Jmir Mental Health最新文献

{"title":"Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.","authors":"Pavol Mikolas, Fabian Huth, Kyra Bröckel-Bundt, Christina Berndt, Julia Martini, Birgit Maicher, Michael Marxen, Falk Gerrik Verhees, Paula Marie Henneberg, Christoph Vogelbacher, Andreas Jansen, Tilo Kircher, Irina Falkenberg, Florian Thomas-Odenthal, Martin Lambert, Vivien Kraft, Gregor Leicht, Christoph Mulert, Andreas J Fallgatter, Thomas Ethofer, Anne Rau, Karolina Leopold, Andreas Bechdolf, Reif Andreas, Silke Matura, Jonathan Repple, Felix Bermpohl, Jana Fiebig, Thomas Stamm, Christoph U Correll, Georg Juckel, Vera Flasbeck, Isabella C Wiest, Jakob N Kather, Udo Dannlowski, Eva Mennigen, Philipp Ritter, Michael Bauer, Andrea Pfennig","doi":"10.2196/84424","DOIUrl":"10.2196/84424","url":null,"abstract":"<p><strong>Background: </strong>Functional impairments associated with mental health conditions are on the rise. Predicting functional outcomes may improve the targeting of preventive interventions. While prognostic models have primarily focused on psychosis, early recognition services require a transdiagnostic approach.</p><p><strong>Objective: </strong>This study aimed to predict global functioning within a 2-year follow-up using baseline clinical and structural magnetic resonance imaging (MRI) data in a population-based sample of young, help-seeking individuals presenting with affective and anxiety symptoms as well as attention-deficit hyperactivity disorder.</p><p><strong>Methods: </strong>We classified 357 help-seeking individuals aged 18-35 years recruited from 9 sites as \"impaired\" (Global Assessment of Functioning [GAF] ≤60; n=228) or \"nonimpaired\" (GAF>60; n=129) at year 1 and/or year 2 follow-up. GAF classification group status at follow-up was predicted using linear support vector machine (SVM), decision tree, and large language model (LLM) Llama-3 using clinical assessments and/or structural MRI. Leave-one-site-out (SVM) or external sample (LLM) was used for validation.</p><p><strong>Results: </strong>SVM achieved balanced accuracy of 69.2% using clinical features only. Items related to baseline occupational functioning, interpersonal relationships, cognitive functioning, psychotic and affective symptoms, as well as the presence of anxiety disorder, were most predictive. The decision tree further reduced the feature set to 5 predictive items, achieving balanced accuracy of 76.6%. Although amygdala and hippocampal subregions achieved balanced accuracy of 57.1%, structural MRI did not improve the overall prediction. Llama-3 performed comparably well to SVM (balanced accuracy of 72.6%).</p><p><strong>Conclusions: </strong>Machine learning demonstrated good performance in predicting global functioning. Interestingly, the out-of-the-box LLM performed comparably well without being trained or fine-tuned, highlighting the potential of leveraging free-text data for mental health prognosis.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e84424"},"PeriodicalIF":7.4,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13286077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148303563","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":"Using AI to Detect Psychosis Relapse: Scoping Review.","authors":"Lorenzo Ghelfi, Jack Healy, Francesco Piacenza, Ian French, Nicholas McNamara, Khyber Afridi Rabbi, Benjamin Bond, Emma O'Hora, Darren Roddy, Moyyad Kamali, Sudipto Das, Sandra Anna Just, Enrico Tedeschi, Musarrat Hussain, Karl Øyvind Mikalsen, Stefan Kaiser, Giacomo Cecere, Sanne Koops, Janna de Boer, Elysie Nguyen, Emre Bora, Wolfram Hinzen, Philipp Homan, Iris E Sommer, David Cotter, Mary Cannon, John Paul Lyne","doi":"10.2196/92192","DOIUrl":"10.2196/92192","url":null,"abstract":"<p><strong>Background: </strong>Psychotic disorder represents a leading cause of disability worldwide, and relapse in psychosis is common. Artificial intelligence (AI) is increasingly recognized as a method that could aid clinical monitoring for individuals experiencing psychosis.</p><p><strong>Objective: </strong>This review aims to map the existing literature on AI-based approaches-including machine learning, deep learning, and natural language processing-used to detect relapse in individuals with psychotic disorders.</p><p><strong>Methods: </strong>A systematic search strategy was conducted on PubMed, PsycINFO, and Embase up to January 7, 2026. Observational studies, randomized controlled trials, and quasi-experimental studies that used AI methods to detect relapse in psychosis were eligible for inclusion. Screening and data extraction procedures were conducted by at least 2 reviewers working independently. Findings were extracted, charted, and described using narrative synthesis based on data extraction and consensus meetings with the research team. The scoping review was prospectively registered with the Open Science Framework.</p><p><strong>Results: </strong>Relevant studies identified (N=10) included the use of digital tools such as smartphone- and smartwatch-based monitoring, ecological momentary assessment tools, social media activity, and internet searches. Digital phenotyping via smartphones and wearables emerged as the most common method for data collection. The efficacy of AI models varied with sensitivity (or recall) ranging from 0.25 to 0.77 and specificity (or precision) ranging from 0.06 to 0.88. The reported area under the receiver operating characteristic curve for models ranged from 0.63 to 0.78. AI models were heterogeneous across studies, and most study findings were not replicated.</p><p><strong>Conclusions: </strong>This scoping review highlights both the promise and the current limitations of AI in psychosis relapse detection. Passive digital phenotyping research in the detection of psychosis relapse has progressed, and personalized approaches with individual-level modeling show promise; however, further studies need to include larger numbers of participants and should incorporate methods such as large language models. Future studies will require large collaborations aimed at delivering AI methods for use in real-world clinical practice.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e92192"},"PeriodicalIF":7.4,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13271360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266709","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":"Prevalence and Predictors of Self-Reported Adverse Experiences in Digital Meditation Training: 2 Randomized Controlled Trials.","authors":"Polina Beloborodova, Lillian M Smith, Kevin M Riordan, Otto Simonsson, Lilah T Dottori, Helen Q Song, Nicholas S Shashko, Raquel Tatar, Scott A Baldwin, Amit Bernstein, John D Dunne, Richard J Davidson, Matthew J Hirshberg, Simon B Goldberg","doi":"10.2196/90602","DOIUrl":"10.2196/90602","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Digital meditation-based interventions (MBIs) reach vast global audiences with millions of active users, yet concerns persist about the frequency and nature of adverse experiences (ie, AExs) occurring during meditation training. Some researchers have argued that AExs are substantially underdetected and reflect iatrogenic harm caused by meditation (ie, adverse effects [AEfs]). Others contend that these experiences largely reflect common stressors that would be experienced without meditation. These competing perspectives underscore the need for further research, particularly in the context of digital MBIs, the most widely used form of meditation training.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study examined the prevalence, predictors, and subjective evaluations of AExs during a digital MBI and tested whether reported experiences may be caused by meditation practice via comparisons between meditation-exposed and nonexposed participants.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Data were drawn from 2 trials of the Healthy Minds Program. Exploratory study 1 (n=315) consisted of a sample of distressed US undergraduate students to estimate the prevalence of AExs and identify baseline predictors. Preregistered confirmatory study 2 (n=594) sampled distressed US adults from all 50 states to replicate findings from study 1 and to examine participants' subjective evaluations of AExs. Study 2 additionally compared AEx rates between participants who did and did not complete guided meditations to assess whether AExs could be caused by meditation exposure. Study 3 (n=87) used qualitative methods to analyze study 1 participants' responses to an open-ended question regarding their strategies for coping with AExs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In studies 1 and 2, 27.9% (88/315) and 10.1% (40/396) of participants, respectively, reported at least one AEx during the study period, with 6.7% (21/315) and 3% (12/396) reporting functional impairment, largely aligning with previous research. Critically, in study 2, rates of AExs did not significantly differ between participants who did and did not complete guided meditations, suggesting that these experiences were not caused by meditation practice. Higher baseline depression, anxiety, loneliness, experiential avoidance, and perceived barriers to meditation predicted more frequent AExs. In studies 1 and 2, 89.8% (79/88) and 90% (36/40) of participants who reported AExs, respectively, indicated that they were glad to have learned to meditate. Qualitative analyses showed that participants used diverse coping strategies, often using skills learned through the Healthy Minds Program.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;AExs were relatively common but occurred at comparable rates among participants who did and did not meditate, challenging claims that such experiences were caused by meditation practice in distressed individuals. Although a small subset of participants reported some degree of functional i","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e90602"},"PeriodicalIF":7.4,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13263023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148240236","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: Barriers and Facilitators in the Implementation of the Systematic Medical Appraisal, Referral, and Treatment (SMART) Mental Health Digital Intervention in Rural India: Mixed Methods Process Evaluation Study.","authors":"Ankita Mukherjee, Mercian Daniel, Sudha Kallakuri, Siddhardha Devarapalli, Sandhya Kanaka Yatirajula, Amanpreet Kaur, Praveen Devarsetty, Usha Raman, Beverley M Essue, Rajesh Sagar, Shashi Kant, Shekhar Saxena, Graham Thornicroft, Anushka Patel, David Peiris, Pallab K Maulik","doi":"10.2196/101441","DOIUrl":"10.2196/101441","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/89164.].</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e101441"},"PeriodicalIF":7.4,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13309762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148240239","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":"Between Help and Harm: An Evaluation Study of Mental Health Crisis Handling by Large Language Models.","authors":"Adrian Arnaiz-Rodriguez, Miguel Baidal, Erik Derner, Jenn Layton Annable, Mark Ball, Mark Ince, Elvira Perez Vallejos, Nuria Oliver","doi":"10.2196/88435","DOIUrl":"10.2196/88435","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The use of large language models (LLMs)-powered chatbots has reshaped how people seek information and advice, including for emotional and mental health support. While LLMs can offer scalable support, their ability to safely detect and respond to acute mental health crises-including suicidal ideation, self-harm, and violent thoughts-remains poorly understood. Progress is hampered by the absence of unified mental health crisis taxonomies, annotated benchmarks, and empirical evaluations grounded in clinical best practices.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We addressed these gaps by introducing (1) a unified taxonomy of 6 clinically informed mental health crisis categories; (2) an evaluation dataset of over 2000 user inputs drawn from 12 publicly available conversational mental health datasets, classified into crisis categories; and (3) an expert-designed protocol for assessing response appropriateness. We also used LLMs to automatically identify crisis-indicative inputs and conducted an auditing study of 5 LLMs to evaluate the safety and appropriateness of their responses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed a taxonomy of mental health crisis categories informed by clinical experts and established literature. From over 239,000 mental health-related user inputs collected from 12 Hugging Face datasets, we curated 2252 examples (206 for validation, 2046 for testing) covering all taxonomy categories. We evaluated 3 LLMs on their ability to classify inputs into crisis categories, selecting the model with the strongest agreement with human annotators as the judge to label the test set. We then audited 5 LLMs on their ability to generate safe and appropriate responses to the 2046 test examples. Response quality was measured using a clinically informed 5-point Likert scale (1=harmful and 5=fully appropriate), relying on an LLM-as-a-judge validated against human expert feedback.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Several LLMs exhibited high consistency and generally reliable behavior when responding to explicit crisis disclosures, but significant risks remain. A nonnegligible proportion of responses was rated as inappropriate or harmful, particularly in the self-harm and suicidal ideation categories. Substantial performance differences were observed across models: gpt-5-nano and deepseek-v3.2-exp achieved very low harmful response rates, whereas gpt-4o-mini, Llama-4-Scout-17B-16E-Instruct, and grok-4-fast-non-reasoning generated markedly higher rates of unsafe outputs. All models exhibited systemic weaknesses, including poor handling of indirect or ambiguous risk signals, reliance on formulaic responses, and frequent misalignment with user context.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;These findings underscore the urgent need for enhanced safeguards, improved crisis detection, and context-aware interventions in LLM deployments and highlight the central role of alignment and safety engineering-beyond model scale or openne","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e88435"},"PeriodicalIF":7.4,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13256495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148228785","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":"Examining the Social and Mental Health Benefits of Virtual and In-Person Physical Activity Intervention Among Postsecondary Students: Quasi-Experimental Study.","authors":"Melissa L deJonge, Amy E Nesbitt, Simon C Darnell, Chloe A Hamza, Catherine M Sabiston","doi":"10.2196/92076","DOIUrl":"10.2196/92076","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Physical activity (PA) is a promising prevention approach for supporting mental health and enhancing social inclusion among postsecondary students. However, it is unclear whether similar outcomes are realized when PA programming is delivered in-person versus virtually.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Using data from a multiphase research project, the purpose of the study was to examine the influence of on-campus PA programming (virtual and in-person delivery) on mental ill health symptoms (ie, anxiety and depression), social inclusion indices (ie, social connectedness, emotional ties, and social relationship quality), and well-being. Three objectives were addressed: (1) to assess pre-post change in symptoms, social inclusion indices, and well-being for virtual and in-person delivery; (2) to evaluate whether outcome change over time differed by delivery mode; and (3) to examine whether change in symptoms and social inclusion indices predicted change in well-being for both delivery modes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Physically inactive postsecondary students experiencing mental ill health participated in a 6-week structured and supervised PA program. Pre-post intervention data were collected across 3 phases, and the analytical samples included: 1. In-person delivery (n=87; 82%, 69/84 young adults; 86%, 74/86 women; 38%, 33/86 White; 20%, 17/86 Chinese; 86%, 75/87 with mental illness; 2. Virtual delivery (n=62; 69%, 42/61 young adults; 95%, 59/62 women; 34%, 21/62 White; 21%, 13/62 South Asian; 55%, 34/62 with mental illness), and 3. Data from students who received in-person or virtual delivery: (n=92; 67%, 61/91 young adults; 90%, 83/92 women; 32%, 29/92 White; 20%, 18/92 South Asian; 59%, 54/92 with mental illness). Data were analyzed using 2-tailed paired samples t tests to address objective 1, a 2 (delivery mode) × 2 (time: pre-post) repeated-measures ANOVA to address objective 2, and hierarchical regression analyses to address objective 3.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Both virtual and in-person PA delivery were effective for symptom reduction and social inclusion improvements across all outcomes (P&lt;.001), with moderate-to-large effects. There was no significant time × delivery mode (F5,84=0.72, ηp²=0.04, P=.60) interaction effect. Change in social inclusion indices explained unique variance in well-being, beyond covariates (gender, mental illness, and ethno-racial identity), and symptom reduction for virtual (R2adj = 0.75, ΔR2=0.08, P&lt;.001) and in-person (R2adj = 0.72, ΔR2=0.16, P&lt;.001) PA delivery.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Online distance learning is increasing across postsecondary settings worldwide, underscoring the need for accessible, technology-enabled mental health prevention interventions. The results provide support for the effectiveness of virtual and in-person PA programming for reducing symptoms of anxiety and depression, while also enhancing social inclusion indices and overall we","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e92076"},"PeriodicalIF":7.4,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13257782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148228852","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":"Detection of Self-Harm in Electronic Mental Health Records Using Privacy-Preserving Local Language Models: Methodological Study.","authors":"Andrey Kormilitzin, Dan W Joyce, Apostolos Tsiachristas, Rohan Borschmann, Navneet Kapur, Galit Geulayov","doi":"10.2196/87586","DOIUrl":"10.2196/87586","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Self-harm is the strongest risk factor for suicide and an important outcome for mental health care. Although prevalent in clinical populations, it is often imprecisely captured in routinely collected clinical data, where it is often recorded and stored as unstructured free text. Contemporary language models, such as GPT (OpenAI) and Gemini (Google), can analyze free-text clinical notes, but such models may violate data governance of processing sensitive patient data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate whether a privacy-preserving language model running entirely within an institution's secure computing infrastructure (here, the UK National Health Service [NHS]) could accurately identify the presence and timing of self-harm using electronic health records from secondary mental health care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Clinical notes were drawn from Oxford Health NHS Foundation Trust using a multistage workflow: (1) a random sample of 1000 patients with a psychiatric diagnosis, defined according to the ICD-10 (International Statistical Classification of Diseases, Tenth Revision; codes F00-F99); (2) candidate-note identification using a Gemma3-4b language model to flag notes containing self-harm content; and (3) from those candidates, 1352 randomly sampled notes were selected for expert annotation, resulting in gold-standard corpus enriched for self-harm content. Clinical notes were annotated for the presence of self-harm and its timing (≤90 days, &gt;90 days, or unknown). A privacy-preserving locally served 27-billion-parameter Gemma 3 language model (\"Gemma3-27b\") was used as the core model. Prompts were systematically developed and refined using a labeled development set to identify self-harm and generate a structured output per clinical record. Gemma3-27b performance was compared against a strong baseline multilabel text classification model based on robustly optimized BERT pretraining approach (RoBERTa), a transformer-based language model architecture. Model performance was evaluated using precision, recall, and the F1-score (harmonic mean of precision and recall), with 95% CIs estimated from 1000 bootstrap samples with replacement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Gemma3-27b outperformed the RoBERTa classifier across all categories, achieving Precision=0.92, Recall=0.92 (sensitivity), and F1-score=0.92 for notes containing self-harm, and Precision=0.97, Recall=0.97 (specificity), and F1-score=0.97 for notes without self-harm. For the 51 notes labeled as recent self-harm in the held-out test set, Gemma3-27b achieved Precision=0.84, Recall=0.75, and F1-score=0.79. The global weighted F1-score of Gemma3-27b across all categories was 0.88, compared to 0.85 for RoBERTa.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;With systematic prompt development on a labeled development set, but no gradient-based fine-tuning, the current Gemma3-27b language model matched or exceeded a fine-tuned RoBERTa classifier","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e87586"},"PeriodicalIF":7.4,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13229841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148145196","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":"Recommendations for Research and Clinical Implementation of Ambulatory Assessment, Mood Monitoring, Digital Phenotyping, and Remote Measurement Technology in Mood Disorders: Synthesis of Systematic Review Findings.","authors":"Laurence Astill Wright, Mat Rawsthorne, Neil Nixon, Boliang Guo, Richard Morriss","doi":"10.2196/79501","DOIUrl":"10.2196/79501","url":null,"abstract":"<p><strong>Background: </strong>Ambulatory assessment and active and passive monitoring all offer a real-time, flexible approach to assessing mood and behavior in mood disorders. Despite their potential, concerns remain regarding the performance, usability, adherence, and potential safety of these tools.</p><p><strong>Objective: </strong>This study synthesizes the findings from 7 systematic reviews, integrating quantitative and qualitative data from randomized trials, observational studies, and user experience research to evaluate the performance, feasibility, acceptability, and clinical impact of ambulatory assessment and mood monitoring in people with depression and bipolar disorder. We assessed studies over the medium or long term (3 months or more).</p><p><strong>Methods: </strong>A summary of a series of systematic reviews was carried out by the authors-including meta-analyses (for quantitative data) and meta-syntheses (for qualitative data). Eight electronic databases were searched, and mixed methods studies were included. Studies were assessed for risk of bias. The results were checked for coherence, and recommendations were made by individuals with lived experience, methodologists, and psychiatrists. GRADE (Grading of Recommendations Assessment, Development, and Evaluation) was used to assess the quality and strength of the evidence.</p><p><strong>Results: </strong>The 111 included studies included 19,945 participants and used 69 different ambulatory assessment protocols or mood-monitoring interventions. Key barriers to implementation were identified, including performance inconsistency, adverse effects, and user disengagement. Evidence-based recommendations are provided to guide future clinical and research applications.</p><p><strong>Conclusions: </strong>Ambulatory assessment and mood monitoring hold promise in research and clinical practice, yet their implementation requires more rigorous evaluation, greater personalization, and responsible, user-centered design. Crucially, these measures can add granularity and confirmation, but additional context is often required, and none of these measures are robust enough yet to replace current outcomes.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e79501"},"PeriodicalIF":7.4,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13229395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148151836","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":"BrainBaseline Assessment of Cognition and Everyday Functioning (\"BRACE\"-ing for the Future): Establishing iPad-Based Norms for Cognitive Function in the Multicenter AIDS Cohort Study and Women's Interagency HIV Study Combined Cohort Study.","authors":"Leah H Rubin, Pauline M Maki, Joan Severson, Adam Lieberman, Eran F Shorer, Sabina A Haberlen, Deborah R Gustafson, Michelle Floris-Moore, Valentina Stosor, Matthew J Mimiaga, Jamie Peven, Deborah L Jones, Anandi N Sheth, Kathleen M Weber, Amanda B Spence, Anjali Sharma, David E Vance, Raha M Dastgheyb","doi":"10.2196/70207","DOIUrl":"10.2196/70207","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Digital cognitive assessments are increasingly used in large-scale studies to assess brain health, offering scalable, standardized, and self-directed testing solutions. Cognitive function remains a concern for people with HIV despite antiretroviral therapy. The BRACE (BrainBaseline Assessment of Cognition and Everyday Functioning) is a validated tablet-based screener for cognition in people with HIV. Preliminary pilot norms were established in a small sample (n=144), but full regression-based normative data have not yet been developed. Consequently, HIV serostatus differences based on standardized BRACE scores and cognitive correlates have not been systematically examined.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to develop regression-based normative data for BRACE performance in people without HIV who were demographically and behaviorally comparable to people with HIV within biological sex; to examine differences in cognitive performance by HIV status and biological sex; and to evaluate sociodemographic, behavioral, and clinical correlates of BRACE performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A total of 2937 participants (1063 people without HIV [499 women] and 1874 people with HIV [1053 women]) in the Multicenter AIDS Cohort Study/Women's Interagency HIV Study Combined Cohort Study completed BRACE once between November 2020 and March 2025. BRACE includes the Trail Making Test (A and B), Stroop-Color, and visual spatial learning. Regression-based norms were derived from people without HIV using multiple demographic models (eg, age-only, age + education, and age + education + sex). The age + education model was selected for primary analyses because it provided the best balance of interpretability, parsimony, and generalizability while avoiding race-based corrections. HIV serostatus and sex differences were examined using ANOVA and χ2 tests, with effect sizes calculated using Cohen's d.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Cognitive performance was largely comparable between people with HIV and people without HIV across all BRACE outcome measures. Statistically significant differences were very small in magnitude (all effect sizes&lt;0.11) and primarily observed among men on Stroop-Color. Across groups, older age and fewer years of education were associated with poorer raw BRACE performance, although these associations attenuated after demographic adjustment using T-scores. Most clinical and behavioral factors (eg, hypertension, smoking, and noncannabis substance use) were related to poorer raw scores but not standardized performance. However, diabetes and cannabis use remained independently associated with T-scores across multiple measures-diabetes with poorer scores and cannabis use with higher scores, an association that should be interpreted cautiously. HIV-specific clinical factors, such as nadir CD4 count and antiretroviral therapy duration, were linked primarily to raw scores.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/st","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e70207"},"PeriodicalIF":7.4,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13218720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148057758","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":"When AI Colludes: Clinical Reliability of Training and Preference Data as a Trustworthy-AI Criterion.","authors":"Hina Tahseen","doi":"10.2196/96894","DOIUrl":"10.2196/96894","url":null,"abstract":"<p><strong>Unlabelled: </strong>Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophancy, and AI-associated delusions. Less attention has been paid to a prior question: whether the human-generated text and preference judgments that shape large language models are themselves clinically reliable, particularly when self-report may be distorted. This Viewpoint aims to develop the clinical psychiatric construct of collusion-the uncritical acceptance of an unreliable account-as an analytic lens for AI training and deployment, and to argue that the clinical reliability of training and preference data should be treated as an explicit trustworthy-AI criterion in mental-health-relevant systems. A conceptual synthesis of psychiatry, clinical psychology, and AI safety literature was undertaken. The analysis distinguishes three pipeline layers: pretraining corpora, preference data and posttraining methods, and deployment-time interaction. It maps the clinical construct of collusion against adjacent technical concepts, including sycophancy, reward overoptimization, grounding, refusal training, red-teaming, and live monitoring. The synthesis suggests that collusion-like dynamics are least applicable at the pretraining layer and most applicable at the preference-data and deployment layers, where unassessed user or labeler input can be reinforced without corroboration. Existing mitigations, including data curation, Constitutional AI, reward-model evaluation, grounded generation, refusal training, red-teaming, and postdeployment monitoring, address parts of this problem. However, these approaches are not yet organized around a clinically informed account of when self-report is unreliable. The central novelty is therefore not a generic claim about bias, but the proposal that clinical self-report reliability should be assessed as a distinct data-quality and governance dimension. Trustworthy-AI frameworks for mental-health-relevant applications should incorporate clinical expertise in self-report reliability into preference-data design, red-teaming, and postmarket surveillance. Adding the clinical reliability of training and preference data as an explicit criterion could complement existing technical safeguards while leaving empirical evaluation of clinician involvement as an open research agenda.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e96894"},"PeriodicalIF":7.4,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13205454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148036738","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}