Res Protoc %G English %X Background: Psychiatric disorders, such as schizophrenia, attention deficit hyperactivity disorder (ADHD), and bipolar disorder, may sometimes be difficult to diagnose. There is a great need for a valid and reliable diagnostic tool to aid clinicians in arriving at the diagnoses in a timely and accurate manner. Prior studies have suggested that patients suffering from schizophrenia and ADHD may process certain sound stimuli in the brainstem in an unusual manner. When these patient groups have been examined with the electrophysiological method of brainstem audiometry, some studies have found illness-specific aberrations. Such aberrations may also exist for patients suffering from bipolar disorder. Objective: In this study, we will examine whether the method of brainstem audiometry can be used as a diagnostic tool for patients suffering from schizophrenia, ADHD, and bipolar disorder. Methods: The method includes three steps: (1) auditory stimulation with specific sound stimuli, (2) simultaneous measurement of brainstem activity, and (3) automated interpretation of the resulting brain stem audiograms with data-based signal analysis. We will compare three groups of 12 individuals with confirmed diagnoses of schizophrenia, ADHD, or bipolar disorder with 12 healthy subjects under blinded conditions for a total of 48 participants. The extent to which the method can be used to reach the correct diagnosis will be investigated. Results: The project is now in a recruiting phase. When all patients and controls have been recruited and the measurements have been performed, the data will be analyzed according to a previously arranged algorithm. We expect the recruiting phase and measurements to be completed in early 2015, the analyses to be performed in mid-2015, and the results of the study to be published in early 2016. Conclusions: If the results support previous findings, this will lend strength to the idea that brainstem audiometry can offer objective diagnostic support for patients suffering from schizophrenia, ADHD, and bipolar disorder. A positive result from the study could imply that brainstem audiometry could become an important supportive tool for clinicians in their efforts to diagnose patients with these disorders in a timely and accurate manner. Trial Registration: ClinicalTrials.gov NCT01629355; https://clinicaltrials.gov/ct2/show/NCT01629355 (Archived by WebCite at http://www.webcitation.org/6VBfTwx5H). %M 25679914 %R 10.2196/resprot.3880 %U http://www.researchprotocols.org/2015/1/e16/ %U https://doi.org/10.2196/resprot.3880 %U http://www.ncbi.nlm.nih.gov/pubmed/25679914