Depression is a major cause of disability worldwide. Recent data suggest that, in industrialised countries, the prevalence of depression peaks in middle age. Identifying factors predictive of future depressive episodes is crucial for developing prevention strategies for this age group.

We aimed to identify future depression in middle-aged adults with no previous psychiatric history.

To predict a diagnosis of depression 1 year or more following a comprehensive baseline assessment, we used a data-driven, machine-learning methodology. Our data-set was the UK Biobank of middle-aged participants (N = 245 036) with no psychiatric history.

Overall, 2.18% of the study population developed a depressive episode at least 1 year following baseline. Basing predictions on a single mental health questionnaire led to an area under the curve of the receiver operating characteristic of 0.66, and a predictive model leveraging the combined results of 100 UK Biobank questionnaires and measurements improved this to 0.79. Our findings were robust to demographic variations (place of birth, gender) and variations in methods of depression assessment. Thus, machine-learning-based models best predict diagnoses of depression when allowing the inclusion of multiple features.

Machine-learning approaches show potential for being beneficial for the identification of clinically relevant predictors of depression. Specifically, we can identify, with moderate success, people with no recorded psychiatric history as at risk for depression by using a relatively small number of features. More work is required to improve these models and evaluate their cost-effectiveness before integrating them into the clinical workflow.

‘POD Adventures’ is a gamified problem-solving intervention delivered via smartphone app, and supported by non-specialist counsellors for a target population of secondary school students in India during the COVID-19 pandemic.

To evaluate the feasibility and acceptability of undertaking a randomised controlled trial of POD Adventures when delivered online with telephone support from counsellors.

We conducted a parallel, two-arm, individually randomised pilot-controlled trial with 11 secondary schools in Goa, India. Participants received either the POD Adventures intervention delivered over 4 weeks or usual care comprising information about local mental health services and national helplines. Outcomes were assessed at two timepoints: baseline and 6 weeks post-randomisation.

Seventy-nine classroom sensitisation sessions reaching a total of 1575 students were conducted. Ninety-two self-initiated study referrals (5.8%) were received, but only 11 participants enrolled in the study. No intervention arm participants completed the intervention. Outcomes at 6 weeks were not available for intervention arm participants (n = 5), and only four control arm participants completed outcomes. No qualitative interviews or participant satisfaction measures were completed because participants could not be reached by the study team.

Despite modifications to address barriers arising from COVID-19 restrictions, online delivery was not feasible in the study context. Low recruitment and missing feasibility and acceptability data make it difficult to draw conclusions about intervention engagement and indicative clinical outcomes. Prior findings showing high uptake, adherence and engagement with POD Adventures when delivered in a school-based context suggest that an online study and delivery posed the biggest barriers to study participation and engagement.

To investigate whether a psychiatry-specific virtual on-call training programme improved confidence of junior trainees in key areas of psychiatry practice. The programme comprised one 90 min lecture and a 2 h simulated on-call shift where participants were bleeped to complete a series of common on-call tasks, delivered via Microsoft Teams.

Thirty-eight trainees attended the lecture, with a significant improvement in confidence in performing seclusion reviews (P = 0.001), prescribing psychiatric medications for acute presentations (P < 0.001), working in section 136 suites (places of safety) (P = 0.001) and feeling prepared for psychiatric on-call shifts (P = 0.002). Respondents reported that a virtual on-call practical session would be useful for their training (median score of 7, interquartile range 5–7.75). Eighteen participants completed the virtual on-call session, with significant improvement in 9 out of the 10 tested domains (P < 0.001).

The programme can be conducted virtually, with low resource requirements. We believe it can improve trainee well-being, patient safety, the delivery of training and induction of rotating junior doctors during the COVID-19 pandemic and it supports the development and delivery of practical training in psychiatry.

Healthcare workers (HCWs) have faced considerable pressures during the COVID-19 pandemic. For some, this has resulted in mental health distress and disorder. Although interventions have sought to support HCWs, few have been evaluated.

We aimed to determine the effectiveness of the ‘Foundations’ application (app) on general (non-psychotic) psychiatric morbidity.

We conducted a multicentre randomised controlled trial of HCWs at 16 NHS trusts (trial registration number: EudraCT: 2021-001279-18). Participants were randomly assigned to the app or wait-list control group. Measures were assessed at baseline, after 4 and 8 weeks. The primary outcome was general psychiatric morbidity (using the General Health Questionnaire). Secondary outcomes included: well-being; presenteeism; anxiety; depression and insomnia. The primary analysis used mixed-effects multivariable regression, presented as adjusted mean differences (aMD).

Between 22 March and 3 June 2021, 1002 participants were randomised (500:502), and 894 (89.2%) followed-up. The sample was predominately women (754/894, 84.3%), with a mean age of 44⋅3 years (interquartile range (IQR) 34–53). Participants randomised to the app had a reduction in psychiatric morbidity symptoms (aMD = −1.39, 95% CI −2.05 to −0.74), improvement in well-being (aMD = 0⋅54, 95% CI 0⋅20 to 0⋅89) and reduction in insomnia (adjusted odds ratio (aOR) = 0⋅36, 95% CI 0⋅21 to 0⋅60). No other significant findings were found, or adverse events reported.

The app had an effect in reducing psychiatric morbidity symptoms in a sample of HCWs. Given it is scalable with no adverse effects, the app may be used as part of an organisation's tiered staff support package. Further evidence is needed on long-term effectiveness and cost-effectiveness.

This review aims to clarify the evidence on the effectiveness of telepsychiatry following the COVID-19 pandemic. We conducted a literature review of three databases (Cochrane Library, PubMed and PsycINFO), using the terms virtual consultation/telepsychiatry/video consultation AND psychiatry/mental illness.

We identified 325 eligible papers and conducted a thematic analysis resulting in five themes: patient and clinical satisfaction, diagnostic reliability, outcomes, technology and professional guidance. The most significant factors linked to effectiveness of telepsychiatry were patient and clinician satisfaction and adequate technology to facilitate examination of the patient.

The consistent diagnostic reliability, satisfactory clinical outcomes and patient satisfaction linked to telepsychiatry favour its continued use once the pandemic ends. The main barrier is reluctance among clinicians and lack of professional guidance. We recommend education on the uses of telepsychiatry among clinicians, and the provision of professional guidance for its use from medical bodies and organisations.

Research in schizophrenia and pregnancy has traditionally been conducted in small samples. More recently, secondary analysis of routine healthcare data has facilitated access to data on large numbers of women with schizophrenia.

To discuss four scientific advances using data from Canada, Denmark and the UK from population-level health registers and clinical data sources.

Narrative review of research from these three countries to illustrate key advances in the area of schizophrenia and pregnancy.

Health administrative and clinical data from electronic medical records have been used to identify population-level and clinical cohorts of women with schizophrenia, and follow them longitudinally along with their children. These data have demonstrated that fertility rates in women with schizophrenia have increased over time and have enabled documentation of the course of illness in relation with pregnancy, showing the early postpartum as the time of highest risk. As a result of large sample sizes, we have been able to understand the prevalence of and risk factors for rare outcomes that would be difficult to study in clinical research. Advanced pharmaco-epidemiological methods have been used to address confounding in studies of antipsychotic medications in pregnancy, to provide data about the benefits and risks of treatment for women and their care providers.

Use of these data has advanced the field of research in schizophrenia and pregnancy. Future developments in use of electronic health records include access to richer data sources and use of modern technical advances such as machine learning and supporting team science.

Digitally enabled services can contribute to the support, treatment and prevention of mental health difficulties; however, questions remain regarding how we can most usefully harness such technology in primary and secondary mental healthcare settings.

To identify barriers and facilitators to enable the potential of digital mental health in England, Scotland, Wales and Northern Ireland.

A three-round Delphi exercise was carried out online with 16 participants from across the four nations of the UK representing the following stakeholder groups: service providers, health professionals, policymakers, lived experience, small and medium enterprises and academics. Qualitative data were collected in the first round (80 fragments) that were then coded to produce a 26-item round-two questionnaire for participant rating. Participants were given the opportunity to reconsider their scores in light of the group responses in round three.

Eight statements under the following five themes reached consensus with median scores between 8 and 10 (i.e. important/very important): co-production; the human element; data security; funding; and regulation.

The Delphi process allowed consensus to be achieved regarding the factors that experts consider important for harnessing technology in primary and secondary mental healthcare. Knowledge of these factors can help users and providers of mental health services negotiate how best to move forward with digitally enabled systems of care.