Night Bedtime Research Articles

Associations between nocturnal bedtime and asthma among adults in the United States

BackgroundSleep disorders have a significant impact on asthma. The aim of this study was to explore the association between nocturnal bedtime and asthma among adults in the United States.MethodsThis study was a cross-sectional analysis involving 11,475 participants from the National Health and Nutrition Examination Survey (NHANES) during the period of 2015–2018. Nocturnal bedtime was categorized into three distinct groups: 2100 h or earlier, between 2100 h and 2300 h, and 2300 h or later. The association between night bedtime and asthma was detected using multivariable logistic regression analyses. Additionally, subgroup analyses were conducted to assess the impact of subgroups.ResultsAfter adjustment for confounders, a positive association was revealed between later bedtime (after 2300 h) and the prevalence of asthma (OR = 1.20, 95%CI: 1.01–1.43). In the subgroup analysis, the following factors were associated with increased risk: 18–39 years (OR = 1.23, 95%CI: 1.02–1.48); female sex (OR = 1.30, 95%CI: 1.01–1.68); Hispanic patients (OR = 1.66, 95%CI: 1.17–2.37); heavy drinkers (OR = 1.52, 95%CI: 1.17–1.96); Body Mass Index (BMI) (< 25 kg/m2) (OR = 1.45, 95%CI: 1.13–1.87); vigorous physical activity (OR = 1.32, 95%CI: 1.05–1.65);Significant interactions were found between nocturnal bedtime and asthma based on age, sex, eosinophils (EOS) percent and depression (P Interaction < 0.05).ConclusionOur results confirmed a moderately increased risk of asthma attributed to later bedtime, especially in 18–39 years, women and patients of Hispanic ethnicity. Future studies should investigate the underlying mechanisms of this association and explore the clinical implications for asthma management.

Hubungan Lama Pemakaian Smartphone Dengan Tingkat Kualitas Tidur Pada Remaja Di Kabupaten Brebes

Adolescents had a problem in lack of fulfillment of sleep needs. This problem has increased along with changes in patterns and lifestyles. One of the lifestyles that are currently popular among adolescents is smartphone addiction. Prolonged use of smartphones before night bedtime will affect adolescent sleep patterns. The research aim is to determine the relationship between smartphone use and sleep quality in adolescents at MAN 1 Brebes. The research is a quantitative study with a cross-sectional approach to correlational research design. The research instrument used the “Kuesioner Penggunaan Smartphone” and Pittsburgh Sleep Quality Index Questionnaire. The study sample is 297 students recruited using stratified random sampling techniques. The inclusion criteria in the research were active students, willing to be respondents, and using smartphones. The data was analyzed using the Kendall-Tau correlation. The study result showed the majority of adolescents had a high frequency of smartphone use, and the quality of sleep was in the poor category. There was a relationship between smartphone use and the sleep quality of adolescents in Brebes with a value of Sig. (2-tailed) -0.08 (sig. 2-tailed) 0.005) and Correlation Coefficient 0.184 (very weak). Adolescents show poor sleep quality conditions with a high category in smartphone use.

Pre-Diagnosis Sleep Status and Survival after a Diagnosis of Ovarian Cancer: A Prospective Cohort Study.

Objective: To explore if pre-diagnosis sleep status is associated with overall survival (OS) of ovarian cancer (OC). Methods: This is a prospective cohort study of 853 OC patients newly diagnosed between 2015 and 2020. Sleep status was measured by the Pittsburgh Sleep Quality Index (PSQI). Vital status of patients was obtained through active follow-up and linkage to medical records and cancer registry. The Cox proportional hazards regression model was utilized to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for aforementioned associations. Results: During the follow-up period (median: 37.57 months, interquartile: 25.00 to 50.17 months), 123 (18.39%) OC patients died. The HR (95%CI) for OS of OC was 2.13 (1.42−3.18) for sleeping after 22:00, compared with sleeping before 22:00; 2.43 (1.64−3.62) for poor sleep quality, compared to good sleep quality; 2.26 (1.37−3.72) for late bed-early rise and 1.93 (1.09−3.42) for late bed-late rise, compared with early bed-early rise; 0.40 (0.24−0.67) for night sleep duration of ≥7.5 h/day, compared with 7−7.5 h/day; 0.53 (0.29−0.98) for total sleep duration of ≥8 h/day, compared with 7.5−8 h/day. Further, the interaction effects were significant between residual lesions and wake-up time, night bedtime, sleep pattern, and between total sleep duration and menopausal status, parity. Additionally, there was a significant curvilinear association between PSQI score and OS (p nonlinear <0.05). Conclusions: Pre-diagnosis longer total and night sleep duration were associated with better OS, whereas later sleeping time, poor sleep quality, and bad sleep patterns were associated with poor OS among OC survivors.

Short total sleep duration and poor sleep quality might be associated with asthenozoospermia risk: A case-control study.

Sleep has been related to a variety of health outcomes. However, no association between sleep and asthenozoospermia has been reported. The aim of this study is to first investigate the relationship between sleep status and asthenozoospermia risk. A case-control study, including 540 asthenozoospermia cases and 579 controls, was performed from June 2020 to December 2020 in the infertility clinic from Shengjing Hospital of China Medical University. Data on sleep status were collected by Pittsburgh sleep quality index questionnaires and asthenozoospermia was diagnosed based on the World Health Organization guidelines. Odds ratio (OR) with 95% confidence interval (95% CI) was calculated by logistic regression analysis to assess the aforementioned association. Results of this study demonstrated that compared with total sleep duration of 8–9 h/day, < 8 h/day was related to asthenozoospermia risk (OR: 1.44, 95% CI: 1.05–1.99); compared to good sleep quality, poor sleep quality was associated with asthenozoospermia risk (OR: 1.35; 95% CI: 1.04–1.77). There were multiplicative model interaction effects between sleep quality and tea drinking (p = 0.04), rotating night shift work (p < 0.01) on asthenozoospermia risk. However, we failed to detect any associations between night sleep duration, daytime napping duration, night bedtime, wake-up time, sleep pattern and asthenozoospermia risk. In conclusion, short total sleep duration and poor sleep quality might be related to asthenozoospermia risk. Further well-designed prospective studies are warranted to confirm our findings.

Bedtime social media use, sleep, and affective wellbeing in young adults: an experience sampling study.

Findings from primarily cross-sectional studies have linked more extensive social media use to poorer sleep and affective wellbeing among adolescents and young adults. This study examined bedtime social media use, sleep, and affective wellbeing, using an experience sampling methodology with the aim of establishing a day-to-day temporal link between the variables. The study hypothesized a positive association between increased bedtime social media use and lower affective wellbeing the following day, mediated by poorer sleep. Using a smartphone application, 101 undergraduate students (Mage =19.70years, SD=1.09 years), completed daily questionnaires assessing the previous night's bedtime social media use and sleep duration and satisfaction (one measurement per day, questionnaire sent at 08:00), and momentary affective wellbeing (five measurements per day, at randomly varying times between 08:00 and 22:00 on weekdays and 10:00 and 22:00 on weekends), for 14 consecutive days. Objective assessments of total sleep time and sleep efficiency were obtained via wrist-worn actigraphs. By means of separate multilevel models, it was tested whether increased bedtime social media use predicted poorer sleep the same night, whether poorer sleep was predictive of positive and negative affect the following day, and whether sleep mediated the relationship between social media use and affective wellbeing. Increased bedtime social media use was not associated with poorer sleep the same night. Apart from subjective sleep satisfaction, no other sleep variable (i.e., subjective sleep duration, objective total sleep time and objective sleep efficiency) predicted positive or negative affect the following day. This study found that bedtime social media use is not detrimental to the sleep and affective wellbeing of healthy young adults. However, it is possible that bedtime social media use may be harmful to the sleep of vulnerable individuals.

0963 Perceived Sleep Need in Adolescents and Acceptability of a Morning Bright Light Intervention

Abstract Introduction We are testing strategies to make fall asleep times earlier to extend sleep duration of sleep-deprived, delayed adolescents. We explored whether perceived sleep need was associated with the acceptability of an intervention which included earlier bedtimes on school nights and early morning bright light (BL) on one weekend. Methods Twenty-three participants (16.0±0.7 years; N=11 female) from the intervention group of a study of adolescents reporting ≤7 h sleep on school nights and late bedtimes (school-night≥23:00; non-school night≥midnight) were included. Participants estimated nightly sleep need during baseline. The intervention included shifting school-night bedtimes 1h earlier than each participant’s usual school night bedtime during the first week and 2h earlier than baseline during the second week. Evening time management goals were used to help participants get to bed earlier. Wake times remained stable because of early school start times. During the weekend in between these 2 weeks, participants lived in the laboratory and received very early morning BL on Saturday and Sunday from two light boxes (~6000 lux; three 50-minute exposures with 10-minute breaks). At the end of the study, an acceptability questionnaire asked about hypothetically engaging in different elements of the intervention (earlier bedtimes, morning BL) at home. Items were summed to create a composite acceptability score. Results Participants reported needing 7.8 h (SD=1.2; range=5-9.5h) of sleep. A linear regression demonstrated that needing more sleep was associated with less acceptability when controlling for age and sex, b=1.37, SEb=.60, p=.03. Conclusion Adolescents who felt they needed less sleep were more accepting of an intervention that required earlier bedtimes on school nights and waking up very early to receive BL on the weekend. Interventions that produce less weekend sleep deprivation, like including naps, might be more acceptable to all. Support R01HL105395 (S.J.C.)

Longitudinal Change in Adolescent Bedtimes Measured by Self-Report and Actigraphy.

Introduction:Sleep is often quantified using self-report or actigraphy. Self-report is practical and less technically challenging, but prone to bias. We sought to determine whether these methods have comparable sensitivity to measure longitudinal changes in adolescent bedtimes.Methods:We measured one week of free-living sleep with wrist actigraphy and usual bedtime on school nights and non-school nights with self-report questionnaire in 144 students at 15 y and 17 y.Results:Self-reported and actigraphy-measured bedtimes were correlated with one another at 15 y and 17 y (p < .001), but reported bedtime was consistently earlier (>30 minutes, p < .001) and with wide inter-method confidence intervals (> ±106 minutes). Mean inter-method discrepancy did not differ on school nights at 15 y and 17 y but was greater at 17 y on non-school nights (p = .002). Inter-method discrepancy at 15 y was not correlated to that at 17 y. Mean change in self-reported school night bedtime from 15 y to 17 y did not differ from that by actigraphy, but self-reported bedtime changed less on non-school nights (p = .002). Two-year changes in self-reported bedtime did not correlate with changes measured by actigraphy.Conclusions:Although methods were correlated, consistently earlier self-reported bedtime suggests report-bias. More varied non-school night bedtimes challenge the accuracy of self-report and actigraphy, reducing sensitivity to change. On school nights, the methods did not differ in group-level sensitivity to changes in bedtime. However, lack of correlation between bedtime changes by each method suggests sensitivity to individual-level change was different. Methodological differences in sensitivity to individual- and group-level change should be considered in longitudinal studies of adolescent sleep patterns.

An Educational Intervention to Improve the Sleep Behavior and Well-Being of High School Students.

The objective of this study was to determine whether a sleep education intervention improves knowledge of sleep, sleep behaviors, and depression in high school freshmen. We recruited student volunteers at a single magnet high school in Los Angeles, California through their health class. Twenty-four freshmen participated and 18 students (17 female, 1 male) completed pre- and postsurveys. Curriculum consisted of 4 hours of after-school interactive lectures emphasizing sleep physiology, benefits of sleep, what impacts sleep, and methods to improve sleep, followed by a 9-week sleep behavior change journal. Pre- and postsurveys measuring both sleep behaviors and knowledge, and a Patient Health Questionnaire-9 depression screening were administered to participants prior to and after the intervention. We used t tests and χ 2 tests to analyze knowledge and behavior change. Subjects improved in average sleep hours per night (preintervention 6.9 hours to postintervention 7.8 hours, P=.0134), and average weekend night bedtime (11:36 pm to 10:54 pm, P=.0307). This school sleep behavior intervention demonstrated students' average sleep hours per night and weekend bedtime improved after the lecture and sleep journal intervention. This suggests a sleep education intervention may benefit this population. Further studies are needed to demonstrate effectiveness of this education over time, across sexes, and in high-risk students.

Circadian and homeostatic processes influence daytime nap architecture

Introduction Besides being a useful tool for coping with sleep deprivation, naps can reduce sleepiness and improve cognitive performance. However, few studies so far have examined what factors regulate its structure. Therefore, we aimed to investigate if circadian preference influences daytime nap architecture. Materials and methods Following a week of actigraphy monitoring of the sleep/wake cycle, a total of 43 healthy young adults (19 females, 22.16 ( ± 3.82) years) filled out the Morningness–Eveningness Questionnaire (MEQ) and took a 90 min polysomnography-recorded nap. MEQ score, nocturnal sleep data and minutes of prior wakefulness were correlated with nap sleep variables. Results There is a negative correlation between MEQ Score (47.93 ( ± 10.93)) and Slow Wave Sleep% (SWS) (r = −.364, p = .044) and a positive correlation with Stage N1% (r = .371, p = .014), meaning that morning preference is associated with increased SWS and decreased N1 in a daytime nap. No correlations were found between MEQ Score and total sleep time (nap or nocturnal, for either the previous night or the mean 5 previous nights), Stage N2%, or REM%. The mean 5 previous nights bedtime (r = −.381, p = .038) and wake time (r9 = −.362, p = .049) were also negatively correlated with SWS, whereas the previous night bedtime (r = .375, p = .013) and wake time (r = .302, p = .049) were positively correlated with N1, that is, going to bed and waking up earlier predicted greater SWS% and smaller N1%, supporting MEQ Score correlations. To rule out homeostatic sleep drive as an explanation for increased nap SWS in individuals with a morning preference, minutes of prior wakefulness were correlated with nap sleep data. No association was found between homeostatic sleep drive and SWS (r = 0,244, p > 0.05). However, increasingly amounts of N1 were associated with less time between waking and napping (r = −0.370, p = 0.015). Conclusion Morningness is associated with increased SWS in a daytime nap and prior wakefulness predicts time spent in N1, in healthy young adults. It is possible that this is because the scheduled nap timing was more suitable for individuals with a morning preference. Both components (circadian and homeostatic) should be taken into account when prescribing naps to ensure maximal restorative effects. Acknowledgements Supported by grants from CAPES and CNPq. We would like to thank all the subjects that volunteered for this study.

Sleep of children living in institutional care facilities

To describe sleep patterns and problems among institutionalized children. In this cross-sectional study, the caregivers of 118 children, aged 4-12 years from six institutional care facilities completed the Children's Sleep Habits Questionnaire (CSHQ). The mean (±SD) of night bedtime was 21:05 ± 2:52, mean morning wake-up time was 06:58 ± 0:31, mean total sleep duration was 10 ± 1.1 h, and mean night-sleep duration was 9.5 ± 0.9 h. The percentage of children who took a daytime nap was 34.7% (n = 41) and the mean duration of nap was 0.5 ± 0.7 h. The most frequently reported sleep problems were bedtime resistance, daytime sleepiness and night awakening. Children with bedtime at or after 9 PM, night-sleep duration less than 10 h and daytime napping had more disturbed sleep. Sleep problems are common among this sample of institutionalized children.

Sleep-Wake Patterns and Sleep Disturbance among Hong Kong Chinese Adolescents

To determine sleep-wake patterns and evaluate sleep disturbance in Hong Kong adolescents; to identify factors that are associated with sleep disturbance; and to examine the relationship of sleep-wake variables and academic performance. A school-based cross-sectional survey. Sample included 1629 adolescents aged 12 to 19 years. Self-report questionnaires, including sleep-wake habit questionnaire, Sleep Quality Index, Morningness/ Eveningness scale, Epworth Sleepiness Scale, Perceived Stress Scale, academic performance, and personal data were administered. The average school-night bedtime was 23:24, and total sleep time was 7.3 hr. During weekends, the average bedtime and rise time was delayed by 64 min and 195 min, respectively. The prevalence of sleep disturbances occurring > or = 3 days per week in the preceding 3 months were: difficulty falling asleep (5.6%), waking up during the night (7.2%), and waking up too early in the morning (10.4%). The prevalence of > or = 1 of these three symptoms was 19.1%. Stepwise regression analyses revealed that circadian phase preference was the most significant predictor for school night bedtime, weekend oversleep, and daytime sleepiness. Perceived stress was the most significant risk factor for sleep disturbance. Students with marginal academic performance reported later bedtimes and shorter sleep during school nights, greater weekend delays in bedtime, and more daytime sleepiness than those with better grades. The prevalence of sleep deprivation and sleep disturbance among Hong Kong adolescents is comparable to those found in other countries. An intervention program for sleep problems in adolescents should be considered.