Jmir Mental Health最新文献

{"title":"Generative AI in Youth Mental Health Apps: Rapid Review.","authors":"Helene Høgsdal, Henriette Kyrrestad, Sabine Kaiser","doi":"10.2196/90589","DOIUrl":"10.2196/90589","url":null,"abstract":"<p><strong>Background: </strong>Mental health apps are frequently used as platforms for delivering digital health interventions to young people. New technology such as generative AI enables a wider range of engaging and more personalized features that can be included in mental health apps. However, there is limited insight into the opportunities for integrating generative AI into such apps.</p><p><strong>Objective: </strong>This rapid review aimed to provide a systematic overview of the possibilities of integrating generative AI into mental health apps for young people. Furthermore, the review aimed to report potential benefits and disadvantages of the integration of generative AI into such apps.</p><p><strong>Methods: </strong>A systematic search was conducted in 4 databases (PsycInfo, Embase, MEDLINE, and CINAHL) in November 2025 to identify studies that evaluated mental health apps with integrated generative AI. Eligible studies included primary research published between 2022 and 2025, involving a sample of young people aged 11 to 24 years, and written in either English or a Scandinavian language.</p><p><strong>Results: </strong>A total of 5 articles were included in the review. The most common method of integrating generative AI into mental health apps for young people was through chatbots. Overall, young people rated the apps as having good usability and quality. Some studies also provided data on effectiveness, showing promising results for outcomes such as depression, anxiety, and distress. None of the studies systematically assessed harmful effects using standardized methods, nor did they report any adverse outcomes related to mental health.</p><p><strong>Conclusions: </strong>Young people are generally positive about apps that include generative AI. There is some evidence suggesting that such tools may contribute to a preventive or health-promoting effect on young people's mental health. However, the existing research is limited and characterized by methodological constraints. The lack of reported adverse mental health outcomes might reflect a lack of investigation rather than evidence of no harm. Further research should explore the potential short- and long-term effects of integrating generative AI into mental health apps for young people, as well as systematically mapping possible adverse events.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e90589"},"PeriodicalIF":7.4,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539175/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suicidal Ideation Across the Lifespan Among Commercially Insured Outpatients: Retrospective Cohort Study.","authors":"Dena M Bravata, James M Hudson, Severin Kibby, Neha P Chaudhary, Taft Parsons Iii, Nicole M Benson","doi":"10.2196/101421","DOIUrl":"https://doi.org/10.2196/101421","url":null,"abstract":"<p><strong>Background: </strong>Suicide remains a leading cause of death in the United States and is on the rise. Limited evidence describes the current burden of suicidal ideation (SI) among commercially insured outpatients, a population that represents a large and rapidly expanding segment of those seeking care.</p><p><strong>Objective: </strong>The objective of this study was to characterize the prevalence of SI among a national cohort of commercially insured individuals presenting for outpatient mental health services, and to examine differences across key demographic and social factors.</p><p><strong>Methods: </strong>This was a retrospective evaluation of patients, aged 6 to 64 years, presenting for their first visit with an outpatient mental health -clinician between January 1, 2024, and October 31, 2025. The prevalence of SI (defined as a nonzero response on question 9 of the Patient Health Questionnaire-9) was compared by age, sex, and geography using regression analysis.</p><p><strong>Results: </strong>Among 189,225 commercially insured individuals included in the analysis, mean age was 33.60 (SD 11.33) years. Among those reporting sex, 68% (34,168/50,378) identified as female, 29% (14,491/50,378) as male, and 3% (1719/50,378) as nonbinary. Most had a diagnosis of anxiety (56,176/189,225, 29.7%) or depression (50,075/189,225, 26.5%). In total, 18.6% (35,215/189,225) of individuals indicated some level of SI. Across age groups, SI was highest among adolescents (age 13-17 years) with 32.8% (2107/6426) reporting any suicidality, and 4.8% (306/6426) reporting SI nearly every day (<i>P</i><.001). The population with the greatest SI comprised individuals identifying as nonbinary (644/1719, 37.5%; <i>P</i><.001). SI increased as a function of patients' social vulnerability (<i>P</i><.001) and varied by geography.</p><p><strong>Conclusions: </strong>Nearly 1 in 5 individuals endorsed some degree of SI, with one third of adolescents reporting SI, proportions that are notably higher than estimates from the general US population. These findings underscore the need for comprehensive screening for SI and the expansion of clinical support for suicidal populations.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e101421"},"PeriodicalIF":7.4,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13489109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Topic Modeling of Spontaneous Speech in Depression Using Large Language Models: Multilingual Four-Cohort Study.","authors":"Gustave Cortal, Sélim Benjamin Guessoum, Xuan-Nga Cao, Santiago de Leon-Martinez, Enrique Baca-García, Rachid Riad","doi":"10.2196/99185","DOIUrl":"https://doi.org/10.2196/99185","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Depression is underdiagnosed worldwide, and clinicians rely on interpreting patients' subjective speech. Qualitative analysis of patient language does not scale, and existing computational approaches describe topics with keyword lists that miss clinical nuance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We evaluated whether clustering spontaneous speech transcripts with large language models (LLMs) yields clusters whose membership is associated with validated clinical scales across multilingual cohorts, and whether LLMs can render those clusters human-readable through fine-grained natural-language descriptions. We further examined which interview questions yield clusters most strongly associated with clinical status, and how sociodemographic factors relate to cluster membership.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed spontaneous speech transcripts from 4 independent cohorts: a French general population sample (1338 participants) and 3 clinical samples in Italian (n=116), Chinese (n=52), and Spanish (n=90). Responses to open-ended questions were transcribed, embedded with a multilingual language model, dimensionally reduced, and grouped by density-based clustering. An LLM then summarized each cluster into a natural-language description. Cluster membership was tested for association with validated clinical scales (Patient Health Questionnaire-9, Beck Depression Inventory, Generalized Anxiety Disorder 7-item scale, Athens Insomnia Scale, Multidimensional Fatigue Inventory, and Columbia Suicide Severity Rating Scale), clinician-assigned depression diagnoses, and sociodemographic factors (age, education, and sex).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Unsupervised clustering yielded clusters significantly associated with clinical scores in the French, Italian, and Chinese cohorts, with an exploratory association in the smaller Spanish cohort. In the French general population, Patient Health Questionnaire-9 depression scores differed across clusters (η²=0.19, 95% CI 0.17 to 0.24, &lt;i&gt;P&lt;/i&gt;&lt;.001), as did anxiety (Generalized Anxiety Disorder 7-item scale), insomnia (Athens Insomnia Scale), and fatigue (Multidimensional Fatigue Inventory) scores (η²=0.14 to 0.16, all &lt;i&gt;P&lt;/i&gt;&lt;.001). In the clinical cohorts, cluster membership was associated with clinician-diagnosed depression in the Italian sample (Cramér &lt;i&gt;V&lt;/i&gt;=0.74, 0.65 to 0.85, &lt;i&gt;P&lt;/i&gt;&lt;.001) and with major depressive disorder diagnosis in the Chinese sample (&lt;i&gt;V&lt;/i&gt;=0.55, 0.36 to 0.78, &lt;i&gt;P&lt;/i&gt;=.001). A suicide-risk association in the smaller Spanish sample (Columbia Suicide Severity Rating Scale) was unstable and is reported as exploratory (mean &lt;i&gt;V&lt;/i&gt;=0.27). The feelings-and-sleep question yielded the strongest clinical associations, whereas questions about past or future events yielded small effect sizes (η²≤0.05). Age (η²=0.27, 0.24 to 0.32) and sex (Cramér &lt;i&gt;V&lt;/i&gt;=0.22, 0.21 to 0.30) were each associated with cluster membership for the \"describe your last 24 hours\" qu","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e99185"},"PeriodicalIF":7.4,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13480703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technology-Themed Persecutory and Related Presentations of Delusions in the Digital Age: Narrative Review.","authors":"Syed Ali Bokhari, Abdelaziz A Osman, Muhanad Elnoor, Mohamed A Alnor, Syed Fahad Javaid","doi":"10.2196/102927","DOIUrl":"https://doi.org/10.2196/102927","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Persecutory delusions have long mirrored prevailing cultural and technological concerns. Beliefs involving implanted devices, internet surveillance, hacked smartphones, algorithmic targeting, and AI-mediated control are increasingly visible in contemporary psychosis. However, we identified no prior review dedicated specifically to synthesizing technology-themed delusional content across historical and contemporary clinical contexts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This narrative review aimed to trace the historical evolution of technology-themed delusions, characterize their contemporary clinical phenomenology, examine relevant neurocognitive and sociocultural mechanisms, and discuss implications for psychiatric assessment and intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;PubMed/MEDLINE, Scopus, and APA PsycInfo were searched without date restrictions, supplemented by backward citation tracking and targeted identification of historical primary sources. Eligible sources included original research, case reports and case series, reviews, and relevant conceptual or historical publications addressing delusional content involving technology. A narrative synthesis approach informed by the Scale for the Assessment of Narrative Review Articles (SANRA) framework was used. The final search was completed on May 1, 2026.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Forty-three sources were included in the narrative synthesis, comprising 9 case reports or case series, 14 empirical studies, 8 reviews, and 12 historical or conceptual sources. Technology-themed delusions appeared as pathoplastic variants of enduring persecutory, control, and referential motifs, clustering into 5 overlapping categories: surveillance and hacking beliefs, implant and control delusions, Truman Show-type broadcast phenomena, social media-specific referential ideas, and emerging algorithmic and AI-mediated themes. In the largest contemporary cohort (228 patients with psychosis), technology-themed delusions were described by 104 of the 201 (51.7%) patients with delusional thought content, and the odds of technology-themed delusions rose by approximately 15% per admission year (odds ratio 1.15, 95% CI 1.01-1.31; P=.04). Cognitive models of persecutory ideation, the aberrant salience hypothesis, technological unfamiliarity, digital immersion, and wider sociocultural narratives all appeared relevant to these presentations. Pandemic-era conspiracy material, including 5G and vaccine-microchip narratives, further illustrated how culturally available technological explanations may scaffold delusional elaboration in vulnerable individuals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Technology-themed delusions represent contemporary expressions of enduring delusional motifs shaped by digital culture. Their assessment requires psychiatric expertise combined with sufficient technological literacy to distinguish proportionate privacy concerns, overvalued conspiracy beliefs, and fixed ","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e102927"},"PeriodicalIF":7.4,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13476003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148765517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Usage Time: Rethinking Therapeutic Dose and Effect in Digital Therapeutics.","authors":"Björn Meyer, Linda T Betz, Mario Weiss, Wolfgang Lutz","doi":"10.2196/98660","DOIUrl":"https://doi.org/10.2196/98660","url":null,"abstract":"<p><strong>Unlabelled: </strong>Digital therapeutics (DTx) have become increasingly prominent in mental health care, offering scalable, evidence-based interventions. A common assumption underlying their design and evaluation is that greater usage time leads to superior therapeutic outcomes, reflecting an implicit linear dose-response model. However, accumulating evidence challenges this simplified perspective and suggests that the relationship between usage time and clinical benefit is substantially more complex. In this viewpoint article, we critically examine the assumption that higher usage time is a necessary prerequisite for therapeutic success in digital mental health interventions. Drawing on psychotherapy dose-effect research, findings from digital intervention trials, and our own empirical work, we highlight several factors that complicate linear exposure models. These include rapid early response and plateau effects, substantial heterogeneity in user trajectories, motivational and contextual determinants of use, episodic patterns of engagement, and the distinction between on-platform activity and off-platform skill application. The linear dose metaphor, implicitly inherited from pharmacotherapy models, appears conceptually mismatched with learning-based digital interventions. Importantly, equating greater use with greater effectiveness risks conflating exposure with therapeutic mechanism, and may inadvertently promote evaluation frameworks that prioritize duration over meaningful change. Together, these insights suggest that usage time is an imperfect proxy for therapeutic engagement, and that high or continuous use should not be treated as a universal indicator of intervention quality. We argue for a shift toward mechanism-informed and individualized evaluation frameworks that prioritize the quality, timing, and functional impact of intervention use over cumulative exposure.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e98660"},"PeriodicalIF":7.4,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13475932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148762965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review.","authors":"Thomas P Kutcher, Isha Chakraborty, Kristin Kostick-Quenet, Akane Sano, Nidal Moukaddam, Jeffrey A Herron, Wayne K Goodman, Sameer A Sheth, Ashutosh Sabharwal, Nicole R Provenza","doi":"10.2196/94819","DOIUrl":"10.2196/94819","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Bipolar disorder (BD) features episodic shifts among mania, hypomania, depression, mixed states, and euthymia. Timely detection of mood transitions is difficult due to infrequent clinical touchpoints. Digital health technologies, including wearables and smartphones, offer a unique opportunity to passively and continuously monitor behavior and physiology that could reflect underlying mood dynamics in real-world settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to systematically review passively collected digital markers for BD mood states, characterize devices/modalities and analytic approaches, appraise risk of bias, and identify design gaps and priorities for clinical translation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched MEDLINE, PsycINFO, Scopus, IEEE Xplore, and ACM Digital Library (February 16, 2026). We included peer-reviewed studies of adults with bipolar I disorder or bipolar II disorder (BDI or BDII) that measured passively collected markers and related them to depressive, manic, hypomanic, mixed, or euthymic states. Studies that relied exclusively on active measures (eg, lab tests and ecological momentary assessment) were excluded. Two independent reviewers screened studies, extracted study characteristics and results, conducted narrative synthesis, and assessed risk of bias.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 23,727 records, 57 studies met criteria. Most enrolled ≤50 participants (n=34, 60%) and monitored ≤365 days (n=46, 81%); 11 out of 57 studies (19%) collected data only in the clinic. Eight digital marker domains emerged: physical activity, heart rate (HR), electrodermal activity (EDA), geolocation, smartphone use, light exposure, sleep, and speech. Consistent patterns linked depression to reduced mobility and social interaction, later/variable sleep, and lower daytime light; mania and hypomania were associated with higher and more variable activity, shorter/advanced sleep, and increased communication. Circadian features derived from sleep/activity repeatedly aided prediction. EDA tended to be lower in depression; HR variability findings were mixed across settings and methods. Keyboard and speech features (eg, timing and prosody) showed associations and performed well in classification models. Twenty-one studies used machine learning; several reported strong performance for episode prediction/classification. However, external validation was usually absent, samples were small, monitoring windows were often short relative to episode timescales, clinical labels were infrequent/misaligned, and missingness was rarely modeled despite likely informativeness.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Passive digital markers for BD show promise, with the most robust signals aligning with DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) diagnostic features (sleep-wake patter","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e94819"},"PeriodicalIF":7.4,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13472530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns of Engagement With an AI Conversational Agent for Mental Health and Associations With Anxiety and Depression: Cross-Sectional Study.","authors":"Kelsey McAlister, Courtney Jewell, Jennifer Huberty","doi":"10.2196/98690","DOIUrl":"10.2196/98690","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Digital mental health interventions using conversational AI agents are increasingly being adopted as scalable alternatives to traditional care. Engagement is typically measured using volume-based metrics (eg, session counts and total time on a platform). However, these metrics overlook engagement patterns over time, which are not well understood in relation to mental health outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The purpose of this cross-sectional study was to explore how different patterns of engagement with Mental's AI conversational agent relate to self-reported depression and anxiety. We aimed to (1) identify and describe engagement profiles based on patterns of interaction depth and temporal consistency, (2) compare depression and anxiety symptoms across engagement profiles, and (3) explore whether engagement profiles were associated with mental health symptom severity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This cross-sectional observational study linked survey responses to back-end app usage data from 112 Mental app users who completed at least 5 sessions with the conversational AI agent. Engagement profiles were derived using median splits on interaction depth (α parameter) and temporal consistency (Gini coefficient). Depression was assessed using the Patient Health Questionnaire-8 (PHQ-8), and anxiety was assessed using the Generalized Anxiety Disorder-7 (GAD-7). One-way ANOVAs compared symptoms across profiles. Linear regression models examined associations between profiles and symptom severity, adjusting for age, gender, and total duration of use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We identified 4 distinct engagement profiles based on interaction depth and temporal consistency: extended and episodic (profile 1; n=25), extended and consistent (profile 2; n=31), brief and episodic (profile 3; n=31), and brief and consistent (profile 4; n=25). Users in profile 1 (extended and episodic) reported the lowest anxiety (mean 2.68, SD 2.43) and depression (mean 3.48, SD 4.06), while profile 4 (brief and consistent) reported the highest anxiety (mean 10.00, SD 7.03) and depression (mean 10.60, SD 8.75). Significant differences were observed for anxiety (F3,108=8.07, P&lt;.001, η²=0.18) and depression (F3,108=5.47, P=.002, η²=0.13). In adjusted models, engagement profile was significantly associated with depression (R²=0.16, F7,104=2.87, and P=.009) and anxiety (R²=0.21, F7,104=4.04, and P&lt;.001). Compared to profile 1, users in profiles 2 and 4 reported significantly higher depression and anxiety. Profile 3 differed from profile 1 for anxiety only (β=3.11, P=.047).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Users with longer, clustered sessions reported the lowest symptoms, whereas those with brief, evenly distributed use reported the highest symptom levels, suggesting that the structure of engagement may be associated with symptom levels in ways that aggregate usage metrics do not capture. These findings are preliminary and hypot","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e98690"},"PeriodicalIF":7.4,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13456072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Momentary Mood and Affiliation Following Social Interactions in the Digital Age: Longitudinal Study Investigating Associations With Anxiety and Depression.","authors":"Anna Bilston, Sarah Daniels, Yasmin Hasan, Susanne Schweizer","doi":"10.2196/94753","DOIUrl":"10.2196/94753","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Two-fold increases in the prevalence of youth anxiety and depression over the last two decades have mirrored exponential growth in opportunities for adolescent online social interaction via social media, short messaging service (SMS), and internet text messaging apps on smartphones. However, studies to date of self-reported online social interaction time have produced conflicting results. Understanding the role of dispositional and developmental differences in individuals' responses to online versus offline social interactions may help elucidate whether and how online social interaction is related to anxiety and depression.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to investigate the relationship between older adolescents' and emerging adults' (18-24-year-olds) mental health and (1) objectively measured time spent on smartphones and online social interaction apps, (2) momentary affective and affiliative responses to online and offline social interactions, and (3) the moderating role of developmentally and dispositionally elevated social sensitivity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Smartphone, social media (eg, Instagram), SMS, and internet (eg, WhatsApp) text messaging app time from participants' screen use settings, as well as symptoms of anxiety and depression, and social sensitivity, were measured in 190 older adolescents and emerging adults (mean age 20.4, SD 2.2 years). Participants then completed a novel ecological momentary assessment (EMA) capturing affective and affiliative responses to recent online or offline social interactions 3× daily for 1 week. Symptoms of mental health were assessed again after 1 month.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Total online social interaction (combined social media and text messaging) app time, but not total smartphone time, was associated with greater anxiety, at both baseline and one month later. Affective and affiliative responses were less positive for online social interactions compared to in-person interactions. Anxiety, but not depression, was associated with feeling less happy, but not less included, after social interactions. Affective and affiliative responses to in-person, but not online, social interactions were negatively associated with depression across the 1-month study period. Finally, social sensitivity moderated the relationship between affective and affiliative responses to social media interactions and depression at baseline. Overall effect sizes were small.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;These findings emphasize the need to investigate individual factors influencing for whom online social interaction is harmful or beneficial. To do so, this study provides a novel, ecologically valid tool for understanding young people's momentary responses to online and offline social interactions, as well as initial evidence for stronger associations between in-person than online social interaction responses and mental health for older adolescents and eme","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e94753"},"PeriodicalIF":7.4,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148702532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Should I and Can I Use AI for Forensic Psychiatry Report Writing?","authors":"Alexandre Hudon","doi":"10.2196/99749","DOIUrl":"10.2196/99749","url":null,"abstract":"<p><strong>Unlabelled: </strong>The rapid evolution of AI, particularly large language models (LLMs), has renewed interest in their potential role in forensic psychiatry report writing. Recent evidence demonstrates that contemporary LLMs perform well in selected medical knowledge, documentation, and information management tasks and may reduce the administrative burden when deployed under appropriate clinical supervision. However, forensic psychiatric reports differ fundamentally from routine clinical documentation. They constitute expert evidence prepared for legal proceedings and therefore require transparent reasoning, explicit weighing of competing evidence, a robust factual foundation, and personal professional accountability. This viewpoint examines whether AI can and should be used in forensic psychiatry report writing by integrating recent empirical evidence, forensic psychiatry guidance, legal and regulatory frameworks, and emerging governance recommendations. Rather than comparing AI with an idealized human evaluator, the manuscript argues that the appropriate comparison is between 2 imperfect systems of reasoning. Human experts remain susceptible to cognitive biases, omission errors, and disagreement, whereas contemporary LLMs exhibit distinct vulnerabilities, including hallucinations, hidden omissions, probabilistic reasoning, and limited explainability. Although the mechanisms differ, both may ultimately compromise the reliability of expert evidence if left unchecked. Current evidence supports AI for bounded, reversible, and independently verifiable tasks, such as document organization, chronology construction, indexing, transcription, and structured summarization, particularly within secure and validated environments. By contrast, there remains insufficient evidence to support AI-assisted generation or material shaping of psycholegal reasoning, credibility assessments, or final forensic opinions. Because these activities require interpretation, accountability, and reasoning that can withstand judicial scrutiny, they remain fundamentally human responsibilities. The most defensible implementation model is, therefore, one of AI around the report rather than AI writing the report, in which AI serves as a supervised productivity tool while the forensic psychiatrist retains full authorship, accountability, and justification of all substantive conclusions.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e99749"},"PeriodicalIF":7.4,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455175/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148702537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Piloting a Digital App Based on the Friendship Bench for Depression and Anxiety in Palestine: Mixed Methods Study.","authors":"Chantal Lakis, Jamilah Sherally, Anne Braakman, Shruthi Abirami Ramiah, Maarten van Herpen, Sireen Khammash, Luma Tarazi, Umaiyeh Khammash, Jennifer Dabis, Elaine Rabello, Mahdi Adelwahab, Pierre Pratley, Dixon Chibanda","doi":"10.2196/77507","DOIUrl":"10.2196/77507","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The burden of mental disorders is high in conflict-affected populations. In Palestine, we piloted Inuka Coaching, a digital intervention adapted from the Friendship Bench delivered by trained and supervised lay coaches. This paper documents the implementation of the intervention in this highly volatile context after October 7, 2023.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to describe the implementation of Inuka Coaching, a digital mental health tool based on task shifting, in Palestine and examine contextual challenges, fidelity to the coaching model, and lessons learned regarding recruitment, retention, and delivery during escalating ethnic cleansing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Two Palestinian mental health professionals were trained and certified in the Inuka method as head coaches, and subsequently trained 5 lay coaches. Palestinian adults in Gaza and the West Bank were recruited primarily through social media and received up to 4 structured, text-based coaching sessions typically delivered over 4 weeks depending on participant availability and preference. Standardized mental health screening questionnaires (Self-Reporting Questionnaire-20 [SRQ-20] and Posttraumatic Stress Disorder Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5]) were collected at baseline, immediately after the first coaching session, and 3 months after the final session. Session transcripts were reviewed to assess coaches' fidelity to the Inuka method, and a focus group discussion explored coaches' experiences with training, delivery, and contextual challenges.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Between August 2023 and February 2024, a total of 70 participants were enrolled. Baseline assessments indicated high levels of psychological distress: 95.7% (67/70) scored above the PCL-5 threshold of 31, suggesting likely posttraumatic stress disorder, and 69.4% (43/62) scored in the \"at risk\" range on the SRQ-20. The effectiveness of the method could not be determined as retention was low, with only 7.1% (5/70) completing the program. Coach fidelity was high, with 94.6% (35/37) of transcripts adhering to all 5 steps of the intervention. Coaches reported positive experiences with the method but identified challenges related to recruitment, session continuity, platform usability, and the need for flexibility during acute crises. Key implementation learnings included the importance of early in-person collaboration and training, culturally sensitive framing, flexible delivery and session structures, and robust support for lay coaches.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While digital, task-shifted mental health interventions can be delivered with fidelity in conflict settings, sustaining engagement during escalating violence remains challenging. Future implementations require flexible design, context-sensitive recruitment and retention strategies, adaptive delivery models, and strong support for","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e77507"},"PeriodicalIF":7.4,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}