Sleep Health最新文献

Background: Social inequalities in sleep have been reported, but there is less research on the mechanisms underlying this association. This study investigates the relationship between financial hardship and sleep within the general adult population, focusing on the mediating effects of psychosocial and lifestyle-related factors.

Methods: We used data from the Specchio cohort, a population-based study in Geneva, Switzerland, initiated in December 2020. Perceived financial hardship and sleep outcomes (insomnia, sleep quality, and sleep duration) were assessed by questionnaire in 2020 to 2021. Counterfactual mediation analysis was conducted to examine the extent to which perceived financial hardship impacts sleep through psychosocial (psychological distress and loneliness) and lifestyle-related (weight, smoking, and physical inactivity) pathways. Models were adjusted for age, sex, education, living alone, and chronic disease.

Results: Among 4388 participants, those experiencing financial hardship had a greater risk of insomnia (odds ratio: 2.11; 95% confidence interval: 1.70-2.61), poor sleep quality (odds ratio: 1.69; 95%confidence interval: 1.41-2.02), and not meeting sleep duration guidelines (odds ratio: 1.40; 95% confidence interval: 1.18-1.66) compared to those without financial difficulties. Psychosocial factors explained 40% of the relationship of financial hardship with insomnia, 35% of the relationship with poor sleep quality, and 10% of the association with suboptimal sleep duration. The contribution of lifestyle-related factors was 8%, 12%, and 17%, respectively.

Conclusion: Perceived financial hardship is a significant predictor of poor sleep, and this association is mediated by psychosocial and, to a lesser extent, lifestyle-related factors. These findings highlight the need for integrative approaches addressing social inequalities in sleep.

Background: Previous research on the interaction of physical activity and sleep on depressive symptoms was mostly cross-sectional or conducted with children or young adults. This study examines the main and interactive associations of physical activity and sleep duration with depressive symptoms over a 3-year period among middle-aged and older Chinese adults.

Methods: Data from 4269 Chinese adults aged 45 or older from the China Health and Retirement Longitudinal Study (CHARLS) were used. Physical activity was categorized as inadequate (<600 MET), adequate (600-8000 MET), and extremely high (>8000 MET). Sleep was classified as inadequate (<6 hours), adequate (6-9 hours), and excessive (>9 hours). The Center for Epidemiologic Studies Depression Scale (CES-D) was used to measure depressive symptoms.

Results: Inadequate sleep was linked to greater increases in depressive symptoms over 3years. A significant interaction between baseline physical activity and sleep duration in predicting depressive symptoms at the 3-year follow-up showed that inadequate sleep, when combined with either inadequate or extremely high physical activity, was associated with higher depressive symptoms at the 3-year follow-up. In middle-aged subgroups, for people with either inadequate physical activity or an extremely high level of physical activity, inadequate sleep was associated with higher CES-D score compared to adequate sleep; for older adults, only inadequate sleep was associated with a higher follow-up CES-D score.

Conclusion: Physical activity and sleep interactively impacted depressive symptoms, suggesting future personalized interventions that simultaneously target physical activity and sleep. Adequate sleep was associated with lower levels of future depressive symptoms in people with inadequate or extremely high physical activity.

Objectives: Children with asthma living in urban environments are at risk for disrupted sleep due to the presence of nocturnal asthma symptoms and urban stressors. Suboptimal sleep can affect children's daily functioning. The current study examined the effects of experimental sleep disruption on daytime performance in children with persistent asthma from urban backgrounds.

Method: Twenty-four children (8-10 years old) with asthma living in urban environments participated in an experimental, laboratory-based sleep disruption protocol. Children completed a baseline night consisting of uninterrupted sleep, followed by a disruption night, with 2-minute arousals every 20 minutes of sleep. Sleep and sleep disruptions were monitored via polysomnography. Daytime performance measurements (Psychomotor Vigilance Task; Daytime Sleepiness, child- and caregiver-report) were evaluated at baseline and after sleep disruption using t-tests and percent change calculations.

Results: No significant differences in attention or daytime sleepiness were observed between the uninterrupted night of sleep and the disrupted night of sleep (p-values >.05). Percent change calculations showed that children demonstrated poorer attention (decreased response speed; increased reaction time, lapses, total errors, false starts) and more daytime sleepiness (caregiver- and child-report) following a night of sleep disruption compared to an uninterrupted night of sleep. Gender and racial/ethnic group differences in outcomes were also examined.

Conclusions: Children with asthma living in urban environments may be at risk for sleep disruption and impaired daytime functioning. More experimental sleep research with larger samples is necessary to further explore these associations.

Study objectives: Sleep is essential for proper function of the mind and body. Studies report the effect of sleep problems on cognition but focus on only a single or limited number of sleep indicators or on clinical populations (e.g., sleep apnea), and/or provide only cross-sectional results. This study examined cross-sectional and longitudinal associations between multidimensional assessment of sleep health and cognitive function.

Methods: 3398 adults (M_age=56years) provided self-reported sleep and objective cognitive data for the Midlife in the United States study. A subsample of 2119 participants also provided sleep and cognitive data at follow-up approximately 9years later. A multidimensional, composite measure of sleep health composed of regularity, satisfaction, alertness, efficiency, and duration based on the Ru-SATED model was utilized (higher score=better sleep health) to evaluate self-reported sleep, and cognitive function was assessed using the Brief Test of Adult Cognition by Telephone.

Results: Cross-sectionally, better sleep health was associated with better cognition (B=0.121, SE=0.017, p<.001). This relationship remained significant even after adjusting for sociodemographic and health covariates (B=0.039, SE=0.014, p=.006). Longitudinally, improvement in sleep health from baseline to follow-up was associated with better cognitive performance at follow-up (B=0.031, SE=0.011, p=.004); however, this relationship did not remain significant after adjusting for covariates (B=0.015, p=.139).

Conclusion: Findings suggest better sleep health measured across multiple domains is associated with higher cognitive function. Future studies may want to examine potential mechanisms by which better sleep health relates to better cognitive function over time, such as reduction in stress or inflammation.

Objectives: Insufficient sleep is linked to various health issues, while physical activity is a protective measure against chronic diseases. Despite the importance of sleep and physical activity for supporting public health, there remains scant research investigating daily and cumulative associations between objectively measured physical activity and sleep. Understanding the associations of physical activity and sleep behaviors over multiple days may inform the efficacy of interventions to synergistically support both behaviors.

Method: Data were from the Community of Mine study (N=367 with complete data). Participants wore ActiGraph GT3X+ accelerometers on their wrist and hip for 14days. Sleep was defined as total sleep time (h/night), wakefulness after sleep onset (min), and sleep efficiency (%). Moderate to vigorous physical activity was defined as ≥760 counts per minute. Mixed-effects linear models with distributed lag effects, adjusted for age, Hispanic/Latino ethnicity, body mass index, education, smoking, and residence type, investigated the effect of sleep on prospective moderate to vigorous physical activity (and moderate to vigorous physical activity on prospective sleep): on the same or previous day, 2-day lag, and 3-day lag.

Results: An increase in same day, 2-day lag, and 3-day lag moderate to vigorous physical activity was associated with decreased total sleep time. Moderate to vigorous physical activity was not associated with sleep efficiency or wakefulness after sleep onset. An increase in same day and 3-day lag of total sleep time was associated with decreased moderate to vigorous physical activity. An increase in 3-day lag sleep efficiency was associated with decreased moderate to vigorous physical activity. wakefulness after sleep onset was not associated with moderate to vigorous physical activity.

Conclusions: These insights contribute to understanding the dynamic interplay between moderate to vigorous physical activity and sleep in adults, highlighting same day and cumulative associations.

Goal and aims: One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a machine learning algorithm supplemented by dynamic features to discern wear/nonwear episodes.

Focus technology: Actigraphy data from wrist actigraph (Spectrum, Philips-Respironics).

Reference technology: The built-in nonwear sensor as "ground truth" to classify nonwear periods using other data, mimicking features of Actiwatch 2.

Sample: Data were collected over 1week from employed adults (n = 853).

Design: Extreme gradient boosting (XGBoost), a tree-based classifier algorithm, was used to classify wear/nonwear, supplemented by dynamic features calculated over various time windows.

Core analytics: The performance of the proposed algorithm was tested over 30-second epochs. Additional analytics and exploratory analyses: Evaluation of the SHapley Additive exPlanations (SHAP) values to find the effectiveness of the dynamic features.

Core outcomes: The XGBoost classifier yielded substantial improvements in balanced accuracy, sensitivity, and specificity, including dynamic features and comparison to default actiwatch classification algorithms.

Important supplemental outcomes: The proposed classifier effectively distinguished between valid and invalid days, and the duration of contiguous periods of nonwear correctly identified.

Core conclusion: Our findings highlight the potential of XGBoost using dynamic features of varying activity levels across the time series to provide insights on wear/nonwear classification using a large dataset. The methodology provides an alternative to laborious manual benchmarking of the data for similar devices that do not have a nonwear sensor.

Objectives: Drawing on the socioecological model of sleep health, we formally examine the association between neighborhood disorder and sleep efficiency. While most studies focus on direct associations with neighborhood context, we also consider whether the relationship between religious attendance and sleep efficiency varies as a function of neighborhood disorder.

Design: We use ordinary least squares regression to model cross-sectional survey data.

Setting: The United States.

Participants: The All of Us Research Program is based on a nonprobability sample of 5168 adults aged 18 and over.

Measurements: Our analyses include an index of perceived neighborhood disorder, a single-item measure of religious attendance, and an objective measure of sleep efficiency based on wrist actigraphy.

Results: While perceptions of neighborhood disorder are inversely associated with sleep efficiency, religious attendance is positively associated with sleep efficiency. The association between religious attendance and sleep efficiency did not vary across levels of neighborhood disorder.

Conclusion: Our analyses add to a growing literature on the association of neighborhood disorder with objective indicators of sleep health. To our knowledge, we are among the first to observe any association between religious attendance and sleep efficiency. We extended the socioecological model of sleep health by framing neighborhood disorder as a moderator.

Objectives: Long work hours and weekend work can negatively impact worker sleep; however, gender differences in these relationships have not been sufficiently studied. We explored the association between long work hours, weekend work, and insomnia symptoms, as well as the moderating effect of gender on this association.

Methods: A nationwide sample of 42,476 Korean workers (52.8% women) was analyzed. The main exposure variables were weekly work hours and monthly weekend days worked. Insomnia symptoms were measured using the Minimal Insomnia Symptom Scale. Logistic regression was used to estimate odds ratios (ORs) and confidence intervals (CIs).

Results: Among the study sample, 10.5% worked ≥55hours weekly and 9.6% worked ≥5 weekend days monthly. The OR (95% CI) of an association between long work hours and insomnia symptoms was 1.72 (1.48-2.00) for 49-54hours, and 2.01 (1.71-2.37) for ≥55hours among men and 1.26 (1.03-1.55) for 49-54hours, and 1.03 (0.83-1.27) for ≥55hours among women. The OR (95% CI) of an association between monthly weekend days worked and insomnia symptoms was 1.68 (1.50-1.90) for 1-4days and 1.92 (1.62-2.29) for ≥5days among men and 1.20 (1.05-1.36) for 1-4days and 1.54 (1.28-1.86) for ≥5days among women.

Conclusion: Long work hours and weekend work are associated with insomnia symptoms, and this association is more pronounced among men than women. Policy interventions are warranted to reduce the burden of long work hours and weekend work.

Objectives: To investigate potential sleep inequities between the infants of Māori and non-Māori mothers in Aotearoa New Zealand, identify socio-ecological factors associated with infant sleep, and determine features of infant sleep that contribute to a mother-perceived infant sleep problem.

Design: Secondary analysis of longitudinal data from the Moe Kura: Mother and Child, Sleep and Well-being in Aotearoa New Zealand study when infants were approximately 12 weeks old.

Participants: 383 Māori and 702 non-Māori mother-infant dyads.

Methods: Chi-square and independent t-tests measured bivariate associations between maternal ethnicity and infant sleep characteristics. Multivariable and ordinal logistic regression models assessed the relative impact of different socio-ecological factors on infant sleep outcome variables.

Results: Key developmental markers of infant sleep did not differ by maternal ethnicity. There were some ethnicity-based differences in sleep location. Maternal ethnicity, maternal age, parity, maternal depression, maternal relationship status, life stress, breastfeeding, work status, and bedsharing were related to different dimensions of infant sleep, and to maternal perceptions of a sleep problem.

Conclusion: Sleep at 12weeks is highly variable between infants and is associated with numerous socio-ecological factors. Findings support a social determinants explanation for sleep health inequities seen later in childhood.