Sleep Health最新文献

Objectives: Sleep disturbances can impair cognition, and cognitive impairment can dysregulate sleep-wake cycles. Despite biological plausibility, disparities in the sleep-cognition relationship are understudied in nationally representative samples.

Methods: Using National Health Interview Survey data from 2011-2018, we investigated cross-sectional associations between sleep and cognition among adults aged ≥40 years overall and by age, sex, race, and ethnicity. Participants self-reported sleep duration (short [<7-hours], long [>9-hours]); insomnia symptoms (trouble falling asleep or staying asleep ≥3 times/week); and non-restorative sleep (waking feeling unrested ≥4 days/week). Cognitive impairment was defined as "not being able to/having a lot of difficulty remembering or concentrating" based on the Washington Group Short Set on Functioning. Adjusted Poisson regression with robust variance estimated prevalence ratios (PRs) and 95% confidence intervals.

Results: Among 73,477 adults (mean age ± standard error 58.3 ± 0.07 years), 31.8% reported short sleep, 4.1% long sleep, 35.7% insomnia symptoms, and 35.5% non-restorative sleep. Overall, 2.2% reported cognitive impairment. All sleep dimensions were associated with a higher prevalence of cognitive impairment (range: PR_short-sleep = 1.23[95% confidence interval:1.07-1.42] to PR_long-sleep = 4.10[3.48-4.83]). Short sleep, insomnia symptom, and non-restorative sleep were more strongly associated with cognitive impairment among middle-aged adults. Associations with long sleep were stronger among older adults. Differences by sex were not observed. While the prevalence of cognitive impairment was slightly higher among Hispanic/Latine and Black adults, associations with short sleep were strongest among White adults and among Asian adults for long sleep (p-interaction<0.05).

Conclusions: Sleep disturbances were associated with cognitive impairment. Most associations were stronger among middle-aged adults, although future studies with objective measures are warranted.

Objectives: This study investigates the association between sleep and precarious working conditions in the digital platform economy. Specifically, we examine how six dimensions of employment precariousness are associated with the likelihood of experiencing sleep problems among online platform workers (OPW).

Methods: We analyze survey data from German-speaking OPW (N = 1969). Sleep outcomes are measured using the Jenkins Sleep Scale (JSS-4), and precariousness is captured by six factor scores derived from an exploratory factor analysis, based on adapted questionnaire items from the Employment Precariousness Scale (EPRES). Logistic regression models with average marginal effects are used to estimate associations, controlling for sociodemographic and work-related characteristics.

Results: Among the six dimensions of precariousness, two are robustly associated with higher likelihood of reporting sleep problems: Time poverty (the inability to take breaks and leave when needed) and vulnerability (reflecting opaque rating and payment systems). A typical variation across each of these scales corresponds to a 7.7%-8.1% increase in the probability of reporting elevated sleep problems.

Conclusions: Our findings highlight that platform-related working conditions, particularly opaque management systems and limited ability to take time off, are important correlates of sleep problems among OPW. Addressing these factors may contribute to improving sleep health in this growing segment of the workforce.

Introduction: Sleep disorders are a major public health issue, and urban noise may contribute to their prevalence. However, no large-scale geospatial studies have specifically examined the relationship between sleep quality and noise in urban settings.

Methodology: This cross-sectional study, conducted in Geneva, Switzerland, between June and September 2023, involved 3691 participants (mean age 54.84 years, 59.7% women). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while nighttime traffic noise exposure was measured using the sonBASE georeferenced database from the Swiss Federal Office for the Environment. Spatial statistics (Getis-Ord Gi*) were employed to identify spatial clusters of sleep disturbances and noise exposure.

Results: Analysis revealed that sleep quality issues and associated indicators were not randomly distributed. Spatial patterns were observed in both subjective noise perception and objective noise measures.

Conclusion: Poor sleep quality in urban environments is spatially correlated with noise exposure, suggesting the need for targeted interventions to mitigate these effects.

Objectives: Attention-deficit/hyperactivity disorder (ADHD) has been linked to sleep disturbances. There is little evidence of this association among a nationally representative sample of school-aged children. This study explored the association between current ADHD diagnosis and short sleep duration among U.S. school-aged children. Parent-reported severity of ADHD and short sleep duration among those with a current ADHD diagnosis was also assessed.

Methods: A secondary analysis of 2020-2021 National Survey of Children's Health (NSCH) data including 26,148 U.S. children ages 6-11 years was performed. Sleep duration was categorized using the National Sleep Foundation's age-specific recommendations defining <9 hours as short sleep duration. Weighted unadjusted and adjusted logistic regression models were performed. The adjusted models included child age, sex, race/ethnicity, health status, overweight status, screen time, physical activity, parent education, family structure, and federal poverty level.

Results: Nearly 10% of children currently had an ADHD diagnosis, with 62% having moderate or severe ADHD. Over one-third (35%) had reports of short sleep duration. Unadjusted (odds ratio [OR] = 1.58, 95% confidence interval [CI] = 1.35-1.84) and adjusted (adjusted OR [AOR] = 1.25, 95%CI = 1.06-1.47) results indicated that children with ADHD were at increased odds of having short sleep duration compared to children without ADHD. Among children with ADHD only, unadjusted (OR = 1.70, 95%CI = 1.26-2.31) and adjusted (AOR = 1.61, 95%CI = 1.19-2.19) results indicated that children with a moderate or severe diagnosis were at increased odds of having short sleep duration than children with a mild diagnosis.

Conclusion: ADHD may influence short sleep duration among U.S. school-aged children. Behavioral sleep treatments should aim toward improving sleep duration among this population.

Objectives: The potential reasons why long sleepers are at increased risk, including cardiovascular events and mortality, are intriguing. This study aimed to explore independent variables related to long (primary aim) and short sleep duration (SDUR) (secondary aim) using objective measurements in a large sample of adults.

Methods: Participants from the ELSA-Brasil study performed a standard clinical evaluation, sleep questionnaires, a home sleep monitoring to determine obstructive sleep apnea (OSA; apnea-hypopnea index ≥15 events/hour), and a 7-day actigraphy to measure SDUR. A multinomial regression analysis was used to identify the predictors of long (≥8 hours) and short (<6 hours) SDUR relative to the reference group (6 to <8 hours).

Results: A total of 2062 participants were included in the analysis (age: 49 ± 8 years; 57.3% women). A third of them had OSA. The mean SDUR was 6.55 ± 0.49 hours. Long and short SDUR were observed in 6.5% and 26.9%, respectively. In the multinomial logistic regression analysis, older individuals (OR = 1.36; 95% CI 1.07-1.75) and depression disorder (OR = 2.59; 95% CI 1.26-5.3) were independently associated with long SDUR. In contrast, the independent variables associated with short SDUR were male sex (OR = 1.9; 95% CI 1.51-2.4), Black participants (OR = 1.75; 95% CI 1.29-2.36), excessive daytime sleepiness (OR = 1.49; 95% CI 1.20-1.86), and OSA (OR = 1.48; 95% CI 1.16-1.88).

Conclusions: In a large multiethnic cohort, we observed significant heterogeneity in the long and short SDUR predictors. Important characteristics such as age and depressive disorder might provide tips for the curious higher cardiovascular risk associated with long SDUR.

Objective: We investigated whether kindergarten bedtime and later sleep duration predicted executive function (EF), behavior, and academic achievement in middle childhood, and whether associations varied by socioeconomic status (SES), race or ethnicity, and disability. By accounting for the independent effects of these factors, findings reflect the unique associations between early sleep patterns and later developmental outcomes.

Methods: Data were drawn from the nationally representative Early Childhood Longitudinal Study, Kindergarten Cohort of 2010-11 (N = 6945). Structural equation modeling (SEM) tested whether kindergarten bedtime (age 5, 2010-2011) predicted third-grade sleep duration (age 9), which in turn influenced fourth-grade EF and behavior (age 10), and fifth-grade academic test scores (age 11, 2015-2016). We examined whether sleep duration mediated bedtime's association with EF, behavior, and achievement; whether EF and behavior mediated sleep duration's association with achievement; and whether these associations were also moderated by SES, disability, and race or ethnicity.

Results: Bedtime predicted duration, which predicted higher achievement through effects on cold EF (i.e., emotionally neutral cognitive processes like working memory and cognitive flexibility). These indirect effects were observed across racial or ethnic and SES groups but became nonsignificant after adjusting for covariates. Although cold EF was the strongest predictor of achievement, duration remained a small but significant contributor.

Conclusions: Disparities in achievement by SES and race or ethnicity may be driven by structural factors beyond sleep duration or bedtime. Promoting early bedtime routines is important, but broader systemic interventions are needed to close achievement gaps.

Objective: To examine how load shedding (scheduled power outages) affects multiple dimensions of sleep health, associated behaviors, and lived experiences among South African adults.

Methods: This study used a convergent mixed-method cross-sectional design. Data were collected through an online survey, that included demographic questions, the RU SATED questionnaire, structured items evaluating sleep-related behavioral changes, and an open-ended question for qualitative insights. A total of 173 participants experiencing load shedding in South Africa completed the survey between May and August of 2023. Quantitative analyses comprised nonparametric tests and multiple linear regression, whereas qualitative responses were subjected to thematic analysis. Data integration was achieved through joint display and thematic triangulation.

Results: Overall sleep health scores significantly declined with increasing load shedding severity (p = .023), particularly affecting sleep satisfaction (p < .001) and duration (p = .002). Participants exposed to stage six load shedding demonstrated significantly poorer sleep health scores compared to those exposed to lower stages (p < .001). Two themes were developed through qualitative analysis: (1) disruption of sleep quality due to environmental disturbances (noise, light, and temperature), and (2) emotional and psychological stress (anxiety, and safety concerns). Structured survey items further indicated disruptions to sleep continuity, regularity and duration.

Conclusion: In this study, load shedding affected multiple dimensions of sleep health through environmental disruption, psychological strain, and adaptive behavioral responses. These preliminary findings highlight the need for replication in diverse populations using objective measures and suggest that integrating sleep health considerations into energy policy may be warranted.

Objectives: Few studies have examined sleep health among African adolescents. We aimed to understand sleep health among Ugandan secondary school students.

Methods: We collected quantitative data in two schools through a survey with items on sleep health and insomnia (using the Cleveland Adolescent Sleepiness Questionnaire, Munich Chronotype Questionnaire and Insomnia Severity Index [ISI]) and mental health with the UNICEF Measuring Mental Health Among Adolescents and Young People at the Population Level (MMAPP) tool. We used regression models to assess characteristics associated with ISI score, and of sleep health with depression and anxiety. We conducted focus group discussions and in-depth interviews with students, parents, teachers, and officials. Quantitative and qualitative analyses were guided by the social ecological model of sleep health.

Results: The 358 participants generally reported poor sleep health (assessed by satisfaction, alertness, timing, efficiency and duration), especially among boarding students. The median sleep duration was 5.1 hours (interquartile range 4.2-6.2). Overall, 36 (10.1%) participants screened positive for moderate/severe insomnia (ISI ≥15), with higher prevalence among females than males (12.7% vs. 6.2%; p = .05). Qualitative interviews highlighted that individual (knowledge and attitudes), social-cultural (religious beliefs, family dynamics, academic demands, peer pressure), environmental (school and home conditions, technological influences), and societal factors (national school schedule guidelines) influenced sleep patterns. Depression and anxiety were associated with multiple dimensions of poor sleep health.

Conclusions: Ugandan adolescents face substantial sleep challenges, which are associated with poor mental health. Evidence-based interventions should be adapted for specific social-ecological contexts to improve sleep and mental health in this population.

Objectives: Daily temperature variability is projected to increase in some world regions up to 100% by 2100. This study investigated associations between night-to-night temperature variability with sleep duration timing and regularity.

Methods: Data were collected from global users of the Withings Sleep Analyzer (n = 125,295) and ScanWatch (n = 238,550) between January 2020-September 2023. Sleep regularity was assessed using intra-monthly variation in sleep duration and timing (onset, midpoint and offset). Ambient temperature data were extracted from established climate models to calculate night-to-night temperature variability. Fixed-effects models were used to assess the association between night-to-night temperature variability and sleep regularity (duration and timing).

Results: Participants (n = 313,822) were middle-aged (50 ± 15 years), 70% male, with an average 17 months of data. During months with high vs. low night-to-night temperature variability (99th percentile: 4.3°C vs. 50th percentile: 1.9°C), sleep onset variability was higher in southern (mean [95%CI]; 17.9 [16.1, 19.6]min), central (6.5°C vs. 1.0°C; 32.2 [29.8, 34.6]min) and northern locations (5.5°C vs. 2.1°C; 11.4 [11.2, 11.7]min). There was greater variability in sleep midpoint and offset with higher night-to-night temperature variability, with similar effect sizes. High night-to-night temperature variability was associated with <10 minutes difference in sleep duration irregularity.

Conclusions: Our findings highlight that high night-to-night temperature variability is associated with a 20%-50% increase in sleep timing irregularity, with central and southern regions being more affected. Given that sleep irregularity is associated with adverse health outcomes, targeted interventions to mitigate the impact of temperature variability on sleep health is an important public health priority.

Introduction: We previously developed a brief, behavioral intervention (Sleep Promotion Program, SPP) that demonstrated feasibility, acceptability, and increased self-reported sleep. Here, we tested the effects of SPP on the trial's secondary outcomes: sleep duration, regularity, and timing as measured by actigraphy and conducted a secondary analysis of SPP's effects on rest-activity rhythms.

Methods: Forty participants (ages 13-15) with insufficient and irregular sleep randomized to SPP-continuation (n = 22), or to sleep monitoring-SPP (n = 18), had analyzable actigraphy data. SPP included sleep psychoeducation and one individual clinician session. Participants wore an actigraph for 1-2 weeks during baseline, Period 1, and Period 2. We hypothesized SPP would lead to longer and more regular sleep from baseline to Period 1 compared to sleep monitoring that would be sustained at Period 2. We used multilevel models with a treatment by time interaction to test whether SPP improved sleep relative to sleep monitoring. We also compared sleep changes from Period 1 to Period 2 among participants randomized to SPP-continuation.

Results: Participants receiving SPP decreased the difference in weekend-weekday sleep onset by 54-minute (β = 1.1; p = .03) from baseline to Period 1 (i.e., increased regularity). Increased regularity was maintained at Period 2. Total sleep time did not change significantly.

Conclusion: SPP regularized weekend-weekday sleep onset timing in adolescents with insufficient and irregular sleep but did not change actigraphic sleep duration, which differed from increases in self-reported sleep duration previously reported. Regularizing sleep may be an attainable first step to improving adolescent sleep. Future work is needed to test whether sleep regularity predicts adolescent health outcomes.

Clinical trials: Targeted Intervention for Insufficient Sleep among Typically-Developing Adolescents (TAPAS); https://clinicaltrials.gov/ct2/show/NCT04163003; NCT04163003.