Frontline occupations, including military, healthcare, and first responders, often include frequent exposure to traumatic events, increasing the risk of substance use disorders (SUDs). Research has shown that those in high-intensity occupations are at higher risk of developing SUDs compared to the general population. Women face unique experiences related to substance use, including greater functional impairment and barriers to treatment access. Yet, understanding of the effectiveness of digital health technologies in addressing substance use among women in frontline occupations is limited. This systematic review evaluates the effectiveness of digital health interventions in reducing substance use among women in frontline roles. Four databases (PsycINFO, Ovid MEDLINE, Embase, PsycArticles) were searched for English language full-text articles (2007-2024) that (1) evaluated a digital intervention designed to reduce substance use, (2) reported changes in substance use outcomes such as frequency, intensity or duration, using validated tools (3) included current or former frontline public service workers, and (4) included women as the primary target population or as a subgroup within the sample. 13 papers met inclusion criteria, focusing on eight distinct web and mobile-based interventions for alcohol, tobacco and illicit substances. Most studies (n = 11) reported substantial post-intervention reductions in alcohol and tobacco use, although results for PTSD symptoms, illicit drug use, and quality of life were mixed. This review highlights the potential of digital health interventions for reducing substance use but underscores significant gaps in research. The scarcity of studies focused on women, small and heterogeneous samples, and focus on veterans limits the generalisability to women in frontline roles. These gaps present a pressing challenge in understanding gender-specific digital intervention efficacy. Future research should prioritise larger, representative samples of women across diverse frontline occupations to drive the development of digital technologies tailored to the unique challenges faced by women in these roles.

BackgroundVeterans face an increased risk of common mental disorders when compared to civilian groups. However, veteran disengagement from treatment is a concern among health care providers, resulting in a need to explore novel ways of managing veteran mental health. Wearable devices, such as fitness trackers and smartwatches, have been explored for their potential to assess, monitor, and predict mental health outcomes in the general population. Such devices provide continuous data on metrics including physical activity, heart rate, sleep quality, and stress levels, offering a comprehensive view of the lifestyle and physiological factors influencing mental health. ObjectiveThis study aims to explore the feasibility of using wearable technology as a data collection and potential health monitoring tool among UK veterans. It also aims to explore the associations between mental health, physical activity, and functioning factors among UK veterans. MethodsThis is an observational feasibility study measuring mental health via validated questionnaires completed at baseline (T0), day 28 (T1), day 56 (T2), and day 84 (T3), and physiological metrics measured continuously via wrist-worn fitness trackers (Garmin vívosmart-5 watches) over 3 months (84 days). UK veterans will be recruited through convenience sampling methods. Statistical analysis will be exploratory, and machine learning models will be trained to detect changes in mental health and well-being outcomes. ResultsData collection was conducted between February 2025 and October 2025, and data analysis is scheduled to begin in January 2026. ConclusionsThis study will provide information on the feasibility of using wearable technology devices within a UK veteran population and may inform potential future interventions seeking to integrate wearable-derived data alongside the management of common mental disorders in veterans experiencing mental health difficulties. Findings would also enhance understanding of the relationship between mental health and physiological factors (eg, physical activity and sleep) in UK veterans. International Registered Report Identifier (IRRID)PRR1-10.2196/73060

Abstract Background Public services are increasingly delivered through digital platforms. Although digital health may improve access and scalability, they may also widen inequalities for people who lack reliable access, confidence, skills, affordability or trust. Objective This study examined the prevalence of self-reported digital exclusion among UK veterans and assessed its association with depression, anxiety and loneliness. Methods A cross-sectional online survey was conducted between July 2025 and March 2026. Participants were UK Armed Forces veterans and resident in the UK. The survey collected sociodemographic, military service, digital access and health data. Self-reported digital exclusion was defined as reporting feeling excluded or disadvantaged due to lack of digital access or skills. Probable depression, anxiety and loneliness were assessed using the PHQ-2, GAD-2 and three-item UCLA Loneliness Scale, respectively. Associations between digital exclusion and each outcome were examined using adjusted multivariable logistic regression. Results Of 1,911 responses received, 1,607 were included after data quality exclusions. Among participants with valid responses to the primary digital exclusion item, 553 (41.7%) reported digital exclusion. Digital exclusion was more common among females, younger veterans and those with lower household income. Probable depression, anxiety and loneliness were more prevalent among digitally excluded participants than among non-excluded participants. In adjusted models, self-reported digital exclusion was associated with higher odds of probable depression (AOR 1.38; 95% CI 1.04 to 1.83; p =0.028), probable anxiety (AOR 1.63, 95% CI 1.23 to 2.16; p <0.001), and probable loneliness (AOR 1.85; 95% CI 1.43 to 2.40; p <0.001). Conclusion More than two-fifths of veterans with valid exposure data reported digital exclusion, despite high reported device access and confidence. Self-reported digital exclusion was associated with poorer mental health and loneliness, although causality cannot be inferred from these cross-sectional data. Digital-first services for veterans should include routine digital needs screening, targeted support and clear non-digital routes to care.

Objectives Studies have found that moral injury (MI) can be easily missed in clinical assessments and most measures of MI have been developed for and with US military samples. As MI is increasingly recognised in non-military samples, existing scales may not be appropriate to civilian experiences. Design To design and validate the Moral Injury Scale (MORIS), a self-report measure of exposure to potentially morally injurious events (PMIEs), moral injury-related distress, and associated risk and protective factors, in UK samples pre-screened for reporting morally or psychologically distressing experiences. Setting UK general population. Participants We administered an initial set of 47 items to 592 participants and conducted exploratory factor analysis. The reduced MORIS was then administered to an independent sample of 382 participants and confirmatory factor analysis was conducted. Primary outcome measures The validity and performance characteristics of the MORIS were assessed against validated measures of mental ill health. Receiver operating characteristic (ROC) curves were used to calculate a likely cut off score for detecting conditions that are associated with moral injury. Results Analysis yielded factors for assessing exposure to PMIEs; time since event; MI-related distress; and risk and protective factors. Good convergent validity was evidenced against the measure of MI and mental health symptoms. Confirmatory factor analysis demonstrated a good fit for the model. ROC analyses suggested that a score of ≥18 (ROC 0.83; 95% CI 0.77 - 0.90) on the MORIS Distress subscale may indicate elevated levels of moral injury-related distress within this sample; however, this finding should be considered exploratory at this stage. Conclusions This study provides preliminary evidence supporting the reliability, unidimensional factor structure, and convergent validity of the MORIS among UK adults reporting morally or psychologically distressing experiences. Data set information Please see data availability statement. Trial registration number N/A

Introduction Alcohol misuse remains a significant cause of morbidity among serving UK Armed Forces (UKAF) personnel, with prevalence exceeding that of comparable civilian populations. Digital interventions offer a potentially scalable approach to alcohol reduction, and the DrinksRation smartphone application has previously demonstrated effectiveness among UK military veterans. This study evaluated the effectiveness of DrinksRation in reducing alcohol consumption among serving UKAF personnel drinking at levels which risk alcohol related harm. Methods Military DrinksRation was a two-arm, parallel-group, superiority randomised controlled trial nested within the Drinking and Wellbeing study. Serving UKAF personnel screening positive for at-risk drinking (Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) score > 4) were randomised to receive either the DrinksRation smartphone application or treatment as usual. The primary outcome was alcohol consumption at 84 days. Secondary outcomes included motivations for drinking, barriers to changing alcohol use, common mental disorders and loneliness. Recruitment took place between October 2023 and July 2024. Results A total of 613 serving personnel completed AUDIT-C screening, of whom 338 (55.1%) met the eligibility criteria for the trial. 113 participants were randomised (58 treatment as usual; 55 DrinksRation). Personnel screening positive for at-risk drinking were significantly less likely to consent to participate than those drinking at lower risk levels (33.4% vs 42.2%; χ 2 = 4.96, p = 0.03 ). Recruitment and follow up were substantially lower than anticipated with no participants in the intervention arm completing the primary end point assessment at 84 days. Consequently, the prespecified comparative analyses of the primary and secondary outcomes could not be undertaken. Participants who activated the DrinksRation application demonstrated sustained engagement with the intervention despite poor completion of research follow up assessments. Conclusions The trial did not generate sufficient outcome data to evaluate effectiveness. However, it demonstrates that routine screening can identify serving personnel drinking alcohol at risky levels, while recruitment, intervention activation and research follow-up remain substantial barriers to evaluating digital alcohol interventions in serving military populations. Although routine screening successfully identified personnel drinking at-risk of alcohol-related harm, engaging those individuals with research participation and sustaining follow up proved considerably more challenging. Trial registration ISRCTN 14977034 1 . Prospectively registered. The study protocol was published prior to commencement of recruitment 2 .

BACKGROUND:Post-traumatic stress disorder (PTSD) symptoms can fluctuate substantially over short periods, yet routine screening typically relies on infrequent self-report. Wearable sensors provide continuous behavioural and physiological signals that may help identify periods of elevated risk. AIMS:This study aimed to evaluate whether combining wearable sensor features with daily self-report data could identify short-term PTSD symptom increases among recently discharged veterans. METHOD:Seventy-four veterans wore commercial activity trackers and completed brief daily questionnaires over 87 days. For each participant, we defined an individual baseline by using the first 14 days of PTSD scores. Wearable variables were transformed into baseline-referenced deviation features to capture departures from personal norms. Missing data were addressed with multiple imputation by chained equations. Candidate predictors were prioritised with least absolute shrinkage and selection operator regression, and a set of machine-learning classifiers was evaluated. Primary performance was assessed by using the area under the precision-recall curve (PR AUC). RESULTS:Across feature set sizes (k = 1-25), performance peaked at k = 17. At this iteration, LightGBM achieved the strongest discrimination (PR AUC 0.86 (s.d. 0.07); area under the receiver-operating characteristic curve 0.89 (s.d. 0.04)) with a precision of 0.67 (s.d. 0.08), recall of 0.64 (s.d. 0.08) and F1 of 0.65 (s.d. 0.07). Key predictors reflected a multimodal profile, combining self-reported affect and perceived stress with wearable indicators of sleep continuity, activity variability and autonomic regulation. CONCLUSIONS:Baseline-referenced wearable features combined with daily self-report may help identify near-term PTSD symptom increases among recently discharged veterans with elevated PTSD symptoms and problematic cannabis use. Future work should validate performance in broader PTSD populations, including samples without problematic cannabis use.

Objectives Little is known about how young people use social media during periods of self-harm. This study aimed to explore how they express themselves online through images posted on social media before and after self-harm and how this expression may change across these periods, employing visual content and thematic analyses.Design A prospective cohort study, with qualitative analysis conducted using a recurrent cross-sectional approach and codebook methodology, accounting for chronological changes across time points before, during and after episodes of self-harm.Setting Participants were recruited from a mental health NHS Trust in the UK.Participants Image data during episodes of self-harm was available for 20 participants. The majority of whom were aged 18 years or older (n=15), female (n=14) and met criteria for moderate or severe anxiety and depression (n=18). The sample reflected diverse ethnic backgrounds, with six participants identifying as Asian or Mixed/Multiple ethnic backgrounds.Results None of the images investigated had direct visual presentations of self-harm. A few images referenced self-harm through the medium of text, and this was largely to normalise and promote help-seeking. Several themes were identified, including participation in activities that support well-being, love and relationships, connecting through humour, expressions of distress, and promoting mental health awareness and support. Subtle temporal changes were also observed.Conclusions Findings suggest that young people may temporarily withdraw from social media on the day of a self-harm event and rarely post graphic self-harm images around that time. This may reflect concerns about being stigmatised, but also improved platform moderation. Instead, platforms may serve as spaces for expressing self-care behaviours and connecting with others about both positive and challenging emotions, and across a range of topics including mental health.Trial registration number ClinicalTrials.gov: NCT04601220.

Cognitive impairment represents a core feature of major depressive disorder (MDD), often persisting after mood symptoms remit and not addressed by usual antidepressant treatments. Despite its relevance, cognition is typically assessed with infrequent tests in clinical settings, overlooking its contextual nature. Smartphones and wearables enable ecologically valid, repeated measurements of cognition and daily life behaviors that may impact it. We examined whether sleep duration, step count, and smartphone screen time are associated with cognitive functioning in MDD. We conducted secondary analyses of RADAR-MDD, a multicenter study following individuals with recurrent MDD. Cognitive functioning - self-reported and performance-based - was assessed with the THINC-it® app. Sleep duration and step count were measured with Fitbit devices, and screen time with the RADAR-Base app. Cognitive assessments (outcomes) were linked to behavioral measures (predictors) from the day of and the day preceding each assessment. Two-level multilevel models estimated between-person (differences in participant means) and within-person (deviations from participant means) effects. The sample included 502 participants, further subdivided by behavior-cognitive outcome pair. For performance-based cognitive assessments, positive associations at the between-person level were found for step count (β = 0.104, SE = 0.031, p < 0.001) and screen time (β = 0.075, SE = 0.036, p = 0.038), and sleep duration showed a quadratic negative effect (β = -0.080, SE = 0.018, p < 0.001). No within-person effects were detected. For self-reported cognitive functioning, step count showed positive associations both between (β = 0.161, SE = 0.037, p < 0.001) and within persons (β = 0.027, SE = 0.010, p = 0.005), while screen time was negatively associated within persons (β = -0.033, SE = 0.011, p = 0.002). Our findings illustrate that smartphones and wearables can collect meaningful daily life data of MDD patients that can be used to support cognitive health. Step count emerges as a promising behavioral target as it is simple to track and is correlated with better cognitive outcomes.

INTRODUCTION:Remotely piloted aircraft systems (RPAS) are integral to military operations. Although geographically removed from the battlefield, RPAS personnel are exposed to operational, organisational and moral stressors. This review synthesises evidence on the mental health and well-being of military RPAS personnel. METHODS:This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and Cochrane methodology. The protocol was preregistered on PROSPERO. Searches were conducted in MEDLINE, EMBASE, PsycINFO, CINAHL, Web of Science, ScienceDirect and PILOTS for studies from January 2002 to June 2025. Peer-reviewed studies examining mental health outcomes among military RPAS personnel were included while grey literature and technical repositories were screened for context. Study quality was assessed using NICE (National Institute for Health and Care Excellence) public health guidance checklists. Given variation in study designs and outcome measures, findings were synthesised narratively. RESULTS:Fourteen studies met the inclusion criteria, comprising nine cross-sectional surveys, two retrospective record analyses, one cohort study, one physiological experiment and one qualitative study. The prevalence of probable post-traumatic stress disorder (PTSD) ranged from 3.3% to 6.7%, which is broadly comparable to other military populations. Psychological distress was reported in 13.0%-16.0% of personnel, and high emotional exhaustion in 25.0%-37.0%. Suicidal ideation ranged from 4.4% to 8.0%. Subthreshold PTSD symptoms, sleep disturbance and functional impairment were frequently reported. The identified risk factors were predominantly organisational rather than combat-related, including long working hours, shift instability, understaffing, role conflict and sleep disruption. Protective factors included leadership support, team cohesion, adequate sleep and positive help-seeking attitudes. CONCLUSIONS:RPAS personnel do not show significantly higher rates of diagnosable mental disorder than other military groups. However, they do experience considerable occupational strain. Across the literature, adverse outcomes were more often linked to chronic organisational stressors than to direct battlefield exposure. Future research should prioritise longitudinal designs, comparative cohorts and the evaluation of targeted occupational interventions. PROSPERO REGISTRATION NUMBER:CRD42022329937.

Background. Alcohol use has historically been higher among UK Armed Forces personnel than in the general population, but much of the evidence was generated during sustained military campaigns in Iraq and Afghanistan. Contemporary patterns of alcohol use following substantial changes in the operational context are less well understood. Aims. To assess the prevalence of at-risk drinking and associated demographic and military factors among serving regular UK Armed Forces personnel. Methods. A cross-sectional study used AUDIT-C screening to categorise alcohol consumption as low, increasing, higher risk or potentially dependent. Demographic and military factors associated with at-risk drinking were examined using multivariable logistic regression. Results. Of 613 personnel screened, 229 (37.4%) consented to the full study. Overall, 338 (55.1%) screened at risk (AUDIT-C ≥ 5), including 110 (17.9%) meeting criteria for higher-risk or potentially dependent drinking. Female personnel had lower adjusted odds of at-risk drinking than males (aOR 0.36, 95% CI 0.14–0.93), while single personnel (aOR 3.63, 95% CI 1.18–11.16) and those with previous combat deployment (aOR 2.43, 95% CI 1.01–5.83) had higher odds. Personnel meeting criteria for higher-risk or potentially dependent drinking were less likely to consent to the full study (26.4% versus 39.8%; p = 0.009). Conclusions. At-risk drinking remains common in the contemporary UK Armed Forces, with most alcohol-related risk occurring below thresholds for high risk and potential dependence. Occupational health approaches should address increasing-risk drinking as well as the heaviest consumption. Lower participation among higher-risk drinkers also warrants consideration in future alcohol research.

BACKGROUND:Workplace mental health is a growing global priority. Traditional approaches to intervention delivery often face barriers of scalability and engagement. Recent advances in artificial intelligence (AI) offer new opportunities for dynamic, personalized support, but their effectiveness and implementation in occupational settings remain unclear. SOURCES OF DATA:This systematic review included 17 studies published between 2018 and 2024, identified from six databases. Studies were appraised using Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines, and risk of bias was assessed with Cochrane Risk of Bias 2.0 (RoB 2.0) and ROBINS-I tools. AREAS OF AGREEMENT:AI-based interventions, such as chatbot using cognitive behavioural therapy and predictive analytics, show promise for improving worker's mental health, enhancing resilience, and improving engagement. Acceptability was generally high across studies. AREAS OF CONTROVERSY:Despite positive findings, intervention maturity remains low, and outcome reporting is inconsistent. Few studies systematically addressed adverse events, rollout scalability, or ethical concerns, and the added value of AI over traditional approaches is uncertain. GROWING POINTS:AI interventions may offer flexible, adaptive solutions for improving workplace mental health, with strong engagement indicators. There is a pressing need to support clinicians and occupational health teams in evaluating potentially useful AI tools. AREAS TIMELY FOR DEVELOPING RESEARCH:Future research must prioritize high quality randomized trials, long-term follow-up, and real-world implementation studies. Standardized frameworks for reporting effectiveness, harms, and ethical considerations are important for safe, trustable, and sustainable adoption in occupational health.

Chronic pain is prevalent among military veterans and commonly presents with perceived stress and alcohol use. Allostatic load models suggest moderate-severe chronic pain may reflect a state of physiological dysregulation with heightened associations between pain and behavioural health symptoms. Yet little is known about daily associations between pain intensity, perceived stress, and alcohol use in veterans with and without moderate-severe chronic pain. This study examined day-to-day associations between pain intensity, perceived stress, and alcohol use among veterans, and whether associations differ between those with moderate-severe chronic pain and those with less severe pain. A sample of United States military veterans (n = 74) completed smartphone-based daily diary surveys for up to 3 months, providing 4307 days of data. Multi-group dynamic structural equation modelling examined within-person, day-to-day associations between symptoms, among veterans with moderate-severe chronic pain and those with less severe pain. Among veterans with moderate-severe chronic pain, bidirectional positive day-to-day associations emerged between pain intensity and perceived stress (b = 0.06-0.13), and between perceived stress and alcohol use (b = 0.05-0.07). Pain intensity also predicted increased next-day alcohol use (b = 0.04). Perceived stress appeared to act as a mechanism linking alcohol use to subsequent pain intensity (b = 0.01). For veterans with less severe pain, symptom associations differed markedly; higher perceived stress predicted lower next-day pain intensity (b = -0.05). Among veterans with moderate-severe chronic pain, day-to-day associations between pain intensity, perceived stress, and alcohol use appear more entangled. These veterans may experience heightened stress reactivity and poorer coping, requiring tailored interventions to monitor and address day-to-day symptom fluctuations.

The COVID-19 pandemic altered alcohol consumption across civilian populations, but evidence among serving military personnel remains limited. In a cross-sectional survey of 227 UK Armed Forces personnel, individuals currently classified in increasing- and higher-risk AUDIT-C categories were more likely to retrospectively report drinking more than before the pandemic, while demographic and military characteristics showed little association apart from younger age.

This study assessed the feasibility of deploying RationAI, a personalized AI-supported messaging framework, to reduce alcohol consumption among UK Armed Forces veterans. Participants were given DrinksRation, a mobile phone app, and allocated to receive either personalized or generic behavior change messages over 12 weeks. A total of 2,871 participants registered for an account during the study period. Feasibility was evaluated through recruitment (n = 2,871), retention (25.4% met engagement criteria), app usage and message delivery. Of those eligible, 343 participants were allocated to the personalized messaging group and 385 to the generic message group. The personalized group had higher early engagement, with app usage peaking at 212.4 (95% CI: 207.32 to 217.45) seconds in Week 2 compared to 183.7 seconds in the control group (95% CI: 178.90 to 188.46; p < 0.001) and received more notifications on average, reflecting additional personalized and event-triggered messages delivered as part of the intervention (47.7 [SD = 18.8] vs 16.3 [SD = 5.3]). Alcohol consumption declined in both groups over the 12-week period, with the personalized group showing a greater reduction from 31.08 to 13.20 units per week, compared to 31.24 to 15.17 units in the control group. Statistically significant between-group differences were observed at Week 2 (p = 0.027), Week 3 (p = 0.041), Week 4 (p = 0.008), and Week 10 (p = 0.049), favoring the personalized group, although between-group differences attenuated towards Week 12. Despite high attrition, the app engaged participants from an important population. These findings suggest the feasibility of personalized digital interventions for alcohol reduction, but there is a need for improved strategies to enhance long-term engagement.

BackgroundEfforts to advance our understanding of depression have long been constrained by the disorder’s vast symptom heterogeneity and by the reliance on self-report, which offers only a partial view of phenotypic expression. Digital phenotyping provides an opportunity to address these core challenges by generating real-time, objective data on behavior and physiology, offering new perspectives on understanding depression phenotypes. Yet, prior efforts to identify such objectively derived subtypes have relied on predefined diagnostic labels or supervised models, limiting discovery to existing clinical categories. ObjectiveThis study aimed to identify subtypes of depression based on objective sleep and activity data using an unsupervised learning method and to explore how participants transition between these subtypes over time. MethodsWe analyzed longitudinal Fitbit data from 623 participants with recurrent depression enrolled in the Remote Assessment of Disease and Relapse in Major Depressive Disorder study. To identify our subtypes, we applied Gaussian mixture models and hidden Markov models, incorporating a thorough model selection approach that combined grouped cross-validation and seed selection to ensure robustness. ResultsThree activity subtypes (high, light, and low activity) and 4 sleep subtypes (efficient early sleepers, efficient late sleepers, disrupted sleepers, and variable late sleepers) were consistently identified. These subtypes align with known associations between depression and behavioral patterns. Transition modeling revealed stability within individuals over follow-up, further suggesting the presence of behavioral phenotypes rather than momentary fluctuations. ConclusionsThe results demonstrate that wearable-derived features can identify reproducible and clinically relevant behavioral subtypes of sleep and activity in individuals with major depressive disorder. These subtypes reflect known behavioral correlates of depression and may offer a data-driven framework for reducing phenotypic heterogeneity, improving research stratification, and supporting personalized patient monitoring. Further work is needed to validate these findings in independent cohorts and evaluate their potential use in reducing noise when using sleep or activity data to predict depression outcomes.

OBJECTIVES:Twenty years since the start of UK Armed Forces participation in the Iraq and Afghanistan conflicts post-2001, the extent to which these deployments continue to impact mental health outcomes and alcohol misuse in UK military personnel is unknown. This is the reporting of the fourth phase, cross-sectional study of a longitudinal cohort study that has assessed the health and well-being of UK serving and ex-serving personnel since 2004. METHODS:Participants were eligible for the most recent phase (2022-2023) if they took part previously (2014-2016) and consented to recontact. Primary outcome measures included symptoms of common mental disorders (CMD), such as depression and anxiety, probable posttraumatic stress disorder (PTSD), complex PTSD (C-PTSD) and alcohol misuse. RESULTS:In the overall sample (n=4104, response rate=54.6%), CMD were the most prevalent outcome (27.8%), followed by probable PTSD (9.4%) and alcohol misuse (8.4%). The majority of PTSD experienced met the criteria for C-PTSD (72.7%). Ex-serving Regulars compared with serving Regulars reported a higher prevalence of PTSD (10.5% vs 7.4%, adjusted OR (AOR)=1.68, 95% CI 1.12 to 2.51) and C-PTSD (6.5% vs 3.9%, AOR=1.80, 95% CI (1.07 to 3.05); a higher prevalence of both disorders was also reported in serving/ex-serving Regulars whose last deployment to Iraq/Afghanistan was in a combat role. CONCLUSION:Although the majority of those who deployed to Iraq or Afghanistan remain well, there is an enduring impact of combat deployment on PTSD. Attention should continue to be directed towards the prevention, early detection and treatment needs of this cohort.

Cognitive difficulties are prevalent in depression and are linked to various negative life outcomes such as psychosocial impairment, absenteeism, lower chance of recovery or remission, and overall poor quality of life. Thus, assessing cognitive functioning over time is key to expanding our understanding of depression. Recent methodological advances and the ubiquity of smartphones enable remote assessment of cognitive functioning through smartphone-based tasks and surveys. However, the association of smartphone-based assessments of cognitive functioning to depression severity remains underexplored. Using a dedicated mobile application for assessing cognitive functioning (THINC-it), we investigate within- and between-person associations between performance-based (attention, working memory, processing speed, attention switching) and self-report measures of cognitive functioning with depression severity in 475 participants from the RADAR-MDD (Remote Assessment of Disease and Relapse-Major Depressive Disorder) cohort study (t = 2036 observations over an average of 14 months of follow-up). At the between-person level, we found stronger negative associations between the self-reported cognitive functioning measure and depression severity (β = -0.649, p  < 0.001) than between the performance-based measures and depression severity (βs = -0.220 to -0.349, p s  < 0.001). At the within-person level, we found negative associations between depression severity and the self-reported measure (β = -0.223, p  < 0.001), processing speed (β = -0.026, p=0.032) and attention (β = -0.037, p=0.003). These findings suggest that although THINC-it could adequately and remotely detect poorer cognitive performance in people with higher depressive symptoms, it was not capable of tracking within-person change over time. Nonetheless, repeatedly measuring self-reports of cognitive functioning showed more potential in tracking within-person changes in depression severity, underscoring their relevance for patient monitoring.

BACKGROUND:Gambling-related harm is a global public health concern. Suicide mortality is increased among people who experience gambling harm, and people who die by suicide often have contact with mental health treatment services in the months preceding their death. AIMS:To assess via a case-control study how gambling diagnosis predicts suicidal death and mental healthcare utilisation using linked routinely collected healthcare data. METHOD:We linked the Welsh Longitudinal General Practice Dataset, Annual District Death Extract, Patient Episode Database for Wales, and Outpatient Appointments Dataset Wales using the Secure Anonymised Information Linkage (SAIL) Databank. A sample of individuals with gambling diagnosis who died by suicide and an age- and sex-matched comparator group of all-cause decedents between 1993 and 2023 were extracted. Predictors of suicidal death, including mental health diagnosis and treatment contacts, were analysed using binary logistic regression models and chi-squared tests. RESULTS:A matched cohort of 92 individuals diagnosed with a gambling diagnosis (mean age 61.5 years, s.d. 13.1; 71% male) who died by suicide and 2990 comparators were identified. Gambling diagnosis status was a significant predictor of suicide (odds ratio 30.94; 95% CI 3.57-268.28; P = 0.002). Individuals with gambling disorder had significantly more mental health treatment contacts (P < 0.001), particularly in-patient contacts (P < 0.001). No difference in out-patient contacts was found. CONCLUSIONS:Historical diagnosis of gambling harm is a significant predictor of suicidal death and mental health treatment utilisation. Improved screening and coding practices would facilitate greater data linkage research on gambling-related suicide and suicide prevention.

Importance Rapid digitalization of health care and a dearth of digital health education for medical students and junior physicians worldwide means there is an imperative for more training in this dynamic and evolving field. Objective To develop an evidence-informed, consensus-guided, adaptable digital health competencies framework for the design and development of digital health curricula in medical institutions globally. Evidence Review A core group was assembled to oversee the development of the Digital Health Competencies in Medical Education (DECODE) framework. First, an initial list was created based on findings from a scoping review and expert consultations. A multidisciplinary and geographically diverse panel of 211 experts from 79 countries and territories was convened for a 2-round, modified Delphi survey conducted between December 2022 and July 2023, with an a priori consensus level of 70%. The framework structure, wordings, and learning outcomes with marginal percentage of agreement were discussed and determined in a consensus meeting organized on September 8, 2023, and subsequent postmeeting qualitative feedback. In total, 211 experts participated in round 1, 149 participated in round 2, 12 participated in the consensus meeting, and 58 participated in postmeeting feedback. Findings The DECODE framework uses 3 main terminologies: domain, competency, and learning outcome. Competencies were grouped into 4 domains: professionalism in digital health, patient and population digital health, health information systems, and health data science. Each competency is accompanied by a set of learning outcomes that are either mandatory or discretionary. The final framework comprises 4 domains, 19 competencies, and 33 mandatory and 145 discretionary learning outcomes, with descriptions for each domain and competency. Six highlighted areas of considerations for medical educators are the variations in nomenclature, the distinctiveness of digital health, the concept of digital health literacy, curriculum space and implementation, the inclusion of discretionary learning outcomes, and socioeconomic inequities in digital health education. Conclusions and Relevance This evidence-informed and consensus-guided framework will play an important role in enabling medical institutions to better prepare future physicians for the ongoing digital transformation in health care. Medical schools are encouraged to adopt and adapt this framework to align with their needs, resources, and circumstances.