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This study aimed to examine the association between electronic device use duration and depressive symptoms among Chinese college students, while also exploring the mediating role of sleep onset time. Data were collected from university students in Xuzhou City, Jiangsu Province, China. Statistical analyses were conducted using STATA 17.0. Ordinary least squares (OLS) regression was employed for both univariate and multivariate analyses. Additionally, a stepwise regression approach was used to assess the mediating effect of sleep onset time. A total of 1,160 valid cases were included in the final analysis. The regression results indicated a significant positive association between electronic device use duration and depressive symptoms, even after adjusting for all control variables. Furthermore, stepwise regression analysis confirmed that sleep onset time partially mediated the relationship between electronic device use and depressive symptoms. Sleep onset time serves as a mediating factor in the link between electronic device use and depressive symptoms. Interventions aimed at promoting healthier lifestyle habits, such as reducing screen time and encouraging physical activity, may help improve the mental well-being of college students.

Introduction Fertility outcomes are increasingly influenced by modern lifestyle factors, including sleep behaviors. However, the relationship between sleep and time to pregnancy (TTP) is underexplored. Methods We conducted a prospective cohort study of 1,684 couples in Guangzhou, China. Sleep behaviors were assessed via structured interviews. Cox proportional hazards models were used to estimate fecundability ratios (FRs), adjusting for potential confounders. Sleep-wake regularity was assessed for all women. Among those with regular patterns ( n  = 1506), we further analyzed sleep duration, bedtime, perceived sleep sufficiency, and insomnia. Results Among all participants, 178 (10.6%) had irregular sleep. Time-varying models revealed that compared to regular sleepers, irregular sleepers exhibited a decreasing fecundability ratio (FR < 1) after approximately 2.6 months of attempting pregnancy, with the association becoming statistically significant after 4.1 months. In women with regular sleep, longer sleep duration was associated with higher fecundability (adjusted FR = 1.18, 95% CI: 1.09–1.27; p  < 0.001). Spline analysis indicated a linear increase in fecundability with sleep durations exceeding 7.5 h. Perceived insufficient sleep was linked to reduced fecundability (adjusted FR = 0.62, 95% CI: 0.48–0.81; p  < 0.001), while later bedtime was associated with lower fecundability (adjusted FR = 0.91, 95% CI: 0.84–0.98; p  = 0.045). Insomnia showed no significant effect (adjusted FR = 0.86, 95% CI: 0.67–1.11, p  = 0.241). Conclusions Irregular sleep patterns may reduce fecundability over time. Among women with regular sleep, longer duration, earlier bedtime, and sufficient perceived sleep were associated with improved reproductive potential. Sleep optimization could be a modifiable behavioral target to enhance fertility. Trial registration ChiCTR2300068809 registered 1/3/2023.

Disturbances in circadian rhythms cause several health problems, such as psychosis, metabolic syndrome, and cancer; however, their effect on kidney disease remains unclear. This study aimed to evaluate the association between chronic kidney disease (CKD) and sleep disturbance in a Korean adult population. A total of 17,408 participants who completed the National Health and Nutrition Examination Survey from 2016 to 2018 were assessed for their sleep patterns and renal function. CKD was defined as an estimated glomerular filtration rate ≤ 60 mL/min/1.73 m² or a positive dipstick urinalysis. Sleep onset time and sleep duration showed significant differences between the control and CKD groups (p < 0.001). After adjusting for the covariates, sleep onset time rather than sleep duration was independently associated with incidence of CKD, and this association was more significant in people who were older, in women, and in those with low body mass index and no comorbidities. When comparing the prevalence of newly diagnosed CKD according to sleep onset time in a population with no CKD risk factors or no history of CKD, the early bedtime group showed an independent association with incidence of new CKD (odds ratio (OR), 1.535; 95% confidence interval (CI), 1.011–2.330) even after adjusting for covariates. Impaired circadian rhythm along with sleep disturbance could be associated with CKD development; therefore, sleep disturbance might be an important therapeutic target for CKD.

Given the rapid development of the digital economy and the sustained proliferation of the internet, digital engagement in older adults has garnered mounting attention from the academic community. However, research has yet to systematically examine the impact of digital engagement on sleep in this demographic. This study aims to examine the association of digital engagement-operationalized as digital access and internet use duration-with the sleep schedules (nocturnal sleep duration, afternoon nap duration, and sleep onset time) of older adults in China, using longitudinal data and robust statistical modeling to explore longitudinal associations and potential mechanisms. Data were derived from 4 waves (2014, 2016, 2018, and 2020) of the China Family Panel Studies, involving 16,784 older adults (≥60 y). We used panel fixed effects models and a random-effects ordered logit model to analyze the effects on continuous outcomes (nocturnal and nap sleep duration), controlling for time-invariant individual characteristics. As sleep onset time is an ordinal variable, a random-effects ordered logit model was used for this outcome. Moderation analyses were conducted by introducing interaction terms (digital engagement×sex and digital engagement×residence) into the models to examine heterogeneity across subgroups (urban or rural, men or women). Mediation analyses were performed using the Sobel test with year-fixed effects and the nonparametric bootstrap method (1000 resamples) to assess the significance of indirect effects via mechanistic pathways (nonfarm employment, protein intake, memory, depressive mood, and instrumental activities of daily living). The study included a total of 16,784 older adults, with an average age of 69 (SE 6.946) years, including 9100 (54.22%) women and 7684 (45.78%) men. The results showed that both digital access (β=-.15, 95% CI -.25 to -.06; P=.002) and internet use time (β=-.07, 95% CI -.13 to -.01; P=.027) were significantly associated with significantly shorter sleep duration of older adults. Digital access was significantly associated with a significant reduction in the length of afternoon naps among older adults, while internet use did not have this effect; both digital access and internet use were significantly associated with a significant delay in older adults' sleep onset time. Digital access was associated with older adults' sleep schedules through its correlations with nonfarm employment, protein intake, memory, depressive mood, and instrumental activities of daily living. Digital access had a greater and more significant impact on men and urban older adults, while internet use had a greater and more significant impact on women and urban older adults. The study indicates that digital engagement, such as the use of electronic devices, is associated with a reduction in both daily and nap sleep duration, as well as a delay in sleep onset, among older adults.

Adolescent suicide has emerged as a global public health concern. Among various risk factors for suicide, sleep-particularly sleep timing-is valuable for its modifiability. However, the relationship between sleep timing and suicidality remains unclear. This study aimed to explore the association between sleep timing and suicidal ideation among Korean adolescents. This cross-sectional study analyzed data from 759,350 adolescents who participated in the Korea Youth Risk Behavior Web-based Survey from 2007 to 2019. Self-reported weekday sleep timing (categorized by \"go-to-bed\" times from 8 PM to 3 AM) and suicidal ideation were analyzed. Hierarchical multivariable logistic regression was performed to evaluate the association between sleep timing and suicidal ideation, adjusting for potential confounders, including sleep duration, sleep quality, and depressive mood. Of the 759,350 adolescents, 17.4% reported suicidal ideation. Using 11 PM as the reference, both the early sleep timing (8 PM: OR = 1.54, 95% CI = 1.29-1.84) and late sleep timing (3 AM: OR = 2.17, 95% CI = 2.09-2.26) were associated with higher odds of suicidal ideation, demonstrating a J-shaped relationship. This independent association remained after adjusting for sociodemographic factors, sleep duration, sleep quality, and depressive mood. The J-shaped pattern appeared consistently across sex and school level. Age-stratified analyses also showed a consistent J-shaped pattern across all ages, with the nadir shifting slightly later with age (from 10 PM in younger adolescents to midnight in older adolescents). A J-shaped association between sleep timing and suicidal ideation was observed after controlling for sleep duration, sleep quality, and depressive mood. These findings indicate that sleep timing may serve as a candidate behavioral marker associated with suicidal ideation. Further longitudinal and intervention studies are warranted to confirm temporality and clarify underlying mechanisms.

Background Late sleep onset time (SOT) is a common social phenomenon in modern society, and it was associated with a higher risk of obesity. However, the literature gap exists about the SOT and cardiometabolic biomarkers which closely associated with obesity. The present study aimed to explore the association of SOT with cardiometabolic biomarkers in Chinese communities. Methods A cross-sectional study enrolled a total of 2418 participants was conducted in Ningxia province of China. The cardiometabolic biomarkers included triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein and fasting plasma glucose were measured quantitatively using the standard method. The SOT and sleep duration were acquired by a self-report questionnaire. The multiple mixed-effect linear regression model was employed to examine the association. Results Binary analysis found an inverse association of SOT with high-density lipoprotein (β = − 0.05, 95% CI : − 0.06, − 0.03), with 1 h delayed in SOT the high-density lipoprotein decreased 0.05 mmol/L. After controlling for demographic variables, health-related behaviors, and physical health covariates, late SOT was associated with a higher level of triglyceride (β = 0.12, 95% CI : 0.06, 0.18), a higher level of low-density lipoprotein (β = 0.06, 95% CI : 0.02, 0.09), and a lower level of high-density lipoprotein (β = − 0.05, 95% CI : − 0.06, − 0.03). when stratified by sleep duration (less than eight hours vs. eight and longer hours), a positive association between SOT and LDL (β = 0.08, 95% CI : 0.04, 0.12) was found among participants with sleep duration eight hours and longer. Conclusion Late sleep onset time with the negative effect on the cardiometabolic biomarkers, and individuals with late SOT coupled with longer sleep duration may take risk of a higher level of low-density lipoprotein which in turn lead to increase the risk of cardiovascular disease.

Background The link between sleep onset time and depression is not well understood. We aimed to investigate the association of sleep onset time with depression. Methods Data from the 2015 to March 2020 National Health and Nutrition Examination Survey were analyzed. Sleep onset time was categorized into five intervals: [22:00–23:00), [23:00–00:00), [00:00–01:00), [01:00–20:00), and [20:00–22:00). Depression was assessed using the Patient Health Questionnaire. Multivariate logistic regression and generalized linear regression analyses were conducted to evaluate the association of sleep onset time with depression. Results The study sample consisted of 6991 adults (weighted mean age 45.6 years [SE, 0.5]; 49.5% female). Depression prevalence varied by sleep onset time intervals: 4.12% for [00:00–01:00), 5.94% for [01:00–20:00), 3.89% for [20:00–22:00), 1.98% for [22:00–23:00), and 3.26% for [23:00–00:00). After adjusting for sleep duration and other covariates, the odds ratios (ORs) for depressive symptoms were significantly greater at sleep onset time during [01:00–20:00) (OR, 2.39; 95% CI 1.20–4.74) and marginally higher at [20:00–22:00) (OR, 1.78; 95% CI 0.99–3.20) compared to the sleep onset time during [22:00–23:00). Higher PHQ-9 scores were associated with sleep onset time outside [22:00–23:00). Conclusion Sleep onset time between [22:00–23:00) was associated with the lowest odds of depression. This suggests new directions for depression research and interventions, emphasizing the importance of considering sleep onset time in mental health strategies.

Background: While sleep duration’s association with stroke is established, the combined influence of sleep onset time and duration on stroke subtypes remains inadequately explored. Since circadian biology links sleep onset timing to vascular risk via mechanisms operating independently of sleep duration, we quantified their joint contributions to the risk of stroke. Methods: In this population-based cross-sectional study, from 31 December 2021 to 31 March 2022, we recruited 8168 ischemic stroke cases, 3172 intracerebral hemorrhage cases, and 13,458 control participants across 152 survey centers in 28 counties in Hunan Province, China. Standardized computer-assisted interviews collected sleep parameters. Conjoint analysis identified protective sleep profiles. Results: Short sleep duration (<6 h) was consistently associated with elevated ischemic risk across all sleep onset times (p < 0.05 in all cases, i.e., sleep before 10 p.m. [odds ratio (95%CI): 1.886(1.606, 2.214)], 10–11 p.m. [1.740(1.336, 2.265)], 11 p.m.–12 a.m. [2.335(1.190, 4.581)], and after 12 a.m. [2.834(1.193, 6.728)]). A sleep duration of 6–8 h with a sleep onset time between 10 p.m. and 12 a.m. was associated with the lowest ischemic risk (p < 0.001 in all cases). Conversely, prolonged sleep (>8 h) with an early sleep onset time (<10 p.m.) increased ischemic risk (OR 1.194, 95% CI 1.090–1.308, p < 0.001), whereas a late sleep onset time (11 p.m.–12 a.m.) in long sleepers was protective (OR 0.580, 95% CI 0.352–0.956, p < 0.001). Similar trends were observed for ICH, though the effect sizes were attenuated. Conclusion: Sleep duration and onset time interact to influence stroke risk. Optimal cerebrovascular protection requires ≥6 h of sleep, ideally initiated between 10 p.m. and 11 p.m. These findings highlight sleep optimization as a potential modifiable target for high-risk populations.

Background Many reports argue that sleep is important for children’s health, learning, and academic performance. The purpose of this longitudinal study was to examine the association between sleep and the development of social competence in infants. Methods This study was conducted as part of a Japan Science and Technology Agency (JST) project. Caregivers responded to the Japan Children’s Study Sleep Questionnaire when children were 18 months old. The interactions of caregivers and children were observed when children were 18, 30, and 42 months old, and rated with the Interaction Rating Scale, which is a measure of social competence. Results Nocturnal sleep duration of more than 10 h and an earlier bed time than 22:00 were significantly correlated with two trajectory groups (low point and high point transition groups) of children’s social competence at 18, 30, and 42 months. Further, total sleep duration of more than 12.25 h and an earlier bed time than 22:00 were significantly correlated with the trajectory of children’s social competence at 18, 30, and 42 months. Conclusions Sleep duration and sleep onset time are important factors in children’s development of social competence. Trial registration The ethics committee of the JST approved this study on March 19, 2001. The registration number is 356-1.

Aims: To investigate factors that may contribute to performance adaptation during permanent night work. Methods: Fifteen healthy subjects participated in an adaptation and baseline night sleep, directly followed by seven simulated eight-hour night shifts (2300 to 0700 hours). At the end of each shift they were taken outside and exposed to natural light for 20 minutes. They then slept from approximately 0800 hours until they naturally awoke. Results: There was a significant increase in mean performance on a visual psychomotor vigilance task across the week. Daytime sleep quality and quantity were not negatively affected. Total sleep time (TST) for each of the daytime sleeps was reduced, resulting in an average cumulative sleep debt of 3.53 hours prior to the final night shift. TST for each of the daytime sleep periods did not significantly differ from the baseline night, nor did TST significantly vary across the week. There was a significant decrease in wake time after sleep onset and sleep onset latency across the week; sleep efficiency showed a trend towards greater efficiency across the consecutive daytime sleeps. Hours of wakefulness prior to each simulated night shift significantly varied across the week. The melatonin profile significantly shifted across the week. Conclusions: Results suggest that under optimal conditions, the sleep debt that accumulates during consecutive night shifts is relatively small and does not exacerbate decrements in night-time performance resulting from other factors. When sleep loss is minimised, adaptation of performance during consecutive night shifts can occur in conjunction with circadian adaptation.