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This study aimed to examine the effects of "adaptive" bedtime routines on a child's well-being, either directly or indirectly through sleep health. A web-based survey was conducted on 700 adults (321 male, 379 female, mean age = 39.98 years, SD = 6.33 years) responsible for preschool children aged 4-6 years old. Results of the mediation analysis showed that the bedtime routines index (BTR-Index[S]) could not confirm any significant regression coefficient with the total disturbance score of the Strengths and Difficulties Questionnaire (SDQ_TDS) (ß = -0.063, p = 0.094) and the total sleep disturbance of Children's Sleep Habits Questionnaire (CSHQ_TSD) (ß = -0.013, p = 0.736) in a single regression analysis. Sobel's test did not confirm any significant indirect effect (Z = -0.337, p = 0.736). As exploratory examination of the relationships between each of the items of BTR-Index(S) with SDQ_TDS and CSHQ_TSD, multiple regression analysis showed a significant positive partial regression coefficient for "Reading/sharing a story before bed" (ß = 0.228, p = 0.006) and a significant negative partial regression coefficient for"Avoiding the use of electronic devices before bed" (ß = -0.222, p = 0.011) towards CSHQ_TSD, with no significant partial regression coefficient identified for SDQ_TDS in any of the items. These findings suggest that bedtime routines do not directly either indirectly, through their sleep health, affect a child's well-being. However, caregivers' deliberate attempt to avoid stimuli that increases children's wakefulness before bedtime may serve as protection for the child's sleep health.

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Objective: Concerns regarding sleep problems in emerging adults and their antecedents, such as problematic smartphone use (PSU), have been growing. This study tested the association between PSU and sleep problems and further investigated the mechanisms of this relationship based on the theory of compensatory health beliefs (CHBs). Methods: This study included 999 participants (74.87% female) in China, aged 17 to 25 years (M = 21.16; standard deviation = 1.60), who voluntarily filled in an anonymous survey. Results: The findings showed positive correlations between sleep problems and PSU, sleep-related CHBs, and bedtime procrastination (rs = .25-.52, p < .001). Furthermore, the positive link between PSU and sleep problems was mediated by bedtime procrastination alone (ß=.21, 95% confidence interval (CI) [.17, .26]) or a serial path of sleep-related CHBs and bedtime procrastination (ß=.04, 95% CI [.02, .05]). Conclusion: This study provides a new perspective to understand the internal mechanism underlying the PSU-sleep problem link. Interventions for sleep disorders ought to consider the theoretical guidelines of the CHBs model to reduce the risk of bedtime procrastination and sleep disorders in emerging adults.

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BACKGROUND: Loneliness was associated with interpersonal sensitivity, but the factors contributing to this relationship in nursing students remain unclear. This study investigated the relationship between loneliness and interpersonal sensitivity among nursing undergraduates, with a specific focus on the mediating roles played by problematic internet use and bedtime procrastination. METHOD: This study was conducted as a cross-sectional survey at a university in China between November and December 2022. Data were collected using a self-administered online questionnaire that included demographic characteristics, the Three-Item Loneliness Scale (T-ILS), the Chinese Version of Short Form of Interpersonal Sensitivity Measure (IPSM-CS), the 6-item short form of the Problematic Internet Use Questionnaire (PIUQ-SF-6), and the Bedtime Procrastination Scale (BPS). Pearson correlation analysis was employed to explore the relationships among loneliness, interpersonal sensitivity, problematic internet use, and bedtime procrastination. Structural equation modeling (SEM) was conducted using AMOS software to examine the mediating role of problematic internet use and bedtime procrastination between loneliness and interpersonal sensitivity. RESULTS: Loneliness was positively related to interpersonal sensitivity among nursing undergraduates (ß = 0.44, P < 0.001). There was a significant chain mediation role of problematic internet use and bedtime procrastination in the relationship between loneliness and interpersonal sensitivity. CONCLUSIONS: The study contributed to deepening the understanding of the relationship between loneliness and interpersonal sensitivity and provided valuable insights into the improvement of interpersonal sensitivity in nursing undergraduates.

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BACKGROUND AND OBJECTIVES: This study aimed to evaluate the effects of milk and banana given as a bedtime snack to patients with primary insomnia on sleep parameters and some biochemical parameters such as brain-derived neurotrophic factor, leptin, and ghrelin. METHODS AND STUDY DESIGN: 21 patients with insomnia who met the inclusion criteria participated in this study. The patients were divided into 3 parallel groups: banana, milk and control. The intervention group were given either 1 portion of banana or just 200 mL of whole-fat milk at bedtime. The control group did not consume any non-routine food. Venous blood samples were taken at baseline and after the study from patients to measure brain-derived neurotrophic factor, leptin and ghrelin concentrations. Sleep quality and architecture were determined by polysomnography and Pittsburg Sleep Quality Index. RESULTS: Pittsburg Sleep Quality Index scores of the banana and milk group were found to be lower after intervention (p<0.05). In terms of polysomnography, the total sleep time of the milk group was found to be significantly higher than baseline. Serum ghrelin concentration of the milk group decreased significantly compared to baseline. CONCLUSIONS: Bedtime milk or banana intake was effective in dealing with insomnia. Foods rich in tryptophan, such as banana and milk, given at bedtime, may improve sleep parameters and appetite hormones.

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BACKGROUND: The association between sleep patterns and young students' mental health, which is crucial for their development, remains understudied in rural China. Therefore, the relationship between sleep patterns and mental health among primary and junior high school students in rural China was examined. METHOD: A total of 1592 primary and junior high school students from rural areas of Gansu Province were surveyed, and the Depression Anxiety and Stress Scale (DASS) was utilized to assess mental health, alongside self-reported data on their daily sleep patterns. RESULTS: Significant sleep inadequacies were identified: 28% of students received less than 8 h of sleep on weekdays, and 19% went to bed later than recommended. On weekends, 38% of students had delayed bedtimes, though only 7.2% received less than 8 h of sleep. Notably, a "U-shaped" relationship was uncovered between sleep duration and mental health for students on weekends, with optimal mental health correlated with receiving 10-11 h of sleep, while both shorter and longer sleep durations on weekends worsened outcomes. This pattern is absent on weekdays. Additionally, adequate sleep and an earlier bedtime was linked to a 6-8% decrease in mental health risks. CONCLUSIONS: These findings provide valuable insights for policymakers seeking to enhance student mental well-being in rural settings, emphasizing the importance of implementing measures that promote balanced sleep habits among young students.

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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.

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Objectives: This study investigated associations between the use of electronic media and sleep in children and young adolescents, with a specific focus on the moderation of associations by sex and age. Methods: Between 2021 and 2022, 453 10- to 14-year-old children participating in the LIFE Child cohort study (Germany) reported on their use of electronic media (daily screen time, use at bedtime, device ownership) and on their sleep difficulties (Sleep Self Report). Associations between media use and sleep as well as interactions with age and sex were assessed using linear regression analyses. Results: The analyses revealed significant associations between the use of media at bedtime and bedtime problems (in girls only), sleep behavior problems (in girls only), and daytime sleepiness (in girls and boys). Daily screen time, in contrast, was associated with none of the sleep difficulties. The number of media devices owned by the child was only associated with bedtime problems in girls, and this association lost statistical significance once media use at bedtime was included as further predictor. Conclusion: The findings underline the potentially sleep-disturbing role of electronic media at bedtime. Furthermore, they suggest that this effect is more pronounced in girls than in boys.

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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.

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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.

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PURPOSE: To determine prospective associations between bedtime screen use behaviors and sleep outcomes one year later in a national study of early adolescents in the United States. METHODS: We analyzed prospective cohort data from 9,398 early adolescents aged 11-12 years (48.4% female, 45% racial/ethnic minority) in the Adolescent Brain Cognitive Development Study (Years 2-3, 2018-2021). Regression analyses examined the associations between self-reported bedtime screen use (Year 2) and sleep variables (Year 3; self-reported sleep duration; caregiver-reported sleep disturbance), adjusting for sociodemographic covariates and sleep variables (Year 2). RESULTS: Having a television or Internet-connected electronic device in the bedroom was prospectively associated with shorter sleep duration one year later. Adolescents who left their phone ringer activated overnight had greater odds of experiencing sleep disturbance and experienced shorter sleep duration one year later, compared to those who turned off their phones at bedtime. Talking/texting on the phone, listening to music, and using social media were all prospectively associated with shorter sleep duration, greater overall sleep disturbance, and a higher factor score for disorders of initiating and maintaining sleep one year later. DISCUSSION: In early adolescents, several bedtime screen use behaviors are associated with adverse sleep outcomes one year later, including sleep disturbance and shorter weekly sleep duration. Screening for and providing anticipatory guidance on specific bedtime screen behaviors in early adolescents may be warranted.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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