Associations between bedtime media use and sleep outcomes in an adult population-based cohort.

OBJECTIVE: To further examine the relationship between bedtime media use and sleep in adults by taking relevant covariates into account and testing hypothesised mediating and moderating pathways. METHODS: Bedtime media use and sleep outcomes were examined by questionnaire in 4188 adults (59 % women, aged 19-94 years) from the Specchio cohort based in Geneva, Switzerland. We tested associations between bedtime media use and sleep (bedtimes, rise times, sleep latency, sleep duration, sleep quality, insomnia, and daytime sleepiness), adjusting for prior sleep, mental health, and health behaviours; whether bedtime media use mediates associations between individual susceptibility factors (age, chronotype, and mental health) and sleep; and whether individual susceptibility factors moderate associations between bedtime media use and sleep. RESULTS: Often using a screen in the 30 minutes before going to sleep at night was associated with a late bedtime (≥midnight; OR [95 % CI] = 1.90 [1.44,2.51], p < 0.001), a short sleep duration (<7 h; 1.21 [1.01,1.46], p < 0.05), and excessive daytime sleepiness (Epworth score >9; 1.47 [1.25,1.74], p < 0.001), adjusting for all covariates. Bedtime media use partly mediated the association between younger age and an evening chronotype and these sleep outcomes. Mental health moderated the association between bedtime media use and sleep quality/insomnia, such that the former was only associated with poorer sleep quality/insomnia among individuals with better mental health. CONCLUSIONS: Frequent bedtime media use was associated with various sleep outcomes, independently of relevant covariates. Limiting the use of screens at bedtime is important to promote sleep among adults. Individuals with poorer mental health likely require additional support to improve their sleep quality.

Association between bedtime and female infertility: a secondary analysis from a cross-sectional study.

Objective: To evaluate the association between bedtime and infertility and to identify the optimal bedtime for women of reproductive age. Methods: We conducted a cross-sectional study using data from 3,903 female participants in the National Health and Nutrition Examination Survey (NHANES) from 2015 to 2020. The effect of bedtime on female infertility was assessed using the binary logistic regression in different models, including crude model and adjusted models. To identify the non-linear correlation between bedtime and infertility, generalized additive models (GAM) were utilized. Subgroup analyses were conducted by age, body mass index (BMI), waist circumference, physical activity total time, marital status, smoking status, drinking status and sleep duration. Results: After adjusting for potential confounders (age, race, sleep duration, waist circumference, marital status, education, BMI, smoking status, drinking status and physical activity total time), a non-linear relationship was observed between bedtime and infertility, with the inflection point at 22:45. To the left side of the inflection point, no significant association was detected. However, to the right of it, bedtime was positively related to the infertility (OR: 1.22; 95% CI: 1.06 to 1.39; P = 0.0049). Subgroup analyses showed that late sleepers with higher BMI were more prone to infertility than those with a lower BMI (BMI: 25-30 kg/m2: OR: 1.26; 95% CI: 1.06 to 1.51; P = 0.0136; BMI ≥ 30 kg/m²: OR: 1.21, 95% CI: 1.09 to 1.34; P = 0.0014). Conclusion: Bedtime was non-linearly associated with infertility, which may provide guidance for sleep behavior in women of childbearing age.

The effects of mobile phone addiction on bedtime procrastination in university students: the masking effect of physical activity and anxiety.

PURPOSE: Good sleep is one of the necessary conditions to ensure the normal performance of the physiological and psychological functions of college students. This study aimed to explore the relationship between mobile phone addiction and bedtime procrastination among Chinese college students and the mediating mechanisms of physical exercise and anxiety between the two, with a view to seek ways to prevent and intervene in college students' sleep procrastination and improve their sleep quality. METHODS: Using SPSS 29.0 analysis with Bootstrap's method, 3,800 first-year students, sophomores, and juniors were given the Mobile Phone Addiction Tendency Scale, Bedtime Procrastination Scale, Physical Activity Scale, and Anxiety Scale. The results of the analyses included mediation tests and effect analyses of anxiety and physical activity. RESULTS: The correlation analysis revealed significant positive correlations between mobile phone addiction and bedtime procrastination (r = 0.149, p < 0.01) as well as anxiety (r = 0.497, p < 0.01). Additionally, there was a significant negative correlation between mobile phone addiction and physical activity (r = -0.447, p < 0.01). Physical activity was also found to have significant negative correlations with anxiety (r = -0.506, p < 0.01) and bedtime procrastination (r = -0.424, p < 0.01). Furthermore, anxiety showed a significant positive correlation with bedtime procrastination (r = 0.334, p < 0.01). Physical activity and anxiety acted as substantial mediators between mobile phone addiction and nighttime procrastination. Both mediators had considerable masking effects, with the mediating effect amounting to 50.3% and 25.1%, respectively. Physical exercise and anxiety played a chain mediating role between mobile phone addiction and bedtime procrastination, and the masking effect was also significant, with a mediating effect size of 13.4%. CONCLUSIONS: This study reveals the special characteristics of the influencing factors and pathways of bedtime procrastination in this group of college students, providing targeted evidence for the prevention and intervention of bedtime procrastination in college students. It also has an important reference value for the effects of exercise and comprehensive intervention to improve bedtime procrastination and enhance the quality of sleep in college students.

Sleep Characteristics of an International Sample of Adult Gamers.

Introduction About 65% of adult Americans report playing video games. Despite potential impacts to functioning, there is limited research on the relationship between video game use and sleep, specifically among adults. The present study expands upon the literature by describing demographic, video game, and sleep characteristics of an international adult sample of gamers. Methods The participants were 3,481 adults aged 18 to 74 who responded to an online questionnaire about video game use (i.e., quantity of play, most common game type), general sleep characteristics (i.e., sleep onset latency [SOL]; duration, sleep timing, and sleep quality), and gaming-specific sleep disruptors (i.e., game-related night awakenings and sleep delays). Most identified as cisgender male (79.8%) and white (77%). Results Participants reported an average SOL of 24.63 minutes, and most (64.5%) had a sleep duration from 7 to 9 hours with an overall average of 8.42 hours. Most (58.7%) reported that their sleep quality was fair to very poor . Bed and wake times were generally delayed, with 51% reporting a late evening or early morning bedtime and an average wake time of 8:28 am. A majority (81.2%) indicated that their bedtime was delayed due to game-related activities, but game-related night awakenings were less common. Conclusion Although many report a sufficient amount of sleep, adult gamers tend to report sleep disruptions in other domains, particularly regarding a delayed sleep schedule and poor sleep quality. This may be attributable to game-related bedtime delays or other game-specific factors (e.g., game type) that should be evaluated in the future.

Latent Profiles and Transitions of Bedtime Procrastination Among Chinese College Students: The Predictive Roles of Anxiety, Depression, Problematic Smartphone Use and Self-Control.

Background: Bedtime procrastination (BP) has become an important factor affecting individual well-being. This study aimed to assess the stability and changes in BP and examine risk and protective factors. Methods: The study recruited 1423 respondents. Latent profile analysis was used to identify subgroups of BP and latent transition analysis to determine transition probabilities for each subgroup. Logistic regression examined associations between identified classes and related factors. Results: Three subgroups of BP were identified. In terms of stability and changes, the moderate bedtime procrastination group showed the highest stability (66%), followed by the severe bedtime procrastination group (62.4%), and the mild bedtime procrastination group had a 52% probability of switching to moderate bedtime procrastination. In terms of influencing factors, more problematic phone use (PSU) (OR: 1.08; 95% CI = 1.05-1.12), more depression (OR: 1.17; 95% CI = 1.06-1.29) and anxiety (OR: 1.16; 95% CI = 1.05-1.28) are all factors that aggravate the transition from mild to moderate sleep procrastination. Similarly, PSU (OR: 1.15; 95% CI = 1.12-1.19), anxiety (OR: 1.10; 95% CI = 1.06-1.14), and depression (OR: 1.10; 95% CI = 1.06-1.14) increased the risk of severe bedtime procrastination. Self-control emerged as a protective factor against BP. Conclusion: This study identified three subgroups of BP at two time points and the rule of transition for each subgroup. Our findings indicate that BP were relatively stable, with some changes over time. The results also highlight the important function that PSU, depression, anxiety, and self-control can play in preventing and intervening in BP.

Association between dinner-bedtime interval and type 2 diabetes mellitus: a large-scale cross-sectional study.

Objective: Glucose metabolism is impacted by circadian disruption. Dinner-bedtime interval (DBI) was an accessible indicator to reflect the alignment between dinner time and circadian clock. We aimed to investigate the association of DBI with type 2 diabetes mellitus (T2DM). Methods: 7676 adult subjects from the Henan Rural Cohort were included. Their demographic information including dinner time and bedtime was collected. Fasting venous blood samples were collected for biochemical determinations. Generalized linear regression model was used to analyze the factors influencing DBI. Furthermore, logistic regression incorporated with restricted cubic spline model was applied to evaluate the association between DBI and T2DM. Results: The results of multiple linear regression model showed that age (ß: -0.018, 95% CI: -0.021, -0.015) was negatively correlated with DBI. Female (ß: 0.311, 95% CI: 0.229, 0.393), junior high school education (ß: 0.246, 95% CI: 0.187, 0.306), high school education or above (ß: 0.346, 95% CI: 0.259, 0.433), average monthly income with 1000-1999 CNY(0.102, 95% CI: 0.032, 0.171), average monthly income ≥ 2000 CNY (ß: 0.164, 95% CI: 0.076, 0.251), moderate physical activity (ß: 0.134, 95% CI: 0.071, 0.197), current smokers (ß: 0.214, 95% CI: 0.118, 0.309), current drinkers (ß: 0.099, 95% CI: 0.008, 0.190) were positively correlated with DBI. Furthermore, DBI was significantly associated with T2DM (adjusted OR: 0.910, 95%CI: 0.845-0.979, P = 0.012). DBI longer than 3 h was associated with decreased risk of T2DM (adjusted OR: 0.773, 95%CI: 0.648-0.921, P = 0.004). Conclusions: DBI larger than 3 h is beneficial to T2DM prevention. Further investigation is required to verify the association.

Bedtime Regularity and Sleep Sufficiency in Children With Tourette Syndrome.

BACKGROUND: Despite research demonstrating sleep disturbance in children with Tourette syndrome (TS), few studies have examined bedtime regularity and sleep sufficiency, two important sleep health dimensions. Therefore, this study examined bedtime regularity and sleep sufficiency in children with TS relative to matched healthy control subjects, and its associated demographic, clinical, and behavioral factors. METHODS: Participants were 384 parents or caregivers of children aged three to 17 years, including 192 with current TS and 192 matched healthy control subjects drawn from the 2020-2021 cycle of the National Survey of Children's Health. Parents completed questions assessing demographic (i.e., age, race, sex), clinical (i.e., attention-deficit/hyperactivity disorder [ADHD], autism spectrum disorder, anxiety, depression, tic severity, behavioral or conduct problems, ADHD medication, health condition-related impairment), and behavioral (i.e., screen time) characteristics. Mann-Whitney U test and chi-square test of independence were performed to compare groups on bedtime regularity and sleep sufficiency, respectively. Ordinal regression and binary logistic regression without and with backward elimination were performed to evaluate indicators of bedtime regularity and sleep sufficiency, respectively, in children with TS. RESULTS: Children with current TS had significantly poorer bedtime regularity, but not sleep sufficiency, relative to matched healthy control subjects. In children with TS, anxiety and two or more hours of daily screen time were associated with higher likelihood of poor bedtime regularity. Autism was associated with lower likelihood of insufficient sleep, and depression was associated with increased likelihood of insufficient sleep. CONCLUSIONS: Findings put forth screen time, anxiety, and depression as intervention targets to optimize sleep health in children with TS.

The association between sleep duration, bedtime, and cognitive ability in Chinese adults: Evidence from the China family panel studies.

Introduction: Dementia is marked by a steady decline or worsening in cognitive abilities, affecting memory, logic, and social competencies. While many studies suggest a potential link between the amount of sleep and dementia risk, the outcomes are not yet consistent. This research delved into the relationship between sleep length and bedtime on cognitive abilities using an extensive dataset from the China Family Panel Studies (CFPS) from 2014 to 2020. Methods: Data from 175,702 observations were collected, including cognitive function test data from 22,848 participants. Various cognitive tests were used to assess cognitive function. Restricted cubic spline (RCS) models were used for data analysis. Results: The optimal sleep duration for cognitive function was found to be 6-7 h, and the optimal bedtime was generally between 22:00-23:00. Longitudinal analysis revealed that sleep duration four years prior had a significant impact on current cognitive function. After accounting for various factors, those who slept for 7-8 h and over 8 h displayed lower cognitive function scores. Conversely, individuals sleeping less than 6 h had higher scores on the Vocabulary Test. Bedtime before 22:00 was associated with lower scores on the Vocabulary Test and Mathematical Test. Subgroup analyses based on age, gender, and urban residence showed variations in optimal sleep duration for different populations. Propensity Score Matching (PSM) analysis supported the findings. Conclusions: Maintaining a sleep duration of 6-7 h and a regular bedtime between 22:00-23:00 is important for optimizing cognitive performance.

Changes in Sleep Duration and Sleep Timing in the General Population from before to during the First COVID-19 Lockdown: A Systematic Review and Meta-Analysis.

BACKGROUND: The COVID-19 lockdown had a profound effect on everyday life, including sleep health. This systematic review and meta-analysis evaluated changes in quantitative sleep parameters during the first lockdown compared with pre-lockdown in the general population. METHODS: A search in scientific databases was performed to identify eligible observational studies from inception to 8 February 2023. We performed a random effects meta-analysis of those studies reporting (a) means of sleep duration, time in bed (TIB), and sleep timing (bedtime and wake-up time); (b) the percentages of atypical sleep duration before and during the lockdown; (c) the percentages of change in sleep duration and sleep timing. RESULTS: A total of 154 studies were included. A small increase in sleep duration (0.25 standardized mean difference, 95% CI 0.180-0.315) was found, with 55.0% of the individuals reporting changes, predominantly an increase (35.2%). The pooled relative risk for sleeping more than 8/9 h per night was 3.31 (95% IC 2.60-4.21). There was a moderately significant delay in sleep timing and a surge in napping. CONCLUSION: An increase in sleep duration and napping, and delayed sleep timing were observed. High-quality studies should evaluate whether these parameters have now become chronic or have returned to pre-lockdown values.

Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use.

GOAL AND AIMS: To test sleep/wake transition detection of consumer sleep trackers and research-grade actigraphy during nocturnal sleep and simulated peri-sleep behavior involving minimal movement. FOCUS TECHNOLOGY: Oura Ring Gen 3, Fitbit Sense, AXTRO Fit 3, Xiaomi Mi Band 7, and ActiGraph GT9X. REFERENCE TECHNOLOGY: Polysomnography. SAMPLE: Sixty-three participants (36 female) aged 20-68. DESIGN: Participants engaged in common peri-sleep behavior (reading news articles, watching videos, and exchanging texts) on a smartphone before and after the sleep period. They were woken up during the night to complete a short questionnaire to simulate responding to an incoming message. CORE ANALYTICS: Detection and timing accuracy for the sleep onset times and wake times. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Discrepancy analysis both including and excluding the peri-sleep activity periods. Epoch-by-epoch analysis of rate and extent of wake misclassification during peri-sleep activity periods. CORE OUTCOMES: Oura and Fitbit were more accurate at detecting sleep/wake transitions than the actigraph and the lower-priced consumer sleep tracker devices. Detection accuracy was less reliable in participants with lower sleep efficiency. IMPORTANT ADDITIONAL OUTCOMES: With inclusion of peri-sleep periods, specificity and Kappa improved significantly for Oura and Fitbit, but not ActiGraph. All devices misclassified motionless wake as sleep to some extent, but this was less prevalent for Oura and Fitbit. CORE CONCLUSIONS: Performance of Oura and Fitbit is robust on nights with suboptimal bedtime routines or minor sleep disturbances. Reduced performance on nights with low sleep efficiency bolsters concerns that these devices are less accurate for fragmented or disturbed sleep.

Follow-up of telehealth parent training for sleep disturbances in young children with autism spectrum disorder.

OBJECTIVE/BACKGROUND: Bedtime and sleep disturbances are ubiquitous in children with autism. The telehealth delivery of a behavior analytic parent training program was earlier reported to be efficacious in improving child sleep and parent sense of competency. Our aim in this brief report was to determine the durability of the telehealth delivered sleep parent training program (SPT) compared to the control condition in this randomized controlled trial. Telehealth delivery could be a means to expand access to such early treatment if efficacious. PARTICIPANTS/METHODS: Parents of young children, ages 2-7 years, with autism and co-occurring sleep disturbances were enrolled in this study. Participants were randomized to either SPT or a comparison arm that included non-sleep related parent education except for one session Sleep Parent Education; SPE). Each participant was individually administered a 5 session program delivered over 10 weeks. Outcome measures were collected at weeks 5 and 10 and at post-treatment follow-up at 16 weeks. RESULTS: Follow-up week 16 data were available for 30 participants randomized to SPT and 24 participants randomized to SPE. Demographics for this follow-up cohort were similar to the full sample. At week 16 follow-up, there was a significant group difference between SPT and SPE the Modified Simonds & Parraga Sleep Questionnaire-Composite Sleep Index (MSPSQ - CSI) (p = 0.011) with an effect size of 0.68. Further, 8 of the 10 children in SPT who were negative responders at the week 10 endpoint were rated as positive responders at week 16 by the treatment masked IE compared to 2 out of 12 in the SPE group (p = 0.008). CONCLUSIONS: Post-treatment follow-up data suggests SPT is durable over time compared to SPE, the active control group. Further, for some participants in SPT, a positive responsive emerged after the treatment endpoint. This brief report adds to the efficacy of SPT as a time-limited intervention for insomnia in young autistic children.

Sleep patterns are associated with cardiometabolic risk factors in nine-year-old Swedish children.

AIM: Sleep duration and bedtime may play a role in children's cardiometabolic health, but research is lacking. This study examined associations between sleep patterns and cardiometabolic risk factors in Swedish nine-year-olds. METHODS: This cross-sectional study used data from three studies, where identical outcome measures were conducted in 411 nine-year-olds, 51% boys, between 2016 and 2020. Sleep was assessed with wrist-worn accelerometers and sleep journals. Children were grouped based on meeting the sleep guidelines of 9-11 h and going to bed early or late based on the median bedtime. Analysis of covariance was used to examine associations between sleep patterns and cardiometabolic risk factors. RESULTS: Meeting sleep guidelines and going to bed early were associated with lower metabolic syndrome score (-0.15 vs. 0.42, p = 0.029), insulin resistance (0.30 vs. 0.60, p = 0.025) and insulin levels (6.80 vs. 8.87 mIU/L, p = 0.034), compared with their peers who did not meet the guidelines and went to bed later. When adjusting for total sleep time, analyses still showed associations with the metabolic syndrome score (-0.19 vs. 0.50, p = 0.011). CONCLUSION: The findings indicate that good sleep patterns could help mediate positive overall cardiometabolic health in children.

A bidirectional model of sleep and technology use: A theoretical review of How much, for whom, and which mechanisms.

The link between technology and sleep is more complex than originally thought. In this updated theoretical review, we propose a new model informed by the growing body of evidence in the area over the past 10 years. The main theoretical change is the addition of bi-directional links between the use of technology and sleep problems. We begin by reviewing the evidence to date for the originally proposed mechanisms of bright light, arousal, nighttime sleep disruptions, and sleep displacement. Then, in support of the new direction of effect (sleep problems preceding technology use), we propose two new mechanisms: technology before sleep might be used as a time filler and/or as an emotional regulation strategy to facilitate the sleep-onset process. Finally, we present potential moderators of the association between technology and sleep, in recognition of protective and vulnerability factors that may mitigate or exacerbate the effects of technology on sleep and vice versa. The goal of this theoretical review is to update the field, guide future public health messages, and to prompt new research into how much technology and sleep affect each other, for whom it may be problematic, and which mechanisms may explain their association.

Association between late bedtime and obesity in children and adolescents: a meta-analysis.

Background: Short sleep duration has been related to obesity in children and adolescents. However, it remains unknown whether late bedtime is also associated with obesity and whether the association is independent of sleep duration. A meta-analysis was performed to address this issue. Methods: In order to accomplish the aim of the meta-analysis, a comprehensive search was conducted on databases including PubMed, Embase, and Web of Science to identify observational studies. The cutoff to determine late bedtime in children in this meta-analysis was consistent with the value used among the included original studies. As for obesity, it was typically defined as a body mass index (BMI) > 95th percentile of age and sex specified reference standards or the International Obesity Task Force defined age- and gender-specific cut-off of BMI. The Cochrane Q test was employed to evaluate heterogeneity among the included studies, while the I2 statistic was estimated. Random-effects models were utilized to merge the results, considering the potential impact of heterogeneity. Results: Tweleve observational studies with 57,728 participants were included. Among them, 6,815 (11.8%) were obese. Pooled results showed that late bedtime reported by the participants or their caregivers was associated with obesity (odds ratio [OR]: 1.27, 95% confidence interval [CI]: 1.16-1.39, p < 0.001; I2 = 0%). Subgroup analysis showed consistent results in studies with (OR: 1.33, 95% CI: 1.04-1.70, p = 0.02) and without adjustment of sleep duration (OR: 1.27, 95% CI: 1.14-1.41, p < 0.001). Further subgroup analysis also showed that the association was not significantly affected by study location, design, age of the participants, or diagnostic methods for obesity (p for subgroup difference all >0.05). Conclusion: Late bedtime is associated with obesity in children and adolescents, which may be independent of sleep duration.

Association of sleep timing with all-cause and cardiovascular mortality: the Sleep Heart Health Study and the Osteoporotic Fractures in Men Study.

STUDY OBJECTIVES: Previous studies have highlighted the importance of sleep patterns for human health. This study aimed to investigate the association of sleep timing with all-cause and cardiovascular disease mortality. METHODS: Participants were screened from two cohort studies: the Sleep Heart Health Study (SHHS; n = 4,824) and the Osteoporotic Fractures in Men Study (n = 2,658). Sleep timing, including bedtime and wake-up time, was obtained from sleep habit questionnaires at baseline. The sleep midpoint was defined as the halfway point between the bedtime and wake-up time. Restricted cubic splines and Cox proportional hazards regression analyses were used to examine the association between sleep timing and mortality. RESULTS: We observed a U-shaped association between bedtime and all-cause mortality in both the SHHS and Osteoporotic Fractures in Men Study groups. Specifically, bedtime at 11:00 pm and waking up at 7:00 am was the nadir for all-cause and cardiovascular disease mortality risks. Individuals with late bedtime (> 12:00 am) had an increased risk of all-cause mortality in SHHS (hazard ratio 1.53, 95% confidence interval 1.28-1.84) and Osteoporotic Fractures in Men Study (hazard ratio 1.27, 95% confidence interval 1.01-1.58). In the SHHS, late wake-up time (> 8:00 am) was associated with increased all-cause mortality (hazard ratio 1.39, 95% confidence interval 1.13-1.72). No significant association was found between wake-up time and cardiovascular disease mortality. Delaying sleep midpoint (> 4:00 am) was also significantly associated with all-cause mortality in the SHHS and Osteoporotic Fractures in Men Study. CONCLUSIONS: Sleep timing is associated with all-cause and cardiovascular disease mortality. Our findings highlight the importance of appropriate sleep timing in reducing mortality risk. CITATION: Ma M, Fan Y, Peng Y, et al. Association of sleep timing with all-cause and cardiovascular mortality: the Sleep Heart Health Study and the Osteoporotic Fractures in Men Study. J Clin Sleep Med. 2024;20(4):545-553.

Self-Reported Sleep Duration and Bedtime Are Associated with Dementia Risk in Community-Dwelling People Aged 40-74 Years: The Murakami Cohort Study.

Background: Sleep is a potentially modifiable factor associated with dementia, including Alzheimer's disease, but current evidence supporting this is insufficient. Objective: This study aimed to determine whether sleep duration and bedtime patterns are associated with the risk of dementia among middle-aged and older people. Methods: This cohort study had an eight-year follow-up period. Participants were 13,601 community-dwelling people aged 40-74 years living in Murakami (Niigata, Japan). Data were collected using a self-administered questionnaire. Predictors were self-reported sleep duration and bedtime, and the outcome was newly-diagnosed dementia determined using the long-term care insurance database. Covariates were demographic characteristics, body mass index, smoking, alcohol consumption, total physical activity, insomnia symptoms, disease history, and either bedtime or sleep duration. Cox proportional hazard models were used to calculate hazard ratios (HRs). Results: The mean age of participants at baseline was 59.2 years. Over a mean follow-up period of 8.0 years, 319 cases of dementia were observed. A long self-reported sleep duration relative to the reference sleep duration (7 hours) was associated with increased dementia risk, with the "8 hours" group (adjusted HR = 1.30, 95% CI:0.99-1.73) and "≥9 hours" group (adjusted HR = 1.46, 95% CI:1.00-2.15) having an increased risk (marginally significant) relative to the reference group. Early bedtime was associated with increased dementia risk (adjusted p for trend = 0.0010), with the "21 : 00 or earlier" group (adjusted HR = 1.61, 95% CI:1.14-2.28) having an increased risk relative to the reference ("23 : 00"). Conclusions: A long self-reported sleep duration and early bedtime are both associated with increased dementia risk in middle-aged and older people.

The relationship between family cohesion and bedtime procrastination among Chinese college students: the chain mediating effect of coping styles and mobile phone addiction.

BACKGROUND: Bedtime procrastination refers to an individual's inability to go to bed at a predetermined time without external obstacles. Previous researchers have found that the bedtime procrastination is harmful to human physical and mental health, but these research on bedtime procrastination have mostly focused on exploring individual factors, while ignoring the external environmental factors. Therefore, this is the first study to investigate bedtime procrastination from the perspective of family environments. METHODS: The study was conducted using a convenient sampling method and online questionnaires. Family Cohesion Scale, Coping Styles Questionnaire, Mobile Phone Addiction Tendency Scale and Bedtime Procrastination Scale were used to measure sleep and psychological condition of 1,048 college students. RESULTS: Family cohesion negatively predicted bedtime procrastination. Additionally, positive coping style and mobile phone addiction had significant independent mediating effects. Furthermore, positive coping style and mobile phone addiction had chain mediating effects between family cohesion and bedtime procrastination. CONCLUSION: This study revealed the effect of coping styles and mobile phone addiction on the relationship between family cohesion and bedtime procrastination among Chinese college students. These findings explained the mechanisms of bedtime procrastination from the perspective of environment, so as to effectively intervene the bedtime procrastination of college students from the perspective of external environment.

Impact of Social Media Addiction and Internet Gaming Disorder on Sleep Quality: Serial Mediation Analyses.

Poor sleep quality is a concerning and prevalent consequence of social media addiction (SMA) and internet gaming disorder (IGD). Due to the lack of research examining how SMA and IGD lead to poor sleep quality, the current study aimed to understand the relationship between SMA and sleep quality, as well as that between IGD and sleep quality, through impulse control and bedtime procrastination. The study tested the hypotheses that higher levels of SMA and IGD would predict lower levels of impulse control, which would then predict higher levels of bedtime procrastination, leading to poorer sleep quality. A serial mediation analysis was performed with a sample of 221 participants (63.3% females, 34.4% males, and 2.3% prefer not to say) aged 18 to 53 years (M = 23.64, SD = 5.72). Participants completed questionnaires that assessed for social media addiction, internet gaming disorder, impulse control factor, bedtime procrastination, and sleep quality. There was a full serial mediation of impulse control and bedtime procrastination in the relationship between SMA and sleep quality, as well as that between IGD and sleep quality, providing support for the hypotheses. The findings provide the knowledge needed to develop and implement strategies that target impulse control issues and reduce bedtime procrastination to improve sleep quality.

Late bedtime combined with more screen time before bed increases the risk of obesity and lowers diet quality in Spanish children.

This cross-sectional study aimed to investigate whether the combination of bedtime and screen time (ST) before bed were associated with obesity and diet quality in toddlers and school-aged children. Parents reported children's bedtimes and ST before bed (0 min, 1-30 min, >30 min). We then defined bed + screen time behavior using bedtime median cut-offs (early [EB] or late [LB]) and ST responses, resulting in four groups: EB-0'ST, EB ≤ 30'ST/LB-0'ST, EB > 30'ST/LB ≤ 30'ST, and LB > 30'ST. For all participants (n = 1133; 5.4 ± 2.7 years, 49.7% girls, 51.9% school-aged) we evaluated body mass index (BMI), diet quality, sleep-related variables, physical activity, and health-related quality of life (HRQoL). Outcome variables were compared across bed + screen time behavior groups, stratified by age group (toddlers and school-aged children) using general linear models for continuous variables, as well as chi-squared tests or logistic regressions for categorical variables. Additionally, we calculated linear p-trends. Analyses were adjusted for sociodemographic variables, BMI, and physical activity (unless the variable was tested). The results showed that toddlers and school-aged children in the LB ≥ 30'ST group were more likely to have overweight/obesity (OR: 3.42 [95%CI:1.41,8.26] and OR: 2.53 [95%CI:1.10,5.03], respectively) than those in the EB-0'ST group. Additionally, toddlers and school-aged children in the EB > 30'ST/LB ≤ 30'ST and LB > 30'ST groups showed significantly lower adherence to the Mediterranean diet compared to the other groups (p < 0.001). Regarding sleep-related outcomes, we observed that the combination of LB and more ST was associated with poorer sleep quality and shorter sleep duration in toddlers and school-aged children (p < 0.001). These findings emphasize the importance of promoting earlier bedtimes and limiting ST before bed as part of obesity prevention strategies for children. Furthermore, such intervention could benefit the quality of children's diet and overall lifestyle.

Demographic predictors of bedtime procrastination in the Japanese population.

Bedtime procrastination (BP) is prevalent and problematic, with no previous study conducted in Japan. This study developed the Japanese version of the Bedtime Procrastination Scale (BPS), assessed its validity, and investigated the relationship between BP, demographic features, and sleep outcomes. Participants were divided into two samples (N = 252 and N = 630). Sample 1 involved a longitudinal study to confirm test-retest reliability of the BPS. Sample 2 involved a cross-sectional study to assess confirmatory factor analysis, criterion-related validity, construct validity, and determine the internal consistency of the BPS. The relationship between BP and demographic features (gender, age, and employment status) and sleep outcomes (Athens Insomnia Scale, sleep hours, sleep onset latency, and sleep efficiency) was investigated using Sample 2. The BPS showed good internal consistency (Cronbach's α coefficients = .90-91), test-retest reliability (ICC = .86), and one factor model was valid; CFI = .95, TLI = .94, RMSEA = .10, and SRMR = .04. The BPS had a moderate positive association with general procrastination, moderate negative associations with self-control, sleep quality, and sleep duration on weekdays, and those who answered "yes" to the item "Do you have trouble with bedtime procrastination?" had higher BPS scores. BPS scores were moderately higher for younger participants (≤ 40 years), slightly higher for females, and non-significantly different between employment statuses. The BPS showed a significant positive correlation with insomnia symptoms, weak positive association with sleep efficiency on weekdays and holidays, and no significant association with sleep onset latencies on holidays and weekdays. This study provides new data on demographic predictors of BP in Japan. No clear effects of gender and employment status were found, but age was a strong predictor of BP, where younger age groups had a higher BP risk. Supplementary Information: The online version contains supplementary material available at 10.1007/s41105-023-00508-7.