ore of five or more. The results suggest that the SDI can be clinically useful for the identification of disrupted sleep when administered by daytime staff in a nursing home context. Clinical Trial Registration www.ClinicalTrials.gov, identifier NCT03357328. Copyright © 2020 Hjetland, Nordhus, Pallesen, Cummings, Tractenberg, Thun, Kolberg and Flo.Background Sleep disturbance is common in perinatal and postnatal women, but the epidemiology of sleep problems is highly variable in these populations. This was a meta-analysis that examined the prevalence of poor sleep quality and its correlates among perinatal and postnatal women. Methods A systematic search of both international and Chinese databases (PubMed, EMBASE, PsycINFO, Web of Science, CNKI, and Wangfang) was performed. Studies with data on sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI) were included. Results Forty-two studies were included for analyses. The prevalence of poor sleep quality was 54.2% (95% CI 47.9-60.5%) in perinatal and postnatal women, with 44.5% (95% CI 37.6-51.6%) in perinatal women and 67.2% (95% CI 57.6-75.5%) in postnatal women. The pooled total PSQI score was 7.54 ± 0.40 (95% CI 6.75-8.33), while the average PSQI component scores varied from 0.13 ± 0.04 for use of sleeping medication to 1.51 ± 0.17 for habitual sleep efficiency. Maternal age, study site, survey year, comorbidity, PSQI cut-off value, and quality assessment score had significant moderating effects on the prevalence of poor sleep quality. Conclusion Given the negative impact of poor sleep quality on health outcomes and well-being, regular screening for poor sleep quality and effective interventions should be conducted for this population. Copyright © 2020 Yang, Li, Ma, Zhang, Hall, Ungvari and Xiang.Threatening faces are potent cues in social anxiety disorder (SAD); therefore, neural response to threatening faces, particularly regions in the "fear" circuit such as amygdala, may classify individuals with SAD. Previous studies of indirect/implicit processing of threatening faces have shown that support vector machine (SVM) pattern recognition significantly differentiates individuals with SAD from healthy participants, though evidence for the role of the fear circuit in classification has been inconsistent. We extend this literature by using SVM during direct face processing. Individuals with SAD (n=47) and healthy controls (n=46) completed a validated emotional face matching task during functional MRI, which included a matching shapes control condition. SVM was based on brain response to threat (vs. happy) faces, threat faces (vs. shapes), and threat/happy faces (vs. shapes) in 90 regions encompassing frontal, limbic, parietal, temporal, and occipital systems. Recursive feature elimination (RFE) was used for feature selection and to rank the contribution of regions in predicting SAD diagnosis. SVM results for threat (vs. happy) faces revealed satisfactory accuracy (e.g., area under the curve=0.72); results with shapes as "baseline" yielded less optimal classification. S64315 RFE for threat (vs. happy) indicated that all 90 brain regions were necessary for classification. RFE-based ranking suggested diffuse neurofunctional activation to threat (vs. happy) faces in classification. When using an RFE cut-point, regions implicated in sensory and goal-directed processes contributed relatively more in differentiating SAD from controls than other regions. Results suggest that neural activity across large-scale systems, as opposed to fear circuitry alone, may aid in the diagnosis of SAD. Copyright © 2020 Xing, Fitzgerald and Klumpp.Background There is a growing body of evidence to show that low-intensity self-help internet-delivered interventions are effective in the treatment of mental disorders. Despite the promising effectiveness of internet-delivered interventions, there is still a challenge for mental health services to implement internet-delivered interventions in routine health care. The aim of this study was to analyze the predictors of adherence to a self-help internet-delivered intervention for adjustment disorder. Methods This was a secondary report of data, including unpublished data, from a randomized controlled trial of an internet-delivered self-help intervention for adjustment disorder. The study included 1,077 participants who had completed online baseline assessments. All participants had experienced significant life stressors over the last 2 years and had high levels of adjustment disorder symptoms. We analyzed the role of sociodemographic variables, pre-treatment adjustment disorder symptoms, outcome expectations, and perceived barriers to mental health services on the use of the intervention. Results We found that usage of internet-delivered self-help intervention and higher adherence was associated with female gender, greater age, higher pre-intervention outcome expectations, exposure to other forms of psychological therapy in addition to the internet-intervention at the time of the study, and reported perceived barriers to mental health services by the study participants. Conclusions The findings of the study indicated the importance of non-specific therapeutic factors on adherence during internet-delivered intervention. Perceived barriers to mental health services were associated with higher adherence to self-help intervention, which indicated that communities with restricted access to mental health services could benefit from low-intensity internet-delivered interventions. Copyright © 2020 Kazlauskas, Eimontas, Olff, Zelviene and Andersson.Background and Objective Suicide is a leading cause of death in young people. Suicidal thoughts and behaviors can be triggered by life and study stresses; therefore, it is important to understand the role of coping strategies. The current study analyzed the link between different coping strategies and suicidality in university students in China. Methods A cross-sectional study of 2,074 undergraduate students from China used a stratified-clustered-random sampling method (response rate 94.4%). The Suicidal Behaviors Questionnaire-Revised Scale was used to identify suicidal risks, while the Brief COPE scale was used to measure different coping strategies. Univariate and multivariate logistic regression analyses were utilized to examine coping strategies and suicidality. Results A negative association of some coping skills (active coping and positive reframing) with suicidality and a positive association of some other coping skills (self-distraction, substance abuse, behavioral disengagement, venting, and self-blame) with suicidality were observed after adjusting for sociodemographic and mental health variables.