Millions of children and adolescents are exposed to wars, affecting their psychological well-being. This review focuses on psychosocial interventions in low and middle-income countries (LMICs) in the Middle East, where mental health services are limited.

Our primary aim was to evaluate the effectiveness of trial-assessed psychosocial interventions in reducing post-traumatic stress disorder (PTSD) symptoms in children and adolescents aged ≤18 years who were exposed to war in LMICs in the Middle East. Changes in other psychological conditions and symptoms were evaluated where reported.

PubMed, Cochrane Library and Ovid were searched without year restriction, in December 2021. Previous review reference lists were also checked. Only studies published in English were included. Each study was evaluated for risk of bias and results are presented as a narrative synthesis.

Three group-based interventions were identified and evaluated across six studies: ‘Teaching Recovery Techniques’, ‘Writing for Recovery’ and ‘Advancing Adolescents’. Two studies took place in post-war settings, and four in a context of ongoing conflict. Positive experiences and improved social skills were indicated following most interventions, but Teaching Recovery Techniques was the only programme associated with a statistically significant reduction in PTSD score. Differences in follow-up interval limited comparability of outcomes.

This review highlights a paucity of evidence for effective treatment options for children and adolescents affected by war from LMICs in the Middle East. Promising indications of reductions in PTSD symptoms, specifically from Teaching Recovery Techniques, require further rigorous evaluation and long-term follow-up.

There is cumulative evidence of the importance of exploring the change of dynamics between symptoms over time as reflective of consolidation of psychopathology.

To explore the interactions between symptoms of ICD-11 adjustment disorder before and after the second lockdown of the COVID-19 pandemic in Israel and identify the most central symptoms and their concurrent and prospective associations with probable adjustment disorder.

This is a population-based study drawn from a probability-based internet panel. A representative sample of the adult Israeli population was assessed at two time points (T1, pre-second lockdown, n = 1029, response rate 76.17%; T2, post-second lockdown, n = 764, response rate 74.24%). Symptoms of adjustment disorder were assessed by the International Adjustment Disorder Questionnaire (IADQ).

Although the overall strength of associations at the two measurement points was similar and two same communities were found, there was a significant change in their structure, with a more consolidated network at T2. The most central item was ‘difficult to relax’ in both networks. Cross-sectionally, all symptoms of failure to adapt significantly predicted adjustment disorder. ‘Worry a lot more’ (preoccupation) and ‘difficult to adapt to life’ (failure to adapt) at T1 significantly predicted this diagnosis at T2.

Adjustment disorder symptoms consolidated during the second lockdown of the pandemic. In line with the ICD-11 conceptualisation of adjustment disorder, both preoccupation and failure-to-adapt symptoms have prognostic validity. This highlights the importance of identifying and targeting adjustment disorder symptoms during a period of stress such as the COVID-19 pandemic.

Cognitive therapy and behavioural activation are both widely applied and effective psychotherapies for depression, but it is unclear which works best for whom. Individual participant data (IPD) meta-analysis allows for examining moderators at the participant level and can provide more precise effect estimates than conventional meta-analysis, which is based on study-level data.

This article describes the protocol for a systematic review and IPD meta-analysis that aims to compare the efficacy of cognitive therapy and behavioural activation for adults with depression, and to explore moderators of treatment effect. (PROSPERO: CRD42022341602)

Systematic literature searches will be conducted in PubMed, PsycINFO, EMBASE and the Cochrane Library, to identify randomised clinical trials comparing cognitive therapy and behavioural activation for adult acute-phase depression. Investigators of these trials will be invited to share their participant-level data. One-stage IPD meta-analyses will be conducted with mixed-effects models to assess treatment effects and to examine various available demographic, clinical and psychological participant characteristics as potential moderators. The primary outcome measure will be depressive symptom level at treatment completion. Secondary outcomes will include post-treatment anxiety, interpersonal functioning and quality of life, as well as follow-up outcomes.

To the best of our knowledge, this will be the first IPD meta-analysis concerning cognitive therapy versus behavioural activation for adult depression. This study has the potential to enhance our knowledge of depression treatment by using state-of-the-art statistical techniques to compare the efficacy of two widely used psychotherapies, and by shedding more light on which of these treatments might work best for whom.

Psychological models of suicidal experiences are largely based on cross-sectional or long-term prospective data with follow-up intervals typically greater than 1 year. Recent time-series analyses suggest that these models may not account for fluctuations in suicidal thinking that occur within a period of hours and/or days.

We explored whether previously posited causal relationships between defeat, entrapment and suicidal ideation accounted for temporal associations between these experiences at small time intervals from 3 to 12 h.

Participants (N = 51) completed an ecological momentary assessment (EMA) study, comprising repeated assessments at semi-random time points up to six times per day for 1 week, resulting in 1852 completed questionnaires. Multilevel vector autoregression was used to calculate temporal associations between variables at different time intervals (i.e. 3 to 12 h between measurements).

The results showed that entrapment severity was temporally associated with current and later suicidal ideation, consistently over these time intervals. Furthermore, entrapment had two-way temporal associations with defeat and suicidal ideation at time intervals of approximately 3 h. The residual and contemporaneous network revealed significant associations between all variables, of which the association between entrapment and defeat was the strongest.

Although entrapment is key in the pathways leading to suicidal ideation over time periods of months, our results suggest that entrapment may also account for the emergence of suicidal thoughts across time periods spanning a few hours.

The sodium valproate PREVENT programme was introduced by the Medicines and Healthcare products Regulatory Agency (MHRA) in March 2018, and is now a legal requirement due to valproate's risks in pregnancy. This project had two main aims: 1) To assess clinician knowledge of PREVENT, and identify deficiencies in current education. 2) To assess the barriers psychiatrists face in achieving compliance with PREVENT.

Knowledge and awareness of PREVENT was assessed through an online survey sent to local consultants and specialty doctors in February 2021. The survey included ten questions, four were Likert style to assess attitudes, two assessed local arrangements and four were knowledge based. A free-text section allowed respondents to describe challenges faced implementing PREVENT. Results were analysed, an educational presentation given at local teaching and a poster was created and distributed - both targeting areas of weakest knowledge. A repeat survey was sent out in June 2021, and results collected to reassess.

The pre-teaching survey received twelve responses, the post teaching survey received eleven. In both, 75% of respondents represented general adult services, and 25% represented intellectual disability services.

There was an improvement in confidence of knowledge with all respondents being either “somewhat” (55%) or “very confident” (45%) post-teaching compared to 75% being “somewhat” confident, 10% “unsure” and 17% “very confident” prior to intervention. Pre-teaching, 10% of respondents were unaware that a risk acknowledgement form must be signed annually, while post-teaching 100% correctly identified this should be annual.

Respondents correctly identifying “highly effective” forms of contraception rose from 83% to 100% following teaching. Post-teaching there was an increase of 31% in the number of respondents correctly identifying the necessary documentation where a patient declines the PREVENT programme.

Pre-teaching, half of respondents were unsure if their team had a reminder system for risk acknowledgement forms, and 42% reported having no system. Post-teaching, 27% of respondents reported now having a reminder system in place, and 27% had plans to implement one.

Initial results showed variable knowledge of the PREVENT programme, and a lack of awareness of the administrative requirements including risk acknowledgement forms. Results demonstrated an improvement in knowledge and organisation to help support compliance with the PREVENT programme. Respondents highlighted that knowledge of the PREVENT programme quickly deteriorates given how rarely it is used. Further work includes a full audit of compliance with PREVENT across the health board, as well as considering “refresher” sessions to prevent atrophy of knowledge.

There is often a waiting period for people who seek psychiatric treatment for depression or anxiety. As this delay risks worsening symptoms, an alternative could be to provide an intervention that requires minimal resources during the waiting period.

The aim was to investigate if a digital problem-solving intervention delivered in a self-guided format with automated features is feasible to provide for patients on the waiting list in routine psychiatric care.

A total of 12 patients with symptoms of depression or anxiety on the waiting list for treatment in routine psychiatric care were given access to a self-guided and monitored digital problem-solving intervention over 4 weeks. Primary outcome measures were treatment credibility and usability. Secondary outcome measures were behavioural engagement, symptoms of depression and anxiety, and negative effects.

A majority of participants rated the intervention as both credible and usable. The intervention was used at least once by nine out of 12 individuals, with an average of 11 logins. The participants did, on average, initiate 2.8 problem-solving attempts and 10.1 solutions. A few participants reached a clinically relevant symptom improvement of depression and anxiety. No serious negative effects were reported.

The credibility and usability of the intervention was perceived as good, and the behavioural engagement with the intervention was deemed sufficient compared with similar self-guided interventions. A self-guided and monitored digital problem-solving intervention may be a beneficial option for patients waiting for or receiving treatment in routine psychiatric care, and should be further evaluated.

Perfectionism, low self-esteem and external locus of control are psychological constructs linked to insomnia, anxiety and depression. Examining how these constructs impact mental health and serve as risk factors for the development of clinically significant symptoms may help direct psychological support resources and preventative measures for university students.

To longitudinally examine associations between the aforementioned psychological constructs and symptoms of insomnia, anxiety and depression in a large representative sample of first-year university students.

Electronic surveys including validated measures of the predictors and outcomes were emailed to all first-year undergraduate students at entry to a major Canadian university, and followed up on at conclusion of the academic year.

Compared with healthy sleepers, students screening positive for insomnia had lower self-esteem, higher self-evaluative perfectionism and increased external locus of control (all P < 0.001). Self-evaluative perfectionism (standardised β = 0.13, P < 0.01), self-esteem (β = −0.30, P < 0.001) and external locus of control (β = 0.07, P = 0.02) measured at entry were significantly associated with insomnia symptoms at follow-up. Insomnia symptoms at entry were strong predictors of symptoms of depression (β = 0.15, P < 0.001) and anxiety (β = 0.16, P < 0.001) at follow-up, even after controlling for baseline symptoms of those disorders.

Perfectionism, low self-esteem and external locus of control may predispose the development of insomnia symptoms in university students. In turn, insomnia symptoms appear to be robust predictors for depressive and anxiety symptoms. Sleep may be an important prevention target in university students.

An early and prolonged lockdown was adopted in Argentina during the first wave of COVID-19. Early reports evidenced elevated psychological symptoms.

To explore if the prolonged lockdown was associated with elevated anxiety and depressive symptoms; if mental fatigue was associated with lockdown adherence (a phenomenon called ‘behavioural fatigue’); and if financial concerns were associated with lockdown adherence and emotional symptoms.

The survey included standardised questionnaires to assess depressive (PHQ-9) and anxious (GAD-7) symptoms, mental fatigue, risk perception, lockdown adherence, financial concerns, daily stress, loneliness, intolerance to uncertainty, negative repetitive thinking and cognitive problems. LASSO regression analyses were carried out to predict depression, anxiety and lockdown adherence

The survey reached 3617 adults (85.2% female) from all provinces of Argentina after 72 days of lockdown. Data were collected between 21 May 2020 and 4 June 2020. In that period, Argentina had an Oxford stringency index of 85/100. Of those surveyed, 45.6% and 27% met the cut-offs for depression and anxiety, respectively. Mental fatigue, cognitive failures and financial concerns were correlated with psychological symptoms, but not with adherence to lockdown. In regression models, mental fatigue, cognitive failures and loneliness were the most important variables to predict depression, intolerance to uncertainty and lockdown difficulty were the most important for anxiety, and perceived threat was the most important for predicting lockdown adherence.

During the extended lockdown, psychological symptoms increased, being enhanced by mental fatigue, cognitive difficulties and financial concerns. We found no evidence of behavioural fatigue. Thus, feeling mentally fatigued is not the same as being behaviourally fatigued.

Problematic internet use, especially in people with substance use disorder, may negatively affect their quality of life (QoL). However, it is unclear whether sleep quality is a key mediator in the association between problematic internet use and QoL among people with substance use disorder.

This study aimed to investigate the relationship between problematic internet use and QoL and how sleep quality may mediate the association between these two variables.

Overall, 319 people (85% male) with substance use disorder (mean age 42.2 years, s.d. 8.9) participated in a cross-sectional study in Taiwan. The Smartphone Application-Based Addiction Scale, Bergan Social Media Addiction Scale, Internet Gaming Disorder-Short Form, Pittsburgh Sleep Quality Index and World Health Organization Quality of Life Questionnaire Brief Version were used.

The prevalence of sleep problems was 56%. There were significant and direct associations between sleep quality and two types of problematic internet use, and between sleep quality and different dimensions of QoL. All types of problematic internet use were significantly and negatively correlated with QoL. Mediated effects of sleep quality in relationships between the different types of problematic internet use and all dimensions of QoL were significant, except for problematic use of social media.

Different types of problematic internet use in people with substance use disorder may be directly associated with reduced QoL. Sleep quality as a significant mediator in this association may be an underlying mechanism to explain pathways between problematic internet use and QoL in this population.

Meditation is commonly implemented in psychological therapies since the ‘third wave’ of cognitive–behavioural therapy has increased the focus on mindfulness-based interventions. Although extensive research literature demonstrates its benefits, little is known about potential adverse effects.

The aim of this study is to report the prevalence, type and severity of particularly unpleasant meditation-related experiences in the largest cross-sectional study on this topic to date, with 1370 regular meditators.

The participants were asked whether they ever encountered particularly unpleasant experiences as a result of their meditation experience. For the first time, the type and severity of those experiences were assessed and the association with several predictors, such as pre-existing mental disorders, were explored via logistic and linear regression.

Similar to previous studies, 22% of participants (95% CI 20–24) reported having encountered unpleasant meditation-related experiences, and 13% of participants (95% CI 3–5) reported experiences that were categorised as adverse. Those were mostly of affective, somatic and cognitive nature. Unpleasant meditation-related experiences were more likely to occur in participants with pre-existing mental illnesses (P = 0.000, 95% CI 1.25–2.12).

This study demonstrates that unpleasant meditation-related experiences are prevalent among meditators and, to a relevant extent, severe enough to warrant further scientific inquiry. Longitudinal studies are needed to examine whether the unpleasant meditation-related experiences are merely negative and thus should be avoided, or are an inherent part of the contemplative path.

An increasing number of children, adolescents and adults with intellectual disabilities and/or autism are being admitted to general psychiatric wards and cared for by general psychiatrists.

The aim of this systematic review was to consider the likely effectiveness of in-patient treatment for this population, and compare and contrast differing models of in-patient care.

A systematic search was completed to identify papers where authors had reported data about the effectiveness of in-patient admissions with reference to one of three domains: treatment effect (e.g. length of stay, clinical outcome, readmission), patient safety (e.g. restrictive practices) and patient experience (e.g. patient or family satisfaction). Where possible, outcomes associated with admission were considered further within the context of differing models of in-patient care (e.g. specialist in-patient services versus general mental health in-patient services).

A total of 106 studies were included and there was evidence that improvements in mental health, social functioning, behaviour and forensic risk were associated with in-patient admission. There were two main models of in-patient psychiatric care described within the literature: admission to a specialist intellectual disability or general mental health in-patient service. Patients admitted to specialist intellectual disability in-patient services had greater complexity, but there were additional benefits, including fewer out-of-area discharges and lower seclusion rates.

There was evidence that admission to in-patient services was associated with improvements in mental health for this population. There was some evidence indicating better outcomes for those admitted to specialist services.

The authors designed a simulation training programme for foundation doctors beginning psychiatry placements across a large mental health trust. The simulation training aimed to improve the confidence, competence, and well-being of foundation doctors through exposing them to realistic psychiatry scenarios and teaching clinical skills in a safe environment.

Four clinical scenarios were filmed with a 360-degree camera, professional actress, and doctors working in psychiatry. The scenarios depicted the journey of a patient being admitted onto a psychiatry ward from the community. Various clinical skills were embedded into the videos including psychiatric history taking, risk assessment, managing acute distress, managing comorbid physical and mental health problems, using the Mental Health Act, and teamwork with colleagues. All videos were delivered to learners using simulation with head-mounted-displays (HMDs). Each video lasted 6–8 minutes and was accompanied by pre-briefing and de-briefing with experienced psychiatrists for a further 15–20 minutes. Participants rated their confidence regarding several skills in psychiatry on Likert scales from 1 to 5 immediately before and after the session. Wilcoxon signed rank tests were conducted to detect statistically significant differences in learner's median confidence ratings before and after the training. Free-text questions explored trainee's most and least favourite aspects of the simulation. A survey also was distributed to learners 2-months after the training to assess how it had influenced their clinical practice.

20 foundation doctors completed the training and provided feedback. Following the simulation training, there were statistically significant improvements in foundation doctor's confidence in: completing psychiatric assessments (p < 0.01), managing physical health problems in psychiatry (p < 0.05), managing acute distress (p < 0.01), reporting information to senior colleagues (p < 0.05), and containing anxiety when communicating with patients (p < 0.05). Trainees highlighted the debriefing, group discussions, and “interactive” simulation videos as the most useful aspects of the training. Some trainees enjoyed viewing the 360-degree videos, whilst others found the HMDs difficult to use. Of the 8 trainees who completed feedback 2 months after the training, 7 (87.5%) felt that it had helped them in their current roles. All trainees agreed (37.5%) or strongly agreed (62.5%) that the simulation scenarios were closely aligned to real-life clinical encounters.

Simulation training in psychiatry using 360-degree videos and HMDs is generally well-received amongst foundation doctors. Embedding simulation training into placement induction can improve the confidence and skills of junior doctors starting psychiatry placements.

Smartphones can facilitate patients completing surveys and collecting sensor data to gain insight into their mental health conditions. However, the utility of sensor data is still being explored. Prior studies have reported a wide range of correlations between passive data and survey scores.

To explore correlations in a large data-set collected with the mindLAMP app. Additionally, we explored whether passive data features could be used in models to predict survey results.

Participants were asked to complete daily and weekly mental health surveys. After screening for data quality, our sample included 147 college student participants and 270 weeks of data. We examined correlations between six weekly surveys and 13 metrics derived from passive data features. Finally, we trained logistic regression models to predict survey scores from passive data with and without daily surveys.

Similar to other large studies, our correlations were lower than prior reports from smaller studies. We found that the most useful features came from GPS, call, and sleep duration data. Logistic regression models performed poorly with only passive data, but when daily survey scores were included, performance greatly increased.

Although passive data alone may not provide enough information to predict survey scores, augmenting this data with short daily surveys can improve performance. Therefore, it may be that passive data can be used to refine survey score predictions and clinical utility may be derived from the combination of active and passive data.

Psychiatric readmissions cause a burden on the healthcare system, incur a monetary cost and cause additional distress to acutely unwell patients. This project explores the use of the free-text of electronic patient records to predict inpatients in psychiatric hospitals at risk of readmission using predictive models generated by machine learning.

Free-text was extracted from the electronic patient records of patients admitted to hospitals in Birmingham and Solihull Mental Health Foundation Trust (BSMHFT) during the five years 2015–2019 inclusive. The anonymised records were obtained via the CRIS (Clinical Record Interactive Search) database. A total of 17208 records were extracted.

The free-text entered by clinicians during an admission was extracted and processed using techniques of natural language processing to generate input vectors suitable to be used with machine learning algorithms. tf-idf (term frequency-inverse document frequency) vectors were used.

A selection of algorithms were used to train predictive models. Two-thirds of the records were used as training data with the remainder as test data. Baseline model performance was assessed and then best-performing candidates underwent hyperparameter optimisation using five-fold cross-validation to improve performance. Bayesian optimisation was used to automate hyperparameter tuning during cross-validation. Hyperparameters were optimised on the log loss function. As the dataset was imbalanced with negative instances outnumbering positive instances to a significant degree, various techniques such as random undersampling of negative instances in the training data were used to deal with class imbalance throughout this process. Following cross-validation, the best-performing models underwent performance analysis.

Models were used to make predictions on the test data. Performance was assessed using F1-measures, precision-recall curves and the average precision metric (equivalent to area under the precision-recall curve). These metrics were chosen due to their suitability in assessing models trained on imbalanced datasets.

The best F1 score obtained was 0.233 using a Random Forest model trained using unigram tf-idf vectors of 500 token dimension.

The best average precision obtained was 0.157 using a Support Vector Machine trained using unigram tf-idf vectors of 2000 token dimension.

Both the above results required the use of random oversampling of positive instances to improve performance on the imbalanced dataset.

The performance indicates that the models generated are unlikely to have significant practical utility. Nevertheless, this exploratory project has produced a processed dataset with knowledge about its characteristics. This could be used for the further development of models using more complex techniques such as language modelling using neural networks.

Little is known about which factors exacerbate and buffer the impact of coronavirus disease 2019 (COVID-19)-related adversities on changes in thinking about and engaging in self-harm over time.

To examine how changes in four social factors contribute to changes in self-harm thoughts and behaviours over time and how these factors in turn interact with adversities and worries about adversities to increase risk for these outcomes.

Data from 49 227 UK adults in the UCL COVID-19 Social Study were analysed across the first 59 weeks of the pandemic. Fixed-effects logistic regressions examined time-varying associations between social support quality, loneliness, number of days of face-to-face contact for >15 min and number of days phoning/video calling for ≥15 min with self-harm thoughts and behaviours. We then examined how these four factors in turn interacted with the total number of adversities and worries about adversity and how this affected outcomes.

Increases in the quality of social support were associated with decreases in the likelihood of both outcomes, whereas greater loneliness was associated with an increase in their likelihood. Associations were less clear for telephone/video contact and face-to-face contact with outcomes. Social support buffered and loneliness exacerbated the impact of adversity experiences on self-harm behaviours.

These findings suggest the importance of the quality of one's social support network, rather than the mere presence of contact, for reducing the likelihood of self-harm behaviours in the context of COVID-19 pandemic-related adversity and worry.