Jmir Mental Health最新文献

{"title":"Effects of Remotely Delivered and Web-Based Interventions on Depression Severity During the COVID-19 Pandemic: 3-Arm Randomized Controlled Trial.","authors":"Zev Schuman-Olivier, Liv Valö, Joseph A Rosansky, Gareth Parry, Alexandra Comeau, Fiona Kate Rice, Carl Fulwiler","doi":"10.2196/88388","DOIUrl":"10.2196/88388","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic highlighted a critical need for effective population mental health approaches to target the most prevalent disorders (eg, depression) during periods of elevated community distress. The effectiveness of remotely delivered and web-based interventions should be investigated to identify and innovate high-quality models for population mental health service delivery.</p><p><strong>Objective: </strong>The primary objective investigated the effectiveness of adding Mindfulness-Based Cognitive Therapy for Resilience (MBCT-R)-a live, online, synchronous, remotely delivered, group-based intervention-to Cambridge Health Alliance MindWell (CHA-MW), a web-based population health screening and stratified support program, compared with CHA-MW alone, on depression symptom severity. The secondary objective evaluated adding internet Cognitive Behavioral Therapy (iCBT)-an asynchronous, web-based, individual, digital intervention-to CHA-MW, compared with CHA-MW alone.</p><p><strong>Methods: </strong>Participants (N=97) were randomized in a 2:2:1 ratio to receive MBCT-R+CHA-MW (n=37), iCBT+CHA-MW (n=41), or CHA-MW alone (n=19) in a 3-arm randomized clinical trial, from May 2021 to September 2022 in an urban public safety net hospital outpatient setting. CHA-MW served as a low-intensity control condition. For the MBCT-R+CHA-MW arm, MBCT-R was an 8-session program mildly adapted from MBCT to address COVID-19-related risks for depression. For the iCBT+CHA-MW arm, iCBT was a 6-session curriculum added to CHA-MW. All study procedures, including regular mental health symptom screenings, were conducted remotely or via a web-based platform. The primary outcome was change in depression symptom severity during the 24-week study period using an intention-to-treat approach that used generalized linear mixed-effects models to evaluate the comparative effectiveness of MBCT-R+CHA-MW vs CHA-MW over time. A secondary analysis compared iCBT+CHA-MW vs CHA-MW on depression severity. Completer analyses were conducted (per-protocol 6+ sessions). The secondary outcome was mental health visit utilization frequency during the study period.</p><p><strong>Results: </strong>Both MBCT-R+CHA-MW (mean difference -14.1, 95% CI -21.0 to -7.2) and CHA-MW (mean difference -15.2, 95% CI -21.8 to -8.6) had significant reductions in depression symptom severity, with no statistically significant between-group differences. iCBT+CHA-MW (mean difference -12.7, 95% CI -17.4 to -8.1) also reduced depression symptoms but without between-group differences when compared with CHA-MW. Intervention completion rates were low (MBCT-R: 30% and iCBT: 24%), and completers demonstrated significantly greater reductions in depression severity than noncompleters (mean difference -8.5, 95% CI -16.2 to -0.8). Overall mental health clinician visits by group had no statistically significant differences. CHA-MW had the largest increase in participants with new psychopharmacology","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e88388"},"PeriodicalIF":7.4,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680422","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: The Effectiveness and Mechanisms of Action of App-Based Interventions for Improving Mental Health and Workplace Well-Being: Randomized Controlled Trial.","authors":"Alexander MacLellan, Graeme Fairchild, Katherine S Button","doi":"10.2196/105585","DOIUrl":"10.2196/105585","url":null,"abstract":"","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e105585"},"PeriodicalIF":7.4,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148649550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suicidal Ideation After Discharge From Psychiatric Hospital: Momentary Assessment Study.","authors":"Wenche Ryberg, Lien My Diep, Mathilde Husky, Roar Fosse","doi":"10.2196/88745","DOIUrl":"10.2196/88745","url":null,"abstract":"<p><strong>Background: </strong>High suicide risk is observed after discharge among patients hospitalized with suicidal thoughts and behaviors. Suicidal ideation (SI) is a well-known precursor to suicide attempts (SAs) and suicide, and it is a central indicator of subjective distress.</p><p><strong>Objective: </strong>This study aimed to examine patient characteristics and predischarge symptoms that might (1) distinguish between participants with and without postdischarge SI, (2) predict peak SI frequency, and (3) predict the intensity and longitudinal trajectory of SI.</p><p><strong>Methods: </strong>Before discharge, patients hospitalized due to high suicide risk were screened for diagnosis (MINI 7.0.2 [Mini International Neuropsychiatric Interview] and SCID-5-PD [Structured Clinical Interview for DSM-5 Personality Disorders]), general symptom severity (Outcome Questionnaire-45 [OQ-45]), depression (Patient Health Questionnaire-9 [PHQ-9]), SI (Suicide Status Form-IV [SSF-IV] and Beck Scale for Suicide Ideation), and suicidal behaviors (Suicide Attempt Self-Injury Count). Twenty male and 16 female adult participants installed a mobile app on their smartphones and reported their level of SI on a 5-point Likert scale (1=not at all to 5=very much) 5 times per day for 10 days after discharge. Associations between baseline characteristics and postdischarge SI were analyzed using logistic regression, median regression, and mixed-effects linear regression.</p><p><strong>Results: </strong>SI was present in 49.2% (528/1073) of all completed surveys. Women reported SI more frequently than men (χ21=63.39; P=.001). No baseline variables differentiated participants who later did (28/36, 77.8%) and did not (8/36, 22.2%) report momentary SI after discharge. Participants hospitalized due to a high risk of suicide (12/36, 33.3%), compared with those hospitalized following an SA (24/36, 66.7%), reported SI more often (χ21=22.64; P=.001) and had a higher frequency of peak SI scores (β=17.74, 95% CI 8.56-26.91; P=.001). Moreover, the variation in the temporal trajectory of postdischarge SI was characterized by large differences between participants (intraclass correlation coefficient [ICC]=0.682), while the intensity of SI did not change over the 10-day period at the group level (β=0.03, 95% CI -0.01 to 0.06; P=.09). Predischarge scores on the SSF-IV chronic subscale, including hopelessness, self-hate, and psychological pain, predicted the intensity of SI after discharge (β=0.13, 95% CI 0.05-0.20; P=.002). No baseline variables predicted the trajectory of SI.</p><p><strong>Conclusions: </strong>SI after discharge was common, varied substantially between participants, and may be more prevalent than previously reported, particularly in groups with high morbidity and prior SAs. The SSF-IV may be a valuable tool for treatment planning and assessment during the inpatient stay.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e88745"},"PeriodicalIF":7.4,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148649527","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":"Governing AI for Mental Health: Fragmented State Approaches and the Case for a Federal Framework.","authors":"Abir Aldhalimi","doi":"10.2196/96389","DOIUrl":"10.2196/96389","url":null,"abstract":"<p><p>State-level regulation of AI used for mental health is emerging in the absence of a federal framework. States are taking different approaches to regulation, resulting in a fragmented regulatory landscape. This Viewpoint aims to identify the governance approaches that US states are using to regulate the use of AI in mental health and analyze the limitations of each. A 4-state case analysis was conducted using the statutory text of bills and laws in Illinois, Utah, New York, and Nevada. Two governance approaches were identified. The first regulates the use of AI in clinical contexts, and the second regulates the technology itself. Some states have combined elements of both approaches to address AI use more comprehensively. While these approaches aim to mitigate harm, they differ in where they believe risk lies in the use of AI for mental health support. The limitations of these divergent approaches include uneven protections for consumers and regulatory uncertainty for developers, vendors, deployers, and clinicians. Because AI in mental health operates across both clinical and consumer domains, neither approach alone can address the risks associated with its use for mental health support. A coordinated, risk-based federal regulatory floor is needed to ensure consistent protections across states.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e96389"},"PeriodicalIF":7.4,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13462728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148608840","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":"Structuring Digital Mental Health Care Navigation: Co-Design Nominal Group Technique Study to Develop the MChart Definition and Typology of the Characteristics of Digital Mental Health Care Navigation Tools.","authors":"Jane Koerner, Luis Salvador-Carulla, Cindy Woods, Sebastian Rosenberg, MaryAnne Furst, Sue Lukersmith, Hossein Tabatabaei-Jafari, Amir Aryani","doi":"10.2196/91462","DOIUrl":"10.2196/91462","url":null,"abstract":"<p><strong>Background: </strong>Australia's mental health care system has been characterized by complexity and fragmentation, as highlighted by numerous reports, commissions, and inquiries. In response, digital mental health care navigation tools have emerged as a promising solution to help individuals locate appropriate mental health services. The rapid proliferation of these tools-without a clear understanding of their definitions and characteristics-risks creating confusion rather than clarity for users. Terms such as \"navigation\" and \"navigators\" are often used interchangeably, further complicating the landscape.</p><p><strong>Objective: </strong>This study addressed the need for a standardized definition and typology of the characteristics of digital mental health care navigation tools.</p><p><strong>Methods: </strong>This study was part of the development of a digital mental health care navigation tool for navigators and planners (MChart). It used a co-design approach using expert-based cooperative analysis, which is a nominal group technique to develop a definition and typology of the characteristics of digital mental health care navigation tools. This process was guided by the Technology Readiness Level for Implementation Sciences framework. The co-design process involved two 2-hour sessions with an expert panel comprising 28 participants, including representatives from mental health planning, primary health care, health care financing and delivery, community-managed organizations, clinical settings (psychiatrists, psychologists, and general practitioners), and consumers.</p><p><strong>Results: </strong>The expert panel collaboratively developed a consensus definition of digital mental health care navigation tools, outlining their scope and intended targets. Through the co-design process, the panel identified 157 characteristics of digital mental health care navigation tools. These characteristics were organized into 5 primary domains: type, management, content, design, and quality. The definition and typology characteristics provide a structured framework for understanding and evaluating the diverse range of digital mental health care navigation tools currently available.</p><p><strong>Conclusions: </strong>The co-designed definition and typology offer a foundational step toward reducing confusion in the digital mental health care navigation space. This study supports the development of quality standards that can be used to assess and compare existing and future tools. This framework has the potential to guide developers, end users, and policymakers in creating more effective, user-centered navigation solutions within Australia's mental health care system and internationally.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e91462"},"PeriodicalIF":7.4,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13458349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148608905","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":"Clinical Outcomes and Predictors of Improvement With Virtual Behavioral Health Care for Gambling Disorder: Retrospective Cohort Study.","authors":"Kelsey McAlister, Elizabeth Knight, Cynthia Grant, Jennifer Huberty","doi":"10.2196/101517","DOIUrl":"10.2196/101517","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Gambling disorder is associated with substantial psychiatric and functional burden, yet few individuals receive treatment. Limited real-world evidence exists evaluating outcomes of virtually delivered behavioral health care for gambling disorder, particularly among clinically complex patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The purpose of this study was to evaluate gambling symptom severity outcomes among adults with gambling disorder receiving care from Birches Health. We aimed to (1) characterize the clinical profile of adults seeking treatment for gambling disorder; (2) quantify changes in gambling symptom severity over the initial 12 weeks of treatment and examine whether baseline clinical complexity, such as gambling symptom severity, depression severity, and psychiatric comorbidities, was associated with differences in gambling symptom severity improvement over time; and (3) estimate the timing and likelihood of achieving clinically meaningful improvement in gambling symptom severity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This retrospective cohort study included 1305 adults receiving virtual behavioral health treatment for gambling disorder through Birches Health between June 2024 and April 2026. Gambling symptom severity was assessed using the Gambling Symptom Assessment Scale (G-SAS) weekly. Linear mixed-effects models evaluated changes in gambling symptom severity over 12 weeks and associations with baseline clinical characteristics. Clinically meaningful improvement was defined as a reduction of 4 or more points in the G-SAS score.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants had a mean age of 41.5 (SD 13.1) years, 65.2% (851/1305) were male, and baseline gambling symptom severity was moderate (mean G-SAS score 20.5, SD 11.83). Over half (730/1305, 56%) of participants presented with at least one psychiatric comorbidity, most commonly anxiety disorder (351/1305, 26.9%) and depressive disorder (276/1305, 21.1%). Gambling symptom severity declined significantly over the first 12 weeks of treatment, with G-SAS scores decreasing by approximately 0.099 points per day (P&lt;.001), corresponding to an estimated 8.3-point reduction over 12 weeks. Higher baseline depressive symptom severity was associated with faster improvement in gambling symptoms (P=.01), whereas depressive disorder (P=.03) and attention-deficit/hyperactivity disorder (P=.008) diagnoses were associated with slower improvement trajectories. Among patients with routine follow-up assessments recorded during the initial 12 weeks of treatment (1071/1305, 82.1%), 71.7% (935/1305) achieved clinically meaningful improvement in gambling symptom severity, with a median time to improvement of 14 days.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;A clinically complex population of adults receiving care through a national virtual behavioral health care provider demonstrated rapid and clinically meaningful reductions in gambling symptom severity. These findings highlight t","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e101517"},"PeriodicalIF":7.4,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148585606","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":"A Digital Acceptance and Commitment Therapy and Education Intervention for Caregivers of Very Preterm Infants in the Neonatal Intensive Care Unit: Randomized Controlled Trial.","authors":"Kristin Harrison Ginsberg, Jane Alsweiler, Jenny Rogers, Alana Cavadino, Meihana Douglas, Anna Serlachius","doi":"10.2196/92021","DOIUrl":"10.2196/92021","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Parents of very preterm infants admitted to the neonatal intensive care unit (NICU) experience high levels of psychological distress, yet access to timely, evidence-based mental health support is limited by staffing and resource constraints. Digital mental health interventions offer a scalable approach to addressing this gap; however, their effectiveness has not been well established in NICU caregiver populations, particularly during periods of acute stress.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to evaluate the effectiveness of a self-guided digital acceptance and commitment therapy (ACT)-based intervention combined with NICU-specific education (NICU parent acceptance and commitment therapy [NPACT]). The study explored the intervention's effects on stress among parents and primary caregivers of very preterm infants, compared to a digital education-only intervention, and active control.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a 3-arm, single-center, randomized controlled cluster trial in a tertiary NICU. Parents and primary caregivers of very preterm infants (&lt;32 wk' gestational age,&lt;1 wk old) were randomized by family cluster to (1) NPACT (ACT+ education), (2) a digital education-only intervention, or (3) active control. Digital interventions were delivered via a web-based platform over 2 weeks. The primary outcome was NICU-related stress on the Parent Stressor Scale: Neonatal Intensive Care Unit (PSS:NICU) at 2 weeks postrandomization. Secondary outcomes included caregiver anxiety, depression, perceived stress, and selected neonatal outcomes. Engagement and perceived helpfulness were assessed for digital interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 102 caregivers from 68 family clusters (79 infants; mean gestational age 28.1, SD 2.2 wk) were enrolled. There were no statistically significant between-group differences in the mean PSS:NICU scores at 2 weeks (NPACT 3.0, SD 0.9; education-only 2.5, SD 1; active control 2.6, SD 0.9; adjusted mean difference for NPACT vs active control 0.04, 95% CI -0.39 to 0.47). No between-group differences were observed for secondary psychological outcomes at any time point. However, caregivers in both digital intervention groups had higher odds of full breastfeeding at discharge compared with active control. Engagement with the digital interventions was high, with 97% (28/29) of NPACT participants and 76% (19/25) of education-only participants completing at least 5 of 7 modules, and both interventions were rated as very helpful.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;In this trial, an unguided digital mental health intervention delivered during NICU admission did not reduce NICU-specific parental stress or other psychological outcomes relative to active control. However, the intervention was highly used by caregivers. These findings suggest that while a brief digital mental health intervention can be successfully implemented in a high-stress clinical settin","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e92021"},"PeriodicalIF":7.4,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13395256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580617","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":"Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability.","authors":"Silvia De Francesco, Damiano Archetti, Cesare Michele Baronio, Claudio Demaria, Alberto Boccali, Claudio Crema, Giovanni Battista Tura, Alberto Redolfi","doi":"10.2196/93307","DOIUrl":"10.2196/93307","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Bipolar disorder (BD) is a complex and heterogeneous psychiatric condition, characterized by fluctuating clinical courses that affect approximately 1%-2% of the global population in their lifetime. Despite pharmacological advances, treatment response varies significantly among patients, making the identification of individualized treatment strategies a major challenge. Artificial Intelligence (AI), through its classical approaches, has emerged as a powerful tool in precision psychiatry to identify subtle patterns in complex data and inform personalized clinical decisions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The present systematic review aimed to examine the current evidence on classical AI-supported treatment optimization in the BD spectrum.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Four databases (PubMed, Web of Science, Scopus, and Embase) were searched for original studies published after 2015 on the application of classical AI in the treatment of BD in adult patients. Publication bias was evaluated by visual inspection of a funnel plot. The methodological quality, risk of bias, and clinical applicability of the predictive models were assessed using the Prediction Model Risk Of Bias Assessment Tool for prediction models using regression or AI methods (PROBAST+AI; PROBAST+AI Working Group) tool.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 35 studies were included and classified into 5 outcome-based categories, including acute symptomatic response, long-term maintenance response, relapse and readmission risk, safety and dose optimization, and brain aging and phenotyping. Acute symptomatic response models performed modestly (pooled area under the curve [AUC] 0.68), while imaging improved accuracy (74%-77%). Long-term maintenance response models showed moderate-to-high performance (pooled AUC 0.80), with biomarker- and cellular-based models reaching 96%-99% accuracy. Relapse and readmission prediction achieved a pooled AUC of 0.71, with digital phenotyping and rule-based methods performing best (AUC 0.85-0.88). Safety and dose optimization models achieved 85%-97% accuracy. Brain aging and phenotyping studies highlighted accelerated brain aging in BD, partially mitigated by lithium, and revealed novel data-driven subgroups. However, 3 studies were considered at high risk of bias due to small sample sizes associated with disproportionately high-performance estimates. An additional study was identified as potentially biased because it lay markedly distant from the funnel plot's confidence line. Finally, the PROBAST+AI assessment revealed a high risk of bias in most studies, primarily due to data analysis limitations, small sample sizes, and lack of external validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The adoption of classical AI tools in BD serves as a driver for therapeutic optimization, ","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e93307"},"PeriodicalIF":7.4,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13387422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148551086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Should I and Can I use AI for forensic psychiatry report writing?: A Viewpoint.","authors":"Alexandre Hudon","doi":"10.2196/99749","DOIUrl":"10.2196/99749","url":null,"abstract":"<p><strong>Unstructured: </strong>The rapid evolution of artificial intelligence (AI), particularly large language models (LLMs), has renewed interest in their potential role in forensic psychiatry report writing. Recent evidence demonstrates that contemporary LLMs perform strongly on selected medical knowledge, documentation, and information-management tasks and may reduce administrative burden when deployed under appropriate clinical supervision. However, forensic psychiatric reports differ fundamentally from routine clinical documentation. They constitute expert evidence prepared for legal proceedings and therefore require transparent reasoning, explicit weighing of competing evidence, a robust factual foundation, and personal professional accountability. This viewpoint examines whether AI can and should be used in forensic psychiatry report writing by integrating recent empirical evidence, forensic psychiatry guidance, legal and regulatory frameworks, and emerging governance recommendations. Rather than comparing AI with an idealized human evaluator, the manuscript argues that the appropriate comparison is between two imperfect systems of reasoning. Human experts remain susceptible to cognitive biases, omission errors, and disagreement, whereas contemporary LLMs exhibit distinct vulnerabilities, including hallucinations, hidden omissions, probabilistic reasoning, and limited explainability. Although the mechanisms differ, both may ultimately compromise the reliability of expert evidence if left unchecked. Current evidence supports AI for bounded, reversible, and independently verifiable tasks such as document organization, chronology construction, indexing, transcription, and structured summarization, particularly within secure and validated environments. By contrast, there remains insufficient evidence to support AI-assisted generation or material shaping of psycholegal reasoning, credibility assessments, or final forensic opinions. Because these activities require interpretation, accountability, and reasoning that can withstand judicial scrutiny, they remain fundamentally human responsibilities. The most defensible implementation model is therefore one of AI around the report rather than AI writing the report, in which AI serves as a supervised productivity tool while the forensic psychiatrist retains full authorship, accountability, and justification of all substantive conclusions.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":" ","pages":""},"PeriodicalIF":7.4,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455175/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148521548","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":"Immersive Technologies in Forensic Mental Health and Prison Settings: Scoping Review.","authors":"Ivana Nakarada-Kordic, Dilshani Kumarapeli, Lana Chisholm, Emma Marie Buitenhek, Stephen Reay","doi":"10.2196/95581","DOIUrl":"10.2196/95581","url":null,"abstract":"<p><strong>Background: </strong>The application of immersive technologies, particularly virtual reality, has expanded rapidly across health care domains, including mental health, rehabilitation, and education. These technologies enable the creation of controlled, interactive, and ecologically valid environments that can support therapeutic interventions, skill development, and behavioral assessment. Within forensic mental health services (FMHS) and prison settings, where individuals often present with complex psychological needs in restrictive and highly regulated environments, immersive technologies offer potential advantages such as safe simulation of real-world scenarios, enhanced engagement, and personalized intervention delivery. However, despite increasing interest, the evidence base remains fragmented, and questions persist regarding effectiveness, ethical implications, and feasibility of implementation in secure and resource-constrained contexts.</p><p><strong>Objective: </strong>Interest in immersive technologies in FMHS and prison settings is growing, yet their role remains unclear. This scoping review mapped current uses, highlighted opportunities, and identified key gaps and considerations for future implementation.</p><p><strong>Methods: </strong>A scoping review of English-language publications (2010-2025) was conducted using the Scopus, PubMed, and CINAHL databases. Data extraction followed the Joanna Briggs Institute framework, and thematic analysis explored benefits, drawbacks, and implementation barriers.</p><p><strong>Results: </strong>Thirty sources were identified. Primary research focused mainly on virtual reality for therapy, skill training, education, and assessment. There was evidence suggesting benefits such as increased engagement, emotional regulation, skill acquisition, autonomy, and improved clinician-patient dialogue. However, the studies were small, heterogeneous, and inconsistently reported, with limited long-term follow-up. Implementation barriers included institutional, ethical, and technical constraints and limited personalization and end user involvement. Co-design and participatory approaches surfaced as key enablers of acceptability, relevance, and safe use.</p><p><strong>Conclusions: </strong>The existing evidence base is preliminary and exploratory but indicates that immersive technologies may have potential value in FMHS and prison contexts. Current findings should be interpreted cautiously because studies are small, heterogeneous, and rarely include long-term follow-up. More robust evidence, careful implementation, and meaningful end user input are needed to support safe, relevant, ethical, and effective use. The emphasis on coproduction and guidance for safe, user-centered implementation is a novel contribution.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"13 ","pages":"e95581"},"PeriodicalIF":7.4,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13428208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473375","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}