Jmir Mental Health最新文献

{"title":"AI Chatbots for Mental Health Self-Management: Lived Experience-Centered Qualitative Study.","authors":"Dong Whi Yoo, Jiayue Melissa Shi, Violeta J Rodriguez, Koustuv Saha","doi":"10.2196/78288","DOIUrl":"10.2196/78288","url":null,"abstract":"<p><strong>Background: </strong>Large language models (LLMs) now enable chatbots to engage in sensitive mental health conversations, including depression self-management. Yet their rapid deployment often overlooks how well these tools align with the priorities of people with lived experiences, which can introduce harms such as inaccurate information, lack of empathy, or inadequate crisis support.</p><p><strong>Objective: </strong>This study explores how people with lived experience of depression experience an LLM-based mental health chatbot in self-management contexts, and what perceived benefits, limitations, and concerns inform harm-mitigating design implications.</p><p><strong>Methods: </strong>We developed a technology probe (a GPT-4o-based chatbot named Zenny) designed to simulate depression self-management scenarios grounded in prior research. We conducted interviews with 17 individuals with lived experiences of depression, who interacted with Zenny during the session. We applied qualitative content analysis to interview transcripts, notes, and chat logs using sensitizing concepts related to values and harms.</p><p><strong>Results: </strong>We identified 3 themes shaping participants' evaluations: (1) informational accuracy and applicability, including concerns about incorrect or misleading information, vagueness, and fit with personal constraints; (2) emotional support vs need for human connection, including validation and a judgment-free space alongside perceived limits of machine empathy; and (3) a personalization-privacy dilemma, where participants wanted more tailored guidance while withholding sensitive information and using privacy-preserving tactics.</p><p><strong>Conclusions: </strong>People with lived experience of depression evaluated LLM-based mental health chatbots through intertwined priorities of actionable information, emotional validation with clear limits, and personalization that does not require unsafe data disclosure. These findings suggest concrete design strategies to mitigate harms and support LLM-based tools as complements to, rather than replacements for, human support and recovery.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e78288"},"PeriodicalIF":5.8,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13046095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147610474","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":"Help-Seeking in the Age of AI: Cross-Sectional Survey of the Use and Perceptions of AI-Based Mental Health Support Among US Adults.","authors":"Michiko Ueda, Michael L Birnbaum, Yanhong Liu, Qingyi Yu, Xihe Tian, Anna Mirer, Seethalakshmi Ramanathan, Mark Sinyor","doi":"10.2196/88196","DOIUrl":"10.2196/88196","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Anecdotal evidence suggests that an increasing number of people are turning to generative artificial intelligence (GenAI) tools or artificial intelligence (AI)-assisted chatbots to discuss and manage mental health concerns. However, systematic data on the use and perception of such tools remain scarce.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to examine how young and middle-aged adults in the United States use GenAI and AI-assisted mental health chatbots as mental health resources and assess their preferences for these tools relative to human mental health professionals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;An anonymous online survey was conducted in October 2025 among US adults in a commercial online panel sample of US adults aged 18-49 years (N=1805). Respondents were asked about the sources they typically turn to when facing mental health concerns, their frequency of using GenAI tools or chatbots for mental health support, and whether the frequency of seeing human mental health professionals had changed since they started using AI tools for mental health support. Attitudes toward AI-based mental health support were assessed and compared with attitudes toward human mental health professionals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In this sample, of the 1805 respondents, 638 (35.2%) reported using AI tools at least once a week in a typical week for mental health support, and 99 (5.5%) were classified as \"heavy users\" who reported regularly spending hours discussing their mental health concerns through AI. However, nearly 60% of respondents reported that they would turn first to family (1078/1805) and friends (1046/1805) when facing mental health concerns. Respondents who screened positive for moderate to severe depressive or anxiety symptoms were more likely to use AI-based mental health support compared to those without these symptoms (adjusted odds ratio 1.71, 95% CI 1.36-2.15) and those with suicidal ideation were more likely to be heavy AI users (adjusted odds ratio 2.42, 95% CI 1.49-3.95). Among those who had ever seen a human mental health professional (n=511), 28.4% (145/511) reported a perceived decline in visit frequency to human mental health professionals since they started using AI tools for the same purpose. Participants expressed more favorable attitudes toward human mental health professionals than toward AI-based tools. However, among heavy AI users, perceptions of AI-based mental health support and human counseling were nearly equivalent in positivity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;AI appears to be an important component of the mental health help-seeking landscape among respondents in this sample. Although most respondents still preferred human professionals, a subset reported relying on AI tools for comparable support. Ongoing monitoring and ethical guidelines are needed to ensure that AI technologies expand access to care while being safely and effectively integrated into the broader conti","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":" ","pages":"e88196"},"PeriodicalIF":5.8,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13077273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373307","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":"Mass Media Narratives of Psychiatric Adverse Events Associated With Generative AI Chatbots: Rapid Scoping Review.","authors":"Van-Han-Alex Chung, Pénélope Bernier, Alexandre Hudon","doi":"10.2196/93040","DOIUrl":"10.2196/93040","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Generative artificial intelligence (AI) chatbots have rapidly entered public use, including in contexts involving emotional support and mental health-related interactions. Although these systems are increasingly accessible, concerns have emerged regarding potential adverse psychiatric outcomes reported in public discourse, including psychosis, suicidal ideation, self-harm, and suicide. However, these reports largely originate from journalistic accounts rather than systematically verified clinical data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This rapid scoping review aimed to systematically map and characterize mass media narratives describing alleged adverse psychiatric outcomes temporally associated with generative AI chatbot interactions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A rapid scoping review methodology was applied to publicly accessible news articles identified primarily through Google News searches. Articles published from November 2022 onward were screened for eligibility if they described a specific case in which psychiatric deterioration or crisis was temporally linked to generative AI use. Data were extracted using a structured coding template capturing article characteristics, demographic information, AI platform features, interaction intensity, outcome type and severity, type of evidence reported, and causal attribution language. Descriptive statistics and cross-tabulations were performed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 71 news articles representing 36 unique cases were included. Suicide death was the most frequently reported outcome (35/61, 57.4% cases with complete severity coding), followed by psychiatric hospitalization (12/61, 19.7%). Fatal outcomes were disproportionately represented among minors (19/21, 90.5%) compared with adults (17/35, 48.6%). ChatGPT was the most frequently cited platform (51/71, 71.8%), followed by Character AI (10/71, 14.1%). Causal attribution most commonly referenced AI system behavior (45/61, 73.8%), and the term \"alleged\" was the predominant causal descriptor (33/61, 54.1%). Evidence sources were primarily chat logs or screenshots (34/61, 55.7%), while police or medical documentation was rare (1/61, 1.6%). Regulatory calls were present in 51 of 60 (85%) articles with nonmissing data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Mass media reporting of generative AI-related psychiatric harms is concentrated around severe outcomes, particularly suicide deaths among youth, and is frequently framed within regulatory and corporate accountability narratives. While causality cannot be established from media reports, consistent patterns of high-intensity interactions, user vulnerability, and limited safeguard reporting highlight the need for structured safety surveillance, transparent AI risk auditing, and clearer governance frameworks. As generative AI becomes increasingly integrated into everyday psychosocial contexts, systematic research and formal safety monitoring will be nec","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e93040"},"PeriodicalIF":5.8,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13077275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575605","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":"Quantifying Consumer Interest in Medicare Advantage: Development and Usability Study Using Google Trends Data.","authors":"Amy Dunn Tramontozzi, Gregory J Downing, Lucas Tramontozzi","doi":"10.2196/89355","DOIUrl":"10.2196/89355","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Since 2020, Medicare Advantage (MA)-related internet searches have tripled, accompanied by increased regional marketing by private insurers. Commercial health insurance dominates the internet during enrollment periods, often outpacing public sources in accessibility. Prior studies suggest that MA advertising significantly shapes enrollment and may fuel choices over traditional Medicare in certain subpopulations. We sought to better understand how health plan marketing strategies affect consumers by using Google Trends data and MA health plan enrollment selection. We applied novel analysis to assess statistical relationships among marketing, internet searches, and enrollment data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objectives of this paper are (1) to establish the validity of Google Trends data as a surrogate measure for consumer MA plan selection by demonstrating stable, repeatable seasonality and domain specificity using control terms such as \"car insurance\" and \"life insurance\" at national and Designated Market Area levels; (2) to quantify the congruency between MA search interest and Centers for Medicare & Medicaid Services enrollment data by testing whether search peaks coincide with or precede enrollment surges nationally within a year; and (3) to assess whether local search intensity aligns with advertising exposure by evaluating search behavior as a potential proxy for marketing impact and consumer engagement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study is a retrospective Google Trends analysis of consumer search patterns from January 2004 to December 2024, using relative search volume and conducting correlations with MA enrollment. Search data are accessible via the Google Trends website Explore tool or by applying for Google Trends application programming interface alpha access. MA enrollment data originated from the Centers for Medicare & Medicaid Services MA Dashboard. KFF (formerly the Kaiser Family Foundation) provided the medical advertising marketing data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A consistent, significant correlation between MA advertising and searches on MA exists across US markets, particularly before and during MA enrollment windows. Findings suggest a linkage in user behavior between volume of searches and subsequent enrollment in an MA plan.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Internet search data can provide an open, near-real-time means of tracking patterns in MA-related search activity across time and geography, offering insight into how consumer interest fluctuates around enrollment periods. Our analysis reveals repeatable patterns in consumer interest over time that may be useful for contextualizing insurance marketing dynamics of consumers choosing commercial MA over traditional Medicare benefits. We also identified a significant correlation of seasonal trends in searches using terms associated with MA plans that peaked during the annual enrollment period (October-December). Impr","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e89355"},"PeriodicalIF":5.8,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13069369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147533153","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":"The Performance of Wearable Device-Based Artificial Intelligence in Detecting Depression: Systematic Review and Meta-Analysis.","authors":"Jiawen Liu, Junhui Wang, Zhaobin Wu, Mohamad Ibrani Shahrimin Bin Adam Assim","doi":"10.2196/85319","DOIUrl":"10.2196/85319","url":null,"abstract":"<p><strong>Background: </strong>In recent years, advances in wearable sensor technology and artificial intelligence (AI) have provided new possibilities for detecting and monitoring depression.</p><p><strong>Objective: </strong>This study systematically reviewed and meta-analyzed the diagnostic and predictive performance of wearable device-based AI models for detecting depression and predicting depressive episodes and explored factors influencing outcomes.</p><p><strong>Methods: </strong>Following PRISMA-DTA (Preferred Reporting Items for a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy) guidelines, the PubMed, Embase, Web of Science, and PsycINFO databases were searched from inception to May 27, 2025. Eligible studies used AI algorithms on wearable device data for depression detection or episode prediction. Sensitivity, specificity, diagnostic odds ratio, and area under the curve (AUC) were pooled using a bivariate random effects model. Risk of bias was assessed using Prediction Model Risk of Bias Assessment Tool plus artificial intelligence (PROBAST+ AI), and certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool.</p><p><strong>Results: </strong>We included 16 studies (32 datasets) with 1189 patients and 13,593 samples. For depression detection, pooled sensitivity and specificity were 0.89 (95% CI 0.83-0.93) and 0.93 (95% CI 0.87-0.96), with a diagnostic odds ratio of 110.47 (95% CI 33.33-366.17) and AUC of 0.96 (95% CI 0.94-0.98). Random forest models showed the best performance (sensitivity=0.89, specificity=0.91, AUC=0.97). Subgroup analyses indicated that study design, AI method, reference standard, and input type significantly affected diagnostic accuracy (P<.05). For depressive episode prediction (3 datasets), pooled sensitivity was 0.86 (95% CI 0.80-0.91), and pooled specificity was 0.65 (95% CI 0.59-0.71). The overall risk of bias was low to moderate, with no evidence of publication bias.</p><p><strong>Conclusions: </strong>Wearable device-based AI models achieved high accuracy for detecting depression and moderate utility in predicting episodes. However, heterogeneity, reliance on retrospective and public datasets, and lack of standardized methods limited generalizability.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e85319"},"PeriodicalIF":5.8,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12974932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436906","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":"Virtual Reality Implementation in Mental Health Care Is a Marathon, Not a Sprint: Qualitative Longitudinal Study of a Virtual Reality Training Program.","authors":"Marileen Mte Kouijzer, Laura Am Koenis, David Huizinga, Saskia M Kelders, Yvonne Ha Bouman, Hanneke Kip","doi":"10.2196/83453","DOIUrl":"https://doi.org/10.2196/83453","url":null,"abstract":"<p><strong>Background: </strong>Despite the potential of virtual reality (VR) for treatment and assessment in mental health care, its practical implementation remains limited. Much implementation research explores barriers and facilitators; fewer studies actually evaluate targeted implementation strategies and track how their effects evolve over time in mental health care practice.</p><p><strong>Objective: </strong>This study aims to examine how a structured VR training program functioned as an implementation strategy in routine mental health care and to identify how therapists' adoption trajectories and implementation needs shifted across stages of the process.</p><p><strong>Methods: </strong>Eleven therapists from a Dutch mental health care organization completed a 6-session VR training. Semistructured interviews were conducted at 3 time points: pretraining, immediately posttraining, and 3 months posttraining. Data were deductively analyzed using theoretical thematic analysis based on the capability, opportunity, motivation - behavior model and the Theoretical Domains Framework to map stage-specific changes in implementation needs relating to VR use.</p><p><strong>Results: </strong>The training improved therapists' perceived knowledge, skills, and confidence in using VR. Nonetheless, actual uptake of VR in clinical routines remained limited. Enduring barriers included workflow misalignment, hierarchical decision-making structures, and the absence of a shared organizational vision and sustained leadership support. The longitudinal design revealed a dynamic pattern: early adoption hinged on individual capability and motivation, whereas maintenance depended on organizational opportunity and communicated support. These stage-specific shifts clarify why training alone does not translate into routine use and which organizational levers are most important when.</p><p><strong>Conclusions: </strong>VR training for therapists is a necessary but insufficient implementation strategy in mental health care. A longitudinal approach shows that successful implementation requires pairing training with organization-level changes that address opportunity barriers over time. By shifting from static evaluations of whether training works to a process-oriented focus on what support is needed at each stage of implementation, this study advances implementation science in digital mental health and offers actionable guidance for embedding VR in routine care.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e83453"},"PeriodicalIF":5.8,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12974359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436871","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":"Associations of Problematic Smartphone Use and Smartphone Screen Time With Eating Disorder Psychopathology in Non-Clinical Samples: A Systematic Review.","authors":"Johanna Keeler, Laura Conde Ludtke, Qingyu Yang, Valentina Raschke Rameh, Rebecca Ward, Janet Treasure, Ben Carter","doi":"10.2196/88572","DOIUrl":"10.2196/88572","url":null,"abstract":"<p><strong>Background: </strong>The ubiquitous use of smartphones has given rise to maladaptive patterns of use, often termed \"problematic smartphone use\" (PSU), which disproportionately impacts children and young people and is associated with poor mental health. Emerging evidence suggests that patterns of smartphone use (eg, PSU and high smartphone screen time) may also influence eating patterns and contribute to symptoms associated with eating disorders (ED), although the nature of this relationship remains poorly understood.</p><p><strong>Objective: </strong>The aim of this systematic review was to examine the association between PSU and ED psychopathology or ED-related outcomes (eg, body dissatisfaction, emotional eating, and food addiction) in clinical and nonclinical populations and explore potential moderators and mediators.</p><p><strong>Methods: </strong>This preregistered systematic review conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines searched 3 databases (PubMed, Embase, and Web of Science) for studies published after January 2011 reporting data on PSU and ED psychopathology.</p><p><strong>Results: </strong>Thirty-five studies met the prespecified eligibility criteria, with almost all reporting cross-sectional data in nonclinical populations (n=52,584; mean age 17.0, SD 5.5 years). Most studies were assessed as being of good quality (n=28, 78%) according to a modified version of the Newcastle-Ottawa Scale. In these nonclinical samples, the vast majority of studies reported a positive association between PSU and ED psychopathology, which was largely consistent across age groups and countries. Identified mediators of this relationship included greater emotional regulation difficulties and anxious and depressive symptoms. Positive associations were also found across studies between PSU and several ED-related outcomes including food addiction, body dissatisfaction, uncontrolled eating, and emotional overeating. Daily smartphone screen time was consistently related to higher ED psychopathology. According to a GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) assessment of the evidence, most outcomes were rated as low certainty, largely due to the cross-sectional nature of evidence, which contributed to a high risk of bias.</p><p><strong>Conclusions: </strong>PSU and greater daily smartphone screen time are associated with higher ED symptoms, body image dissatisfaction, and broader disordered eating behaviors. Due to a paucity of studies in clinical populations, these conclusions are generalizable only to nonclinical populations (ie, those without a formal diagnosis of an ED). Further longitudinal research in clinical populations is needed to fully contextualize the impact of PSU and smartphone screen time on ED risk and severity.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e88572"},"PeriodicalIF":5.8,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12980065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436892","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":"AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.","authors":"Syeda Umme Salma, Chandra Rekha Renduchintala, Isa Siddique, Evelina Sterling, Sweta Sneha, Nazmus Sakib","doi":"10.2196/79973","DOIUrl":"10.2196/79973","url":null,"abstract":"<p><strong>Background: </strong>Caregivers supporting individuals with Alzheimer disease and related dementias (AD/ADRD) frequently encounter prolonged emotional strain, psychological distress, and social isolation, yet their needs are largely overlooked in current technological and clinical interventions. The special routines and obligations of caregivers of individuals with AD/ADRD are frequently not well-suited to the many artificial intelligence-driven (AI-driven) mental health solutions that are currently available. This reveals a critical need for sophisticated, customized solutions created especially to help the mental health of caregivers for patients with AD/ADRD.</p><p><strong>Objective: </strong>To address the existing limitations of personalized mental health interventions, we aimed to identify existing literature on personalized mental health interventions using AI for specific purposes and to develop a new framework for the caregivers of individuals with AD/ADRD.</p><p><strong>Methods: </strong>We followed an iterative approach to design the new framework. First, we did a systematic literature review of current literature to identify data analysis, AI methods, and personalized interventions. Second, we focused on the underlying gaps of this research, and by synthesizing our findings from the review, we proposed a conceptual framework.</p><p><strong>Results: </strong>The systematic literature review identified 73 unique results, and from external sources, we found 3 unique potential papers. Of these, 28 papers were eligible for inclusion, on which we performed our analysis. Based on the findings, we developed a new conceptual framework with 3 special features that are specifically for caregivers of patients with AD/ADRD. The 3 unique features are a personalized daily routine scheduler, which will take both patients with AD/ADRD and caregiver's information to make it personalized, a daily reward system to keep patients motivated, and an educational repository to get the bite-sized knowledge for the lesson of handling patients in an efficient manner and taking care of one's own mental health.</p><p><strong>Conclusions: </strong>The proposed framework provides a chance for caregivers to receive mental health care, which will be personalized. The framework is developed with more updated methods than existing approaches, with a lack of personalization in this sector. This framework can be implemented with a goal of personalization and explainable approaches and can undergo further iterations to ensure it is appropriate for specific purposes.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e79973"},"PeriodicalIF":5.8,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13005065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147370492","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":"Effectiveness and Experiences of Online Mental Health Peer Support for Young People: Systematic Scoping Review.","authors":"Shuting Yuan, Gavin Davidson, Sebastian Kurten, Paul Best","doi":"10.2196/83139","DOIUrl":"10.2196/83139","url":null,"abstract":"<p><strong>Background: </strong>The prevalence of mental health conditions among young people is high and further increasing. Despite this considerable need, barriers remain to accessing and engaging with traditional mental health services. Online mental health peer support is increasingly popular among young people seeking help. However, research examining the effectiveness of online mental health peer support and user-centered experiences remains limited.</p><p><strong>Objective: </strong>This systematic scoping review aimed to synthesize research evidence on the effectiveness and experiences of online mental health peer support for young people, compare these across different forms, and identify possible applications of online peer support.</p><p><strong>Methods: </strong>This scoping review followed the 5-stage framework proposed by Arksey and O'Malley and revised by Levac et al. Three reviewers screened the articles. The IBSS, SSCI, Scopus, PsycINFO, MEDLINE, and Social Policy and Practice databases were searched by title and abstract. Retrieved studies (N=8327) were double-screened, and 38 articles met the inclusion criteria. Studies were included if they focused on young people aged up to and including 25 years and if the intervention was online peer support primarily aimed at supporting mental health.</p><p><strong>Results: </strong>The number of participants (posts/comments) in each study ranged from 10 to 36,934. Seventeen studies reported on the effectiveness of online peer support, and 28 studies reported on young users' experiences. This review summarized evidence of overall positive clinical outcomes, personal recovery outcomes (including improved social connectedness and other personal recovery outcomes), and multidimensional experiences of online mental health peer support (such as fostering resonance or fatigue).</p><p><strong>Conclusions: </strong>Overall, online mental health peer support demonstrated positive effects on clinical and personal recovery outcomes. However, findings related to user experiences were mixed. Experiences were influenced by factors such as safety, anonymity, and the quality of peer interactions. These insights may inform the role alongside traditional services, attractive platform design, and safeguarding. Future research should further explore the integration of online peer support with traditional services and various digital platforms to better address young people's mental health needs and further examine its effectiveness as well as experiences in practice to maximize the peer support benefits and reduce risks.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e83139"},"PeriodicalIF":5.8,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12935419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147291566","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":"Correction: Using Smartphone-Tracked Behavioral Markers to Recognize Depression and Anxiety Symptoms: Cross-Sectional Digital Phenotyping Study.","authors":"George Aalbers, Andrea Costanzo, Raj Jagesar, Femke Lamers, Martien J H Kas, Brenda W J H Penninx","doi":"10.2196/92888","DOIUrl":"10.2196/92888","url":null,"abstract":"","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e92888"},"PeriodicalIF":5.8,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12916086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146221629","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}