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Prior research has shown that those with Post-Traumatic Stress Disorder (PTSD) have persistent reductions in quality of life (QoL), and higher rates of alcohol misuse. As such, it is important that we explore QoL and alcohol misuse on PTSD diagnosis. Therefore, the aim of this study was to assess the association between PTSD, QoL and alcohol misuse among United Kingdom (UK) veterans. 163 UK veterans who sought help for a mental health disorder were recruited to take part. Linear regressions were used to assess the association between probable PTSD, QoL and alcohol misuse. Pearson’s correlation analyses were used to assess the relationship between PTSD symptom clusters and QoL domains. We found unadjusted regressions showed evidence that, compared to those without PTSD, those with PTSD had lower QoL scores on physical health, psychosocial, social relationships and environment domains. Adjusting for age, sex, and outcome variables, only associations with the physical health domain and psychosocial domain remained statistically significant. Correlation analyses between PTSD and QoL domains showed the strongest negative correlations between the functional impairment and physical health domain, and between the functional impairment and psychosocial domain. We found that those with probable PTSD had lower QoL and higher alcohol misuse scores.

and circadian function are leading candidate markers for early relapse identification in MDD. Consumer-grade wearable devices may offer opportunity for remote and real-time examination of dynamic changes in sleep. Objective: We used FitBit data from individuals with recurrent MDD to describe longitudinal associations of sleep duration, quality, and regularity with subsequent depressive relapse and depression severity.Design: Data were collected as part of a longitudinal remote measurement technologies (RMT) cohort study in people with recurrent MDD. Participants: A total of 623 people with MDD wore a FitBit and completed regular outcome assessments via email for a median follow-up of 541 days. Multivariable regression models tested for associations between sleep features and depression outcomes. We considered two samples of people with at least one assessment of relapse (n=213) or at least one assessment of depression severity (n=390). Results: Increased intra-individual variability in total sleep time, greater sleep fragmentation, and later sleep mid-points were associated with worse depression outcomes. Adjusted Population Attributable Fractions (PAFs) suggested that an intervention to increase sleep consistency in adults with MDD could reduce the population risk for depression by up to 18-37%. Conclusion: We found consistent associations between wearable-derived sleep features and the probability of depressive relapse and increased depressive symptom severity. Disordered sleep is prevalent and disruptive, and challenging to capture longitudinally via conventional laboratory sleep assessments. Our study demonstrates a role for consumer-grade activity trackers to predict relapse risk and depression severity in people with recurrent MDD.

ObjectivesThe COVID-19 pandemic has disrupted the social and working lives of many. Past studies have highlighted worsening mental health during the pandemic, but often rely on small samples or infrequent follow-up. This study draws on fortnightly assessments from a large occupational cohort to describe differing trajectories of mental health between April 2020 and April 2021 and individual characteristics associated with these trajectory types.MethodsKing’s College London Coronavirus Health and Experiences of Colleagues at King’s is an occupational cohort study at a large university in London, UK. Participants (n=2241) completed online questionnaires fortnightly between April 2020 and April 2021. Symptoms of anxiety and depression were assessed using Generalised Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9).ResultsOn average, participants reported low levels of anxiety and depression (GAD-7 and PHQ-9 scores of 0–9, consistent with ‘none’, ‘minimal’ or ‘mild’ symptoms) throughout the year, with symptoms highest in April 2020 and decreasing over the summer months when no lockdown measures were in place. However, we observed more severe and variable symptoms among subgroups of participants. Four trajectory types for anxiety and depression were identified: ‘persistent high severity’ (6%–7% of participants), ‘varying symptoms, opposing national cases’ (4%–8%), ‘varying symptoms, consistent with national cases’ (6%–11%) and ‘persistent low severity’ (74%–84%). Younger age, female gender, caring responsibilities and shielding were associated with higher severity trajectory types.ConclusionsThese data highlight differing individual responses to the pandemic and underscore the need to consider individual circumstances when assessing and treating mental health. Aggregate trends in anxiety and depression may hide greater variation and symptom severity among subgroups.

BACKGROUND Multi-parametric remote measurement technologies (RMTs), comprising smartphones and wearable devices, have the potential to revolutionise understanding of the etiology and trajectory of major depressive disorder (MDD). Engagement with RMTs in MDD research is of the upmost importance for validity of predictive analytical methods and long-term use, and can be conceptualized as both objective engagement (data availability) or subjective engagement (system usability and experiential factors). In-app components provide an opportunity to promote engagement with RMTs while minimizing research team resources. Randomized controlled trials (RCTs) are the gold standard in quantifying the effect of in-app components on engagement with RMTs in those with MDD. OBJECTIVE This study aimed to evaluate whether a multi-parametric RMT system with a visual progress report tracking data completion, theoretically informed notifications, and access to research team contact details could promote objective and subjective engagement with remote symptom tracking over and above the system as usual. METHODS A 2-arm, parallel-group RCT (participant-blinded) with 1:1 randomization was conducted with 100 participants with MDD over 12 weeks. Participants in both arms employed the RADAR-base system, involving a smartphone app for weekly symptom assessments and a wearable Fitbit device for continuous passive tracking. Participants in the intervention arm (n=50) also had access to the additional in-app components. The primary outcome was objective engagement with the system, measured as the percentage of weekly questionnaires during follow-up. Secondary outcomes comprised measures of subjective engagement (system engagement, system usability, and emotional self-awareness). RESULTS Levels of completion of the PHQ-9 task were similar in the control (69.2%) and intervention (68.1%) arms (p-value for difference between arms = 0.83, 95% CI -9.32 to 11.65). Those in the intervention group reported slightly higher user engagement (1.93, 95% CI -1.91 to 5.78), emotional self-awareness (1.13, 95% CI -2.93 to 5.19) and system usability (2.29, 95% CI -5.93 to 10.52) scores at follow-up, however all confidence intervals were wide and included 0. CONCLUSIONS The adapted system did not increase objective or subjective engagement with remote symptom tracking in our research cohort. This study provides important foundations for understanding engagement with RMTs for research, and the methodologies by which this work can be replicated in both community and clinical settings. CLINICALTRIAL This study was registered as a clinical trial (reference number NCT04972474; https://clinicaltrials.gov/ct2/show/NCT04972474), and published as a protocol (https://doi.org/10.2196/32653). INTERNATIONAL REGISTERED REPORT RR2-10.2196/32653

The COVID-19 pandemic has resulted in a digital technology revolution which included widespread use in remote healthcare settings, remote working and use of technology to support friends and family to stay in touch. The armed forces have also increased its use of digital technology, but not at the same rate, and it is important that they do not fall behind in the revolution. One area where digital technology could be helpful is the treatment and management of mental health conditions. In a civilian setting, digital technology adoption has been found to be acceptable and feasible yet there is little use in the armed forces. In this personal view, we explore the potential use of personalised digital technology for mental health, the hype surrounding it and the dangers.This paper forms part of the special issue ofBMJ Military Healthdedicated to personalised digital technology for mental health in the armed forces.

Background: Cognitive symptoms are common during and following episodes of depression. Little is known about the persistence of self-reported and performance-based cognition with depression and functional outcomes. Methods: This is a secondary analysis of a prospective naturalistic observational clinical cohort study of individuals with recurrent Major Depressive Disorder (MDD; N=623). Participants completed app-based self-reported and performance-based cognitive function assessments alongside validated measures of depression, functional disability, and self-esteem every three months. Participants were followed-up for a maximum of 2-years. Multilevel hierarchically nested modelling was employed to explore between- and within-participant variation over time to identify whether persistent cognitive difficulties are related to levels of depression and functional impairment during follow-up.Results: 508 individuals (81.5%) provided data (mean age: 46.6, SD: 15.6; 76.2% female). Increasing persistence of self-reported cognitive difficulty was associated with higher levels of depression and functional impairment throughout the follow-up. In comparison to low persistence of objective cognitive difficulty (75% of timepoints) reported significantly higher levels of depression (B=5.17, SE=2.21, p=0.019) and functional impairment (B=4.82, SE=1.79, p=0.002) over time. Examination of the individual cognitive modules shows that persistently impaired executive function is associated with worse functioning, and poor processing speed is particularly important for worsened depressive symptoms.Conclusions: We replicated previous findings of greater persistence of cognitive difficulty with increasing severity of depression and further demonstrate that these cognitive difficulties are associated with pervasive functional disability. Difficulties with cognition may be an indicator and target for further treatment input.

ObjectivesThis study reports preliminary findings on the prevalence of, and factors associated with, mental health and well-being outcomes of healthcare workers during the early months (April–June) of the COVID-19 pandemic in the UK.MethodsPreliminary cross-sectional data were analysed from a cohort study (n=4378). Clinical and non-clinical staff of three London-based NHS Trusts, including acute and mental health Trusts, took part in an online baseline survey. The primary outcome measure used is the presence of probable common mental disorders (CMDs), measured by the General Health Questionnaire. Secondary outcomes are probable anxiety (seven-item Generalised Anxiety Disorder), depression (nine-item Patient Health Questionnaire), post-traumatic stress disorder (PTSD) (six-item Post-Traumatic Stress Disorder checklist), suicidal ideation (Clinical Interview Schedule) and alcohol use (Alcohol Use Disorder Identification Test). Moral injury is measured using the Moray Injury Event Scale.ResultsAnalyses showed substantial levels of probable CMDs (58.9%, 95% CI 58.1 to 60.8) and of PTSD (30.2%, 95% CI 28.1 to 32.5) with lower levels of depression (27.3%, 95% CI 25.3 to 29.4), anxiety (23.2%, 95% CI 21.3 to 25.3) and alcohol misuse (10.5%, 95% CI 9.2 to 11.9). Women, younger staff and nurses tended to have poorer outcomes than other staff, except for alcohol misuse. Higher reported exposure to moral injury (distress resulting from violation of one’s moral code) was strongly associated with increased levels of probable CMDs, anxiety, depression, PTSD symptoms and alcohol misuse.ConclusionsOur findings suggest that mental health support for healthcare workers should consider those demographics and occupations at highest risk. Rigorous longitudinal data are needed in order to respond to the potential long-term mental health impacts of the pandemic.

Purpose Healthcare provision in the United Kingdom (UK) falls primarily to the National Health Service (NHS) which is free at the point of access. In the UK, there is currently no national marker to identify military veterans in electronic health records, nor a requirement to record it. This study aimed to compare the sociodemographic characteristics and recorded mental health diagnoses of a sample of veterans and civilians accessing secondary mental health services. Methods The Military Service Identification Tool, a machine learning computer tool, was employed to identify veterans and civilians from electronic health records. This study compared the sociodemographic characteristics and recorded mental health diagnoses of veterans and civilians accessing secondary mental health care from South London and Maudsley NHS Foundation Trust, UK. Data from 2,576 patients were analysed; 1288 civilians and 1288 veterans matched on age and gender. Results Depressive disorder was the most prevalent across both groups in the sample (26.2% veterans, 15.5% civilians). The present sample of veterans accessing support for mental health conditions were significantly more likely to have diagnoses of anxiety, depressive, psychosis, personality, and stress disorders (AORs ranging 1.41–2.84) but less likely to have a drug disorder (AOR = 0.51) than age- and gender-matched civilians. Conclusion Veterans accessing secondary mental health services in South London had higher risks for many mental health problems than civilians accessing the same services. Findings suggest that military career history is a key consideration for probable prognosis and treatment, but this needs corroborating in other geographical areas including national population-based studies in the UK.

IntroductionYoung people are the most frequent users of social media and smartphones and there has been an increasing speculation about the potential negative impacts of their use on mental health. This has coincided with a sharp increase in the levels of self-harm in young people. To date, studies researching this potential association are predominantly cross-sectional and reliant on self-report data, which precludes the ability to objectively analyse behaviour over time. This study is one of the first attempts to explore temporal patterns of real-world usage prior to self-harm, to identify whether there are usage patterns associated with an increased risk.Methods and analysisTo study the mechanisms by which social media and smartphone use underpin self-harm in a clinical sample of young people, the Social media, Smartphone use and Self-harm in Young People (3S-YP) study uses a prospective, observational study design. Up to 600 young people aged 13–25 years old from secondary mental health services will be recruited and followed for up to 6 months. Primary analysis will compare real-world data in the 7 days leading up to a participant or clinician recorded self-harm episode, to categorise patterns of problematic usage. Secondary analyses will explore potential mediating effects of anxiety, depression, sleep disturbance, loneliness and bullying.Ethics and disseminationThis study was approved by the National Research Ethics Service, London - Riverside, as well as by the Joint Research and Development Office of the Institute of Psychiatry, Psychology and Neuroscience and South London and Maudsley NHS Foundation Trust (SLaM), and the SLaM Clinical Research Interactive Search (CRIS) Oversight Committee. The findings from this study will be disseminated through peer-reviewed scientific journals, conferences, websites, social media and stakeholder engagement activities.Trial registration numberNCT04601220.

Background Alcohol misuse is higher in the UK armed forces (AF) than in the general population. Research demonstrates that alcohol misuse persists after an individual leaves service, and this is notably the case for those who are seeking help for a mental health difficulty. Despite this, there is no work on testing a mobile alcohol reduction intervention that is personalized to support the UK AF. Objective To address this gap, we investigated the efficacy of a 28-day brief alcohol intervention delivered via a mobile app in reducing weekly self-reported alcohol consumption among UK veterans seeking help for mental health difficulties. Methods We performed a 2-arm participant-blinded randomized controlled trial (RCT). We compared a mobile app that included interactive features designed to enhance participants’ motivation and personalized messaging (intervention arm) with a version that provided government guidance on alcohol consumption only (control arm). Adults were eligible if they had served in the UK AF, were currently receiving or had received clinical support for mental health symptoms, and consumed 14 units (approximately 112 g of ethanol) or more of alcohol per week. Participants received the intervention or the control mobile app (1:1 ratio). The primary outcome was a change in self-reported weekly alcohol consumption between baseline and day 84 assessed using the validated Timeline Follow Back for Alcohol Consumption (TLFB) (prior 7 days), with a secondary outcome exploring self-reported change in the Alcohol Use Disorder Identification Test (AUDIT) score. Results Between October 2020 and April 2021, 2708 individuals were invited to take part, of which 2531 (93.5%) did not respond, 54 (2%) were ineligible, and 123 (4.5%) responded and were randomly allocated (62, 50.4%, intervention; 61, 49.6%, control). At day 84, 41 (66.1%) participants in the intervention arm and 37 (60.7%) in the control arm completed the primary outcome assessment. Between baseline and day 84, weekly alcohol consumption reduced by –10.5 (95% CI –19.5 to –1.5) units in the control arm and –28.2 (95% CI –36.9 to –19.5) units in the intervention arm (P=.003, Cohen d=0.35). We also found a significant reduction in the AUDIT score of –3.9 (95% CI –6.2 to –1.6) in the intervention arm (Cohen d=0.48). Our primary and secondary effects did not persist over the longer term (day 168). Two adverse events were detected during the trial. Conclusions This study examined the efficacy of a fully automated 28-day brief alcohol intervention delivered via a mobile app in a help-seeking sample of UK veterans with hazardous alcohol consumption. We found that participants receiving Drinks:Ration reduced their alcohol consumption more than participants receiving guidance only (at day 84). In the short term, we found Drinks:Ration is efficacious in reducing alcohol consumption in help-seeking veterans. Trial Registration ClinicalTrials.gov NCT04494594; https://tinyurl.com/34em6n9f International Registered Report Identifier (IRRID) RR2-10.2196/19720

BACKGROUND Alcohol misuse is higher in the UK armed forces (AF) than in the general population. Research demonstrates that alcohol misuse persists after an individual leaves service, and this is notably the case for those who are seeking help for a mental health difficulty. Despite this, there is no work on testing a mobile alcohol reduction intervention that is personalized to support the UK AF. OBJECTIVE To address this gap, we investigated the efficacy of a 28-day brief alcohol intervention delivered via a mobile app in reducing weekly self-reported alcohol consumption among UK veterans seeking help for mental health difficulties. METHODS We performed a 2-arm participant-blinded randomized controlled trial (RCT). We compared a mobile app that included interactive features designed to enhance participants’ motivation and personalized messaging (intervention arm) with a version that provided government guidance on alcohol consumption only (control arm). Adults were eligible if they had served in the UK AF, were currently receiving or had received clinical support for mental health symptoms, and consumed 14 units (approximately 112 g of ethanol) or more of alcohol per week. Participants received the intervention or the control mobile app (1:1 ratio). The primary outcome was a change in self-reported weekly alcohol consumption between baseline and day 84 assessed using the validated Timeline Follow Back for Alcohol Consumption (TLFB) (prior 7 days), with a secondary outcome exploring self-reported change in the Alcohol Use Disorder Identification Test (AUDIT) score. RESULTS Between October 2020 and April 2021, 2708 individuals were invited to take part, of which 2531 (93.5%) did not respond, 54 (2%) were ineligible, and 123 (4.5%) responded and were randomly allocated (62, 50.4%, intervention; 61, 49.6%, control). At day 84, 41 (66.1%) participants in the intervention arm and 37 (60.7%) in the control arm completed the primary outcome assessment. Between baseline and day 84, weekly alcohol consumption reduced by –10.5 (95% CI –19.5 to –1.5) units in the control arm and –28.2 (95% CI –36.9 to –19.5) units in the intervention arm (P=.003, Cohen d=0.35). We also found a significant reduction in the AUDIT score of –3.9 (95% CI –6.2 to –1.6) in the intervention arm (Cohen d=0.48). Our primary and secondary effects did not persist over the longer term (day 168). Two adverse events were detected during the trial. CONCLUSIONS This study examined the efficacy of a fully automated 28-day brief alcohol intervention delivered via a mobile app in a help-seeking sample of UK veterans with hazardous alcohol consumption. We found that participants receiving Drinks:Ration reduced their alcohol consumption more than participants receiving guidance only (at day 84). In the short term, we found Drinks:Ration is efficacious in reducing alcohol consumption in help-seeking veterans. CLINICALTRIAL ClinicalTrials.gov NCT04494594; https://tinyurl.com/34em6n9f INTERNATIONAL REGISTERED REPORT RR2-10.2196/19720

Background U.S. veterans report high rates of traumatic experiences and mental health symptomology [e.g. posttraumatic stress disorder (PTSD)]. The stress sensitization hypothesis posits experiences of adversity sensitize individuals to stress reactions which can lead to greater psychiatric problems. We extend this hypothesis by exploring how multiple adversities such as early childhood adversity, combat-related trauma, and military sexual trauma related to heterogeneity in stress over time and, subsequently, greater risk for PTSD. Methods 1230 veterans were recruited for an observational, longitudinal study. Veterans responded to questionnaires on PTSD, stress, and traumatic experiences five times over an 18-month study period. We used latent transition analysis to understand how heterogeneity in adverse experiences is related to transition into stress trajectory classes. We also explored how transition patterns related to PTSD symptomology. Results Across all models, we found support for stress sensitization. In general, combat trauma in combinations with other types of adverse experiences, namely early childhood adversity and military sexual trauma, imposed a greater probability of transitioning into higher risk stress profiles. We also showed differential effects of early childhood and military-specific adversity on PTSD symptomology. Conclusion The present study rigorously integrates both military-specific and early life adversity into analysis on stress sensitivity, and is the first to examine how sensitivity might affect trajectories of stress over time. Our study provides a nuanced, and specific, look at who is risk for sensitization to stress based on previous traumatic experiences as well as what transition patterns are associated with greater PTSD symptomology.

Introduction Paternal psychopathology is associated with various adolescent outcomes. With emotional disorders presenting a significant public health concern in the adolescent age group, the aim of this systematic review was to synthesize evidence on the relationship between paternal mental health and adolescent anxiety or depression. Methods PubMed, Web of Science, Embase, Ovid MEDLINE, Global Health, and PsycINFO were searched for articles which primarily aimed to investigate the relationship between paternal mental health (exposure) and adolescent anxiety or depression (outcome). Articles were assessed for risk of bias, and findings are presented in a narrative synthesis. The protocol is registered on PROSPERO (CRD42018094076). Results Findings from the fourteen included studies indicated that paternal depression is associated with adolescent depression and anxiety. Findings relating to other paternal mental health disorders were inconclusive. Results largely suggested that adolescent depression and anxiety is equally associated with paternal and maternal mental health. The included studies were mostly cross-sectional, and the quality of included studies was mixed. Attempts to focus on the 11–17 year age range were hampered by the variability of age ranges included in studies. Conclusions Further longitudinal research is needed to clarify the association between paternal mental health disorders other than depression, and adolescent anxiety or depression. Mechanisms in this relationship should also be further explored, and could be informed by existing models on younger children.

Background Drinking motivations within the UK military have not been studied despite the high prevalence of alcohol misuse in this group. Aims We aimed to characterize drinking motivations and their demographic, military and mental health associations in UK serving and ex-serving personnel. Methods Serving and ex-serving personnel reporting mental health, stress or emotional problems occurring in the last 3 years were selected from an existing cohort study. A semi-structured telephone interview survey examined participants’ mental health, help-seeking, alcohol use and drinking motivations. Results Exploratory factor analysis of drinking motivations in military personnel (n = 1279; response rate = 84.6%) yielded 2 factors, labelled ‘drinking to cope’ and ‘social pressure’. Higher drinking to cope motivations were associated with probable anxiety (rate ratio [RR] = 1.4; 95% confidence interval [CI] = 1.3–1.5), depression (RR = 1.3; 95% CI = 1.2–1.4) and post-traumatic stress disorder (RR = 1.4; 95% CI = 1.3–1.6). Higher social pressure motivations were associated with probable anxiety (odds ratio = 1.1; 95% CI = 1.0–1.1). Alcohol misuse and binge drinking were associated with reporting higher drinking to cope motivations, drinking at home and drinking alone. Conclusions Amongst military personnel with a stress, emotional or mental health problem, those who drink to cope with mental disorder symptoms or because of social pressure, in addition to those who drink at home or drink alone, are more likely to also drink excessively.

Social media usage impacts upon the mental health and wellbeing of young people, yet there is not enough evidence to determine who is affected, how and to what extent. While it has widened and strengthened communication networks for many, the dangers posed to at-risk youth are serious. Social media data offers unique insights into the minute details of a user's online life. Timely consented access to data could offer many opportunities to transform understanding of its effects on mental wellbeing in different contexts. However, limited data access by researchers is preventing such advances from being made. Our multidisciplinary authorship includes a lived experience adviser, academic and practicing psychiatrists, and academic psychology, as well as computational, statistical, and qualitative researchers. In this Perspective article, we propose a framework to support secure and confidential access to social media platform data for research to make progress toward better public mental health.

Purpose Accidents are the most common cause of death among UK military personnel. It is a common misconception in the general public that accidental injuries are always the result of random events, however research suggests that mental health problems and the increased levels of risky behaviour in military personnel may play a role. The objective of this study was to further our understanding of injuries and deaths not related to deployment by examining the associations of mental health, alcohol misuse and smoking with inpatient admission to hospital for accidents and injuries, and attendance to accident and emergency (A&E) departments. Methods Data on all hospital admissions for accidents and injuries and A&E attendance at NHS hospitals in England, Scotland and Wales were linked to data on self-reported mental health problems, alcohol misuse and smoking from a large, representative UK military cohort of serving and ex-serving personnel (n = 8,602). Logistic regression was used to examine the associations between having a hospital admission for an accident or injury with self-reported mental health problems, alcohol misuse and smoking. Cox proportional-hazards regression was then conducted to assess the associations of mental health problems, alcohol misuse and smoking with time to hospital admission for an accident or injury. Finally, negative binomial regression was used to examine associations between the number of A&E attendances with mental health problems, alcohol misuse and smoking. Results Personnel reporting symptoms of common mental disorder (CMD) or probable post-traumatic stress disorder (PTSD) were more likely to have an admission to hospital for an accident or injury (fully adjusted odds ratio 1.39, 95% confidence interval [CI] 1.05–1.84), than those who did not report these symptoms, and also had more attendances to A&E (fully adjusted incidence rate ratio [IRR] 1.32, 95% CI 1.16–1.51). A&E attendances were also more common in personnel who were smokers (fully adjusted IRR 1.21, 95% CI 1.09–1.35) following adjustment for demographic, military and health characteristics. Conclusions The findings suggest that accidents and injuries among military personnel are not always random events and that there are health and behavioural factors, including poor mental health and smoking, which are associated (with small effect sizes) with an increased risk of being involved in an accident. Clinicians treating individuals attending hospital after an accident should consider their healthcare needs holistically, including issues related to mental health and health damaging behaviours.

BackgroundCOVID-19 antibody testing allows population studies to classify participants by previous SARS-CoV-2 infection status. Home lateral flow immune-antibody testing devices offer a very convenient way of doing this, but relatively little is known about how measurement and antibody variability will affect consistency in results over time. We examined consistency by looking at the outcome of two tests three months apart while COVID-19 infection rates were low (summer 2020 in the UK).MethodsThe KCL-Coronavirus Health and Experiences in Colleagues at King’s is an occupational cohort of staff and postgraduate research students. Lateral flow immune-antibody testing kits were sent to participant’s homes in late June 2020 and late September 2020. Participants also completed regular surveys that included asking about COVID-19 symptoms and whether they thought they had been infected.ResultsWe studied 1489 participants returned valid results in both June and September (59% of those sent kits). Lateral flow immune-antibody test was positive for 7.2% in June and 5.9% in September, with 3.9% positive in both. Being more symptomatic or suspecting infection increased the probability of ever being positive. Of those positive in June, 46% (49/107) were negative in September (seroreversion), and this was similar regardless of symptom characteristics, suspicion, and timing of possible infection. A possible outlier was those aged over 55 years, where only 3 of 13 (23%) had seroreversion.DiscussionThese results do not follow the pattern reported from studies specifically designed to monitor seropositivity, which have found greater consistency over time and the influence of presence, timing and severity of symptoms on seroreversion. We suggest several factors that may have contributed to this difference: our low bar in defining initial seropositivity (single test); a non-quantitative test known to have relatively low sensitivity; participants carrying out testing. We would encourage other studies to use these real-world performance characteristics alongside those from laboratory studies to plan and analyse any antibody testing.

BACKGROUND Multi-parametric remote measurement technologies (RMTs) comprise smartphone apps and wearable devices for both active and passive symptom tracking. They hold potential for understanding current depression status and predicting future depression status. However, the promise of using RMTs for relapse prediction is heavily dependent on user engagement, which is defined as both a behavioral and experiential construct. A better understanding of how to promote engagement in RMT research through various in-app components will aid in providing scalable solutions for future remote research, higher quality results, and applications for implementation in clinical practice. OBJECTIVE The aim of this study is to provide the rationale and protocol for a 2-armed randomized controlled trial to investigate the effect of insightful notifications, progress visualization, and researcher contact details on behavioral and experiential engagement with a multi-parametric mobile health data collection platform, Remote Assessment of Disease and Relapse (RADAR)–base. METHODS We aim to recruit 140 participants upon completion of their participation in the RADAR Major Depressive Disorder study in the London site. Data will be collected using 3 weekly tasks through an active smartphone app, a passive (background) data collection app, and a Fitbit device. Participants will be randomly allocated at a 1:1 ratio to receive either an adapted version of the active app that incorporates insightful notifications, progress visualization, and access to researcher contact details or the active app as usual. Statistical tests will be used to assess the hypotheses that participants using the adapted app will complete a higher percentage of weekly tasks (behavioral engagement: primary outcome) and score higher on self-awareness measures (experiential engagement). RESULTS Recruitment commenced in April 2021. Data collection was completed in September 2021. The results of this study will be communicated via publication in 2022. CONCLUSIONS This study aims to understand how best to promote engagement with RMTs in depression research. The findings will help determine the most effective techniques for implementation in both future rounds of the RADAR Major Depressive Disorder study and, in the long term, clinical practice. CLINICALTRIAL ClinicalTrials.gov NCT04972474; http://clinicaltrials.gov/ct2/show/NCT04972474 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/32653

Background: The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering and clinical science. We aim to summarise the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and to identify leading digital signals for depression. Methods: Medical and computer science databases were searched between January 2007 to November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included (PROSPERO registration: 2019 CRD42019159929). A descriptive approach was taken to synthesise study methodologies. Results We included 52 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show some consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Regression and classification machine learning models found predictive value of aggregated features. Interpretation: Recommendations are put forward to improve aspects of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the potential for reporting bias in studies with numerous features. Funding: National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London.

Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. Methods Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. Results Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. Conclusions RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

The use of remote measurement technologies (RMTs) across mobile health (mHealth) studies is becoming increasingly popular, given their potential for high-frequency symptom monitoring outside of routine clinical appointments. However, many RMT studies fail to report on engagement and recruitment statistics, with the few who do citing a wide range of recruitment rates. There is a need for the standardisation of best practices for successful recruitment into RMT research, critical for both research validity and reproducibility. The current paper aims to create a framework for successful recruitment into RMT studies, reflecting on the experience of RADAR-MDD, a large-scale, multi-site prospective cohort study utilising RMT to explore the clinical course of people with major depressive disorder across the UK, Netherlands, and Spain. More specifically, the paper assesses four key strategies for successful recruitment, alongside a review of the common barriers to participation and how to avoid them. Finally, the strategies and barriers outlined are combined into a single model of recruitment, that can be used as a framework to inform future study design and evaluation. Such a model will be applicable to a variety of stakeholders using RMT in healthcare research and practice.

Background Researchers conducting cohort studies may wish to investigate the effect of episodes of COVID-19 illness on participants. A definitive diagnosis of COVID-19 is not always available, so studies have to rely on proxy indicators. This paper seeks to contribute evidence that may assist the use and interpretation of these COVID-indicators. Methods We described five potential COVID-indicators: self-reported core symptoms, a symptom algorithm; self-reported suspicion of COVID-19; self-reported external results; and home antibody testing based on a 'lateral flow' antibody (IgG/IgM) test cassette. Included were staff and postgraduate research students at a large London university who volunteered for the study and were living in the UK in June 2020. Excluded were those who did not return a valid antibody test result. We provide descriptive statistics of prevalence and overlap of the five indicators. Results Core symptoms were the most common COVID-indicator (770/1882 participants positive, 41%), followed by suspicion of COVID-19 (n = 509/1882, 27%), a positive symptom algorithm (n = 298/1882, 16%), study antibody lateral flow positive (n = 124/1882, 7%) and a positive external test result (n = 39/1882, 2%), thus a 20-fold difference between least and most common. Meeting any one indicator increased the likelihood of all others, with concordance between 65 and 94%. Report of a low suspicion of having had COVID-19 predicted a negative antibody test in 98%, but positive suspicion predicted a positive antibody test in only 20%. Those who reported previous external antibody tests were more likely to have received a positive result from the external test (24%) than the study test (15%). Conclusions Our results support the use of proxy indicators of past COVID-19, with the caveat that none is perfect. Differences from previous antibody studies, most significantly in lower proportions of participants positive for antibodies, may be partly due to a decline in antibody detection over time. Subsequent to our study, vaccination may have further complicated the interpretation of COVID-indicators, only strengthening the need to critically evaluate what criteria should be used to define COVID-19 cases when designing studies and interpreting study results.

ObjectiveTo assess the feasibility of home antibody testing as part of large-scale study, the King’s College London Coronavirus Health and Experiences of Colleagues at King’s (KCL CHECK).MethodsParticipants of the KCL CHECK study were sent a SureScreen Diagnostics COVID-19 IgG/IgM Rapid Test Cassette to complete at home in June 2020 (phase 1) and September 2020 (phase 2). Participants were asked to upload a test result image to a study website. Test result images and sociodemographic information were analysed by the research team.ResultsA total of n=2716 participants enrolled in the KCL CHECK study, with n=2003 (73.7%) and n=1825 (69.3%) consenting and responding to phase 1 and 2. Of these, n=1882 (93.9%; phase 1) and n=1675 (91.8%; phase 2) returned a valid result. n=123 (6.5%; phase 1) and n=91 (5.4%; phase 2) tested positive for SARS-CoV-2 antibodies. A total of n=1488 participants provided a result in both phases, with n=57 (3.8%) testing positive for SARS- CoV-2 antibodies across both phases, suggesting a reduction in the number of positive antibody results over time. Initial comparisons showed variation by age group, gender and clinical role.ConclusionsOur study highlights the feasibility of rapid, repeated and low-cost SARS-CoV-2 serological testing without the need for face-to-face contact.What is already known about this subject?Higher education institutions have a duty of care to minimise the spread and transmission of COVID-19 in its campuses, and among staff and students. The reopening of higher education buildings and campuses has brought about a mass movement of students, academics and support staff from across the UK. Serological antibody studies can assist by highlighting groups of people and behaviours associated with high risk of COVID-19.What are the new findings?We report a framework for SARS-CoV-2 serological antibody testing in an occupational group of postgraduate research students and current members of staff at King’s College London. Over two phases of data collection, 6.5% (phase 1) and 5.4% (phase 2) tested positive for SARS-CoV-2 antibodies, with only 3.8% testing positive for antibodies in both phases, suggesting a reduction in positive antibody results over time.How might this impact on policy or clinical practice in the foreseeable future?Our study highlights the feasibility of rapidly deploying low-cost and repeatable SARS-CoV-2 serological testing, without the need for face-to-face contact, to support the higher education system of the UK.

ObjectivesThis study reports preliminary findings on the prevalence of, and factors associated with, mental health and wellbeing outcomes of healthcare workers during the early months (April-June) of the COVID-19 pandemic in the UK.MethodsPreliminary cross-sectional data were analysed from a cohort study (n=4,378). Clinical and non-clinical staff of three London-based NHS Trusts (UK), including acute and mental health Trusts, took part in an online baseline survey. The primary outcome measure used is the presence of probable common mental disorders (CMDs), measured by the General Health Questionnaire (GHQ-12). Secondary outcomes are probable anxiety (GAD-7), depression (PHQ-9), Post-Traumatic Stress Disorder (PTSD) (PCL-6), suicidal ideation (CIS-R), and alcohol use (AUDIT). Moral injury is measured using the Moray Injury Event Scale (MIES).ResultsAnalyses showed substantial levels of CMDs (58.9%, 95%CI 58.1 to 60.8), and of PTSD (30.2%, 95%CI 28.1 to 32.5) with lower levels of depression (27.3%, 95%CI 25.3 to 29.4), anxiety (23.2%, 95%CI 21.3 to 25.3), and alcohol misuse (10.5%, 95%CI, 9.2 to 11.9). Women, younger staff, and nurses tended to have poorer outcomes than other staff, except for alcohol misuse. Higher reported exposure to moral injury (distress resulting from violation of one’s moral code) was strongly associated with increased levels of CMDs, anxiety, depression, PTSD symptoms, and alcohol misuse.ConclusionsOur findings suggest that mental health support for healthcare workers should consider those demographics and occupations at highest risk. Rigorous longitudinal data are needed in order to respond to the potential long-term mental health impacts of the pandemic.HighlightsWhat is already known about this subject?Large-scale population studies report increased prevalence of depression, anxiety, and psychological distress during the COVID-19 pandemic.Evidence from previous epidemics indicates a high and persistent burden of adverse mental health outcomes among healthcare workers.What are the new findings?Substantial levels of probable common mental disorders and post-traumatic stress disorder were found among healthcare workers.Groups at increased risk of adverse mental health outcomes included women, nurses, and younger staff, as well as those who reported higher levels of moral injury.How might this impact on policy or clinical practice in the foreseeable future?The mental health offering to healthcare workers must consider the interplay of demographic, social, and occupational factors.Additional longitudinal research that emphasises methodological rigor, namely with use of standardised diagnostic interviews to establish mental health diagnoses, is necessary to better understand the mental health burden, identify those most at risk, and provide appropriate support without pathologizing ordinary distress responses.

The ease of in-the-wild speech recording using smartphones has sparked considerable interest in the combined application of speech, remote measurement technology (RMT) and advanced analytics as a research and healthcare tool. For this to be realised, the acceptability of remote speech collection to the user must be established, in addition to feasibility from an analytical perspective. To understand the acceptance, facilitators, and barriers of smartphone-based speech recording, we invited 384 individuals with major depressive disorder (MDD) from the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) research programme in Spain and the UK to complete a survey on their experiences recording their speech. In this analysis, we demonstrate that study participants were more comfortable completing a scripted speech task than a free speech task. For both speech tasks, we found depression severity and country to be significant predictors of comfort. Not seeing smartphone notifications of the scheduled speech tasks, low mood and forgetfulness were the most commonly reported obstacles to providing speech recordings., Comment: Accepted to Interspeech 2021. Formatting changes + minor language edits

BackgroundDefinitive diagnosis of COVID-19 requires resources frequently restricted to the severely ill. Cohort studies must rely on surrogate indicators to define cases of COVID-19 in the community. We describe the prevalence and overlap of potential indicators including self-reported symptoms, suspicion, and routine test results, plus home antibody testing.MethodsAn occupational cohort of 2807 staff and postgraduate students at a large London university. Repeated surveys covering March to June 2020. Antibody test results from ‘lateral flow’ IgG/IgM cassettes in June 2020.Results1882 participants had valid antibody test results, and 124 (7%) were positive. Core symptoms of COVID-19 were common (770 participants positive, 41%), although fewer met criteria on a symptom algorithm (n=297, 16%). Suspicion of COVID-19 (n=509, 27%) was much higher than positive external tests (n=39, 2%). Positive antibody tests were rare in people who had no suspicion (n=4, 1%) or no core symptoms (n=10, 2%). In those who reported external antibody tests, 15% were positive on the study antibody test, compared with 24% on earlier external antibody tests.DiscussionOur results demonstrate the agreement between different COVID indicators. Antibody testing using lateral flow devices at home can detect asymptomatic cases and provide greater certainty to self-report; but due to weak and waning antibody responses to mild infection, may under-ascertain. Multiple indicators used in combination can provide a more complete story than one used alone. Cohort studies need to consider how they deal with different, sometimes conflicting, indicators of COVID-19 illness to understand its long-term outcomes.THUMBNAILWhat is already known on this subject?Research into the effects of COVID-19 in the community is needed to respond to the pandemic, and guidance is needed as to how cohort studies measure COVID-19 infection status retrospectively, particularly given that objective testing for infection was not widely available in the first wave of COVID-19 in many countries. Retrospective testing might be possible using antibodies as a proxy for previous COVID-19 infection.What this study adds?Antibody testing is feasible in community cohorts but sensitivity may be poor. Self-report of suspected infection, recall of symptoms and results of tests received elsewhere add different aspects to the ascertainment of COVID-19 exposure. Combining self-report and objectively measured indicators may enable tailored algorithms for COVID-19 case definition that suits the aims of different research studies.

We report test results for SARS-CoV-2 antibodies in an occupational group of postgraduate research students and current members of staff at King’s College London. Between June and July 2020, antibody testing kits were sent to n = 2296 participants; n = 2004 (86.3%) responded, of whom n = 1882 (93.9%) returned valid test results. Of those that returned valid results, n = 124 (6.6%) tested positive for SARS-CoV-2 antibodies, with initial comparisons showing variation by age group and clinical exposure.

IntroductionWe will use an occupational sample to study the impact of COVID-19 on current staff and postgraduate research students at a large UK university. The cohort study will address some of the key questions about COVID-19 for the international community, while also providing feedback to the employer and educator.Methods and analysisParticipants were recruited via email to their University email address. Administrative records were available to compare the composition of volunteer participants to underlying staff and postgraduate student populations of the University. The study comprises a baseline survey, longitudinal follow-up surveys and a viral antibody study. Baseline information was collected in April 2020 including demographics, working situation, current stresses and worries, mental health and neurological symptoms. Personal experiences of COVID-19, indirect experiences and attitudes towards the pandemic were queried, as well as satisfaction with communication and support at work. Longitudinal surveys will assess changes in COVID-19 exposure and mental health. A viral antibody detection component is being planned and will also be longitudinal in nature.Ethics and disseminationEthical approval has been gained from KCL’s Psychiatry, Nursing and Midwifery Research Ethics Committee (HR-19/20-18247). Participants were provided with information and agreed to a series of consent statements before enrolment. Data are kept on secure servers with access to personally identifiable information limited. Researchers may apply to have access to pseudonymised data. Findings will be disseminated internally to the University and participants, and externally through scientific publications.

BACKGROUND Alcohol misuse is higher in the UK Armed Forces than in the general population. Previous research has shown that interventions delivered via smartphones are efficacious in promoting self-monitoring of alcohol use, have utility in reducing alcohol consumption, and have a broad reach. OBJECTIVE This single-blinded randomized controlled trial (RCT) aims to assess the efficacy of a 28-day brief alcohol intervention delivered via a smartphone app (Drinks:Ration) in reducing weekly self-reported alcohol consumption between baseline and 3-month follow-up among veterans who drink at a hazardous or harmful level and receive or have received support for mental health symptoms in a clinical setting. METHODS In this two-arm, single-blinded RCT, a smartphone app that includes interactive features designed to enhance participants’ motivation and personalized messaging is compared with a smartphone app that provides only government guidance on alcohol consumption. The trial will be conducted in a veteran population that has sought help through Combat Stress, a UK veteran’s mental health charity. Recruitment, consent, and data collection will be carried out automatically through the Drinks:Ration platform. The primary outcome is the change in self-reported weekly alcohol consumption between baseline (day 0) and 3-month follow-up (day 84) as measured using the Time-Line Follow back for Alcohol Consumption. Secondary outcome measures include (1) change in the baseline to 3-month follow-up (day 84) Alcohol Use Disorder Identification Test score and (2) change in the baseline to 3-month follow-up (day 84) World Health Organization Quality of Life-BREF score to assess the quality of adjusted life years. Process evaluation measures include (1) app use and (2) usability ratings as measured by the mHealth App Usability Questionnaire. The primary and secondary outcomes will also be reassessed at the 6-month follow-up (day 168) to assess the longer-term benefits of the intervention, which will be reported as a secondary outcome. RESULTS The study will begin recruitment in October 2020 and is expected to require 12 months to complete. The study results will be published in 2022. CONCLUSIONS This study assesses whether a smartphone app is efficacious in reducing self-reported alcohol consumption in a veteran population that has sought help through Combat Stress using personalized messaging and interactive features. This innovative approach, if successful, may provide a means to deliver a low-cost health promotion program that has the potential to reach large groups, in particular those who are geographically dispersed, such as military personnel. CLINICALTRIAL ClinicalTrials.gov NCT04494594; https://clinicaltrials.gov/ct2/show/NCT04494594 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/19720

Prior research on the United Kingdom (UK) public’s perception towards the British Armed Forces often found a contradicting understanding of the military as both ‘heroes’ and ‘victims’. In order to examine these contradictions further, this study examined public attitudes and perceptions of the British Armed Forces, using a sentiment analysis of Twitter content posted on or after 1 January 2014. Twitter is one of the largest social media platforms, with an estimated 126 million daily active users worldwide, and 17 million active users in the UK. A bespoke data collection platform was developed to identify and extract relevant tweets and replies. In total, 323,512 tweets and 17,234 replies were identified and analysed. We found that tweets related to or discussing the British Armed Forces were significantly more positive than negative, with public perceptions of the Armed Forces stable over time. We also observed that it was more likely for negative tweets to be posted late evening or early morning compared to other hours of the day. Furthermore, this study identified differences in how positive and negative tweets were discussed in relation to politicised hashtags concerning Government policy, political organisations, and mental health. This was an unexpected finding, and more research is required to understand the reasons as to why this is the case.

BACKGROUND Electronic health care records (EHRs) are a rich source of health-related information, with potential for secondary research use. In the United Kingdom, there is no national marker for identifying those who have previously served in the Armed Forces, making analysis of the health and well-being of veterans using EHRs difficult. OBJECTIVE This study aimed to develop a tool to identify veterans from free-text clinical documents recorded in a psychiatric EHR database. METHODS Veterans were manually identified using the South London and Maudsley (SLaM) Biomedical Research Centre Clinical Record Interactive Search—a database holding secondary mental health care electronic records for the SLaM National Health Service Foundation Trust. An iterative approach was taken; first, a structured query language (SQL) method was developed, which was then refined using natural language processing and machine learning to create the Military Service Identification Tool (MSIT) to identify if a patient was a civilian or veteran. Performance, defined as correct classification of veterans compared with incorrect classification, was measured using positive predictive value, negative predictive value, sensitivity, F1 score, and accuracy (otherwise termed Youden Index). RESULTS A gold standard dataset of 6672 free-text clinical documents was manually annotated by human coders. Of these documents, 66.00% (4470/6672) were then used to train the SQL and MSIT approaches and 34.00% (2202/6672) were used for testing the approaches. To develop the MSIT, an iterative 2-stage approach was undertaken. In the first stage, an SQL method was developed to identify veterans using a keyword rule–based approach. This approach obtained an accuracy of 0.93 in correctly predicting civilians and veterans, a positive predictive value of 0.81, a sensitivity of 0.75, and a negative predictive value of 0.95. This method informed the second stage, which was the development of the MSIT using machine learning, which, when tested, obtained an accuracy of 0.97, a positive predictive value of 0.90, a sensitivity of 0.91, and a negative predictive value of 0.98. CONCLUSIONS The MSIT has the potential to be used in identifying veterans in the United Kingdom from free-text clinical documents, providing new and unique insights into the health and well-being of this population and their use of mental health care services.

Purpose A growing body of research has identified correlates (i.e., predictors) of youth homelessness. However, such risk and protective factors have not been identified for youth receiving substance use treatment. Using characteristics collected at treatment intake, the present study sought to identify predictors of youths' first episode of homelessness during the 12 months after substance use treatment entry. Methods Data come from a longitudinal study of adolescents (n = 17,911; aged 12–17 years) receiving substance use treatment throughout the U.S. Participants completed surveys at intake and at 3, 6, and 12 months later. Logistic regression and Lasso machine learning regression were used to predict participants' first episode of homelessness in the 12 months after treatment intake. Results After excluding adolescents reporting previous experiences of homelessness, 5.0% of adolescents reported their first episode of homelessness over the 12 months after treatment intake. The results from logistic and lasso models were generally consistent. Final models revealed that adolescents who were older, male, reported more victimization experiences, mental health problems, family problems, deviant peer relationships, and substance use problems (more treatment episodes and illicit drug dependence) were more likely to report experiencing homelessness. Hispanic/Latino adolescents were less likely to experience homelessness, compared with white adolescents. Conclusions The results point to the important risk and protective factors that can be assessed at treatment entry to identify adolescents at greater risk of experiencing their first episode of homelessness.

Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity formental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer fromlow positive predictive values.More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice, This manuscript was written as a result of a workshop that was held at the Institute of Psychiatry, Psychology and Neuroscience, King’s College London, financially supported by the European Science Foundation (ESF) Research Networking Programme Evaluating Information Access Systems: http://elias-network.eu/. SV is supported by the Swedish Research Council (2015-00359) and the Marie Skłodowska Curie Actions, Cofund, Project INCA 600398. EB-G is partially supported by grants from Instituto de Salud Carlos III (ISCIII PI13/02200; PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073); American Foundation for Suicide Prevention (AFSP) (LSRG- 1-005-16). NW is supported by the UCLH NIHR Biomedical Research Centre. DN is supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1, with an Alan Turing Institute Fellowship (TU/A/000006). RP has received support from a Medical Research Council (MRC) Health Data Research UK Fellowship (MR/S003118/1) and a Starter Grant for Clinical Lecturers (SGL015/1020) supported by the Academy of Medical Sciences, The Wellcome Trust, MRC, British Heart Foundation, Arthritis Research UK, the Royal College of Physicians and Diabetes UK. DL is supported by the UK Medical Research Council under grant MR/N028244/2 and the King’s Centre for Military Health Research. JD is supported by a Medical Research Council (MRC) Clinical Research Training Fellowship (MR/L017105/1). RD is funded by a Clinician Scientist Fellowship (research project e-HOST-IT) from the Health Foundation in partnership with the Academy of Medical Sciences

Background Electronic health care records (EHRs) are a rich source of health-related information, with potential for secondary research use. In the United Kingdom, there is no national marker for identifying those who have previously served in the Armed Forces, making analysis of the health and well-being of veterans using EHRs difficult. Objective This study aimed to develop a tool to identify veterans from free-text clinical documents recorded in a psychiatric EHR database. Methods Veterans were manually identified using the South London and Maudsley (SLaM) Biomedical Research Centre Clinical Record Interactive Search—a database holding secondary mental health care electronic records for the SLaM National Health Service Foundation Trust. An iterative approach was taken; first, a structured query language (SQL) method was developed, which was then refined using natural language processing and machine learning to create the Military Service Identification Tool (MSIT) to identify if a patient was a civilian or veteran. Performance, defined as correct classification of veterans compared with incorrect classification, was measured using positive predictive value, negative predictive value, sensitivity, F1 score, and accuracy (otherwise termed Youden Index). Results A gold standard dataset of 6672 free-text clinical documents was manually annotated by human coders. Of these documents, 66.00% (4470/6672) were then used to train the SQL and MSIT approaches and 34.00% (2202/6672) were used for testing the approaches. To develop the MSIT, an iterative 2-stage approach was undertaken. In the first stage, an SQL method was developed to identify veterans using a keyword rule–based approach. This approach obtained an accuracy of 0.93 in correctly predicting civilians and veterans, a positive predictive value of 0.81, a sensitivity of 0.75, and a negative predictive value of 0.95. This method informed the second stage, which was the development of the MSIT using machine learning, which, when tested, obtained an accuracy of 0.97, a positive predictive value of 0.90, a sensitivity of 0.91, and a negative predictive value of 0.98. Conclusions The MSIT has the potential to be used in identifying veterans in the United Kingdom from free-text clinical documents, providing new and unique insights into the health and well-being of this population and their use of mental health care services.

BACKGROUND Alcohol consumption in the UK Armed Forces is higher than in the general population, and this pattern continues after leaving the service. Smartphone apps may be useful to increase ex-serving personnel’s awareness of their alcohol consumption, support self-monitoring, and prompt a change in behavior. OBJECTIVE The study aimed to explore the acceptability of Information about Drinking in Ex-serving personnel (InDEx), a tailored smartphone app, combined with personalized short message service (SMS) text messaging designed to target ex-serving personnel who meet the criteria for hazardous alcohol use. METHODS The InDEx intervention included 4 key modules: (1) assessment and normative feedback, (2) self-monitoring and feedback, (3) goal setting and review, and (4) personalized SMS text messaging. A semistructured telephone interview study was conducted with ex-serving personnel after using the app for a 28-day period. Interviews were used to explore the acceptability of app modules and its functionality and the perceived changes in participant’s drinking. Interview transcripts were analyzed using inductive thematic analysis. RESULTS Overall, 94% (29/31) participants who used InDEx agreed to take part in a telephone interview. Overall, 4 themes were identified: Credibility, Meeting their needs, Simplicity, and Helpful for ex-serving personnel. The importance of credibility, functionality, and meeting the individual needs of ex-serving personnel was emphasized. Acceptability and engagement with specific modules of the app and text messages were influenced by the following: (1) if they felt it provided credible information, (2) whether the content was appropriately personalized to them, (3) the ease of use, and (4) beliefs about their own drinking behaviors. Participants recommended that the app would be most suitable for personnel about to leave the Armed Forces. CONCLUSIONS InDEx was an acceptable smartphone app for ex-serving personnel for monitoring alcohol consumption and in providing meaningful feedback to the individual. Pages that met the participant’s interests and provided real time personalized, credible feedback on their drinking and text messages tailored to participant’s interactions with the app were particularly favored.

There is a lack of quantitative evidence concerning UK (United Kingdom) Armed Forces (AF) veterans who access secondary mental health care services&mdash, specialist care often delivered in high intensity therapeutic clinics or hospitals&mdash, for their mental health difficulties. The current study aimed to investigate the utility and feasibility of identifying veterans accessing secondary mental health care services using National Health Service (NHS) electronic health records (EHRs) in the UK. Veterans were manually identified using the Clinical Record Interactive Search (CRIS) system&mdash, a database holding secondary mental health care EHRs for an NHS Trust in the UK. We systematically and manually searched CRIS for veterans, by applying a military-related key word search strategy to the free-text clinical notes completed by clinicians. Relevant data on veterans&rsquo, socio-demographic characteristics, mental disorder diagnoses and treatment pathways through care were extracted for analysis. This study showed that it is feasible, although time consuming, to identify veterans through CRIS. Using the military-related key word search strategy identified 1600 potential veteran records. Following manual review, 693 (43.3%) of these records were verified as &ldquo, probable&rdquo, veterans and used for analysis. They had a median age of 74 years (interquartile range (IQR): 53&ndash, 86), the majority were male (90.8%) and lived alone (38.0%). The most common mental diagnoses overall were depressive disorders (22.9%), followed by alcohol use disorders (10.5%). Differences in care pathways were observed between pre and post national service (NS) era veterans. This feasibility study represents a first step in showing that it is possible to identify veterans through free-text clinical notes. It is also the first to compare veterans from pre and post NS era.

BACKGROUND Self-reported alcohol misuse remains high in armed forces personnel even after they have left service. More than 50% of ex-serving personnel meet the criteria for hazardous alcohol use; however, many fail to acknowledge that they have a problem. Previous research indicates that interventions delivered via smartphone apps are suitable in promoting self-monitoring of alcohol use, have a broad reach, and may be more cost-effective than other types of brief interventions. There is currently no such intervention specifically designed for the armed forces. OBJECTIVE This study sought to describe the development of a tailored smartphone app and personalized text messaging (short message service, SMS) framework and to test the usability and feasibility (measured and reported as user engagement) of this app in a hard-to-engage ex-serving population. METHODS App development used Agile methodology (an incremental, iterative approach used in software development) and was informed by behavior change theory, participant feedback, and focus groups. Participants were recruited between May 2017 and June 2017 from an existing United Kingdom longitudinal military health and well-being cohort study, prescreened for eligibility, and directed to download either Android or iOS versions of the ”Information about Drinking for Ex-serving personnel” (InDEx) app. Through the app, participants were asked to record alcohol consumption, complete a range of self-report measures, and set goals using implementation intentions (if-then plans). Alongside the app, participants received daily automated personalized text messages (SMS) corresponding to specific behavior change techniques with content informed by the health action process approach with the intended purpose of promoting the use of the drinks diary, suggesting alternative behaviors, and providing feedback on goals setting. RESULTS Invitations to take part in the study were sent to ex-serving personnel, 22.6% (31/137) of whom accepted and downloaded the app. Participants opened the InDEx app a median of 15.0 (interquartile range [IQR] 8.5-19.0) times during the 4 week period (28 days), received an average of 36.1 (SD 3.2) text messages (SMS), consumed alcohol on a median of 13.0 (IQR 11.0-15.0) days, and consumed a median of 5.6 (IQR 3.3-11.8) units per drinking day in the first week, which decreased to 4.7 (IQR 2.0-6.9) units by the last week and remained active for 4.0 (IQR 3.0-4.0) weeks. CONCLUSIONS Personnel engaged and used the app regularly as demonstrated by the number of initializations, interactions, and time spent using InDEx. Future research is needed to evaluate the engagement with and efficacy of InDEx for the reduction of alcohol consumption and binge drinking in an armed forces population.

Evaluating the execution style of human motion can give insight into the performance and behaviour exhibited by the participant. This could enable support in developing personalised rehabilitation programmes by providing better understanding of motion mechanics and contextual behaviour. However, performing analyses, generating statistical representations and models which are free from external bins, repeatable and robust is a difficult task. In this work, we propose a framework which evaluates clinically valid motions to identify unstable behaviour during performance using Deep Convolutional Neural Networks. The framework is composed of two parts; 1) Instead of using the whole skeleton as input, we divide the human skeleton into five joint groups. For each group, feature encoding is used to represent spatial and temporal domains to permit high-level abstraction and to remove noise these are then represented using distance matrices. 2) The encoded representations are labelled using an automatic labelling method and evaluated using deep learning. Experimental results demonstrates the ability to correctly classify data compared to classical approaches.

ObjectiveSecondary health systems in the United Kingdom (UK) are unique for recording Outpatient, Inpatient and Accident & Emergency (A&E) visits in the form of electronic health (eHealth) records. Linking regional healthcare datasets is a problematic, further challenging when linking externally, such as to the King’s College Military Cohort Study (KCMCS). We introduce our methodology used for eRecord linkage. ApproacheHealth records from England, Scotland and Wales offer a variety of parameters such as admission/discharge date, diagnosis, treatment/procedure undertaken and the cost of treatment. To acquire eHealth records, unique patient identifiers: NHS number, forename, surname, sex and date of birth extracted from KCMCS were provided to each region. The KCMCS contains self-reported questionnaire results for 9,990 serving/ex-serving military personal, 8,602 participants consented to linkage. eHealth records prepared for linkage in two stages. First, admission and discharge date were checked to ensure a valid date. Second, episodes were checked for consistency, ensuring that no records for individual participants were duplicated. Data available varied based on the region, this disparity between regions can result in data type variation. Hence, linkage was performed on mutual variables to ensure a uniform admission history. Creation of the linked dataset was as follows. First, records and episodes relating to an individual were brought together, to create a personal admission history. Secondly, personal admission history were linked to the KCMCS. ResultsLinking to regional health datasets is not without its challenges. England, Scotland and Wales obtain, store and process eHealth records using different methodologies. A total of 6,336 (76.66%) participants were matched by regional health providers, with a total of 61,558 eHealth records. A total of 187 eHealth records were identified and discounted from linkage due to failure to meet criteria listed above. Verifying diagnoses completeness, Inpatient admissions were consistently code, with full completeness. Conversely, Outpatient admissions were poorly coded with 98% lacking any type of diagnosis. In addition, A&E records were sparsely coded; we identified four different regional and local coding systems to identify reason for admission. The eHealth records show promise for identifying health traits of the military. However, further work is required to identify synergy and overcome regional variations. ConclusionLinkage techniques provide new opportunities for exploring the health of serving and veteran population. However, quality of identifier and linkage error are still of major concern. Further, record completeness, diagnoses accuracy and data cleaning impact the data quality.

Background Many adults with post-traumatic stress disorder (PTSD) are unable to access healthcare services for treatment due to logistical, social, and attitudinal barriers. Interventions delivered via mobile applications (apps) may help overcome these barriers. Objective The aim of this study is to systematically evaluate the most recent evidence from trials investigating the efficacy of mobile apps for treating PTSD. Methods PubMed, Web of Science, Embase, PsycINFO, and Medline were searched in February 2018. Randomised controlled trials (RCTs) were included if they quantitatively evaluated the efficacy of a mobile app for treating PTSD as part of the primary aim. Findings were presented in a narrative synthesis. Results In the five identified RCTs, the use of app-based interventions appeared to be associated with reductions in PTSD symptoms. However, the strength of evidence for this association appeared to be inconsistent, and there was little evidence that those using the apps experienced greater reductions in PTSD symptoms than those in control conditions. Nonetheless, there was some evidence that app-based interventions are both a feasible and acceptable treatment pathway option. Conclusions Included studies were often limited by small sample sizes, brief intervention, and follow-up periods, and self-reported measures of PTSD. Evidence for the efficacy of mobile interventions for treating PTSD was inconclusive, but promising. Healthcare professionals should exercise caution in recommending app-based interventions until the potentially adverse effects of app use are better understood and larger-scale studies have taken place.

Analysis and quantification of human motion to support clinicians in the decision-making process is the desired outcome for many clinical-based approaches. However, generating statistical models that are free from human interpretation and yet representative is a difficult task. In this paper, we propose a framework that automatically recognizes and evaluates human mobility impairments using the Microsoft Kinect One depth sensor. The framework is composed of two parts. First, it recognizes motions, such as sit-to-stand or walking 4 m, using abstract feature representation techniques and machine learning. Second, evaluation of the motion sequence in the temporal domain by comparing the test participant with a statistical mobility model, generated from tracking movements of healthy people. To complement the framework, we propose an automatic method to enable a fairer, unbiased approach to label motion capture data. Finally, we demonstrate the ability of the framework to recognize and provide clinically relevant feedback to highlight mobility concerns, hence providing a route toward stratified rehabilitation pathways and clinician-led interventions.

There is a clear advantage to developing automated systems to detect human motion in the field of computer vision for applications associated with healthcare. We have compiled a diverse dataset of clinically-relevant motions using the Microsoft Kinect One sensor and release the dataset to the community as an open source solution for benchmarking detection, quantification and recognition algorithms. The dataset, namely Kinect 3D Active (K3Da), includes motions collected from young and older men and women ranging in age from 18–81 years. Participants performed standardised tests, including the Short Physical Performance Battery, Timed-Up-And-Go, vertical jump and other balance assessments which were recorded using depth sensor technology and extracted to generate motion capture data, sampled at 30 frames-per-second. Preliminary evaluations using Support Vector Machines, Random Forests, Artificial Neural Networks and Boltzmann Machines show age-related differences in many of the movements. These results demonstrate the relevance of the dataset to support benchmarking of algorithms associated and/or intended for use in a healthcare setting.

This paper aims to investigate whether micro-facial movement sequences can be distinguished from neutral face sequences. As a micro-facial movement tends to be very quick and subtle, classifying when a movement occurs compared to the face without movement can be a challenging computer vision problem. Using local binary patterns on three orthogonal planes and Gaussian derivatives, local features, when interpreted by machine learning algorithms, can accurately describe when a movement and non-movement occurs. This method can then be applied to help aid humans in detecting when the small movements occur. This also differs from current literature as most only concentrate in emotional expression recognition. Using the CASME II dataset, the results from the investigation of different descriptors have shown a higher accuracy compared to state-of-the-art methods.

We present the development of a virtual trainer for use by physiotherapists and patients in exercise based physiotherapy programmes. It allows a therapist to tailor exercise requirements to the specific needs and challenges of individual patients. Patients can select different programmes and follow a coach avatar to perform recorded exercises based on their needs. The Microsoft Kinect has been implemented as a means to track user’s body movements. This enables immersive and natural interaction between the user and virtual tuition world. Most importantly, the recorded skeletal joint data facilitates quantitative analysis and feedback of patient’s body movements. The proof of concept has been implemented and tested by 15 volunteers. Preliminary study shows the potential of using Kinect as a low cost solution for virtual physiotherapy training at home or clinic settings.

The recognition of human activity is a challenging topic for machine learning. We present an analysis of Support Vector Machines (SVM) and Random Forests (RF) in their ability to accurately classify Kinect kinematic activities. Twenty participants were captured using the Microsoft Kinect performing ten physical rehabilitation activities. We extracted the kinematic location, velocity and energy of the skeletal joints at each frame of the activity to form a feature vector. Principle Component Analysis (PCA) was applied as a pre-processing step to reduce dimensionality and identify significant features amongst activity classes. SVM and RF are then trained on the PCA feature space to assess classification performance, we undertook an incremental increase in the dataset size. We analyse the classification accuracy, model training and classification time quantitatively at each incremental increase. The experimental results demonstrate that RF outperformed SVM in classification rate for six out of the ten activities. Although SVM has performance advantages in training time, RF would be more suited to real-time activity classification due to its low classification time and high classification accuracy when using eight to ten participants in the training set.

Background Studies examining the prevalence of physical diseases in a particular population tend to rely either on the categorisation and coding of diseases provided by the International Classification of Diseases, Tenth Edition (ICD-10) or on clinical experience. However, the chapters in ICD-10 are broad and do not distinguish between non-communicable and communicable diseases. Thus, the purpose of this study was to develop a framework of non-communicable diseases (NCDs) which could i) be applied to electronic healthcare records and ii) account for multimorbidity. Methods Three databases were searched on 16 November 2015 via the Ovid research tool: PsycINFO (1806–present), MedLine (1946–present) and Embase (1980–present). The search included the keywords “multimorbidity” OR “multi-morbidity” combined with the keywords “chronic disease” OR “chronic illness”. Studies were included according to the following inclusion criteria: (1) studies which investigated the multimorbidity of chronic conditions in a representative sample; (2) studies that generated a list of chronic conditions based on primary and secondary healthcare data. Results After applying these criteria, 34 studies were identified as being relevant to the present study and a further 17 studies were identified from references thus providing a total of 51 lists of chronic conditions. The first step in the development of the framework involved collating the 51 lists and ordering the conditions from most to least common. Conditions were included if they appeared in three or more lists. Of the 51 lists, 7 used ICD-10 or ICD-9 codes. Based on these studies, ICD-10 codes were then assigned to each condition. Conditions sharing similar aetiology were identified and grouped together using the 51 lists identified from the literature review and generic health literature. Finally, condition groupings were validated by clinicians who provided suggestions on changes to some of the categories and reported that the framework had good face validity. The final framework included 26 non-communicable physical conditions which fall under ten disease categories. Conclusion This framework makes a significant contribution to the study of non-communicable physical diseases and may be particularly useful to prevalence studies on multimorbidity which aim to investigate co-occurring physical diseases without the identification of a specific index condition. This coding framework can be directly applied to future studies of NCDs which use electronic healthcare records.

Background Non-communicable diseases (NCD) account for two thirds of deaths globally, yet the focus of military healthcare has been on the consequences of deployment on mental health and provision of services for those wounded or injured in combat. This study will use the data linkage of a representative military cohort to secondary electronic healthcare records, to estimate the prevalence of NCDs in a UK military population and to identify the most common conditions. Methods The King’s Centre for Military Health Research (KCMHR) cohort is a large representative study of military personnel. Data was collected in 2004–2006 (Phase 1) and then again in 2007–2009 (Phase 2). Secondary healthcare data on inpatient admissions, outpatient and A&E visits were linked to the KCMHR cohort (using NHS number, name, sex and date of birth), for all Phase 2 participants who consented for access to their medical records (n = 8602). Three separate linkages were conducted for England, Wales and Scotland by the Health and Social Care Information Centre (HSCIC), Secure Anonymised Information Linkage (SAIL) and Information Services Division (ISD), respectively. The prevalence of non-communicable diseases, stratified by sociodemographic factors was derived. Results The linkages across the three regions resulted in an overall matching rate of 77%. The three most common conditions were gastrointestinal (GI) disorders (5.6%), joint disorders (5.6%) and arthritis/osteoarthritis (2.3%). The most common condition differed by age; joint disorders (5.4%) in the youngest aged group, GI disorders (7.6%) in middle aged adults and GI disorders (9.7%) in the older age group. The most common condition also differed by military rank with joint disorders (5.8%) prevalent in other ranks and non-commissioned officers and GI disorders (6.4%) in the higher ranks, i.e. commissioned officers. Discussion It is not surprising that joint disorders and arthritis are prevalent in this military population, given the physical demands of the job. This supports findings from the US military. One explanation for the high prevalence of GI disorders could relate to the outbreak of diarrhoea and vomiting during recent deployments. The next steps for this study (to be presented) are to examine deployment and combat exposure, mental health and alcohol misuse from the cohort as predictors of NCDs.