Background:Major depressive disorder affects over 280 million people worldwide, and access to effective treatment remains limited. Transcranial direct current stimulation (tDCS) is a noninvasive option, and portable devices now allow for home-based delivery under varying degrees of remote supervision. Objective:This study aimed to systematically review and meta-analyze the efficacy, safety, feasibility, and acceptability of home-based and remotely supervised tDCS for depressive disorders. Methods:Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension) guidelines, we searched MEDLINE, Embase, Web of Science, the Cochrane databases, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform up to July 2025, with backward and forward citation searching. Two reviewers independently screened records, extracted data, and assessed risk of bias (version 2 of the Cochrane risk-of-bias tool for randomized trials, Newcastle-Ottawa Scale for observational studies, and Critical Appraisal Skills Programme for qualitative studies) and certainty of evidence (Grading of Recommendations Assessment, Development, and Evaluation; GRADE). Results:This review included 12 distinct studies (16 reports), of which 6 (50%) were randomized sham-controlled trials forming the meta-analytic pool. Active home-based tDCS produced a small, statistically significant improvement over sham (pooled Hedges g=0.36, 95% CI 0.06-0.66; P=.03; I2=34.3%). The effect was not robust to removal of the single largest positive trial (omitting the one study from 2025: g=0.39, 95% CI -0.12 to 0.91), and trial-level results were mixed: the 2 largest trials (one unsupervised [n=210] and one self-administered [n=141]) were negative on their primary depression outcomes, whereas the largest real-time supervised trial (n=174) was positive (between-group 95% CI 0.51-4.01; P=.01). This estimate was concordant in direction with an independent peer-reviewed meta-analysis of overlapping trials, which reported a pooled Montgomery-Åsberg Depression Rating Scale reduction (weighted mean difference -2.74, 95% CI -4.19 to -1.29) and Hamilton Depression Rating Scale reduction (weighted mean difference -2.24, 95% CI -4.16 to -1.49), attenuating to nonsignificance (P>.05) in major depressive disorder without comorbid cognitive impairment. The pooled effect fell at or near the minimal clinically important difference. GRADE certainty was moderate. Adverse events were predominantly mild: one pilot study was terminated early for skin lesions, and one nonfatal suicide attempt occurred in an unsupervised trial. Conclusions:Home-based and remotely supervised tDCS produces a small, statistically significant but clinically modest antidepressant effect that is sensitive to the inclusion of the largest positive trial, with the 2 largest trials being negative. The available controlled evidence does not establish supervision intensity as a determinant of efficacy. Current data are insufficient to recommend routine clinical adoption; adequately powered trials with standardized supervision and longer follow-up are needed.
Background:Mental health chatbots are increasingly used to support people with depressive symptoms, and large language models make these systems more flexible than rule-based chatbots. However, it remains unclear how well large language model-based chatbots deliver structured psychological interventions. Objective:This study examined how well a GPT-4o-based chatbot delivered a behavioral activation intervention for young people with depression using sessions with artificial users and clinical expert assessment. It also identified limitations and potential refinements. Methods:We implemented a GPT-4o (gpt-4o-2024-08-06; OpenAI)-based chatbot using a structured system prompt to deliver a single-session behavioral activation intervention for people with depression aged 14 to 29 years. We generated 48 sessions with GPT-4o-based artificial users derived from clinical vignettes varying across 7 characteristics. Ten clinical experts, either licensed psychotherapists or advanced psychotherapy trainees, independently assessed the sessions using the 14-item Quality of Behavioral Activation Scale (Q-BAS), rated from 0 to 6, supplemented by rating therapeutic capabilities, artificial user authenticity and difficulty, and qualitative feedback. Results:The chatbot completed all 7 intervention phases in every session. The mean holistic session quality rating was 3.94 (SD 1.23), and the mean Q-BAS rating was 4.03 (SD 1.18). Thirteen of 14 Q-BAS components exceeded the satisfactory threshold of 3. Ratings were highest for mood assessment (mean 5.42, SD 1.09) and activity planning (mean 4.98, SD 1.41) and lowest for explaining positive reinforcement (mean 2.92, SD 2.30) and supporting activity-mood monitoring (mean 3.02, SD 2.04). Therapeutic capability ratings were highest for message safety (mean 5.90, SD 0.37), message clarity (mean 5.56, SD 0.77), and objective, nonjudgmental communication (mean 5.17, SD 1.04) and lowest for therapeutic rapport (mean 4.12, SD 1.45) and natural conversation flow (mean 4.25, SD 1.42). Artificial users were rated below the scale midpoint for authenticity (mean 2.75, SD 1.41) and difficulty (mean 1.23, SD 1.46). Clinical experts described the chatbot as structured, clear, and safe but identified insufficient clinical reasoning as the main limitation, particularly in evaluating the therapeutic suitability and feasibility of activities, barriers, solution strategies, and rewards. Artificial users were often highly compliant, especially when identifying positive activities. Conclusions:In expert-rated sessions with artificial users, the chatbot delivered the behavioral activation intervention as intended and performed strongest on procedural components. It performed less well on positive reinforcement and activity-mood monitoring, indicating refinement needs in clinical reasoning, follow-up questioning, and evaluating whether proposed activities, plans, barriers, solution strategies, and rewards are therapeutically appropriate and feasible. The findings identify targets for improvement before testing with human users, while the artificial user design and expert ratings limit conclusions about real therapeutic interactions.
Background:Suicide remains a leading cause of death among young adults aged 18 to 25 years. Young adults experiencing suicidal ideation (SI) are increasingly using crisis text services (CTSs), a free and accessible option for crisis intervention. Little is known about CTSs from the young adult perspective. Objective:This study aimed to characterize young adults' experiences with and perceptions of CTSs for SI. Methods:We conducted in-depth interviews, by phone, Zoom, or text, with young adults (n=39) in the United States who had a lifetime history of SI. Participants included those who had or had not engaged with CTSs for SI. Semistructured interviews were conducted from January to July 2024. The data were analyzed using a modified grounded theory approach. Results:We constructed 5 key themes to characterize young adults' perceptions of and experiences with CTSs for SI. Young adults perceived CTSs as a unique component of their mental health crisis management. They appreciated CTSs' technological features, particularly the privacy they provided and the ability to reflect on and edit responses. However, they expressed dissatisfaction with the nonspecific nature of many CTS interactions. The perceived anonymity of CTSs served multiple functions, both as a motivator for CTS use and as a potential point of vulnerability, should it be lost during a CTS interaction. Participants' perceptions of CTSs' impact varied; some viewed them as beneficial, whereas others reported neutral or inconsistent effects over time. Conclusions:Among young adults with SI, CTSs are a key yet imperfect resource. Quality improvement and evaluation efforts may be needed to understand how responders can better tailor responses to improve conversational quality and consistency for young adult texters.
Background Mental health apps are frequently used as platforms for delivering digital health interventions to young people. New technology such as generative AI enables a wider range of engaging and more personalized features that can be included in mental health apps. However, there is limited insight into the opportunities for integrating generative AI into such apps. Objective This rapid review aimed to provide a systematic overview of the possibilities of integrating generative AI into mental health apps for young people. Furthermore, the review aimed to report potential benefits and disadvantages of the integration of generative AI into such apps. Methods A systematic search was conducted in 4 databases (PsycInfo, Embase, MEDLINE, and CINAHL) in November 2025 to identify studies that evaluated mental health apps with integrated generative AI. Eligible studies included primary research published between 2022 and 2025, involving a sample of young people aged 11 to 24 years, and written in either English or a Scandinavian language. Results A total of 5 articles were included in the review. The most common method of integrating generative AI into mental health apps for young people was through chatbots. Overall, young people rated the apps as having good usability and quality. Some studies also provided data on effectiveness, showing promising results for outcomes such as depression, anxiety, and distress. None of the studies systematically assessed harmful effects using standardized methods, nor did they report any adverse outcomes related to mental health. Conclusions Young people are generally positive about apps that include generative AI. There is some evidence suggesting that such tools may contribute to a preventive or health-promoting effect on young people’s mental health. However, the existing research is limited and characterized by methodological constraints. The lack of reported adverse mental health outcomes might reflect a lack of investigation rather than evidence of no harm. Further research should explore the potential short- and long-term effects of integrating generative AI into mental health apps for young people, as well as systematically mapping possible adverse events.
Background:Suicide remains a leading cause of death in the United States and is on the rise. Limited evidence describes the current burden of suicidal ideation (SI) among commercially insured outpatients, a population that represents a large and rapidly expanding segment of those seeking care. Objective:The objective of this study was to characterize the prevalence of SI among a national cohort of commercially insured individuals presenting for outpatient mental health services, and to examine differences across key demographic and social factors. Methods:This was a retrospective evaluation of patients, aged 6 to 64 years, presenting for their first visit with an outpatient mental health -clinician between January 1, 2024, and October 31, 2025. The prevalence of SI (defined as a nonzero response on question 9 of the Patient Health Questionnaire-9) was compared by age, sex, and geography using regression analysis. Results:Among 189,225 commercially insured individuals included in the analysis, mean age was 33.60 (SD 11.33) years. Among those reporting sex, 68% (34,168/50,378) identified as female, 29% (14,491/50,378) as male, and 3% (1719/50,378) as nonbinary. Most had a diagnosis of anxiety (56,176/189,225, 29.7%) or depression (50,075/189,225, 26.5%). In total, 18.6% (35,215/189,225) of individuals indicated some level of SI. Across age groups, SI was highest among adolescents (age 13-17 years) with 32.8% (2107/6426) reporting any suicidality, and 4.8% (306/6426) reporting SI nearly every day (P<.001). The population with the greatest SI comprised individuals identifying as nonbinary (644/1719, 37.5%; P<.001). SI increased as a function of patients' social vulnerability (P<.001) and varied by geography. Conclusions:Nearly 1 in 5 individuals endorsed some degree of SI, with one third of adolescents reporting SI, proportions that are notably higher than estimates from the general US population. These findings underscore the need for comprehensive screening for SI and the expansion of clinical support for suicidal populations.
Background:Persecutory delusions have long mirrored prevailing cultural and technological concerns. Beliefs involving implanted devices, internet surveillance, hacked smartphones, algorithmic targeting, and AI-mediated control are increasingly visible in contemporary psychosis. However, we identified no prior review dedicated specifically to synthesizing technology-themed delusional content across historical and contemporary clinical contexts. Objective:This narrative review aimed to trace the historical evolution of technology-themed delusions, characterize their contemporary clinical phenomenology, examine relevant neurocognitive and sociocultural mechanisms, and discuss implications for psychiatric assessment and intervention. Methods:PubMed/MEDLINE, Scopus, and APA PsycInfo were searched without date restrictions, supplemented by backward citation tracking and targeted identification of historical primary sources. Eligible sources included original research, case reports and case series, reviews, and relevant conceptual or historical publications addressing delusional content involving technology. A narrative synthesis approach informed by the Scale for the Assessment of Narrative Review Articles (SANRA) framework was used. The final search was completed on May 1, 2026. Results:Forty-three sources were included in the narrative synthesis, comprising 9 case reports or case series, 14 empirical studies, 8 reviews, and 12 historical or conceptual sources. Technology-themed delusions appeared as pathoplastic variants of enduring persecutory, control, and referential motifs, clustering into 5 overlapping categories: surveillance and hacking beliefs, implant and control delusions, Truman Show-type broadcast phenomena, social media-specific referential ideas, and emerging algorithmic and AI-mediated themes. In the largest contemporary cohort (228 patients with psychosis), technology-themed delusions were described by 104 of the 201 (51.7%) patients with delusional thought content, and the odds of technology-themed delusions rose by approximately 15% per admission year (odds ratio 1.15, 95% CI 1.01-1.31; P=.04). Cognitive models of persecutory ideation, the aberrant salience hypothesis, technological unfamiliarity, digital immersion, and wider sociocultural narratives all appeared relevant to these presentations. Pandemic-era conspiracy material, including 5G and vaccine-microchip narratives, further illustrated how culturally available technological explanations may scaffold delusional elaboration in vulnerable individuals. Conclusions:Technology-themed delusions represent contemporary expressions of enduring delusional motifs shaped by digital culture. Their assessment requires psychiatric expertise combined with sufficient technological literacy to distinguish proportionate privacy concerns, overvalued conspiracy beliefs, and fixed delusional convictions. Future research should use prospective cohort designs, cross-cultural comparisons, and targeted evaluation of cognitive behavioral and digital literacy-informed interventions, while monitoring emerging AI-mediated phenomena.
Background:Digital mental health interventions using conversational AI agents are increasingly being adopted as scalable alternatives to traditional care. Engagement is typically measured using volume-based metrics (eg, session counts and total time on a platform). However, these metrics overlook engagement patterns over time, which are not well understood in relation to mental health outcomes. Objective:The purpose of this cross-sectional study was to explore how different patterns of engagement with Mental's AI conversational agent relate to self-reported depression and anxiety. We aimed to (1) identify and describe engagement profiles based on patterns of interaction depth and temporal consistency, (2) compare depression and anxiety symptoms across engagement profiles, and (3) explore whether engagement profiles were associated with mental health symptom severity. Methods:This cross-sectional observational study linked survey responses to back-end app usage data from 112 Mental app users who completed at least 5 sessions with the conversational AI agent. Engagement profiles were derived using median splits on interaction depth (α parameter) and temporal consistency (Gini coefficient). Depression was assessed using the Patient Health Questionnaire-8 (PHQ-8), and anxiety was assessed using the Generalized Anxiety Disorder-7 (GAD-7). One-way ANOVAs compared symptoms across profiles. Linear regression models examined associations between profiles and symptom severity, adjusting for age, gender, and total duration of use. Results:We identified 4 distinct engagement profiles based on interaction depth and temporal consistency: extended and episodic (profile 1; n=25), extended and consistent (profile 2; n=31), brief and episodic (profile 3; n=31), and brief and consistent (profile 4; n=25). Users in profile 1 (extended and episodic) reported the lowest anxiety (mean 2.68, SD 2.43) and depression (mean 3.48, SD 4.06), while profile 4 (brief and consistent) reported the highest anxiety (mean 10.00, SD 7.03) and depression (mean 10.60, SD 8.75). Significant differences were observed for anxiety (F3,108=8.07, P<.001, η²=0.18) and depression (F3,108=5.47, P=.002, η²=0.13). In adjusted models, engagement profile was significantly associated with depression (R²=0.16, F7,104=2.87, and P=.009) and anxiety (R²=0.21, F7,104=4.04, and P<.001). Compared to profile 1, users in profiles 2 and 4 reported significantly higher depression and anxiety. Profile 3 differed from profile 1 for anxiety only (β=3.11, P=.047). Conclusions:Users with longer, clustered sessions reported the lowest symptoms, whereas those with brief, evenly distributed use reported the highest symptom levels, suggesting that the structure of engagement may be associated with symptom levels in ways that aggregate usage metrics do not capture. These findings are preliminary and hypothesis-generating, highlighting the importance of considering how engagement unfolds over time and suggesting that pattern-based measurement may improve the understanding of user outcomes in AI-powered mental health care. Future work should examine the directionality of these associations and whether distinct engagement patterns reflect meaningfully different modes of interacting with AI-powered care.
Background:The burden of mental disorders is high in conflict-affected populations. In Palestine, we piloted Inuka Coaching, a digital intervention adapted from the Friendship Bench delivered by trained and supervised lay coaches. This paper documents the implementation of the intervention in this highly volatile context after October 7, 2023. Objective:This study aimed to describe the implementation of Inuka Coaching, a digital mental health tool based on task shifting, in Palestine and examine contextual challenges, fidelity to the coaching model, and lessons learned regarding recruitment, retention, and delivery during escalating ethnic cleansing. Methods:Two Palestinian mental health professionals were trained and certified in the Inuka method as head coaches, and subsequently trained 5 lay coaches. Palestinian adults in Gaza and the West Bank were recruited primarily through social media and received up to 4 structured, text-based coaching sessions typically delivered over 4 weeks depending on participant availability and preference. Standardized mental health screening questionnaires (Self-Reporting Questionnaire-20 [SRQ-20] and Posttraumatic Stress Disorder Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5]) were collected at baseline, immediately after the first coaching session, and 3 months after the final session. Session transcripts were reviewed to assess coaches' fidelity to the Inuka method, and a focus group discussion explored coaches' experiences with training, delivery, and contextual challenges. Results:Between August 2023 and February 2024, a total of 70 participants were enrolled. Baseline assessments indicated high levels of psychological distress: 95.7% (67/70) scored above the PCL-5 threshold of 31, suggesting likely posttraumatic stress disorder, and 69.4% (43/62) scored in the "at risk" range on the SRQ-20. The effectiveness of the method could not be determined as retention was low, with only 7.1% (5/70) completing the program. Coach fidelity was high, with 94.6% (35/37) of transcripts adhering to all 5 steps of the intervention. Coaches reported positive experiences with the method but identified challenges related to recruitment, session continuity, platform usability, and the need for flexibility during acute crises. Key implementation learnings included the importance of early in-person collaboration and training, culturally sensitive framing, flexible delivery and session structures, and robust support for lay coaches. Conclusions:While digital, task-shifted mental health interventions can be delivered with fidelity in conflict settings, sustaining engagement during escalating violence remains challenging. Future implementations require flexible design, context-sensitive recruitment and retention strategies, adaptive delivery models, and strong support for lay coaches.
Background: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. Objective: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. Methods: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. Results: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 treatment visits during the 24-week study (CHA-MW +21%, MBCT-R +10%, and iCBT -5%). Conclusions:MBCT-R+CHA-MW, iCBT+CHA-MW, and CHA-MW were each effective in treating depression, without any intervention demonstrating superiority in intention-to-treat analyses. CHA-MW was as efficacious during the COVID-19 pandemic as more resource-intensive interventions that demanded greater time and effort from participants. Low completion rates for MBCT-R and iCBT during the COVID-19 pandemic may have contributed to these results.
Background: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. Objective: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. Methods: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. Results: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. Conclusions: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.
Background:Social media influencers occupy a pervasive role in billions of users' daily digital lives, particularly among adolescents and young adults. Audiences develop parasocial engagement with these figures, including parasocial relationships (PSRs) and parasocial interactions (PSIs). Despite growing concern about their mental health implications, no prior meta-analysis has quantitatively synthesized this evidence. Objective:This systematic review and meta-analysis aimed to estimate the associations between influencer-directed parasocial engagement and mental health outcomes, examine prespecified moderators, and evaluate the quality of the existing evidence base. Methods:Seven databases (PsycINFO, Embase, MEDLINE, ERIC, PubMed, Web of Science, and Scopus) were searched from inception. Studies quantitatively assessing PSRs or PSIs with social media influencers and reporting mental health outcomes were eligible. A 3-level random-effects meta-analysis was conducted using Pearson r. Primary pooled estimates were calculated separately for positive or adaptive outcomes, and negative or maladaptive outcomes. Moderators examined included outcome domain, parasocial construct type, age group, gender, cultural region, and platform. Results:Seventeen studies (52 effect sizes) were included. Parasocial engagement was positively associated with both positive (k=28; r=0.38, 95% CI 0.18-0.55) and negative outcomes (k=24; r=0.24, 95% CI 0.08-0.38), with positive effects significantly stronger. Well-being showed the largest effects (r=0.42), followed by social media addiction (r=0.33). PSI demonstrated stronger associations than PSR (r=0.56 vs 0.22). Effects were largest among adolescents (r=0.44) and in Eastern samples (r=0.61 vs 0.29). No evidence of publication bias was detected (Egger P=.94; fail-safe N=18,643). Conclusions:Parasocial engagement functions as a psychological double-edged sword, reliably linked to both enhanced well-being and problematic engagement. Positive mental health outcomes, particularly those involving well-being, were generally stronger and more consistent than negative ones. These findings suggest that parasocial engagement should not be understood as uniformly beneficial or harmful; rather, its psychological meaning depends on the outcome domain, parasocial construct type, developmental stage, and cultural context. Future longitudinal, mechanism-based research with diverse samples is needed to clarify when parasocial engagement is most beneficial or harmful.
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.
BACKGROUND: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. OBJECTIVE:This study addressed the need for a standardized definition and typology of the characteristics of digital mental health care navigation tools. METHODS: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. RESULTS: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. CONCLUSIONS: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.
Abstract Background Two-fold increases in the prevalence of youth anxiety and depression over the last two decades have mirrored exponential growth in opportunities for adolescent online social interaction via social media, short messaging service (SMS), and internet text messaging apps on smartphones. However, studies to date of self-reported online social interaction time have produced conflicting results. Understanding the role of dispositional and developmental differences in individuals’ responses to online versus offline social interactions may help elucidate whether and how online social interaction is related to anxiety and depression. Objective This study aimed to investigate the relationship between older adolescents’ and emerging adults’ (18‐24-year-olds) mental health and (1) objectively measured time spent on smartphones and online social interaction apps, (2) momentary affective and affiliative responses to online and offline social interactions, and (3) the moderating role of developmentally and dispositionally elevated social sensitivity. Methods Smartphone, social media (eg, Instagram), SMS, and internet (eg, WhatsApp) text messaging app time from participants’ screen use settings, as well as symptoms of anxiety and depression, and social sensitivity, were measured in 190 older adolescents and emerging adults (mean age 20.4, SD 2.2 years). Participants then completed a novel ecological momentary assessment (EMA) capturing affective and affiliative responses to recent online or offline social interactions 3× daily for 1 week. Symptoms of mental health were assessed again after 1 month. Results Total online social interaction (combined social media and text messaging) app time, but not total smartphone time, was associated with greater anxiety, at both baseline and one month later. Affective and affiliative responses were less positive for online social interactions compared to in-person interactions. Anxiety, but not depression, was associated with feeling less happy, but not less included, after social interactions. Affective and affiliative responses to in-person, but not online, social interactions were negatively associated with depression across the 1-month study period. Finally, social sensitivity moderated the relationship between affective and affiliative responses to social media interactions and depression at baseline. Overall effect sizes were small. Conclusions These findings emphasize the need to investigate individual factors influencing for whom online social interaction is harmful or beneficial. To do so, this study provides a novel, ecologically valid tool for understanding young people’s momentary responses to online and offline social interactions, as well as initial evidence for stronger associations between in-person than online social interaction responses and mental health for older adolescents and emerging adults. It also introduces evidence of social sensitivity as a potential, developmentally relevant vulnerability to the effects of online social interaction. Further research is needed in younger adolescent populations over longer timeframes.
Background Internet-based cognitive behavioral therapy (iCBT) is an effective and scalable alternative to face-to-face psychotherapy, but its reach is constrained by the time therapists spend reviewing patient input and manually drafting written responses. Studies suggest that large language models (LLMs) may be capable of generating high-quality therapeutic text, with the potential to support therapists in delivering treatment. Their suitability as therapist-support tools in structured iCBT, however, remains insufficiently studied. Objective This study aims to assess the quality of LLM-generated iCBT responses to patient messages by comparing them to the quality of responses produced by humans. Methods In a preregistered blinded clinician rating experiment, experienced clinicians assessed the quality of human-produced vs LLM-generated therapist responses within a simulated iCBT treatment for functional somatic disorder. Raters were exposed to a stimulus material consisting of 5 fictitious patient messages, each paired with 1 human and 1 LLM-generated response. Raters assessed message/response pairs on 5 quality dimensions (overall quality, helpfulness, empathy, professionalism, and protocol adherence) and were asked to indicate the source of the response (human/LLM). Analyses were primarily descriptive, supplemented by exploratory statistical tests and descriptive thematic content analysis of open-ended text fields. The full preregistered study protocol is available at Open Science Framework. Results A total of 61 raters provided data, while 54 were eligible and included for analysis. Human- and LLM-generated responses were rated similarly across quality dimensions on a 1-5 scale: overall quality (LLM: mean 4.00, SD 0.54 vs human: mean 3.96, SD 0.53; d=0.06), helpfulness (LLM: mean 3.85, SD 0.57 vs human: mean 3.93, SD 0.49; d=0.13), professionalism (LLM: mean 4.25, SD 0.53 vs human: mean 4.11, SD 0.53; d=0.24), protocol adherence (LLM: mean 4.13, SD 0.52 vs human: mean 4.13, SD 0.54; d=0.03) and empathy (LLM: mean 4.31, SD 0.47 vs human: mean 4.08, SD 0.50; d=0.42). Raters correctly identified the source of human-generated responses (mean 79%, SD 19.65%) more accurately than LLM-generated responses (mean 63%, SD 21.30%). In all, 30/54 (55%) raters responded to one or more open text fields. Qualitative analysis indicated that LLM-generated responses were perceived as polished but also generic and at times excessively empathetic. Conclusions LLM-generated responses were judged to be of comparable quality to those written by human therapists, though qualitative feedback indicated they were at times generic and insufficiently challenging. These findings provide initial support for the feasibility of using LLMs as therapist-support tools in iCBT, but further research is needed to determine whether their integration yields tangible clinical and organizational benefits.
Background: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. Objective:The present systematic review aimed to examine the current evidence on classical AI-supported treatment optimization in the BD spectrum. Methods: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. Results: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. Conclusions:The adoption of classical AI tools in BD serves as a driver for therapeutic optimization, although current AI tools in BD should still be considered exploratory rather than ready for clinical use. Effective implementation in real-world clinical scenarios requires more robust, transparent, and externally validated models to ensure reliability and generalizability.
BACKGROUND: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. OBJECTIVE: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. METHODS: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. RESULTS: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<.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. CONCLUSIONS: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 the potential of specialized virtual care models to expand access to gambling treatment and support symptom improvement in routine care settings. Future research should evaluate longer-term recovery trajectories and identify factors associated with sustained improvement and ongoing engagement in care.
Background: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. Objective: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. Methods:We conducted a 3-arm, single-center, randomized controlled cluster trial in a tertiary NICU. Parents and primary caregivers of very preterm infants (<32 wk' gestational age,<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. Results: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. Conclusions: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 setting with caregivers, its capacity to reduce acute psychological distress may be limited. Secondary findings indicate potential benefits of the digital intervention on breastfeeding, generating hypotheses for future research. Digital mental health interventions in neonatal settings may be most effective when integrated within hybrid models of care and/or delivered beyond the acute admission phase.
Abstract The rapid evolution of 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 well in selected medical knowledge, documentation, and information management tasks and may reduce the 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 2 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.
