Race/ethnicity and sleep disturbances are associated with dementia risk.

To explore racial–ethnic disparities in sleep disturbances, and whether race/ethnicity moderates the relationship between sleep disturbances and dementia risk among older adults.

We analysed ten annual waves (2011–2020) of prospective cohort data from the National Health and Aging Trends Study, a nationally representative USA sample of 6284 non-Hispanic White (n = 4394), non-Hispanic Black (n = 1311), Hispanic (n = 342) and non-Hispanic Asian (n = 108) community-dwelling older adults. Sleep disturbances were converted into three longitudinal measures: (a) sleep-initiation difficulty (trouble falling asleep within 30 min), (b) sleep-maintenance difficulty (trouble falling asleep after waking up early) and (c) sleep medication usage (taking medication to sleep). Cox proportional hazards models analysed time to dementia, after applying sampling weights and adjusting for sociodemographic characteristics and health.

Black, Hispanic and Asian respondents exhibited higher frequencies of sleep-initiation and sleep-maintenance difficulties, but had less sleep medication usage, compared with White older adults. Among Hispanic respondents, sleep-initiation difficulty was associated with significantly decreased dementia risk (adjusted hazard ratio (aHR) = 0.34, 95% CI 0.15–0.76), but sleep-maintenance difficulty was associated with increased dementia risk (aHR = 2.68, 95% CI 1.17–6.13), compared with White respondents. Asian respondents using sleep medications had a significantly higher dementia risk (aHR = 3.85, 95% CI 1.64–9.04). There were no significant interactions for Black respondents.

Sleep disturbances are more frequent among older Black, Hispanic and Asian adults, and should be considered when addressing dementia disparities. Research is needed to explore how certain sleep disturbances may elevate dementia risk across different racial and ethnic subgroups.

The study evaluated a package of measures to improve sleep on psychiatric wards admitting patients from children and young people's services (CYPS). Sleep disturbance has significant impact on adolescent mental health, and in-patient wards can directly cause sleep disturbance, independent of the problem that led to admission. We developed a CYPS-specific package (TeenSleepWell) that promoted a better sleep environment, enhanced staff education about sleep, screened for sleep disorders, and raised awareness of benefits and side-effects of hypnotics. This included personalised sleep care plans that allowed a protected 8 h sleep period when safe.

Evaluation over 2 years showed enhanced in-patient care: 57% of patients were able to have a protected sleep period. There was no increase in adverse events and there was a decrease in hypnotics issued.

Improving sleep during in-patient CYPS admissions is possible and personalised sleep care plan should be a care standard.

Sleep problems associated with poor mental health and academic outcomes may have been exacerbated by the COVID-19 pandemic.

To describe sleep in undergraduate students during the COVID-19 pandemic.

This longitudinal analysis included data from 9523 students over 4 years (2018–2022), associated with different pandemic phases. Students completed a biannual survey assessing risk factors, mental health symptoms and lifestyle, using validated measures. Sleep was assessed with the Sleep Condition Indicator (SCI-8). Propensity weights and multivariable log-binomial regressions were used to compare sleep in four successive first-year cohorts. Linear mixed-effects models were used to examine changes in sleep over academic semesters and years.

There was an overall decrease in average SCI-8 scores, indicating worsening sleep across academic years (average change −0.42 per year; P-trend < 0.001), and an increase in probable insomnia at university entry (range 18.1–29.7%; P-trend < 0.001) before and up to the peak of the pandemic. Sleep improved somewhat in autumn 2021, when restrictions loosened. Students commonly reported daytime sleep problems, including mood, energy, relationships (36–48%) and concentration, productivity, and daytime sleepiness (54–66%). There was a consistent pattern of worsening sleep over the academic year. Probable insomnia was associated with increased cannabis use and passive screen time, and reduced recreation and exercise.

Sleep difficulties are common and persistent in students, were amplified by the pandemic and worsen over the academic year. Given the importance of sleep for well-being and academic success, a preventive focus on sleep hygiene, healthy lifestyle and low-intensity sleep interventions seems justified.

The effect of long working hours on mental health has drawn great social attention in recent years.

We investigated how work–family conflict mediates the associations between long working hours and sleep disturbance and burnout.

We included 19 159 individuals from a nationally representative sample of workers in South Korea. We decomposed the total effect into a direct effect (long working hours → sleep disturbance or burnout) and an indirect effect (long working hours → work–family conflict → sleep disturbance or burnout). Logistic mediation models were used.

Long working hours were associated with increased risks of work–family conflict, sleep disturbance and burnout. The longer the working hours, the stronger the direct and indirect effects. The odds ratios of the direct effects of long working hours on sleep disturbance were 1.64 (95% CI 1.39–1.95) for 49–54 h/week and 1.66 (95% CI 1.37–2.01) for ≥55 h/week; those of the indirect effects were 1.16 (95% CI 1.12–1.21) for 49–54 h/week and 1.27 (95% CI 1.21–1.33) for ≥ 55 h/week. Similarly, odds ratios of the direct effects of long working hours on burnout were 1.18 (95% CI 1.05–1.33) for 49–54 h/week and 1.20 (95% CI 1.04–1.37) for ≥55 h/week; those of the indirect effects were 1.11 (95% CI 1.09–1.15) for 49–54 h/week and 1.20 (95% CI 1.16–1.24) for ≥55 h/week.

Our results suggest that work–family conflict mediates the associations between long working hours and sleep disturbance and burnout. Longitudinal studies should be followed to confirm the causal relationship.

Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and overlapping nature of symptom endorsement in a population-based sample across three of the most common categories of psychiatric disorders: depressive disorders, anxiety disorders, and sleep–wake disorders using unsupervised machine learning approaches.

We assessed a total of 43 symptoms in a discovery sample of 6,602 participants of the population-based Rotterdam Study between 2009 and 2013, and in a replication sample of 3,005 participants between 2016 and 2020. Symptoms were assessed using the Center for Epidemiologic Studies Depression Scale, the Hospital Anxiety and Depression Scale, and the Pittsburgh Sleep Quality Index. Hierarchical clustering analysis was applied on test items and participants to investigate common patterns of symptoms co-occurrence, and further quantitatively investigated with clustering methods to find groups that may represent similar psychiatric phenotypes.

First, clustering analyses of the questionnaire items suggested a three-cluster solution representing clusters of “mixed” symptoms, “depressed affect and nervousness”, and “troubled sleep and interpersonal problems”. A highly similar clustering solution was independently established in the replication sample. Second, four groups of participants could be separated, and these groups scored differently on the item clusters.

We identified three clusters of psychiatric symptoms that most commonly co-occur in a population-based sample. These symptoms clustered stable over samples, but across the topics of depression, anxiety, and poor sleep. We identified four groups of participants that share (sub)clinical symptoms and might benefit from similar prevention or treatment strategies, despite potentially diverging, or lack of, diagnoses.