BMJ Mental Health

BackgroundDepression and anxiety are common mental health conditions in the UK. NHS Talking Therapies offers evidence-based therapies and is the largest provider of treatment, yet, only 50% of patients recover. Accurate outcome prediction could identify those at risk of poor outcomes and support more personalised care. This study aimed to develop and internally validate multivariable prediction models using routinely collected data from a large, ethnically diverse sample to enable fair, data-driven treatment decisions. MethodsData included 30,999 adults who completed high-intensity therapy at a single NHS trust between 2018 and mid-2024. Seven NHS post-treatment outcomes were modelled: reliable improvement, recovery, and reliable recovery for both depression and anxiety, and also functional impairment at the end of treatment. Predictors measured at baseline included sociodemographic and clinical characteristics. Models were developed using elastic net logistic regression and internally validated using bootstrap resampling. ResultsThe sample was predominantly female (73%) with a median age of 34; 57% identified as White and 22% as Black. Models showed moderate to good discrimination (AUC 0.63-0.77) and strong calibration. Key predictors aligned with clinical expectations, including baseline symptom severity, unemployment, benefit receipt, reporting a disability or long-term condition, psychotropic medication use among other sociodemographic factors. ConclusionsThis study highlights the potential of data-driven tools to inform clinical decisions and treatment stratification in NHS Talking Therapies. Early identification of patients less likely to benefit from standard care could support timely review, monitoring, or tailored interventions. External validation and implementation research are needed to ensure generalisability and equity in care.

BackgroundProlonged waiting times for Child and Adolescent Mental Health Services (CAMHS) leave many young people without structured support while awaiting specialist treatment. Social prescribing has been proposed as a community-based adjunct within CAMHS pathways; however, evidence regarding its safety and clinical impact remains limited. MethodsWellbeing While Waiting was a multi-site non-randomised controlled trial embedded within a hybrid type II implementation-effectiveness evaluation conducted across 11 CAMHS in England. The protocol was prospectively published prior to recruitment (BMC Psychiatry; 10.1186/s12888-023-04758-0). Between May 2023 and March 2025, 558 young people aged 11-18 years referred to CAMHS were enrolled (225 usual care; 333 social prescribing). Primary outcomes were anxiety and depression symptoms, total emotional and behavioural difficulties, and perceived stress. Secondary outcomes included resilience and wellbeing. ResultsNo intervention-related adverse events were observed. On average, participants had 5 sessions with a Link Worker. Compared with usual care, no significant differences were observed in anxiety or depression symptoms. However, participants receiving social prescribing demonstrated significant improvements in total emotional and behavioural difficulties over six months, driven by reductions in conduct difficulties, hyperactivity and peer problems. Significant improvements for those receiving social prescribing were also found for prosocial behaviour and resilience. ConclusionsWithin routine CAMHS pathways, no intervention-related adverse events were observed for social prescribing, and social prescribing was associated with improvements in behavioural and resilience-related outcomes, although not in anxiety or depressive symptoms. Findings suggest social prescribing may offer a valuable adjunct during delayed access to specialist treatment, with effects distinct from symptom-focused clinical therapies.

Background: The NHS Improving Access to Psychological Therapies (IAPT) programme, now rebranded as NHS Talking Therapies, faces persistent capacity constraints with average wait times exceeding 90 days for cognitive behavioral therapy (CBT) in many Clinical Commissioning Group areas. AI-powered CBT platforms have been introduced as a digital adjunct within stepped care, yet longitudinal evidence on anxiety symptom trajectories and their predictors in routine NHS settings remains limited. Objective: To model individual anxiety symptom trajectories among patients referred to an AI-powered CBT platform within NHS primary care, identify distinct trajectory classes, and examine patient-level and practice-level predictors of differential treatment response using multilevel growth curve modeling. Methods: A prospective cohort study was conducted using linked clinical and administrative data from 6,284 patients (aged 18-65) referred to the CalmLogic AI-CBT platform across 187 general practices in four NHS England Integrated Care Systems (ICSs) between April 2023 and September 2025. Patients completed GAD-7 assessments at baseline, 4 weeks, 8 weeks, 12 weeks, and 24 weeks. Three-level growth curve models (assessments nested within patients nested within practices) with random intercepts and random slopes were fitted. Growth mixture modeling (GMM) was subsequently applied to identify latent trajectory classes. Predictors were examined at Level 2 (patient demographics, baseline severity, comorbidities, digital literacy, engagement intensity) and Level 3 (practice deprivation index, list size, urban/rural classification, and IAPT wait time). Results: The unconditional growth model revealed a significant average linear decline in GAD-7 scores of -0.94 points per month (p < .001), with substantial between-patient variation in both intercepts (variance = 14.82, p < .001) and slopes (variance = 0.38, p < .001). Significant between-practice variation accounted for 8.7% of intercept variance (ICC = 0.087). Growth mixture modeling identified four distinct trajectory classes: Rapid Responders (28.4%, steep early decline stabilising by week 8); Gradual Improvers (34.1%, steady linear decline through 24 weeks); Partial Responders (22.8%, modest early improvement followed by a plateau at clinically significant levels); and Non-Responders (14.7%, minimal change or slight deterioration). Higher baseline severity, female gender, and greater module completion predicted membership in the Rapid Responder class. Practice-level IAPT wait times exceeding 90 days independently predicted faster improvement trajectories (coefficient = -0.31, p = .003), suggesting that AI-CBT has its greatest incremental value in capacity-constrained areas. Patients in the most deprived quintile showed slower trajectories (coefficient = 0.22, p = .011) despite equivalent engagement levels, indicating a deprivation-related treatment response gap. Conclusions: AI-powered CBT platforms integrated within NHS primary care produce significant anxiety symptom reduction on average, but treatment response is heterogeneous, with four distinct trajectory classes identified. The finding that longer IAPT wait times predict better AI-CBT outcomes supports the platform's positioning as a scalable bridge intervention for capacity-constrained services. The deprivation-related response gap warrants targeted support strategies for patients in the most disadvantaged communities.

ImportanceAI-enabled features may improve the effectiveness of routine mental health care, yet large-scale real-world evidence remains limited. ObjectiveTo evaluate whether access to AI-enabled continuous care features embedded within routine psychotherapy delivery is associated with improved treatment engagement and clinical outcomes under real-world conditions. DesignPreregistered cluster-level, matched, quasi-experimental study using a real-world rollout of AI-enabled continuous care features compared with psychotherapy alone (intention-to-treat framework). SettingAn employer-sponsored behavioral health program providing outpatient psychotherapy for employees and dependents. ParticipantsAdults initiating a new episode of psychotherapy from 25 employers with access to continuous care features and 75 matched employers without access. Treatment engagement was assessed over 7 weeks (n=26,208), and clinical outcomes were evaluated for up to 180 days (n=5,518). ExposureEmployer-level access to AI-enabled continuous care features supporting engagement and continuity before and between psychotherapy sessions, compared with psychotherapy alone. Main OutcomesEarly treatment engagement (number of psychotherapy sessions attended and time to second session) and changes in depressive and anxiety symptom severity measured using the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7). ResultsCompared with matched controls receiving psychotherapy alone, the intervention group attended 5% more psychotherapy sessions during the first 7 weeks (rate ratio, 1.05 [1.01, 1.10]) and completed their second session sooner (mean difference, -0.62 days [-1.05, -0.18]). Both groups demonstrated substantial symptom improvement over time; however, access to continuous care features was associated with additional improvement in depressive symptoms (d=0.16) and anxiety symptoms (d=0.15) at the median duration of care (day 44). These effects translated into clinically meaningful differences in reliable improvement by the median duration of care (NNT=25 for both outcomes). Conclusions and RelevanceIn this real-world evaluation, access to AI-enabled continuous care features embedded within routine psychotherapy delivery was associated with greater early engagement and a higher likelihood of reliable symptom improvement beyond psychotherapy alone. These findings suggest that augmenting routine psychotherapy with AI-enabled continuous care can meaningfully shift recovery trajectories during a standard treatment episode, strengthening early treatment momentum and improving outcomes at scale. Key PointsO_ST_ABSQuestionC_ST_ABSIs access to AI-enabled continuous care features embedded within routine psychotherapy delivery associated with improved treatment engagement and clinical outcomes under real-world conditions? FindingsIn this cluster-level, matched, quasi-experimental study of adults receiving psychotherapy within an employer-sponsored behavioral health program, access to AI-enabled continuous care features was associated with significantly greater early treatment engagement and faster improvement in depressive and anxiety symptoms compared with psychotherapy alone. MeaningAI-enabled support features may incrementally enhance the delivery and effectiveness of established psychotherapies when implemented as complements to routine care at scale.

ObjectivesElectroconvulsive Therapy (ECT) is an effective treatment for bipolar disorder, particularly in severe acute cases or for illness resistant to pharmacotherapy. However, the risk of relapse following ECT is high, necessitating intervention to reduce this risk. Based on findings from ECT studies in unipolar depression and its well-known mood-stabilizing properties, it is likely that lithium treatment may reduce the risk of relapse of bipolar disorder following ECT. Therefore, we conducted a target trial emulation using data from Danish nationwide registers to investigate whether lithium protects against relapse following ECT treatment of bipolar disorder. MethodsPatients discharged from their first psychiatric admission with a primary diagnosis of bipolar disorder between January 1, 2006, and June 1, 2024, who received at least six ECT treatments, were included. Follow-up began two weeks after discharge and continued until relapse, death, one year, or January 1, 2025. Patients were considered allocated to lithium treatment if they redeemed a prescription for lithium within the first two weeks after discharge from the index admission (ECT treatment). The outcome was time to relapse, defined by either psychiatric hospital admission or suicide. Cox proportional hazards regression, adjusted for potential confounders, was used to compare the outcome between patients allocated and not allocated to lithium treatment. ResultsAmong the 574 eligible patients (mean age 41.5 years, 61.3% women), 214 (37.3%) were allocated to lithium treatment and 360 (62.7%) were not allocated to lithium treatment. During follow-up, 56 patients (26.2%) in the lithium group and 135 patients (37.5%) in the non-lithium group experienced a relapse. Lithium treatment was associated with a substantially reduced risk of relapse (adjusted hazard rate ratio, 0.60, 95% CI=0.43-0.84). ConclusionLithium treatment after ECT may reduce the risk of relapse in patients with bipolar disorder. These findings should be followed up by a randomized controlled trial.

Introduction People with bipolar disorder (BD) and concurrent opioid use disorder (OUD) experience more severe clinical outcomes, including higher mortality, treatment complexity, and worse psychiatric symptoms, yet they are underserved due to a lack of tailored clinical guidelines and limited supporting research on competing treatment options. While pharmacological treatments for BD are well-established, their use varies widely across settings, and their effectiveness in individuals with co-occurring OUD is unclear. We propose parallel population-based studies to emulate randomized controlled trials to assess the comparative effectiveness of pharmacological treatment options for BD among people with OUD in British Columbia and Ontario, Canada, 2010-2023. Methods and analysis We propose emulating a series of parallel target trials using linked population-level health administrative data for all individuals aged 18 years or older diagnosed with both BD and OUD and who initiated treatments for BD between 1 January 2010 and 31 December 2023. All analyses will be conducted in parallel in British Columbia and Ontario. We propose a series of four successive target trial emulations, comparing (i) lithium versus non-antipsychotic mood stabilizers such as divalproex, lamotrigine, and valproic acid; (ii) lithium versus 2nd generation antipsychotics with mood stabilizing properties such as risperidone, olanzapine, aripiprazole, and quetiapine; (iii) lithium versus combination treatments such as lithium and divalproex, lithium and olanzapine, lithium and aripiprazole, lithium and quetiapine, divalproex and olanzapine, and olanzapine and quetiapine; (iv) lithium and valproate (LATVAL) versus lithium and olanzapine, lithium and aripiprazole, lithium and quetiapine, divalproex and olanzapine, and olanzapine and quetiapine. Incident user and prevalent new user analyses are planned for proposed target trials (i)-(iv), pending sufficient data. Stratified analyses will be conducted for BD-I, manic and depressive phases of BD illness. We propose an initiator analysis (intention-to-treat, conditional on medication dispensation) to determine the effectiveness of the treatments and per-protocol analyses to determine the efficacy of the treatments after dealing with treatment switching and recommended dose adjustment. The outcomes will include psychiatric acute-care visits (hospitalizations and emergency department visits), BD treatment discontinuation and all-cause mortality. Subgroup and sensitivity analyses, including cohort and study timeline restrictions, eligibility criteria modifications, and outcome reclassifications, are proposed to assess the robustness of our results. Executing analyses in parallel across settings using a co-developed protocol will allow us to evaluate the replicability of findings. Ethics and dissemination The protocol, cohort creation, and analysis plan have been classified and approved as a quality improvement initiative by the Providence Health Care Research Ethics Board and the Simon Fraser University Office of Research Ethics. Results will be disseminated to local advocacy groups, clinical groups and decision-makers, national and international clinical guideline developers, presented at international conferences, and published in peer-reviewed journals.

IntroductionSevere mental illness is associated with high mortality rates and cardiovascular disease. Obesity and dysmetabolism associated with antipsychotic treatment comprise modifiable risk factors, which remain undertreated. Interventions such as antipsychotic-switching, lifestyle-interventions and weight-reducing medication have shown varying results indicating a need for a more individualized approach. The Meta-Care Trial aims to assess the effectiveness of a pragmatic, individualized, evidence- and guideline-based cardiometabolic intervention. Methods and analysisMeta-Care is an open-label randomized controlled trial (RCT). Patients between 18-45 years with schizophrenia spectrum disorders or bipolar disorder will be recruited from in- and outpatient Mental Health Services in the Capital Region of Denmark. Inclusion criteria include treatment with antipsychotics and: either i) [&ge;]5% body weight gain or [&ge;]5cm waist circumference increase since initiation of antipsychotic therapy, or ii) a body mass index (BMI) [&ge;]30 kg/m2, or iii) BMI [&ge;]27 kg/m2 and related cardiovascular risk factors. Patients are randomized to a pragmatic, individualized metabolic clinic using evidence- and guideline-based care in a mental health center or standard care. Allocation-ratio is 1:1. The primary outcome is the proportion of patients achieving weight loss [&ge;]5% of initial body weight after 12 months. Secondary and exploratory outcomes cover cardiometabolic risk factors, cognition, personal recovery, and quality of life. Qualitative interviews will explore patient experience and contextual factors. Recruitment started in October 2023 and will include a total of 84 patients. Ethics and disseminationThe Meta-Care trial is funded by The Independent Research Fund Denmark and The Worzner Memorial Fund for Research in Mental Illness. The trial has been approved by the Regional Ethics Committee and Data Protection Agency in the Capital Region of Denmark. Positive, negative, and inconclusive results will be published in scientific peer-reviewed journals, presented at conferences, and dispersed to patient organisations and media. Strengths and limitations- The Meta-Care Trial is the first randomized control trial (RCT) to investigate the effectiveness and acceptability of a pragmatic, individualized metabolic clinic located in a mental health center using evidence- and guideline-based care to treat obesity and cardiometabolic risk factors in patients with severe mental illness - The pragmatic design with limited exclusion criteria and simple outcome measures will generate results that are generalizable to clinical practice - The complex Meta-Care multi-intervention limits inferences of effects explained by specific modifications of pharmacotherapy or lifestyle changes - Potential knowledge exchange from treating personnel in the Meta-Care Trial to caregivers in the standard care group may lead to contamination bias - Although the Meta-Care trial has an open label design, measurements of primary and secondary outcomes will be carried out by blinded assessors

Background Hospital admissions for mental health (MH) and stress related presentations (SRP; symptoms without a clear medical cause which may be psychosomatic in nature) among children and young people (CYP) have risen over time. Rehospitalisation contributes to service costs, may indicate gaps in community based care, and can also disrupt education and social development. Methods This retrospective cohort study used NHS Hospital Episode Statistics to identify all CYP aged 10 to 25 with >1 MH/SRP related hospital admissions in England between 1 April 2014 and 31 March 2018, with follow up until 31 March 2019. Admissions were classified from ICD10 codes into internalising, externalising, personality, and eating disorders, psychosis, self-harm, substance use, postpartum, or potentially psychosomatic diagnostic groups. Outcomes included 30 day all cause readmission, 1 year all cause readmission, and 1 year MH/SRP-specific rehospitalisation. Time to rehospitalisation, and number of MH/SRP readmissions were also evaluated. Clinical and sociodemographic characteristics associated with rehospitalisation were assessed using regression models, time to rehospitalisation using Kaplan Meier analyses, and diagnostic transitions were visualised using Sankey diagrams. Results Of 492,061 CYP with hospital admission for MH/SRP, approximately one third were rehospitalised within one year. Females, older CYP and those from more deprived areas had higher odds of all cause readmission. The odds of MH/SRP rehospitalisation were highest among those aged 14 to 15 years. Co occurring chronic physical health conditions, personality and eating disorders were associated with higher odds, and shorter time, to readmission. Conclusions Rehospitalisation following MH/SRP admissions is common and socioeconomically patterned among CYP. Targeted discharge planning and continuity of care interventions are needed, particularly for high risk CYP admitted with eating and personality disorders.

BackgroundLongitudinal measurement of depression severity in outpatient psychiatric care is limited by infrequent standardized assessments. Although psychiatric clinical notes capture illness burden and functional impairment, this information is rarely quantified for analysis. ObjectiveTo evaluate whether large language models (LLMs) can infer clinically meaningful measures of depression severity from outpatient psychiatry notes. MethodsWe sampled 91,651 outpatient psychiatry notes from 8,287 adult patients across 58 clinics within a large academic medical center between 2015 and 2021. A HIPAA-compliant LLM (OpenAI GPT-5.2) was prompted to independently estimate three depression severity scores (Patient Health Questionnaire-9 [PHQ-9], Hamilton Depression Rating Scale [HAM-D], and depression-specific Clinical Global Impression-Severity [CGI-S]) from notes, with patient-reported PHQ-9 content within notes redacted to prevent biasing. Convergent validity was assessed against patient-reported PHQ-9 (n=3,757), study-clinician chart review (n=125), and treating-clinician suicide risk assessments (SRA; n=2,985). Predictive validity was evaluated using survival models of antidepressant switching and psychiatric emergency visits. Discriminant validity across diagnoses and consistency across demographic groups and clinics were also evaluated. Results10.8% of eligible visits had a PHQ-9 recorded within 7 days before the encounter. LLM-inferred PHQ-9 scores showed moderate agreement with patient-reported PHQ-9 (Cohens {kappa}=0.64, 95%CI:0.62-0.66; Pearson r=0.67, 95%CI: 0.65-0.68). Stronger agreement was found between LLM CGI-S and study-clinician chart review ({kappa}rater1=0.79, 95%CI: 0.70-0.85; {kappa}rater2=0.67, 95%CI: 0.58-0.77; r=0.86 with mean rating, 95%CI: 0.80-0.90). In prospective analyses, LLM CGI-S predicted antidepressant switching (C-index=0.60; CI95%: 0.58-0.62) and psychiatric emergency visits (C-index=0.63; 95%CI: 0.57-0.68), which was comparable to the predictive performance of patient-reported PHQ-9 and treating-clinician SRA. Correlations between LLM CGI-S and patient-reported PHQ-9 were consistent across clinics (I2<0.1) but significantly lower among Black (r=0.48, 95%CI: 0.38-0.57) and Hispanic (r=0.43, 95%CI: 0.27-0.56) patients. ConclusionsLLM-inferred depression severity scores from psychiatric outpatient notes support longitudinal, standardized phenotyping of depression severity, such as for routine outcome monitoring. These results have implications for facilitating genetic, pharmacoepidemiologic, and antidepressant treatment effectiveness studies using real-world evidence.

BackgroundPoor patient retention and family engagement compromise the effectiveness of coordinated specialty care (CSC) for first-episode psychosis (FEP). This mixed methods study aimed to identify program-level characteristics (CSC fidelity and engagement strategies) associated with patient retention and family engagement in Massachusetts CSC programs. MethodsPrimary outcomes were rates of patient retention and family engagement ([&ge;]1 evidence-based family intervention session), based on CSC program census (October 2022 - September 2023). Quantitative analyses explored program characteristics (EPINET Program-Level Core Assessment Battery) and fidelity ratings (Massachusetts Psychosis Fidelity Scale) as predictors using t-tests or univariate linear regressions. Thematic analysis of program interviews compared patient and family engagement strategies employed by high versus low performing programs. ResultsAcross nine programs, mean patient retention was 86% (range: 58-97%) and family engagement was 40% (range: 12-100%). Higher fidelity to evidence-based services (e.g., individual therapy, family intervention, and supported education/employment) was significantly associated with both outcomes (p<.05; R2 range: .51-.72). Mixed-methods analysis showed that high performing programs used case management-related supports to meet service users practical needs. Factors associated with higher patient retention included having comprehensive intake assessments, provider visits during hospitalization, and periodic treatment reviews. Programs that conducted benefits counseling and proactively recommended family services as standard care had higher family engagement. ConclusionsHigher fidelity CSC programs had better patient retention and family engagement. Case management-related supports addressed treatment barriers. Strategies designed to strengthen therapeutic alliance and goal alignment may promote patient engagement, while family engagement may benefit from proactive recommendation of family intervention.

BackgroundDespite widespread recognition of the value of lived experience (LE) involvement in healthcare research and increased LE involvement activity, we lack established implementation methods and instruments for reporting and evaluating impact. We present a protocol for an innovative LE-led Impact Log tool and co-production framework, which may help to address some fundamental barriers to co-production. The Impact Log will be implemented within a five-year multidisciplinary transdiagnostic research project on severe mental illness, the Brain and Genomics Hub of the UKRI Mental Health Platform, and is also designed for wider adaptation and use. Part I presents a short narrative review of literature pertaining to defining, evaluating, and enhancing the impact of co-production, to provide in-depth background and aid future development. Part II presents the Impact Log protocol. MethodsThe Impact Log framework is designed to integrate inclusive and impactful co-production throughout all research stages, and to record and evaluate its impact across three domains using an accessible short form. The three research domains are: design and delivery; interpersonal and environmental aspects; systems and processes. Impact Log design and implementation is led by LE study leads and a specialist advisory panel, who are integrated fully within the wider research team, and all have combined research experience and LE of bipolar or psychotic disorders. All Hub research participants will be offered accessible opportunities for remunerated lived experience input, and there will be outreach to ensure diverse representation, aided by the Hubs charity partners. Data collection and analysis will be LE led and will include iterative analysis to inform continuing development. Diverse formal and informal dissemination throughout the project will maximise wider stakeholder engagement. DiscussionThe potential value of this research is to implement a novel tool and framework for facilitating, recording and evaluating co-production in complex mental health research, which can be adapted for wider use. Strengths in design are LE leadership and cross-cutting LE research integration, incorporation of multiple domains, and a focus on facilitating diversity and inclusion within co-production. Potential limitations for this project and wider adaptation may include limited resources, risk of bias and health challenges. Lay SummaryWe have provided a brief lay summary to help people without a research background understand our project. This article explains our plan to develop and test a new way of understanding how research changes when people with personal experience of a mental health condition are part of the research team. We are a team of mental health researchers and many of us have direct experience of bipolar and psychosis. We work alongside other researchers, including people who might also have worked in mental health services or in charities that provide support. Our research project aims to better understand what is happening in the brain, body, lives and experiences of people who have bipolar and psychosis. Many people believe that research is better when it includes the views of people who have direct experience of the health condition being studied. This is called "lived experience". We have developed a structured approach to make sure that people with lived experience are meaningfully involved in our research team. We have also created a simple tool, called the Impact Log, to record when lived experience members contribute and to help us understand how their involvement influences the research. Finally, we wanted to better understand what other researchers have said about lived experience involvement. We reviewed many published academic studies and reports and brought their findings together in what is called a "narrative review". This review summarises what is already known about the difference lived experience involvement can make in research.

The COVID-19 pandemic had substantial impact on healthcare systems across the globe, including psychiatric services. Use of electroconvulsive therapy (ECT), a lifesaving intervention for severe mental illness, was reported to have declined during the pandemic in several countries, but nationwide data remain scarce. Using nationwide data from the Danish National Patient Register, we examined all ECT treatments administered in Denmark from September 2019 to May 2025. Weekly treatment numbers were visualized across the three national COVID-19 lockdowns to descriptively assess changes in ECT use. A notable reduction in ECT treatments was observed in the weeks preceding and during the first lockdown (March 11 to May 18, 2020). A post-hoc estimation indicated approximately 1,366 "missed" treatments during the initial pandemic phase in 2020. When these were added to the 27,033 treatments delivered in 2020, the adjusted total approximated annual treatment volumes in 2019 and 2022, suggesting a temporary disruption rather than sustained decline. In contrast, ECT activity during the second and third lockdowns appeared largely unaffected. These findings suggest that ECT provision in Denmark was temporarily reduced during the initial phase of the pandemic but remained resilient thereafter. In the case of a future pandemic, safeguarding timely access to ECT--particularly in early phases-- should be prioritized given its critical role in the treatment of severe mental illness.

BackgroundAI-powered cognitive behavioural therapy (AI-CBT) tools hold significant promise for addressing the global mental health treatment gap, yet sustained user engagement remains critically low. While patient attitudes and experiential factors have been qualitatively documented, the psychological mechanisms through which AI literacy translates into long-term engagement remain poorly understood. Existing systematic evidence highlights trust, perceived therapeutic alliance, and stigma as salient themes, but no large-scale quantitative study has modelled these as a mediated pathway. ObjectiveThis study aimed to (1) examine whether trust in AI systems and perceived therapeutic alliance mediate the relationship between AI literacy and sustained AI-CBT engagement, and (2) determine whether mental health stigma moderates these mediated pathways. MethodsA cross-sectional national online survey was conducted in the United Kingdom (N = 1,247). Eligible adults (18+) with a history of anxiety or depression who had used an AI-CBT tool in the preceding 12 months were recruited via stratified random sampling. Structural equation modelling (SEM) with moderated mediation was conducted in R (lavaan 0.6-17). Moderated mediation was evaluated using the PROCESS macro framework adapted for SEM, with 5,000 bootstrap replications for bias-corrected confidence intervals. Model fit was assessed using CFI, TLI, RMSEA, and SRMR indices. ResultsThe final SEM demonstrated excellent fit (CFI = 0.967, TLI = 0.959, RMSEA = 0.043 [90% CI: 0.036-0.051], SRMR = 0.052). AI literacy exerted a significant indirect effect on sustained engagement through trust in AI ({beta} = 0.213, SE = 0.031, p < .001) and perceived therapeutic alliance ({beta} = 0.187, SE = 0.028, p < .001). Mental health stigma significantly moderated the trust[-&gt;]engagement pathway ({Delta}R2 = 0.042, p = .003), with the indirect effect being stronger among individuals with lower stigma scores. The total indirect effect accounted for 58.4% of the total effect of AI literacy on engagement. ConclusionsAI literacy promotes sustained AI-CBT engagement primarily through its effects on trust and perceived therapeutic alliance, pathways that are attenuated by mental health stigma. These findings underscore the need for stigma-reduction interventions and AI literacy programmes as implementation strategies. Findings have direct implications for the design and deployment of AI-CBT tools across UK NHS digital mental health services.

BackgroundUp to one-third of medical inpatients experience clinically relevant mental distress, yet many remain untreated. Stepped and collaborative care (SCC) models may improve access to mental health care, but predictors of service uptake are unclear. We examined patient- and ward-level predictors of psychosomatic-psychiatric consultation (PPC). MethodsWe analyzed data from SomPsyNet, a stepped-wedge cluster randomized trial targeting SOMatic inpatients across three Swiss tertiary hospitals, to prevent PSYchosocial distress by a care NETwork. Analyses focused on inpatients screening positive for mental distress. Multiple-imputed logistic regressions assessed predictors of four sequential service-use stages: PPC considered, offered, accepted, and received. ResultsAmong 589 distressed patients, 93.9% were offered PPC, 63.1% accepted, and 83.9% of acceptors received PPC, yielding a 50% overall receipt rate. Patients without Swiss citizenship showed higher odds of acceptance (odds ratio [OR]=1.82 [1.10, 3.00]) and eventual receipt (OR=1.62 [1.01, 2.62]). Being in a Geriatric ward facilitated PCC uptake, while patients from gynecology showed reduced progression through the care pathway. Age, gender, income, education, marital status, and living arrangement showed no statistically robust associations. ConclusionsAlmost two-thirds of mentally distressed medical hospital inpatients accepted an offered PPC, indicating high acceptability. About half ultimately received a consultation, highlighting substantial attrition along the SCC pathway. Ward specialty and nationality were key determinants of PPC uptake. These findings suggest that proactive, ward-oriented consultation-liaison models embedded in routine inpatient care may improve timely and equitable access to mental healthcare, including for migrant and minority patients who are otherwise less likely to access such care. HighlightsO_LIPsychosomatic-psychiatric consultation pathway of medically hospitalized inpatients C_LIO_LI63% accepted such a consultation when offered; overall 50% reached receipt. C_LIO_LINon-Swiss nationality increased odds of acceptance (OR 1.8) and receipt (OR 1.6). C_LIO_LIPatients at geriatrics wards showed higher, at gynecology wards lower transitions. C_LIO_LIResults support low-threshold, ward-oriented consultation-liaison models. C_LI

Task-sharing approaches have shown promise in low-resource settings, yet few culturally adapted interventions have been systematically evaluated for forcibly displaced populations. Since 2016, over 1.7 million Venezuelans have migrated to Peru, facing significant barriers to healthcare and elevated risks of anxiety, depression, and post-traumatic stress disorder (PTSD). This protocol describes COMPASS (Cognitive-behavioral Open-source Mental-health Program Adapted for migrants, Sustainably delivered by lay providers and Supported by evidence). COMPASS is a transdiagnostic, open-source cognitive behavioral therapy program co-designed with forcibly displaced populations. This protocol describes the procedures for an ongoing randomized pilot trial with n = 90 forcibly displaced Venezuelan people (Clinicaltrials.gov: NCT06635486). COMPASS guides, or lay providers, trained through an intensive apprenticeship model, will deliver 6-12 weekly remote sessions. Primary outcomes include changes in anxiety, depression, and PTSD symptoms, assessed with validated Spanish-language measures. Secondary outcomes include feasibility (recruitment, retention, fidelity) and acceptability (therapist and participant ratings). Exploratory outcomes will examine integration, migration experiences, and demographic moderators of intervention effectiveness. Analyses will follow the intention-to-treat principle, using descriptive statistics and regression models to evaluate symptom trajectories across baseline, post-intervention, and 3- and 6-month follow-ups. This study represents the first effectiveness evaluation of an open-source, lay-delivered CBT program tailored for forcibly displaced people in Peru. Findings will inform feasibility, acceptability, and preliminary effectiveness of COMPASS, with potential to expand scalable, culturally relevant mental health services for forcibly displaced populations in resource-constrained settings worldwide.

BackgroundA study conducted in Norway showed that the association between pupil mental health diagnoses and educational attainment has weakened over time. One possible explanation is that earlier detection of mental health problems in recent years has facilitated earlier treatment, intervention, and educational support that might improve academic outcomes. We investigated whether the weakening association between mental health and attainment could be replicated in England, and explained by earlier age at first diagnosis. MethodsThis was a secondary longitudinal data analysis of de-identified records from a secondary mental healthcare provider in England, which have been linked to the Department for Educations National Pupil Database. We included n=149,841 pupils residing in South East London, born 1993-2003, who completed their end-of-school exams 2009-2019. The main exposure variables were ADHD and internalising disorder diagnosis. In linear regressions, we investigated their associations with Year 11 attainment (typically assessed age 15-16 years), whether this was modified by birth year, and the role of age at first diagnosis. ResultsOn average, ADHD (n=844, 0.6%) and internalising disorder (n=2,523, 1.7%) were associated with lower Year 11 attainment. However, significant interactions between diagnosis and birth year suggested that pupils with these disorders showed increases in standardised exam scores over successive birth cohorts, resulting in a closing attainment gap over time. While age at first diagnosis became younger over the period, this did not confound the observed associations. ConclusionsWe replicated findings from Norway that suggest a narrowing attainment gap between those with and without ADHD and internalising disorder diagnoses. Building on this, we ruled out earlier age of diagnosis as a possible explanation for this phenomenon. With administrative data research growing internationally, we are increasingly able to replicate mental health and education trends in different countries, opening more opportunities for international collaboration.

BackgroundIn 2020, Oregon became the first U.S. state to establish a regulated framework for adults to access psilocybin services using naturally-derived mushroom products. No studies have examined mental health outcomes among individuals receiving psilocybin in this context. AimsTo evaluate changes in self-reported symptoms of depression, anxiety, and well-being 30-days post-psilocybin session under the Oregon state-regulated model , and document session-related adverse events and doses consumed. MethodsThis was a naturalistic study (March 2024-April 2025) among adults [&ge;]21 years participating in a legal psilocybin services program. Online surveys were completed pre-session, 1-day, and 30-days post-session. Primary outcomes were change in depression, anxiety, and well-being symptoms pre-session to 30-days post-session evaluated using linear mixed-effects models (random effect: timepoint; fixed effects: sex, concurrent psychiatric medication use, age, session dose [total psilocybin equivalents, TPE, mg: psilocybin mg + 1.39 * psilocin mg]). Adverse events (e.g., hallucinogen persisting perception disorder [HPPD]) were assessed at 1-day and 30-days post-session. ResultsParticipants (n=88; median age 43 years; 52% male) were predominantly Oregon residents (53.4%), psychedelic-experienced (64.8%), and concurrently using psychiatric medication (46.6%). All outcomes improved significantly at 30-days post-session (p<0.001), including in sensitivity analyses stratified by concurrent psychiatric medication usage (p<0.001 all outcomes, both groups). Two participants (2.3%) reported symptoms consistent with HPPD at 1-day post-session, but none at 30-days. Mean dose was 27.8 mg (SD 8.2) TPE. ConclusionsPsilocybin sessions delivered under the Oregon regulatory model were associated with clinically meaningful improvements in depression, anxiety, and well-being 30-days post-session, supporting therapeutic effectiveness of legal psilocybin services.

Background Chronic pain and depression are leading causes of disability and frequently co-occur. Depression presents with diverse symptoms, but despite this variability, the prevalence of individual depressive symptoms in chronic pain and the genetic and causal associations linking these traits remain poorly characterised. Methods Using data from 142,688 age- and sex-matched UK Biobank participants, we compared depressive symptom severity levels and item-level Patient Health Questionnaire-9 (PHQ-9) prevalences, spanning affective, cognitive and somatic domains, between participants with and without chronic pain. Using genome-wide association study (GWAS) summary statistics of multisite chronic pain (MCP), major depressive disorder (MDD), and individual symptoms of depression, genetic correlations and bidirectional causal effects between MCP and depressive phenotypes (MDD and individual symptoms) were estimated via linkage disequilibrium score regression (LDSC) and two-sample Mendelian randomisation (MR), respectively. Results Depression (at every severity level) was more common in the chronic pain group compared to controls, with the largest between-group difference for severe symptoms (7.50-fold increase). All individual depressive symptoms were at least 2.79 times as prevalent in chronic pain. Additionally, chronic pain had a significant and positive genetic correlation with MDD (rg = 0.59) and all depressive symptoms (rg = [0.24, 0.55]). MR supported a bidirectional causal association between MCP and MDD (MCP[-&gt;]MDD: OR = 1.85, pFDR < 0.001, MDD[-&gt;]MCP: {beta} = 0.17, pFDR < 0.001). At the symptom level, MR indicated bidirectional effects between MCP and anhedonia (MCP[-&gt;]anhedonia: OR = 1.60, pFDR < 0.001, anhedonia[-&gt;]MCP: {beta} = 0.08, pFDR = 0.005), and unidirectional effects of MCP on appetite/weight gain (OR = 1.90, pFDR = 0.022) and appetite/weight loss (OR = 1.63, pFDR = 0.005), concentration problems (OR = 1.63, pFDR = 0.044), and suicidal thoughts (OR = 1.46, pFDR = 0.021). Additionally, genetic liability to concentration problems was associated with a lower risk of MCP ({beta} = -0.04, pFDR = 0.022). Conclusion Chronic pain is associated with a marked depressive burden spanning all symptom domains. Shared genetic architecture and symptom-specific causal pathways, particularly involving anhedonia, highlight potential targets for improved treatment of comorbid chronic pain and depression.

Digital therapeutics for mental health often face low patient engagement, which limits their clinical impact. Interventions that deliver treatment using a video game medium may improve engagement and therapeutic efficacy, but the putative emergence of gaming-related problems remains a concern among clinical stakeholders. We examined whether long-term engagement with Meliora, a video game therapeutic for adult major depressive disorder, was associated with changes in gaming-related problems in a three-arm randomized controlled trial. The intention-to-treat cohort (n = 1,001) had a mean age of 33.4 years (SD 9.3) and 64% were female. The Gaming Addiction Scale (GAS-7) scores decreased from baseline (week 0) to post-intervention (week 12) in the Meliora arm (p = 8.1x10-4) and in the treatment-as-usual arm (p = 6.0x10-6), with no significant change observed in the Sham arm (p = 0.39). Changes in GAS-7 scores were not associated with intervention use hours (Meliora: p = 0.17; Sham: p = 0.28) or with experienced immersion (Meliora: p = 0.93; Sham: p = 0.19). Deterioration analysis found worsening rates from baseline to post-intervention low and comparable across study arms. Analyses in the per-protocol completer cohort ([&ge;]24 h use) corroborated these findings, indicating that even higher use did not lead to increases in gaming-related problems. These results provide evidence that long-term use of a video game therapeutic does not increase gaming-related problems when risks are properly mitigated, suggesting that video games may provide a safe medium for digital therapeutics. Author summaryMany patients use digital therapeutics insufficiently or drop out early, which limits their effectiveness and applicability in healthcare. Video game therapeutics deliver the treatment using an interactive video game as a medium to improve both engagement and therapeutic efficacy. However, extended use of video game therapeutics could inadvertently increase gaming-related problems. We examined whether long-term use of Meliora, a video game therapeutic for adults living with depression, was associated with increased gaming-related problems. We found that using Meliora or a highly similar Sham device did not increase gaming-related problems. Changes in gaming-related problems were not associated with the amount of time participants used the interventions, suggesting that typical use patterns are safe. We also found no relationship between experienced immersion and changes in gaming-related problems, suggesting that subjective immersion is distinct from problematic gaming. This study provides the first clinical evidence that extended engagement with a video game therapeutic does not increase gaming-related problems. These findings suggest that video games can be a safe medium for digital therapeutics in healthcare.

BackgroundMajor depressive disorder (MDD), a leading cause of disability worldwide, exhibits substantial heterogeneity in treatment outcomes. Patients who do not respond to standard antidepressant therapy account for the majority of MDDs disease burden. Risk factors have been implicated in treatment response, including genes impacting on how antidepressants are metabolised. Yet, despite its clinical importance, risk factors for treatment-resistant depression (TRD) remain unexplored in low- and middle-income countries (LMIC). We used data from the DIVERGE study on MDD to investigate the risk factors of TRD in Pakistan. MethodsDIVERGE is a genetic epidemiological study that recruited adult MDD patients ([&ge;]18 years) between Sep 27,2021 to Jun 30, 2025, from psychiatric care facilities across Pakistan. Detailed phenotypic information was collected by trained interviewers and blood samples taken. Infinium Global Diversity Array with Enhanced PGx-8 from Illumina was used for genotyping followed by DRAGEN calling to infer metaboliser phenotypes for Cytochrome P450 (CYP) enzyme genes. We defined TRD as minimal to no improvement after [&ge;]12 weeks of adherent antidepressant therapy. We conducted multi-level logistic regression to test the association of demographic, clinical and pharmacogenetic variables with TRD. FindingsAmong 3,677 eligible patients, polypharmacy was rampant; 86% were prescribed another psychotropic drug along with an antidepressant. Psychological therapies were uncommon (6%) while 49% of patients had previously visited to a religious leader/faith healer in relation to their mental health problems. TRD was experienced by 34% (95%CI: 32-36%) patients. The TRD group was characterised by more psychotic symptoms and suicidal behaviour (OR=1.39, 95%CI=1.04-1.84, p=0.02; OR=1.03, 95%CI=1.01-1.05, p=0.005). Social support (OR=0.55, 95%CI=0.44-0.69, p=1.4x10-7) and parents being first cousins (OR=0.81, 95%CI=0.69-0.96, p=0.01) were associated with lower odds of TRD. In 1,085 patients with CYP enzyme data, poor (OR=1.85, 95%CI=1.11-3.07, p=0.01) and ultra-rapid (OR=3.11, 95%CI=1.59-6.12, p=0.0009) metabolizers for CYP2C19 had increased risk of TRD compared with normal metabolisers. InterpretationThere was an excessive use of polypharmacy in the treatment of depression while psychological therapies were uncommon highlighting the need for more evidence-based practice. This first large study of MDD from Pakistan uncovered the importance of culture-specific forms of social support in preventing TRD, highlighting opportunities for interventions in low-income settings. Pharmacogenetic markers can be leveraged to predict TRD.