Jmir Mental Health最新文献

{"title":"Incorporating Data From Text Messages and Email Into Psychotherapy: Randomized Controlled Trial.","authors":"Ipsit V Vahia, Julia Kimball, Boyu Ren, Hailey Cray, Rebecca Dickinson, Heejung Kim, Dylan X Guan, Kerry J Ressler","doi":"10.2196/93446","DOIUrl":"10.2196/93446","url":null,"abstract":"<p><strong>Background: </strong>As digital communication becomes central to daily life, psychotherapy increasingly has access to patients' electronic media data. Although digital phenotyping has been widely studied, less is known about whether incorporating personal communication data, such as text messages, improves clinical outcomes in psychotherapy.</p><p><strong>Objective: </strong>This study aimed to determine whether integrating personalized text message data into psychotherapy improves depression, anxiety, health-related quality of life, and therapeutic alliance and to examine whether increased access to collateral information influences clinical decision-making.</p><p><strong>Methods: </strong>The Electronic Media and its Impact on Psychotherapy (EMAP) study was a randomized controlled trial conducted in adult and geriatric outpatient and partial hospitalization settings at an academic psychiatric hospital. Participants receiving psychotherapy for primary depression or anxiety (n=101) were randomized to electronic media-enhanced therapy (EME; n=52) or treatment as usual (TAU; n=49). In EME, research staff reviewed participants' text messages prior to sessions using personalized mood-related key terms and presented aggregated findings on a Health Insurance Portability and Accountability Act (HIPAA)-compliant clinician dashboard. TAU participants received standard care without message review. Outcomes included change scores for the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), the 36-item Short Form Health Survey (SF-36), and the Working Alliance Inventory-Short Revised (WAI-SR). Linear regression and nonparametric tests compared groups. Moderation analyses assessed whether the frequency of information access influenced treatment changes.</p><p><strong>Results: </strong>No statistically significant between-group differences were observed in anxiety or working alliance. Among participants with SF-36 follow-up data (n=65), EME participants demonstrated greater improvement in the pain subscale. Greater frequency of information access was associated with increased treatment-related changes in the EME group. Access to additional collateral information was associated with more clinical actions but not with improved distal outcomes.</p><p><strong>Conclusions: </strong>Integrating personalized text message data into short-term psychotherapy did not significantly improve depression or anxiety, and it neither improved nor worsened therapeutic alliance. However, access to greater amounts of collateral information, including electronic media, influenced clinical decision-making, suggesting nuanced and context-dependent effects.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e93446"},"PeriodicalIF":7.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13679906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225242","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":"\"There is a Place for it\"-Practitioners' Perspectives on Situated Ethical Acceptability of AI Technologies for Mental Health Practice: Qualitative Study.","authors":"Abeer Alotaibi, Corina Sas","doi":"10.2196/91884","DOIUrl":"https://doi.org/10.2196/91884","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Design research on AI technologies for mental health has expanded rapidly, yet existing work has largely focused on practitioners' perspectives within isolated therapeutic stages or specific services. As a result, there is limited understanding of how practitioners view the role of AI across the full therapeutic journey, including assessment, diagnosis, and treatment, or how these perspectives can be translated into actionable and ethically grounded design guidance. This lack of stage-specific guidance introduces several risks: (1) the development of AI tools that practitioners may not trust or adopt, (2) heightened ethical concerns, particularly in diagnosis where bias and erosion of human judgment are most likely, and (3) missed opportunities to leverage AI to support ongoing assessment and treatment adaptation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to explore mental health practitioners' perspectives on how AI may support or hinder therapeutic practice across all 3 therapeutic stages, with particular attention to their ethical considerations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted semistructured interviews with 18 mental health practitioners to explore how AI may support or hinder therapeutic practice across assessment, diagnosis, and treatment. Data were analyzed thematically, and ethical challenges were organized using 4 biomedical principles. This study received approval from the Lancaster University Institutional Ethics Committee (ID 3530).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Findings suggest a stage-specific pattern: practitioners identified valuable roles for AI in supporting clinical assessment and treatment, while expressing strong resistance to autonomous AI diagnosis, which was regarded as the most ethically sensitive stage. Across contexts, AI was seen as supporting and validating, rather than replacing, clinical judgment. Ethical concerns were organized using these principles, with key issues including data validity, inclusivity, privacy, transparency, and explainability. Grounded in these findings, four design directions are proposed: (1) AI-enabled clinical assessment hubs for analyzing multimodal patient data to support transparent decision-making; (2) natural language processing-based tools to enable richer understanding of patients' lived experiences; (3) new models of trust to support ethically sensitive AI-assisted diagnostic decision-making; and (4) large language model-powered personalized and adaptive mental health support systems that maintain human-AI partnership and preserve practitioners' central role in care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study provides a stage-specific understanding of practitioners' perspectives on AI use across assessment, diagnosis, and treatment and systematically frames ethical challenges using biomedical principles. The findings translate practitioner insights into actionable design guidance for ethically informed AI-supported mental he","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e91884"},"PeriodicalIF":7.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of Therapist-Client Dynamics in Psychotherapy Using LLM-Based Assessments: An Empirical Study.","authors":"Angela Chen, Siwei Jin, Canwen Wang, Holly Swartz, Tongshuang Wu, Robert Kraut, Haiyi Zhu","doi":"10.2196/95389","DOIUrl":"https://doi.org/10.2196/95389","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Psychotherapy is a primary treatment for many mental health conditions, yet the dynamic interplay among therapist behaviors, client responses, and the therapeutic relationship remains difficult to disentangle at scale. Traditional process research relies on labor-intensive human coding, limiting the ability to model moment-to-moment therapeutic dynamics across large datasets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop and validate a computational framework for modeling therapist-client interaction processes and to examine how therapist behaviors and relational qualities relate to client self-disclosure and emotional expression in psychotherapy sessions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed automated assessment pipelines using large language models (LLMs) to measure therapist behaviors (empathy, exploration), relational quality (rapport), and client outcomes (self-disclosure, self-directed negative emotion, outward-directed negative emotion). Model-generated scores were validated against human annotations. We then applied these measures to approximately 2,000 hours of psychotherapy transcripts from the Alexander Street corpus. This yielded an estimation sample of 119,233 client utterances from 1,573 sessions and 37 clients. Because utterances are nested within sessions and sessions within clients, we estimated a saturated multivariate structural equation model (SEM) with standard errors clustered at the session level. Results were confirmed in a multilevel regression model with random intercepts for session and client.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Following comparative validation of six candidate language models, automated measures showed good to excellent agreement with human annotations (ICC = 0.60-0.81), with rapport (ICC = 0.81), self-disclosure (ICC = 0.78), empathy components (ICC = 0.60-0.75), and emotion measurements (ICC = 0.66-0.80). SEM analyses revealed that therapist empathy and exploration were each associated with increased client disclosure (empathy β=0.086, 95% CI 0.078 to 0.095; exploration β=0.086, 95% CI 0.078 to 0.094; both P&lt;.001) and increased expression of self-directed negative emotions, comprising sadness, anxiety, fear, and depression (empathy β=0.118, 95% CI 0.110 to 0.127; exploration β=0.078, 95% CI 0.070 to 0.087; both P&lt;.001). Both behaviors were also associated with reduced expressions of enjoyment (empathy β=-0.037; exploration β=-0.041; both P&lt;.001). Surprisingly, rapport in the prior session showed no association with client self-disclosure or negative emotion expression.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrates that LLM-based measurement combined with structural modeling can capture core therapeutic processes at scale. Empathy and exploration showed small but consistent turn-level associations with client disclosure and emotional expression, whereas prior-session rapport did not show a robust association. These findings provide a c","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":" ","pages":""},"PeriodicalIF":7.4,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble of Domain-Specific Natural Language Processing and Large Language Models for Detecting Suicidal Ideation and Mental Health Conditions in Social Media Text: Development and Evaluation Study.","authors":"Thomas Kallstenius, Basil Duvernoy, Adam Kallstenius, Andrea Johansson Capusan, Adam Williamson","doi":"10.2196/98925","DOIUrl":"https://doi.org/10.2196/98925","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mental illness contributes substantially to global disability, and public adoption of AI for mental health support is accelerating without commensurate safety evaluation. General-purpose large language models can fail to recognize naturalistic text expressing suicidal ideation, with immediate clinical consequences. Domain-specific natural language processing methods offer a contrasting approach, but ensembles integrating the two have not been formally characterized.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to (1) benchmark 5 modeling approaches for classifying mental health-related social media text, (2) develop and optimize ensembles integrating a domain-specific natural language processing classifier with a fine-tuned large language model, and (3) derive a closed-form framework constraining ensemble weighting in safety-critical classification.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed 60,889 publicly available Reddit and Twitter texts spanning 9 categories (anxiety, bipolar disorder, depression, a normal baseline, personality disorder, stress, suicidal ideation, attention-deficit/hyperactivity disorder, and autism spectrum disorder). Labels were derived from the originating subreddit or a self-stated condition and are not clinical diagnoses. Five architectures were compared: prompt-engineered base GPT-4o-mini; fine-tuned GPT-4o-mini; a domain-specific classifier (support vector machine over symptom-informed lexical features); and 2 ensembles of these models, hybrid probability-indicator fusion and soft probability fusion. Data were split into 70%, 10%, and 20% at the post level (n=42,622, n=6089, and n=12,178); weights were selected on the validation split and all reported performance comes from the held-out test split. Closed-form bounds on permissible language model weights were derived for both strategies. CIs are paired bootstrap intervals (n=10,000 replicates); accuracy differences were tested with exact McNemar tests.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The domain-specific classifier achieved 91.9% accuracy (95% CI 91.4%-92.4%), exceeding the base (58.7%) and fine-tuned (88.6%) large language models. Both ensembles outperformed either constituent model, reaching 93.9% (soft fusion; 95% CI 93.5%-94.3%) and 93.8% (hybrid fusion; 95% CI 93.4%-94.2%) at a weighting of 60% classifier to 40% fine-tuned model, selected on validation (P&lt;.001 for both against the classifier alone; the 2 ensembles did not differ from each other [P=.16]). The hybrid ensemble reduced the miss rate for the suicidal ideation category from 32.5% (650/2000) to 1.9% (37/2000), a 17.6-fold reduction relative to the base model, although its advantage over the classifier alone within that category was not significant (P=.68). Accuracy fell discontinuously above a 50% language model weight, where the hybrid ensemble reduced exactly to the fine-tuned model; the selected weight satisfies the derived instance-level bound of 44.0%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Co","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e98925"},"PeriodicalIF":7.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149138661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of Chatbots in Mental Health Screening and Assessment: Systematic Review.","authors":"Isabel Morales Gil, Inés Marti-Estevez, Sandra Doval, José Miguel Gutiérrez-Carrillo, Rocío Fausor, Silvia Arribas-García, Miguel Ángel Álvarez-Mon, Javier Quintero","doi":"10.2196/93672","DOIUrl":"10.2196/93672","url":null,"abstract":"<p><strong>Background: </strong>Mental health disorders affect 970 million people globally; yet, over 50% do not access timely evaluation due to structural barriers and professional shortages. Chatbots and AI-based conversational agents have emerged as promising tools for mental health screening and assessment.</p><p><strong>Objective: </strong>This study systematically evaluated the effectiveness, accuracy, reliability, and acceptability of chatbots and AI-based conversational agents for mental health screening and assessment in adults.</p><p><strong>Methods: </strong>Systematic search conducted in May 2025 across PubMed/MEDLINE, PsycINFO, Scopus, and Web of Science (2019-2025), following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Eligible studies evaluated chatbots or AI for mental health screening/assessment in adults (≥18 y). Risk of bias was assessed using appropriate tools (Risk of Bias 2, Quality Assessment of Diagnostic Accuracy Studies-2, Joanna Briggs Institute checklists, and Mixed Methods Appraisal Tool). This systematic review was registered with PROSPERO (International Prospective Register of Systematic Reviews; CRD420251072392).</p><p><strong>Results: </strong>Eighteen studies (2021-2025) were included, with samples ranging from 20 to 3902 participants. Rule-based chatbots demonstrated high reliability (Cronbach α >0.85) and good acceptability (Acceptability of Intervention Measure >19/25). Generative models (large language models) achieved sensitivities of 0.84 to 0.93 and specificities of 0.80 to 0.96 for depression and anxiety, with correlations up to <i>r</i>=0.96 with expert clinicians in suicide risk assessment. Hybrid approaches combining large language models with machine learning achieved exceptional performance for cognitive impairment (<i>F</i>₁-score of 92.1%, specificity of 99.6%). Most studies reported high user satisfaction (≥70%), although barriers existed among older populations. Methodological quality was heterogeneous with a moderate risk of bias in critical dimensions.</p><p><strong>Conclusions: </strong>Chatbots and AI conversational agents demonstrate clinically relevant performance in mental health screening and assessment. However, safe implementation requires clear clinical protocols, professional supervision, integration with electronic health records, and active mitigation of algorithmic bias. These technologies should complement rather than replace clinical judgment.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e93672"},"PeriodicalIF":7.4,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13641319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116816","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":"Electronic Health Record-Based Phenotyping for Obsessive-Compulsive Disorder: Algorithm Development and Multicenter Validation Study.","authors":"Bo Wang, Tyne W Miller-Fleming, Dongmei Yu, Donald Hucks, Emily Gantz, Rebecca Johnston, Angela Maxwell-Horn, Nancy Cox, James Sutcliffe, Carol A Mathews, Evonne McArthur, Patrick L McGuire, Dia Kabir, Ashley Dankese, Evan J Giangrande, Rebecca G Fortgang, Shirley B Wang, Rakesh Karmacharya, Joshua L Roffman, Jeremiah M Scharf, Jordan W Smoller, Takahiro Soda, James J Crowley, Lea K Davis","doi":"10.2196/89213","DOIUrl":"https://doi.org/10.2196/89213","url":null,"abstract":"<p><strong>Background: </strong>Obsessive-compulsive disorder (OCD) is a common psychiatric disorder, with two-thirds of affected individuals reporting severe impairment. Despite its substantial burden and moderate heritability, the etiology of OCD remains poorly understood, and treatments are often suboptimal. Although recent genome-wide association studies (GWAS) have identified some risk loci, much of the genetic architecture of OCD remains undiscovered, underscoring the need for scalable approaches to identify large, well-defined patient cohorts.</p><p><strong>Objective: </strong>This study aimed to develop and validate a scalable electronic health record (EHR)-based phenotyping algorithm for identifying OCD cases to support large-scale genetic and translational research.</p><p><strong>Methods: </strong>We leveraged EHR-linked biobank data from 2 large hospital systems, namely Vanderbilt University Medical Center (VUMC) and Mass General Brigham (MGB), to develop a high-throughput phenotyping algorithm integrating diagnostic codes, medication records, and natural language processing (NLP) of clinical notes. Algorithm performance was evaluated through expert chart review, and genetic analyses were performed in individuals of European genetic ancestry using the polygenic scores (PGS) of OCD, major depressive disorder (MDD), and height derived from the most recent GWAS.</p><p><strong>Results: </strong>Expert chart reviews demonstrated our algorithm combining both <i>International Statistical Classification of Diseases</i> (<i>ICD</i>) codes and NLP achieved the highest positive predictive values (PPV) for OCD case identification (0.84 at VUMC; 0.91 at MGB) compared to using either <i>ICD</i> codes or NLP alone, albeit with reduced case yield. At both sites, algorithm-defined OCD cases of European genetic ancestry showed significantly higher OCD PGS than controls. In sensitivity analyses adjusting for MDD status, OCD PGS associations were more robust than MDD PGS associations, while height PGS showed no association, supporting the genetic plausibility and relative specificity of the phenotype.</p><p><strong>Conclusions: </strong>This study presents a scalable and cost-efficient EHR-based approach for identifying OCD cases across health systems. The algorithm achieves high PPV, and among individuals of European genetic ancestry, algorithm-defined cases show significant OCD PGS enrichment, supporting its utility for large-scale genetic studies and advancing understanding of the disorder's complex etiology.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e89213"},"PeriodicalIF":7.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13635843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116976","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":"Generative AI Adoption in Psychotherapy Practice Among Mental Health Professionals: Multicountry Cross-Sectional Survey.","authors":"Charlotte Blease, Josefin Hagström, Jens Gaab, Alexis Carey, Francesco Cipriani, Colin Gorman, Antje Frey Nascimento, Amanda Fitzgerald, Lena Holtz, Julian Schwarz, Gillian Strudwick, Maria Tibbs, John Torous, Jeffrey H D Cornelius-White","doi":"10.2196/105052","DOIUrl":"10.2196/105052","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Generative AI (GenAI) tools, such as OpenAI's ChatGPT, Google's Gemini, and Microsoft Copilot, are being adopted across health care settings. Despite the popularity of GenAI, empirical data on the extent of adoption by mental health clinicians in routine psychotherapy practice are scarce.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to characterize purposeful GenAI use among mental health professionals providing psychotherapy, including tools used, purposes, perceived workload effects, and the organizational context surrounding use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a cross-sectional online survey between January 13, 2026, and March 30, 2026. A convenience sample of trained mental health professionals who had provided psychotherapy within the previous 12 months was recruited through professional associations and academic and clinical mailing lists, with snowball dissemination by email. The survey assessed lifetime purposeful GenAI use in psychotherapy practice; tools and purposes of use; perceived work burden reduction; and, for the previous 12 months, employer or professional organization encouragement, prohibition, and participation in AI training. Descriptive statistics were calculated. Univariable logistic regression models were used to examine associations between GenAI use and candidate factors. Variables meeting an entry criterion of P&lt;.10 were entered into a multivariable logistic regression; statistical significance was set at P&lt;.05.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 766 mental health professionals who provide psychotherapy in 30 countries completed the survey. Of these, 54.6% (n=418, 95% CI 51.0%-58.1%) reported having purposely used at least 1 GenAI tool in clinical psychotherapy practice. ChatGPT was the most frequently used tool (354/418, 84.7%, 95% CI 81.2%-88.2%). The most commonly reported clinical purpose was assisting with treatment planning (175/418, 41.9%, 95% CI 37.1%-46.7%), followed by managing administrative tasks (n=173, 41.4%, 95% CI 36.6%-46.1%) and generating psychoeducational materials for clients (n=166, 39.7%, 95% CI 35.0%-44.4%). Among AI users, 82.8% (346/418, 95% CI 79.1%-86.4%) reported that these tools reduced their overall work burden. Only 18.1% (139/766, 95% CI 15.4%-20.9%) of respondents reported institutional encouragement to use GenAI tools, while 81.1% (n=621, 95% CI 78.3%-83.9%) reported not having received any professional training on AI use. In multivariable logistic regression (α=.05), younger age was associated with GenAI use, while practice setting showed mixed evidence of association. The overall adjusted effect of practice setting did not reach statistical significance (P=.07), although urban practitioners had lower odds of GenAI use than rural practitioners (adjusted odds ratio 0.614, 95% CI 0.388-0.971; P=.04).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study extends previous predominantly national and profession-specific research by provi","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e105052"},"PeriodicalIF":7.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116919","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":"Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review.","authors":"Yifan Sun, Kemeng Che, Tella Lantta, Anna Axelin, Iman Azimi, Pasi Liljeberg","doi":"10.2196/101677","DOIUrl":"10.2196/101677","url":null,"abstract":"<p><strong>Background: </strong>Perinatal mental health disorders affect approximately 20% of pregnant and postpartum individuals, and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjective self-reports. Mobile devices, including smartphones and wearables, offer opportunities for continuous and objective measurement, but evidence on their assessment utility in perinatal populations remains fragmented.</p><p><strong>Objective: </strong>This review aimed to examine the application of wearable devices and smartphones for detecting and predicting perinatal mental health outcomes, with emphasis on predictive performance, informative features, and methodological rigor.</p><p><strong>Methods: </strong>We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (PROSPERO CRD420251249218). Six databases (PubMed, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ACM Digital Library) were searched initially in January 2026 and supplemented by an amended search in June 2026, with no publication date restrictions. Evidence was synthesized narratively, and the risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool With AI extension).</p><p><strong>Results: </strong>The initial and supplementary searches yielded 1952 unique records after deduplication, of which 10 studies met the inclusion criteria. The included studies covered postpartum depression, prenatal stress, discrete emotions during pregnancy (eg, happiness, anxiety, and sadness), and maternal loneliness. High discrimination metrics were reported for postpartum depression in individual studies, including a multiclass area under the curve of 0.85, a binary area under the curve of 0.871, and an <i>F</i>₁-score of 0.9872. Heart rate variability, GPS-derived mobility, physical activity, and sleep features were most frequently reported as useful, and their interpretation requires perinatal-specific contextualization. Methodological quality was limited, with 80% (12/15) of PROBAST+AI assessment units rated as having high overall quality concern or risk of bias, mainly due to small samples, limited validation, inadequate handling of missing data, and potential overfitting in the analysis domain.</p><p><strong>Conclusions: </strong>Mobile sensing shows preliminary potential for perinatal mental health assessment, but current evidence does not yet support clinical screening or decision-making, and independent external validation in perinatal populations is currently lacking. Progress toward clinical utility requires broader mental health outcome coverage, larger longitudinal cohorts, standardized analytical and reporting practices, adoption of modeling approaches better suited to perinatal trajectories, independent external validation, and human-centered monitoring designs.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e101677"},"PeriodicalIF":7.4,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149065387","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":"Assessing the Need for Mental Health Support From Free-Text Responses: Development and Validation of Language-Based Assessments in Adults With Internalizing Symptoms.","authors":"Clara Wiebel, Veerle C Eijsbroek, Vasudha Varadarajan, Katarina Kjell, H Andrew Schwartz, Oscar Kjell","doi":"10.2196/105460","DOIUrl":"10.2196/105460","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Machine learning and natural language processing have demonstrated significant potential for mental health assessment: describing your mental health in your own words can offer a more ecologically valid approach than traditional rating scales. However, most models focus on specific diagnoses, conditions, or symptoms, which may prematurely assign labels and potentially reinforce stigma in the context of early-stage mental health screening.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study develops a language-based assessment model that assesses the need for mental health support based on probed natural language and validates it against best-estimate assessments from multiple experienced psychotherapists.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed an enriched online sample (n=600 for development and n=212 for validation), in which about half reported experiencing internalizing symptoms (depression or anxiety). Participants described their mental health using open-ended responses regarding (1) mental health, (2) suicidal thoughts, (3) medical history, and (4) depression. The responses were converted into contextual word embeddings using a large language model and entered as predictors in a ridge regression using nested cross-validation. Two to three experienced psychotherapists assessed each participant's need for mental health support on a scale from 1 (no support needed) to 5 (potential crisis). Their assessments were based on longitudinal clinical data (natural language, validated scales, clinical interview, sociodemographics, and clinical history) and were averaged into a best-estimate assessment for model validation. We used the Sequential Evaluation With Model Preregistration framework, which separates model development from validation in a held-out set to support robust estimations and generalizability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The language-based assessments closely aligned with the best-estimate assessments (&lt;i&gt;r&lt;/i&gt;=0.82) and showed strong correlations with established clinical rating scales for depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder 7-Item Scale), stress (Perceived Stress Scale 10), and suicidality (Inventory of Depression and Anxiety Symptoms; &lt;i&gt;r&lt;/i&gt;=0.62-0.77). Language-based visualizations of topics and word embeddings showed that low need for support assessments was associated with mentioning well-being and good health, while high assessments were related to depression, anxiety, and suicidality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrates that natural language responses analyzed through large language models and machine learning can be used to assess individuals' need for mental health support in close alignment with best-estimate assessments from experienced psychotherapists. Using less than 5 minutes of respondent time, this approach offers a practical tool for early-stage mental health screening in both clinical and self-guided screening","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e105460"},"PeriodicalIF":7.4,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149070265","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":"Possible Role and Function of AI Conversational Agents in Dialectical Behavior Therapy for Borderline Personality Disorder: Qualitative Interview Study.","authors":"Niklas Liljedahl, Lilas Ali, Sophie I Liljedahl, Örjan Falk, Steinn Steingrimsson","doi":"10.2196/98840","DOIUrl":"10.2196/98840","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Borderline personality disorder (BPD) is associated with substantial distress and a high risk for suicide. Individuals with BPD may be unable to access evidence-based treatments like dialectical behavior therapy (DBT). Artificial intelligence conversational agents (AI-CA) are increasingly discussed as scalable tools for mental health support, but little is known about how DBT clinicians understand the possible role of AI-CA in treatment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to explore DBT psychologists' perspectives on integrating AI-CA into DBT for BPD in the future.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Seventeen psychologists in Sweden, each with at least 1 year of clinical DBT experience (mean 6.4 years, SD 5.6), participated in semistructured interviews as part of this qualitative study. Interviews were conducted in Swedish, transcribed verbatim, and analyzed using reflexive thematic analysis within a constructivist framework. Participants did not test a specific AI-CA.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Three main themes were developed from the data. The first main theme, \"Who Are We in Therapy?\" explored how participants defined AI-CA relationally, positioning it variously as a tool, team member, or supervisor, and a sometimes harmful competitor. How these positionings were configured shaped what AI-CA was seen as allowed to do. The second main theme, \"The Stoic Helper,\" captured how AI-CA was constructed as an extension of the ideal helper: available, competent, adaptable, and tireless, able to provide support in moments when human therapists could not or preferred not to be present. Participants' hopes for what AI-CA could become often mirrored qualities they found difficult to sustain in their own clinical work. The third main theme, \"The Well-Intended Accommodator,\" captured concerns that AI-CA may reinforce dependency and function as a safety behavior by supporting reassurance-seeking rather than autonomy. A central concern was not whether AI-CA could generate validating responses, but whether it could know when validation supports change and when it becomes maladaptive accommodation (AI functional ambiguity).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Perceived benefits mainly centered on accessibility and support for DBT skills generalization, whereas key concerns involved alliance disruption, reinforcement of behaviors that would ideally be targeted for change, dependency, and questions regarding responsibility in high-risk situations. Integrating AI-CA into DBT is not only a technical question but a relational and ethical one. How AI-CA is positioned in relation to the therapist, person in treatment, and team shapes which tasks are considered acceptable and what form integration can take. The findings highlight the need for implementation frameworks that account for relational dynamics, treatment-specific considerations, and AI functional ambiguity that may arise when AI-CA operates in complex therape","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e98840"},"PeriodicalIF":7.4,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149065444","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}