Background: Sexual abuse is a broad category of traumatic experiences that includes rape and any unwanted sexual contact with a body part or foreign object, whether penetrative, oral or otherwise. Although patients with mental illness have a higher risk of becoming victims of sexual abuse in adulthood, few studies investigate the proportion of male victims in this population. Their underrepresentation in research is a barrier to understanding the negative outcomes associated with sexual abuse in men.Aims: We estimated the prevalence of recent (past year) and adulthood sexual abuse perpetrated by any perpetrator and separately by intimate partners in males diagnosed with a mental illness.Method: To model the prevalences and heterogeneity arising from reports, we used Bayesian multilevel models. Prevalences were estimated for mixed-diagnosis, substance misuse, intellectual disability and post-traumatic stress disorder samples, and studies reporting specifically on intimate partner violence. This review was registered through PROSPERO (CRD42020169299)Results: Estimated adult sexual abuse was 5.3% (95% Credibility Interval 1.6–12.8%) for past-year abuse and 14.1% (95% Credibility Interval 7.3–22.4%) for abuse in adulthood. There was considerable heterogeneity of prevalence between studies and diagnosis groups.Conclusions: Our analyses show that the prevalence of sexual abuse of males diagnosed with a mental illness was much higher than for men in the general population. This has important implications regarding the proportion of undetected or untreated sexually abused men in clinical practice.

Study Objectives: Impaired sleep following trauma such as childhood maltreatment is both a prognostic factor for future mental illness and a feasible intervention point. Yet, associations between childhood maltreatment and objectively measured sleep components are rarely found. New approaches advance the use of multidimensional sleep health scores instead of individual sleep components. However, no such methodology has been used to study the consequences of maltreatment on sleep health in adolescent cohorts so far. We hypothesized that childhood maltreatment will be associated with poorer sleep health in adolescence. Methods: A cross-sectional sample of 494 adolescents at high risk of emotional and behavioral problems (mean age 17.9) completed the Childhood Trauma Questionnaire—Short Form to assess five forms of maltreatment (emotional and physical abuse/neglect and sexual abuse) assessed as continuous sum scores. During nine nights of actigraphy and sleep diary measurements, data on sleep regularity, satisfaction, alertness, timing, efficiency, and duration were collected, which were combined into a sleep health composite score ranging from 0 to 6. Linear regression models were adjusted for age, sex, household income, ethnic origin, educational level, urbanization of living environment, and parental psychopathological problems. Results: Associations were found between all forms of maltreatment and poorer sleep health (p < .031), except for sexual abuse (p = .224). Partial r effect sizes ranged from −0.12 (95% CI = −0.22 to −0.01) for emotional neglect to −0.18 (−0.28 to −0.08) for total maltreatment. Conclusions: Maltreatment was associated with impairment in everyday sleep health, reflected in both subjective and objective measurements of sleep.

The rising popularity of wearable devices allows for extensive and unobtrusive collection of personal health data for extended periods of time. Recent studies have used machine learning to create predictive algorithms to assess symptoms of major depressive disorder (MDD) based on these data. This review evaluates the clinical relevance of these models. Studies were selected to represent the range of methodologies and applications of wearables for MDD algorithms, with a focus on wrist-worn devices. The reviewed studies demonstrated that wearable-based algorithms were able to predict symptoms of MDD with considerable accuracy. These models may be used in the clinic to complement the monitoring of treatments or to facilitate early intervention in high-risk populations. In a preventative context, they could prompt users to seek help for earlier intervention and better clinical outcomes. However, the lack of standardized methodologies and variation in which performance metrics are reported complicates direct comparisons between studies. Issues with reproducibility, overfitting, small sample sizes, and limited population demographics also limit the generalizability of findings. As such, wearable-based algorithms show considerable promise for predicting and monitoring MDD, but there is significant room for improvement before this promise can be fulfilled.

Background: Sheltered housing is associated with quality-of-life improvements for individuals with serious mental illness (SMI). However, there are equivocal findings around safety outcomes related to this type of living condition. Aims: We aimed to investigate raw differences in prevalence and incidence of crime victimisation in sheltered housing compared with living alone or with family; and to identify groups at high risk for victimisation, using demographic and clinical factors. We do so by reporting estimated victimisation incidents for each risk group. Method: A large, community-based, cross-sectional survey of 956 people with SMI completed the Dutch Crime and Victimisation Survey. Data was collected on victimisation prevalence and number of incidents in the past year. Results: Victimisation prevalence was highest among residents in sheltered housing (50.8%) compared with persons living alone (43%) or with family (37.8%). We found that sheltered housing was associated with increased raw victimisation incidence (incidence rate ratio: 2.80, 95% CI 2.36–3.34 compared with living with family; 1.87, 95% CI 1.59–2.20 compared with living alone). Incidence was especially high for some high-risk groups, including men, people with comorbid post-traumatic stress disorder and those with high levels of education. However, women reported less victimisation in sheltered housing than living alone or with family, if they also reported drug or alcohol use. Conclusions: The high prevalence and incidence of victimisation among residents in sheltered housing highlights the need for more awareness and surveillance of victimisation in this population group, to better facilitate a recovery-enabling environment for residents with SMI.

Despite increased prevalence of domestic violence and abuse (DVA), victimization through DVA often remains undetected in mental health care. To estimate the effectiveness of a system provider level training intervention by comparing the detection and referral rates of DVA of intervention community mental health (CMH) teams with rates in control CMH teams. We also aimed to determine whether improvements in knowledge, skills and attitudes to DVA were greater in clinicians working in intervention CMH teams than those working in control teams. We conducted a cluster randomized controlled trial in two urban areas of the Netherlands. Detection and referral rates were assessed at baseline and at 6 and 12 months after the start of the intervention. DVA knowledge, skills and attitudes were assessed using a survey at baseline and at 6 and 12 months after start of the intervention. Electronic patient files were used to identify detected and referred cases of DVA. Outcomes were compared between the intervention and control teams using a generalized linear mixed model. During the 12-month follow-up, detection and referral rates did not differ between the intervention and control teams. However, improvements in knowledge, skills and attitude during that follow-up period were greater in intervention teams than in control teams: β 3.21 (95% CI 1.18-4.60). Our trial showed that a training program on DVA knowledge and skills in CMH teams can increase knowledge and attitude towards DVA. However, our intervention does not appear to increase the detection or referral rates of DVA in patients with a severe mental illness. A low detection rate of DVA remains a major problem. Interventions with more obligatory elements and a focus on improving communication between CMH teams and DVA services are recommended.