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Development of the Healthy Women Intervention to Increase Women’s Engagement in Medication Treatment for Opioid Use Disorder: Mixed Methods, User-Centered Design Approach

Background: Rates of opioid use disorder (OUD) have increased among women over the past 2 decades. Medication treatment for opioid use disorder (MOUD) is effective but underused. Gender-specific treatments for women have been associated with improved substance use outcomes. However, these treatments have not specifically targeted women’s engagement in MOUD, and the impact of existing gender-specific treatments is restricted by in-person delivery. Objective: The aim of this study was to develop a digital intervention to feasibly deliver gender-specific care that addresses the individualized needs of women with OUD to increase engagement in MOUD. Methods: A mixed methods, user-centered design approach was used to inform the development of a digital intervention. In phase 1, qualitative interviews were conducted with women with lived experience of OUD (n=20) and providers who treat women with OUD (n=8). Interviews were recorded, transcribed, and coded for themes. In addition, a larger sample of treatment providers (n=55) completed an online survey to further inform the content of the digital intervention. Phase 2 consisted of designing, beta-testing (n=5), and refining the intervention. Results: The age of women with lived experience ranged from 21 to 59 (mean 38.5, SD 9.4) years; 63% (5/8) of providers interviewed were female participants. The qualitative interview data from women with lived experience and providers were grouped into 6 thematic categories: 3 treatment-related (1) barriers to treatment, (2) facilitators to successful recovery, and (3) important issues to address in treatment, and 3 technology-related (4) positives of using technology as part of treatment, (5) suggested technology features, and (6) barriers to using technology. Across the treatment-related categories, several themes touched on women-specific factors including family responsibilities, abusive partners, stigma, and motivation for treatment (eg, pregnancy). The technology-related categories provided information for designing the features of the intervention, as well as revealing barriers to technology use, which could be helpful in developing implementation strategies. Provider survey participants were primarily female participants (40/55, 73%), with a mean age of 42.5 (SD 12.5) years. Survey data provided additional information on barriers to treatment and suggested technology features. Based on these data and preliminary work, the intervention was created. Minor edits to content and visual design were made in the beta-testing phase. The final version includes a web-based component with 6 topic modules and a mobile component. Topics in the web-based component are presented through infographics, text, videos, and interactive questions. The mobile component includes daily motivational messages, skills practice activities (2/wk), weekly check-ins, and resources (always available). Conclusions: Important themes and suggested features from women with lived experience and providers were incorporated into a digital intervention for women with OUD. Data on feasibility, satisfaction, and engagement with the intervention are currently being collected in phase 3, a pilot randomized controlled trial.
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Help-Seeking in the Age of AI: Cross-Sectional Survey of the Use and Perceptions of AI-Based Mental Health Support Among US Adults

<strong>Background:</strong> Anecdotal evidence suggests that an increasing number of people are turning to generative artificial intelligence (GenAI) tools or artificial intelligence (AI)-assisted chatbots to discuss and manage mental health concerns. However, systematic data on the use and perception of such tools remain scarce. <strong>Objective:</strong> This study aimed to examine how young and middle-aged adults in the United States use GenAI and AI-assisted mental health chatbots as mental health resources and assess their preferences for these tools relative to human mental health professionals. <strong>Methods:</strong> An anonymous online survey was conducted in October 2025 among US adults in a commercial online panel sample of US adults aged 18-49 years (N=1805). Respondents were asked about the sources they typically turn to when facing mental health concerns, their frequency of using GenAI tools or chatbots for mental health support, and whether the frequency of seeing human mental health professionals had changed since they started using AI tools for mental health support. Attitudes toward AI-based mental health support were assessed and compared with attitudes toward human mental health professionals. <strong>Results:</strong> In this sample, of the 1805 respondents, 638 (35.2%) reported using AI tools at least once a week in a typical week for mental health support, and 99 (5.5%) were classified as “heavy users” who reported regularly spending hours discussing their mental health concerns through AI. However, nearly 60% of respondents reported that they would turn first to family (1078/1805) and friends (1046/1805) when facing mental health concerns. Respondents who screened positive for moderate to severe depressive or anxiety symptoms were more likely to use AI-based mental health support compared to those without these symptoms (adjusted odds ratio 1.71, 95% CI 1.36-2.15) and those with suicidal ideation were more likely to be heavy AI users (adjusted odds ratio 2.42, 95% CI 1.49-3.95). Among those who had ever seen a human mental health professional (n=511), 28.4% (145/511) reported a perceived decline in visit frequency to human mental health professionals since they started using AI tools for the same purpose. Participants expressed more favorable attitudes toward human mental health professionals than toward AI-based tools. However, among heavy AI users, perceptions of AI-based mental health support and human counseling were nearly equivalent in positivity. <strong>Conclusions:</strong> AI appears to be an important component of the mental health help-seeking landscape among respondents in this sample. Although most respondents still preferred human professionals, a subset reported relying on AI tools for comparable support. Ongoing monitoring and ethical guidelines are needed to ensure that AI technologies expand access to care while being safely and effectively integrated into the broader continuum of mental health services.
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Mass Media Narratives of Psychiatric Adverse Events Associated With Generative AI Chatbots: Rapid Scoping Review

<strong>Background:</strong> Generative artificial intelligence (AI) chatbots have rapidly entered public use, including in contexts involving emotional support and mental health–related interactions. Although these systems are increasingly accessible, concerns have emerged regarding potential adverse psychiatric outcomes reported in public discourse, including psychosis, suicidal ideation, self-harm, and suicide. However, these reports largely originate from journalistic accounts rather than systematically verified clinical data. <strong>Objective:</strong> This rapid scoping review aimed to systematically map and characterize mass media narratives describing alleged adverse psychiatric outcomes temporally associated with generative AI chatbot interactions. <strong>Methods:</strong> A rapid scoping review methodology was applied to publicly accessible news articles identified primarily through Google News searches. Articles published from November 2022 onward were screened for eligibility if they described a specific case in which psychiatric deterioration or crisis was temporally linked to generative AI use. Data were extracted using a structured coding template capturing article characteristics, demographic information, AI platform features, interaction intensity, outcome type and severity, type of evidence reported, and causal attribution language. Descriptive statistics and cross-tabulations were performed. <strong>Results:</strong> A total of 71 news articles representing 36 unique cases were included. Suicide death was the most frequently reported outcome (35/61, 57.4% cases with complete severity coding), followed by psychiatric hospitalization (12/61, 19.7%). Fatal outcomes were disproportionately represented among minors (19/21, 90.5%) compared with adults (17/35, 48.6%). ChatGPT was the most frequently cited platform (51/71, 71.8%), followed by Character AI (10/71, 14.1%). Causal attribution most commonly referenced AI system behavior (45/61, 73.8%), and the term “alleged” was the predominant causal descriptor (33/61, 54.1%). Evidence sources were primarily chat logs or screenshots (34/61, 55.7%), while police or medical documentation was rare (1/61, 1.6%). Regulatory calls were present in 51 of 60 (85%) articles with nonmissing data. <strong>Conclusions:</strong> Mass media reporting of generative AI–related psychiatric harms is concentrated around severe outcomes, particularly suicide deaths among youth, and is frequently framed within regulatory and corporate accountability narratives. While causality cannot be established from media reports, consistent patterns of high-intensity interactions, user vulnerability, and limited safeguard reporting highlight the need for structured safety surveillance, transparent AI risk auditing, and clearer governance frameworks. As generative AI becomes increasingly integrated into everyday psychosocial contexts, systematic research and formal safety monitoring will be necessary to determine whether media-reported harms correspond to verifiable clinical risk patterns.
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The Performance of Wearable Device–Based Artificial Intelligence in Detecting Depression: Systematic Review and Meta-Analysis

Background: In recent years, advances in wearable sensor technology and artificial intelligence (AI) have provided new possibilities for detecting and monitoring depression. Objective: This study systematically reviewed and meta-analyzed the diagnostic and predictive performance of wearable device–based AI models for detecting depression and predicting depressive episodes and explored factors influencing outcomes. Methods: Following PRISMA-DTA (Preferred Reporting Items for a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy) guidelines, the PubMed, Embase, Web of Science, and PsycINFO databases were searched from inception to May 27, 2025. Eligible studies used AI algorithms on wearable device data for depression detection or episode prediction. Sensitivity, specificity, diagnostic odds ratio, and area under the curve (AUC) were pooled using a bivariate random effects model. Risk of bias was assessed using Prediction Model Risk of Bias Assessment Tool plus artificial intelligence (PROBAST+ AI), and certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool. Results: We included 16 studies (32 datasets) with 1189 patients and 13,593 samples. For depression detection, pooled sensitivity and specificity were 0.89 (95% CI 0.83‐0.93) and 0.93 (95% CI 0.87‐0.96), with a diagnostic odds ratio of 110.47 (95% CI 33.33‐366.17) and AUC of 0.96 (95% CI 0.94‐0.98). Random forest models showed the best performance (sensitivity=0.89, specificity=0.91, AUC=0.97). Subgroup analyses indicated that study design, AI method, reference standard, and input type significantly affected diagnostic accuracy (<.05). For depressive episode prediction (3 datasets), pooled sensitivity was 0.86 (95% CI 0.80‐0.91), and pooled specificity was 0.65 (95% CI 0.59‐0.71). The overall risk of bias was low to moderate, with no evidence of publication bias. Conclusions: Wearable device–based AI models achieved high accuracy for detecting depression and moderate utility in predicting episodes. However, heterogeneity, reliance on retrospective and public datasets, and lack of standardized methods limited generalizability. Trial Registration: PROSPERO CRD420251070778; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251070778
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Virtual Reality Implementation in Mental Health Care Is a Marathon, Not a Sprint: Qualitative Longitudinal Study of a Virtual Reality Training Program

Background: Despite the potential of virtual reality (VR) for treatment and assessment in mental health care, its practical implementation remains limited. Much implementation research explores barriers and facilitators; fewer studies actually evaluate targeted implementation strategies and track how their effects evolve over time in mental health care practice. Objective: This study aims to examine how a structured VR training program functioned as an implementation strategy in routine mental health care and to identify how therapists’ adoption trajectories and implementation needs shifted across stages of the process. Methods: Eleven therapists from a Dutch mental health care organization completed a 6-session VR training. Semistructured interviews were conducted at 3 time points: pretraining, immediately posttraining, and 3 months posttraining. Data were deductively analyzed using theoretical thematic analysis based on the capability, opportunity, motivation – behavior model and the Theoretical Domains Framework to map stage-specific changes in implementation needs relating to VR use. Results: The training improved therapists’ perceived knowledge, skills, and confidence in using VR. Nonetheless, actual uptake of VR in clinical routines remained limited. Enduring barriers included workflow misalignment, hierarchical decision-making structures, and the absence of a shared organizational vision and sustained leadership support. The longitudinal design revealed a dynamic pattern: early adoption hinged on individual capability and motivation, whereas maintenance depended on organizational opportunity and communicated support. These stage-specific shifts clarify why training alone does not translate into routine use and which organizational levers are most important when. Conclusions: VR training for therapists is a necessary but insufficient implementation strategy in mental health care. A longitudinal approach shows that successful implementation requires pairing training with organization-level changes that address opportunity barriers over time. By shifting from static evaluations of whether training works to a process-oriented focus on what support is needed at each stage of implementation, this study advances implementation science in digital mental health and offers actionable guidance for embedding VR in routine care.
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[Comment] Lived experience perspectives on the development of a Psychosis Metabolic Risk Calculator (PsyMetRiC)

In this issue of The Lancet Psychiatry, Benjamin Perry and colleagues1 present a collaboratively developed, refined, and externally validated risk prediction tool (the Psychosis Metabolic Risk Calculator [PsyMetRiC]) that is clinically available, and that can separately predict the risk of clinically significant weight gain, metabolic syndrome, and type 2 diabetes in young people with psychosis. Key to the collaborative development of PsyMetRiC has been the involvement of young people with a lived experience of psychosis, supported by the McPin Foundation and Equally Well UK.

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Implementing Action-Based Cognitive Remediation for Transdiagnostic Cognitive Difficulties in a Tertiary Mental Health Hospital

Conditions: Psychiatric Disorders; Depression – Major Depressive Disorder; Schizophrenia and Other Psychotic Disorders; Anxiety and Mood Disorders; Bipolar and Related Disorders; PTSD – Post Traumatic Stress Disorder; Autism Spectrum Disorder

Interventions: Behavioral: Action-Based Cognitive Remediation

Sponsors: The Royal Ottawa Mental Health Centre

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