Sleep disturbance is common in gestational parents during pregnancy and postpartum periods. This study evaluated the feasibility and efficacy of a scalable cognitive behavioural therapy (CBT) sleep intervention tailored for these periods.

This is a two-arm, parallel-group, single-blind, superiority randomised controlled trial. Nulliparous females without severe medical/psychiatric conditions were randomised 1:1 to CBT or attention- and time-matched control. All participants received a 1 h telephone session and automated multimedia emails from the third trimester until 6 months postpartum. Outcomes were assessed with validated instruments at gestation weeks 30 (baseline) and 35 (pregnancy endpoint), and postpartum months 1.5, 3, 6 (postpartum endpoint), 12 and 24.

In total, 163 eligible participants (age M ± s.d. = 33.35 ± 3.42) were randomised. The CBT intervention was well accepted, with no reported adverse effect. Intention-to-treat analyses showed that compared to control, receiving CBT was associated with lower insomnia severity and sleep disturbance (two primary outcomes), and lower sleep-related impairment at the pregnancy endpoint (p values ⩽ 0.001), as well as at 24 months postpartum (p ranges 0.012–0.052). Group differences across the first postpartum year were non-significant. Participants with elevated insomnia symptoms at baseline benefitted substantially more from CBT (v. control), including having significantly lower insomnia symptoms throughout the first postpartum year. Group differences in symptoms of depression or anxiety were non-significant.

A scalable CBT sleep intervention is efficacious in buffering against sleep disturbance during pregnancy and benefitted sleep at 2-year postpartum, especially for individuals with insomnia symptoms during pregnancy. The intervention holds promise for implementation into routine perinatal care.

Recommendations for promoting mental health during the COVID-19 pandemic include maintaining social contact, through virtual rather than physical contact, moderating substance/alcohol use, and limiting news and media exposure. We seek to understand if these pandemic-related behaviors impact subsequent mental health.

Daily online survey data were collected on adults during May/June 2020. Measures were of daily physical and virtual (online) contact with others; substance and media use; and indices of psychological striving, struggling and COVID-related worry. Using random-intercept cross-lagged panel analysis, dynamic within-person cross-lagged effects were separated from more static individual differences.

In total, 1148 participants completed daily surveys [657 (57.2%) females, 484 (42.1%) males; mean age 40.6 (s.d. 12.4) years]. Daily increases in news consumed increased COVID-related worrying the next day [cross-lagged estimate = 0.034 (95% CI 0.018–0.049), FDR-adjusted p = 0.00005] and vice versa [0.03 (0.012–0.048), FDR-adjusted p = 0.0017]. Increased media consumption also exacerbated subsequent psychological struggling [0.064 (0.03–0.098), FDR-adjusted p = 0.0005]. There were no significant cross-lagged effects of daily changes in social distancing or virtual contact on later mental health.

We delineate a cycle wherein a daily increase in media consumption results in a subsequent increase in COVID-related worries, which in turn increases daily media consumption. Moreover, the adverse impact of news extended to broader measures of psychological struggling. A similar dynamic did not unfold between the daily amount of physical or virtual contact and subsequent mental health. Findings are consistent with current recommendations to moderate news and media consumption in order to promote mental health.

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a classification system that seeks to organize psychopathology using quantitative evidence – yet the current model was established by narrative review. This meta-analysis provides a quantitative synthesis of literature on transdiagnostic dimensions of psychopathology to evaluate the validity of the HiTOP framework.

Published studies estimating factor-analytic models from diagnostic and statistical manual of mental disorders (DSM) diagnoses were screened. A total of 120,596 participants from 35 studies assessing 23 DSM diagnoses were included in the meta-analytic models. Data were pooled into a meta-analytic correlation matrix using a random effects model. Exploratory factor analyses were conducted using the pooled correlation matrix. A hierarchical structure was estimated by extracting one to five factors representing levels of the HiTOP framework, then calculating congruence coefficients between factors at sequential levels.

Five transdiagnostic dimensions fit the DSM diagnoses well (comparative fit index = 0.92, root mean square error of approximation = 0.07, and standardized root-mean-square residual = 0.03). Most diagnoses had factor loadings >|0.30| on the expected factors, and congruence coefficients between factors indicated a hierarchical structure consistent with the HiTOP framework.

A model closely resembling the HiTOP framework fit the data well and placement of DSM diagnoses within transdiagnostic dimensions were largely confirmed, supporting it as valid structure for conceptualizing and organizing psychopathology. Results also suggest transdiagnostic research should (1) use traits, narrow symptoms, and dimensional measures of psychopathology instead of DSM diagnoses, (2) assess a broader array of constructs, and (3) increase focus on understudied pathologies.

Several multivariate algorithms have been developed for predicting psychosis, as attempts to obtain better prognosis prediction than with current clinical high-risk (CHR) criteria. The models have typically been based on samples from specialized clinics. We evaluated the generalizability of 19 prediction models to clinical practice in an unselected adolescent psychiatric sample.

In total, 153 adolescent psychiatric patients in the Helsinki Prodromal Study underwent an extensive baseline assessment including the SIPS interview and a neurocognitive battery, with 50 participants (33%) fulfilling CHR criteria. The adolescents were followed up for 7 years using comprehensive national registers. Assessed outcomes were (1) any psychotic disorder diagnosis (n = 18, 12%) and (2) first psychiatric hospitalization (n = 25, 16%) as an index of overall deterioration of functioning.

Most models improved the overall prediction accuracy over standard CHR criteria (area under the curve estimates ranging between 0.51 and 0.82), although the accuracy was worse than that in the samples used to develop the models, also when applied only to the CHR subsample. The best models for transition to psychosis included the severity of positive symptoms, especially delusions, and negative symptoms. Exploratory models revealed baseline negative symptoms, low functioning, delusions, and sleep problems in combination to be the best predictor of psychiatric hospitalization in the upcoming years.

Including the severity levels of both positive and negative symptomatology proved beneficial in predicting psychosis. Despite these advances, the applicability of extended psychosis-risk models to general psychiatric practice appears limited.

Offspring of parents with major mood disorders (MDDs) are at increased risk for early psychopathology. We aim to compare the rates of neurodevelopmental disorders in offspring of parents with bipolar disorder, major depressive disorder, and controls.

We established a lifetime diagnosis of neurodevelopmental disorders [attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, communication disorders, intellectual disabilities, specific learning disorders, and motor disorders] using the Kiddie Schedule for Affective Disorders and Schizophrenia, Present and Lifetime Version in 400 participants (mean age 11.3 + s.d. 3.9 years), including 93 offspring of parents with bipolar disorder, 182 offspring of parents with major depressive disorder, and 125 control offspring of parents with no mood disorder.

Neurodevelopmental disorders were elevated in offspring of parents with bipolar disorder [odds ratio (OR) 2.34, 95% confidence interval (CI) 1.23–4.47, p = 0.010] and major depressive disorder (OR 1.87, 95% CI 1.03–3.39, p = 0.035) compared to controls. This difference was driven by the rates of ADHD, which were highest among offspring of parents with bipolar disorder (30.1%), intermediate in offspring of parents with major depressive disorder (24.2%), and lowest in controls (14.4%). There were no significant differences in frequencies of other neurodevelopmental disorders between the three groups. Chronic course of mood disorder in parents was associated with higher rates of any neurodevelopmental disorder and higher rates of ADHD in offspring.

Our findings suggest monitoring for ADHD and other neurodevelopmental disorders in offspring of parents with MDDs may be indicated to improve early diagnosis and treatment.

Digital interventions for anxiety disorders are a promising solution to address barriers to evidence-based treatment access. Precise and powerful estimates of digital intervention effectiveness for anxiety disorders are necessary for further adoption in practice. The present systematic review and meta-analysis examined the effectiveness of digital interventions across all anxiety disorders and specific to each disorder v. wait-list and care-as-usual controls.

A systematic search of bibliographic databases identified 15 030 abstracts from inception to 1 January 2020. Forty-seven randomized controlled trials (53 comparisons; 4958 participants) contributed to the meta-analysis. Subgroup analyses were conducted by an anxiety disorder, risk of bias, treatment support, recruitment, location and treatment adherence.

A large, pooled effect size of g = 0.80 [95% Confidence Interval: 0.68–0.93] was found in favor of digital interventions. Moderate to large pooled effect sizes favoring digital interventions were found for generalized anxiety disorder (g = 0.62), mixed anxiety samples (g = 0.68), panic disorder with or without agoraphobia (g = 1.08) and social anxiety disorder (g = 0.76) subgroups. No subgroups were significantly different or related to the pooled effect size. Notably, the effects of guided interventions (g = 0.84) and unguided interventions (g = 0.64) were not significantly different. Supplemental analysis comparing digital and face-to-face interventions (9 comparisons; 683 participants) found no significant difference in effect [g = 0.14 favoring digital interventions; Confidence Interval: −0.01 to 0.30].

The precise and powerful estimates found further justify the application of digital interventions for anxiety disorders in place of wait-list or usual care.

Informant-based questionnaires may have utility for cognitive impairment or dementia screening. Reviews describing the accuracy of respective questionnaires are available, but their focus on individual questionnaires precludes comparisons across tools. We conducted an overview of systematic reviews to assess the comparative accuracy of informant questionnaires and identify areas where evidence is lacking.

We searched six databases to identify systematic reviews describing diagnostic test accuracy of informant questionnaires for cognitive impairment or dementia. We pooled sensitivity and specificity data for each questionnaire and used network approaches to compare accuracy estimates across the differing tests. We used grading of recommendations, assessment, development and evaluation (GRADE) to evaluate the overall certainty of evidence. Finally, we created an evidence ‘heat-map’, describing the availability of accurate data for individual tests in different populations and settings.

We identified 25 reviews, consisting of 93 studies and 13 informant questionnaires. Pooled analysis (37 studies; 11 052 participants) ranked the eight-item interview to ascertain dementia (AD8) highest for sensitivity [90%; 95% credible intervals (CrI) = 82–95; ‘best-test’ probability = 36]; while the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) was most specific (81%; 95% CrI = 66–90; ‘best-test’ probability = 29%). GRADE-based evaluation of evidence suggested certainty was ‘low’ overall. Our heat-map indicated that only AD8 and IQCODE have been extensively evaluated and most studies have been in the secondary care settings.

AD8 and IQCODE appear to be valid questionnaires for cognitive impairment or dementia assessment. Other available informant-based cognitive screening questionnaires lack evidence to justify their use at present. Evidence on the accuracy of available tools in primary care settings and with specific populations is required.

Violence perpetrated by psychiatric inpatients is associated with modifiable factors. Current structured approaches to assess inpatient violence risk lack predictive validity and linkage to interventions.

Adult psychiatric inpatients on forensic and general wards in three psychiatric hospitals were recruited and followed up prospectively for 6 months. Information on modifiable (dynamic) risk factors were collected every 1–4 weeks, and baseline background factors. Data were transferred to a web-based monitoring system (FOxWeb) to calculate a total dynamic risk score. Outcomes were extracted from an incident-reporting system recording aggression and interpersonal violence. The association between total dynamic score and violent incidents was assessed by multilevel logistic regression and compared with dynamic score excluded.

We recruited 89 patients and conducted 624 separate assessments (median 5/patient). Mean age was 39 (s.d. 12.5) years with 20% (n = 18) female. Common diagnoses were schizophrenia-spectrum disorders (70%, n = 62) and personality disorders (20%, n = 18). There were 93 violent incidents. Factors contributing to violence risk were a total dynamic score of ⩾1 (OR 3.39, 95% CI 1.25–9.20), 10-year increase in age (OR 0.67, 0.47–0.96), and female sex (OR 2.78, 1.04–7.40). Non-significant associations with schizophrenia-spectrum disorder were found (OR 0.50, 0.20–1.21). In a fixed-effect model using all covariates, AUC was 0.77 (0.72–0.82) and 0.75 (0.70–0.80) when the dynamic score was excluded.

In predicting violence risk in individuals with psychiatric disorders, modifiable factors added little incremental value beyond static ones in a psychiatric inpatient setting. Future work should make a clear distinction between risk factors that assist in prediction and those linked to needs.