Self-reported Sleep Behaviors Research Articles

Exploring the relationship between wellness behaviors and burnout amongst university faculty and staff

Abstract Higher education has recently experienced unprecedented faculty exodus, largely due to burnout. Burnout is associated with poor health outcomes. Active lifestyles contribute to health and reduced burnout, but research investigating wellness behaviors and burnout amongst university faculty and staff is lacking. The purpose of this study was to assess wellness behaviors including physical activity, nutrition and sleep in university faculty and staff and their associations with burnout. This mixed methods study included two phases. First phase was a quantitative survey assessing burnout total burnout, self-reported physical activity, nutrition and sleep behaviors. Phase II was a qualitative open-ended questionnaire assessing perceptions of workplace factors associated with burnout and campus resources to support wellness. A total of 294 faculty and staff participated in phase I. The majority of respondents identified as female (n= 158, 53.7%) and in faculty roles, n=150 (53.6%). The majority of participants, n=169 (57.5%) reported moderate or high levels of burnout. The majority of participants were inactive or minimally active, n=174 (59.2%). There was a statistically significant, positive, correlation between physical activity status and personal burnout, r(252)=0.21, p <0.001. Of 48 participants in phase II, access to wellness resources and organizational factors emerged as factors associated with burnout. Participants expressed desires for improved access to exercise and wellness resources and described disparities between student resources and those for faculty. This research might be used to inform practice through the development of support programs, wellness initiatives, or facilities for physical activity on campus for faculty and staff.

Joint association of sleep quality and physical activity with metabolic dysfunction-associated fatty liver disease: a population-based cross-sectional study in Western China

BackgroundMetabolic dysfunction-associated fatty liver disease (MAFLD) is a growing threat leading to substantial disease burden globally. Poor sleep and physical inactivity are common in modern societies and independently associated with MAFLD, however, their joint effects on MAFLD remains unclear.MethodsThis population-based cross-sectional study was conducted in Xinjiang Uygur Autonomous Region, China, between July 2019 and September 2021. Self-reported sleep behaviors and physical activity (PA) were assessed using validated questionnaires. The primary outcome was radiological diagnosis of MAFLD.ResultsOf the 10 089 participants aged 47.0 (9.1) years (51.6% men), 3854 (38.2%) individuals had MAFLD. Poor sleep quality and physical inactivity were independently and jointly associated with an increased prevalence of MAFLD, independent of traditional risk factors (P < 0.05). Compared to subjects with guideline-recommended moderate-to-vigorous PA (MVPA) and good sleep quality, individuals with no recommended MVPA and poor sleep had the highest possibility of MAFLD (odds ratio = 2.36, 95% confidence interval: 1.81 – 3.08). Enhancing sleep quality substantially attenuated MAFLD prevalence regardless of the volume of PA, whereas, engaging in PA well above current guidelines did not adequately counteract the adverse impacts of poor sleep on MAFLD.ConclusionsPublic health awareness and strategies concurrently targeting both sleep quality and PA should be encouraged to curb the climbing prevalence of MAFLD.

0193 Sleep profiles of drinking and non-drinking college students

Abstract Introduction Alcohol use and insufficient sleep are prevalent in American college students and studies suggest that these behaviors are reciprocally interrelated. However, there is limited information about the relationship between sleep and alcohol use in college students without sleep or alcohol disorders. This study aimed to characterize the relationship between sleep and alcohol use in healthy college students by considering the effects of using alcohol on same-day nocturnal sleep, next-day nocturnal sleep, and napping. Methods Thirty-one college students (n=12 consumed alcohol during the study, n=19 did not) completed twice-daily online diary reports and wore a wrist-actigraphy device across two weeks. Linear mixed models examined person-specific (e.g., drinker or non-drinker) and event-specific (e.g., drinking or non-drinking night; school or free night) effects of alcohol use on subjective and objective sleep outcomes. Results When averaged across all nights, sleep behavior did not differ between drinkers and non-drinkers (ps&amp;gt;0.05). On drinking nights, participants went to bed later (p&amp;lt; 0.05) but woke up at similar times compared to non-drinking nights, resulting in significantly shorter sleep durations on drinking nights (p&amp;lt; 0.05). There were no effects of alcohol use on subjective sleep quality or daytime sleepiness (ps&amp;gt;0.05) or on next-day nocturnal sleep variables (ps&amp;gt;0.05). Daytime sleep (i.e., napping) was observed in 25 (81%) participants. The proportion of nappers and number of naps per participant did not significantly differ by drinking status (ps&amp;gt;0.05). There were also no differences in actigraphy-derived daytime sleep variables between drinkers and non-drinkers (ps&amp;gt;0.05). Among drinkers who napped (n=9), naps were taken on 27% of days following drinking nights and on 17% of days following non-drinking nights (p&amp;gt;0.05). Conclusion This study bolsters prior reports that insufficient nocturnal sleep and daytime napping are common in college by validating self-reported sleep behaviors with objective actigraphy data. Efforts to convey the importance of proper sleep are still needed in the college context. Future research should examine consequences of layering acute bouts of excessive alcohol consumption onto insufficient nocturnal sleep. Support (if any) This work was supported by the NIH NIAAA grant R01AA028286 to JB and AS.

0033 Genome-wide association analysis of composite sleep scores in 413,904 individuals

Abstract Introduction Recent genome-wide association studies (GWAS) of individual sleep traits have identified hundreds of genetic loci, often implicating genes and biological pathways that are shared across sleep phenotypes. Modeling multiple dimensions of sleep may allow identification of common underlying genetic mechanisms and complement analyses of individual traits. Moreover, construction of novel sleep health scores, accounting for correlations between traits, has the potential to enhance specificity and power for genetic analyses. Methods We performed GWAS of composite sleep scores derived from five sleep questions characterizing 413,904 individuals of European ancestry from the UK Biobank. We constructed an additive sleep score (SS-add) as a sum of up to five favorable self-reported sleep behaviors (sleep duration of 7-8 hours, early chronotype, few insomnia symptoms, no snoring, and no excessive daytime sleepiness) as well as five principal component scores (SS-PC1 – SS-PC5) using the underlying sleep traits. SNP-level association studies of the sleep scores were complemented with multiple follow-up analyses, including investigation of pathway, tissue, and cell-type enrichments, as well as comprehensive genetic correlation (381 representative phenotypes) and bi-directional Mendelian randomization (MR) analyses (40 selected phenotypes). Results Sleep scores SS-PC1 (interpretable as longer duration sleep without insomnia symptoms) and SS-PC2 (healthy sleep without snoring or sleepiness) showed higher heritability (respectively 11.7% and 9.3%) compared with their primary underlying traits. SS-PC3 (loading strongly on chronotype) showed highest overall heritability (15.3%). Accounting for the six GWAS, we identified 28 significant novel loci (p&amp;lt; 8.3e-9), 31 additional novel loci (p&amp;lt; 5e-8), and 341 loci previously reported (p&amp;lt; 5e-8) by GWAS of individual sleep traits. Associated loci mapped to genes enriched in expression in brain tissues, and in metabolic and neuronal pathways. Numerous neurological, cardiometabolic and other traits were genetically correlated with sleep health (104 with|rho_g|&amp;gt;0.3, p&amp;lt; 2e-5). MR associations (p&amp;lt; 2e-4) pointed to causes (BMI [SS-PC2/PC4], smoking [SS-add/PC1], lower socio-economic status [SS-add/PC5]) and consequences (coronary heart disease [SS-add/PC1], pain [SS-PC1/5]) of poor sleep. Conclusion Composite sleep health scores revealed novel genetic mechanisms of related sleep behaviors, helped clarify relationships with neurological and cardiometabolic traits, and deserve further investigation. Support (if any) R01HL153814 (to H.W.), NHLBI R35HL135818 (to S.R.), 1R01HL146751 (to R.S.), UKB Application 6818.

Association of Self-Reported Sleep Characteristics and Hip Fracture: Observational and Mendelian Randomization Studies.

Previous observational studies on the relationship between sleep characteristics and fracture have yielded contradictory results. The goal of this study was to replicate the findings in a large longitudinal cohort and then conduct a Mendelian randomization (MR) analysis to infer the causality between sleep behaviors and fracture risk. Based on data from the China Health and Retirement Longitudinal Study (CHARLS) including 17,708 participants, we found that individuals with short sleep duration (<5 h) (OR [odds ratio] = 1.62, 95% CI: 1.07-2.44) or restless sleep (OR = 1.55, 95% CI: 1.10-2.19) have a higher risk of hip fracture. A U-shaped relationship between nighttime sleep duration and hip fracture risk (p-nonlinear = 0.01) was observed using restricted cubic spline regression analysis. Through joint effect analysis, we found that participants with short sleep duration (<5 h) combined with midday napping could significantly decrease hip fracture incidence. We further inferred the causal relationship between self-reported sleep behaviors and hip fracture using the MR approach. Among four sleep phenotypic parameters (sleep duration, daytime napping, chronotype, and insomnia), we found a modest causal relationship between sleep duration and fracture (OR = 0.69, 95% CI: 0.48 to 0.99, p = 0.04). However, no causal relationship was observed for other sleep traits. In conclusion, our findings suggest that short sleep duration has a potential detrimental effect on hip fracture. Improving sleep patterns is of significance for developing hip fracture preventive strategies in the middle-aged and the elderly populations.

The Effects of Objective Push-Type Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: mHealth Intervention Trial Using a Health Care Internet of Things System.

BackgroundSleep is beneficial for physical and mental health. Several mobile and wearable sleep-tracking devices have been developed, and personalized sleep feedback is the most common functionality among these devices. To date, no study has implemented an objective push-type feedback message and investigated the characteristics of habitual sleep behavior and diurnal symptoms when receiving sleep feedback.ObjectiveWe conducted a mobile health intervention trial to examine whether sending objective push-type sleep feedback changes the self-reported mood, physical symptoms, and sleep behavior of Japanese office workers.MethodsIn total, 31 office workers (mean age 42.3, SD 7.9 years; male-to-female ratio 21:10) participated in a 2-arm intervention trial from November 30 to December 19, 2020. The participants were instructed to indicate their momentary mood and physical symptoms (depressive mood, anxiety, stress, sleepiness, fatigue, and neck and shoulder stiffness) 5 times a day using a smartphone app. In addition, daily work performance was rated once a day after work. They were randomly assigned to either a feedback or control group, wherein they did or did not receive messages about their sleep status on the app every morning, respectively. All participants wore activity monitors on their nondominant wrists, through which objective sleep data were registered on the web on a server. On the basis of the estimated sleep data on the server, personalized sleep feedback messages were generated and sent to the participants in the feedback group using the app. These processes were fully automated.ResultsUsing hierarchical statistical models, we examined the differences in the statistical properties of sleep variables (sleep duration and midpoint of sleep) and daily work performance over the trial period. Group differences in the diurnal slopes for mood and physical symptoms were examined using a linear mixed effect model. We found a significant group difference among within-individual residuals at the midpoint of sleep (expected a posteriori for the difference: −15, 95% credible interval −26 to −4 min), suggesting more stable sleep timing in the feedback group. However, there were no significant group differences in daily work performance. We also found significant group differences in the diurnal slopes for sleepiness (P<.001), fatigue (P=.002), and neck and shoulder stiffness (P<.001), which was largely due to better scores in the feedback group at wake-up time relative to those in the control group.ConclusionsThis is the first mobile health study to demonstrate that objective push-type sleep feedback improves sleep timing of and physical symptoms in healthy office workers. Future research should incorporate specific behavioral instructions intended to improve sleep habits and examine the effectiveness of these instructions.

Earlier Bedtime and Effective Coping Skills Predict a Return to Low-Risk of Depression in Young Adults during the COVID-19 Pandemic.

To determine the persistent effects of the pandemic on mental health in young adults, we categorized depressive symptom trajectories and sought factors that promoted a reduction in depressive symptoms in high-risk individuals. Specifically, longitudinal analysis investigated changes in the risk for depression before and during the pandemic until December 2021 in 399 young adults (57% female; age range: 22.8 ± 2.6 years) in the United States (U.S.) participating in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The Center for Epidemiologic Studies Depression Scale (CES-D-10) was administered multiple times before and during the pandemic. A score ≥10 identified individuals at high-risk for depression. Self-reported sleep behavior, substance use, and coping skills at the start of the pandemic were assessed as predictors for returning to low-risk levels while controlling for demographic factors. The analysis identified four trajectory groups regarding depression risk, with 38% being at low-risk pre-pandemic through 2021, 14% showing persistent high-risk pre-pandemic through 2021, and the remainder converting to high-risk either in June 2020 (30%) or later (18%). Of those who became high-risk in June 2020, 51% were no longer at high-risk in 2021. Logistic regression revealed that earlier bedtime and, for the older participants (mid to late twenties), better coping skills were associated with this declining risk. Results indicate divergence in trajectories of depressive symptoms, with a considerable number of young adults developing persistent depressive symptoms. Healthy sleep behavior and specific coping skills have the potential to promote remittance from depressive symptoms in the context of the pandemic.

Sleep Factors in Relation to Metabolic Dysfunction-Associated Fatty Liver Disease in Middle-Aged and Elderly Chinese.

Accumulating evidence implies that sleep disturbance is involved in metabolic disorders. We comprehensively evaluated the association between various dimensions of sleep behaviors and the risk for metabolic dysfunction-associated fatty liver disease (MAFLD). In this cross-sectional study of 5011 participants with self-reported sleep behaviors and radiologically diagnosed MAFLD, a comprehensive healthy sleep score was generated to evaluate the associations between sleep behaviors and MAFLD risk using multivariate logistic regression adjusting for demographics, lifestyles, medication, and metabolic comorbidities. Furthermore, mediation analysis was utilized to assess the extent to which obesity explains the effect of sleep quality on MAFLD risk. Late bedtime, snoring, and daytime napping for over 30 minutes significantly associated with an increased risk of MAFLD, with odds ratios (OR) of 1.37 (95% CI 1.10, 1.70), 1.59 (95% CI 1.33, 1.91), and 1.17 (95% CI 1.02, 1.35), respectively, after full adjustments including obesity. Participants with disturbance in nighttime sleep and prolonged daytime napping showed the highest risk for MAFLD (OR 2.38, 95% CI 1.73, 3.27). Each additional increase of healthy sleep score was associated with a 16% reduction in MAFLD risk. Further stratified analysis revealed that people with a sedentary lifestyle and central obesity experienced more prominent adverse effects from poor sleep quality than others. Moreover, obesity accounted for only 20.77% of the total effect of sleep quality on MAFLD risk. Sleep behaviors, both cumulatively and individually, are associated with MAFLD risk. Public health awareness and strategies should be encouraged to curb MAFLD.

Predicting stress and depressive symptoms using high-resolution smartphone data and sleep behavior in Danish adults.

The early detection of mental disorders is crucial. Patterns of smartphone behavior have been suggested to predict mental disorders. The aim of this study was to develop and compare prediction models using a novel combination of smartphone and sleep behavior to predict early indicators of mental health problems, specifically high perceived stress and depressive symptoms. The data material included two separate population samples nested within the SmartSleep Study. Prediction models were trained using information from 4522 Danish adults and tested in an independent test set comprising of 1885 adults. The prediction models utilized comprehensive information on subjective smartphone behavior, objective night-time smartphone behavior, and self-reported sleep behavior. Receiver operating characteristics area-under-the-curve (ROC AUC) values obtained in the test set were recorded as the performance metrics for each prediction model. Neither subjective nor objective smartphone behavior was found to add additional predictive information compared to basic sociodemographic factors when forecasting perceived stress or depressive symptoms. Instead, the best performance for predicting poor mental health was found in the sleep prediction model (AUC = 0.75, 95% CI: 0.72-0.78) for perceived stress and (AUC = 0.83, 95%CI: 0.80-0.85) for depressive symptoms, which included self-reported information on sleep quantity, sleep quality and the use of sleep medication. Sleep behavior is an important predictor when forecasting mental health symptoms and it outperforms novel approaches using objective and subjective smartphone behavior.

Relationship Between Sleep Duration and Stroke History in Middle-Aged and Elderly in Guiyang: A Cross-Sectional Survey.

BackgroundWith over 2 million new cases annually, stroke is associated with the higher amount of disability-adjusted life-years lost than any other disease in China; however, the relationship between sleep time and stroke has not been concluded yet. Aim of this study was to analyze the relationship between sleep duration and stroke history in middle-aged and elderly people in Guiyang, China.MethodsThis study was a cross-sectional survey carried out in 40–99-years-old permanent residents of Guiyang. Yunyan, Wudang, and Baiyun districts and Xiuwen County were selected by stratified multilevel sampling for a face-to-face survey. Demographics, history of stroke, and self-reported sleep behavior data were collected, and multivariable logistic regression models were used to gradually adjust possible confounding factors.ResultsA total of 5065 participants were included, of them 126 (2.5%) had a history of stroke. Short sleep (<7 h) was observed in 11.0%, sufficient sleep (7–9 h) in 69.4%, and long sleep (>9 h) in 19.6%. Sleep duration and stroke prevalence showed a U-shaped distribution. When taking the sleep duration of 7–9 h as a reference, sleep duration >9 h was associated with stroke (all P < 0.05) in the univariable model (OR = 2.68, 95% CI: 1.83–3.93) and in the multivariable models 1 (OR = 2.35, 95% CI: 1.59–3.47), 2 (OR = 2.27, 95% CI: 1.53–3.37), 3 (OR = 2.25, 95% CI: 1.51–3.33), and 4 (OR = 2.11, 95% CI: 1.39–3.19). There were no significant differences between the <7 and 7–9 h groups (P > 0.05).ConclusionThus, long sleep duration (>9 h) is independently associated with history of stroke in middle-aged and elderly people in Guiyang.

False beliefs about sleep and their associations with sleep-related behavior

ObjectivesUnderstanding the association between sleep-related beliefs and behaviors may be useful in improving sleep health in the general population. This study examines false beliefs about sleep and their associations with self-reported sleep and related behaviors. MethodsRespondents in an online survey indicated the degree to which they agreed with 20 statements previously identified as sleep myths by experts in the field. A total sleep myths score was calculated for each participant, with higher scores reflecting greater false beliefs. Sociodemographic factors, behaviors, and knowledge related to sleep were also assessed. ResultsTotal sample size was 1120 adults residing in the United States (51.5% female; M age = 47.22). Overall, belief in sleep myths was relatively common, with 10 of 20 false statements endorsed by at least 50% of the sample. Sleep myth scores varied by sociodemographic factors, including age, gender, socioeconomic indicators, and region of residence. Higher sleep myth scores were associated with greater inconsistency in bedtimes (odds ratio: 1.07 [1.04-1.09]), more frequent napping (odds ratio: 1.11 [1.09-1.14]), more in-bed activities (β = 0.35, p < .001), engaging in behaviors incompatible with sleep hygiene recommendations (β = 0.24, p < .001), and perceiving fewer consequences of insufficient sleep (β = −0.13, p < .001). Those endorsing more myths reported shorter sleep on non-worknights (β = −0.09, p = .01) but not on worknights. ConclusionsBelief in sleep myths is related to sleep health behavior and may be a modifiable target for intervention.

Abstract 848: Sleep behaviors and risk of lung cancer in the UK Biobank

Abstract Background. Lung cancer is the second most common malignancy and the leading causes of cancer death in both men and women in the United States. Cross-sectional studies on sleep disorders showed that sleep apnea and nocturnal hypoxemia are associated with an increased risk of lung cancer. Studies on sleep patterns are largely limited to sleep duration, snoring, insomnia and narcolepsy, in which short or long sleep duration, insomnia and narcolepsy are associated with an increased lung cancer risk. However, the association with other sleep patterns such as chronotype has not yet been investigated and the joint collective impact of sleep behaviors on lung cancer risk remains undetermined. In this study, we examined the association of six sleep behaviors with risk of lung cancer overall and among subgroups defined by smoking status and sex. Methods. A total of 410,252 UK Biobank participants free of lung cancer aged 37-73 years recruited in 2006-2010 were included. Lung cancer cases were identified from cancer registry or death records up to September 2020. Self-reported sleep behaviors (chronotype, duration, daytime nap, snoring, insomnia, and narcolepsy) were collected at study baseline. Multivariable Cox proportional hazard regression was used to estimate the hazard ratio (HR) and 95% confidence interval (CIs) for each sleep behavior and risk of lung cancer. A composite unhealthy sleep score (0-3) was further developed by summing the individual score of three unhealthy sleep behaviors: evening chronotype, sleep &amp;lt;7 or &amp;gt;8 h/day, and usual daytime nap, with higher scores indicating a higher-risk sleep pattern. Stratified analyses jointly by smoking status (never vs. ever) and sex were conducted. Results. During up to 13.6 years of follow up with 4,693,213 person-years, a total of 2,897 lung cancer cases were documented. Compared with individuals with morning chronotype, participants with evening chronotype had an 11% increased risk of lung cancer (HR: 1.11, 95%CI: 0.98-1.26). Those who slept &amp;lt;7 or &amp;gt;8 h/day had 9-25% increased risk compared with individuals who slept 7-8 hours. Compared with those who never took daytime nap, participants with usual nap had an 14% increased lung cancer risk (HR: 1.14, 95%CI: 1.00-1.30). Snoring, insomnia and narcolepsy were not associated with lung cancer risk. Compared with participants without any of high-risk sleep behavior (evening chronotype, sleep &amp;lt;7 or &amp;gt;8 h/day, and usual daytime nap), those with 1 high-risk behavior had 12% (HR: 1.12, 95%CI: 1.04-1.21) increased risk of lung cancer, and individuals with 2 or 3 high-risk sleep behaviors had a 30% (HR: 1.30, 95%CI: 1.15-1.47) increased risk of lung cancer (Ptrend &amp;lt; 0.001). Such association was similarly observed among male and female smokers, but was slightly stronger in male never smokers compared to females. Conclusion. Unhealthy sleep behaviors including evening chronotype, sleep &amp;lt;7 or &amp;gt;8 hours per day, and usual daytime nap was associated with increased risk of lung cancer. Citation Format: Xiaoyan Wang, Xiaoyu Zong, Na Li, Ramaswamy Govindan, Graham Colditz, Yin Cao. Sleep behaviors and risk of lung cancer in the UK Biobank [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 848.

Development of a Sleep Assessment Module in the Automated Self-Administered 24-Hour (ASA24) Dietary Assessment Tool: New Research Opportunities

Abstract Objectives To develop a sleep assessment module in ASA24 to capture self-reported sleep behavior as an optional enhancement to the ASA24 Dietary Assessment Tool for adults. Methods Multiple self-reported sleep assessment tools were considered in module development, including the National Sleep Foundation Sleep Diary, the Activities Completed over Time in 24-hours (ACT24), Munich Chronotype Questionnaire (MCTQ), and the Consensus Sleep Diary (CSD) Core. Priority was given to minimal need for adaptation, questionnaire length to reduce survey fatigue, incorporating plain language, and optimizing for implementation in 24 hour recalls (24HR) and food records. Researchers with expertise in meal timing and sleep were consulted for feedback on content and utility and programmers with expertise in survey design were consulted on implementation. Lastly, the online data collection process and ASA24 System's output data files were tested for accuracy. Results The ASA24 sleep module contains ten questions and can be administered immediately following dietary assessment. Eight CSD Core questions were adapted to assess time in bed, time trying to go to sleep, and length of time to fall asleep; number and duration of nocturnal awakenings; wake time and time out of bed for the day; and perceived sleep quality. Two questions were added to capture sleep quality and comparability of reported sleep to a usual night's sleep. For users completing a 24HR, the module includes two questions on time of awakening and sleep quality immediately preceding the first reported meal; all 10 sleep questions are asked for the sleep period immediately following the last meal (i.e., 12 questions total), allowing for assessment of the impact of diet on sleep. In contrast, a food record is completed on the same day users consume the food, and thus all sleep questions address the sleep window prior to the first meal; a single record can be used to assess the impact of sleep on diet. Consecutive days of records can also be collected to capture sleep pre- and post-eating windows. Conclusions The ASA24 sleep module can assess sleep timing and quality and will be available in Fall 2021. Researchers can soon leverage this novel resource to examine the association of sleep with timing of eating and other chrononutrition variables. Funding Sources This project has been funded by the NIH.

24-h movement behaviors among autistic adults: Differences by sex, age, and level of independence

BackgroundAutistic adults have low levels of physical activity [PA], high levels of sedentary behavior [SB], and insufficient sleep. Not known is the extent to which engagement in these movement behaviors vary by sex, age, and level of independence in activities of daily living (ADLs). ObjectiveTo characterize movement behaviors in a national sample of autistic adults by sex, age, and level of independence in ADLs. MethodsA national sample of autistic adults and caregivers of autistic adults self-reported PA, SB and sleep behaviors as well as demographic variables using an electronic survey. Levels of engagement in movement behaviors were described, and compared by sex, age (young-adult versus middle-age), and level of independence in ADLs. ResultsData were collected on 361 autistic adults (60.3% male, n = 217, mage = 30.82 years, SD = 10.24). Overall, 44% did not meet the PA guideline; PA guideline adherence was lowest among males and those who were dependent on others in ADLs. Overall, the SB guideline was not met by 43% of the sample at weekdays and 48% at weekends. SB guideline adherence was lowest among adults who were middle-aged, and those who were fully independent in ADLs. Overall, 35.2% did not meet the total sleep time (TST) guideline. Middle-aged autistic adults had the lowest adherence to the TST guideline. ConclusionsThese finding should prompt researchers to consider these demographic differences, and tailor research and programmatic efforts to account for the unique movement behavior profiles of different segments of this heterogenous population.

A Functional Adenosine Deaminase Polymorphism Associates with Evening Melatonin Levels and Sleep Quality.

Increased adenosine levels throughout the day promote sleepiness. A single nucleotide polymorphism (SNP) in the adenosine deaminase ADA gene (rs73598374) has been shown to affect sleep regulation. The extent to which lower ADA enzymatic activity is associated with the homeostatic sleep factor, melatonin, is uncertain. To test this possibility, we assessed the relationship between the ADA polymorphism and evening melatonin levels, as well as self-reported sleep behavior. Given the close relationship between mood and sleep behavior, we further tested the impact of ADA genotype on self-reported mood. We show that relative to the GG homozygotes, the A allele carriers (higher adenosine levels) had significantly higher evening melatonin levels as well as significantly better sleep quality. We further show the correlations between sleep and mood measures were altered by ADA genotype, with a stronger relationship observed in the GG (lower adenosine) group. Combined, these findings advance our understanding of the biochemistry of melatonin production by showing that there is a relationship between ADA genotype and melatonin levels. The differential relationships between sleep and psychological health between the genotype groups may reveal novel insights about the development of genotype-specific progression of various psychological disorders such as chronic anxiety and stress.

Self-reported current sleep behaviors of adult athletes from different competitive levels and sports.

ObjectivesTo quantify self-reported current sleep behaviors in a range of adult athletes. In addition, to determine any differences in sleep duration and sleep quality, depending on sport type and competitive level.Material and MethodsIn this cross-sectional study, 313 athletes (243 male, 70 female), competing in a variety of sports and competitive level, completed the Pittsburgh sleep quality index (PSQI) and a questionnaire which captured current sleep behaviors. Sleep quality was calculated using the global PSQI score (≥ 5 indicative of poor sleep quality).ResultsOn average, athletes self-reported sleep duration was 7:34 ± 1:00 h:min. Overall, 19% of athletes achieved less than 7 h of sleep, 50% achieved less than 8 h. Global PSQI score was 5.0 ± 2.4, with poor sleep quality found in 55% of athletes. Sleep duration was significantly shorter in runners compared to basketball, soccer and rugby players (p < 0.05). Recreational athletes slept significantly less (7:08 ± 0:54 h:min) than competitive (7:32 ± 1:00 h:min), national (7:50 ± 1:00 h:min) and elite level athletes (7:49 ± 0:51 h:min). No differences in sleep quality were found between sport or competitive level.DiscussionHalf of the athletes failed to achieve 8 h of sleep per night and the majority reported compromised sleep quality. Sport type and competitive level may influence sleep duration; however, these factors do not seem to cause discrepancies in sleep quality. This study provides novel data into the sleep behaviors of adult athletes, and suggests strategies to improve sleep duration and quality may be warranted.

The Discrepancy between Knowledge of Sleep Recommendations and the Actual Sleep Behaviour of Australian Adults

ABSTRACT Introduction: Inadequate sleep is a major public health concern, with large economic, health, and operational costs to Australia. Despite the implementation of public sleep health campaigns, approximately 40% of Australian adults do not obtain the recommended 7–9 hours of sleep. Thus, while people may know how much sleep is required, this knowledge may not be adequately translated to actual sleep behavior. Consequently, this study aims to examine the discrepancy between knowledge of sleep recommendations and self-reported sleep behaviors. Methods: A sample of 1265 Australian adults (54% female, aged 18–65) completed a phone interview as part of the 2017 National Social Survey and were asked questions about their knowledge of sleep guidelines and their actual sleep behavior. Binary logistic regression was used to determine the factors associated with awareness of sleep recommendations and whether this corresponded with reported sleep duration. Results: The final sample size was 998. Although 94% of the sample were aware of current sleep recommendations, 23% of participants did not self-report regularly obtaining 7–9 h sleep per night. These participants were less likely to want to obtain more sleep, less likely to view sleep as a priority before stressful events, and less likely to self-report good health. Conclusion: Although a majority of the sample were aware of sleep recommendations, almost a quarter of the participants’ behavior did not align with their knowledge. Future sleep health campaigns should consider options beyond education, including emphasis on practical strategies and modifiable lifestyle factors to assist individuals to obtain the recommended amount of sleep.

Sleep deficits and cannabis use behaviors: an analysis of shared genetics using linkage disequilibrium score regression and polygenic risk prediction.

Estimate the genetic relationship of cannabis use with sleep deficits and an eveningness chronotype. We used linkage disequilibrium score regression (LDSC) to analyze genetic correlations between sleep deficits and cannabis use behaviors. Secondly, we generated sleep deficit polygenic risk score (PRS) and estimated their ability to predict cannabis use behaviors using linear and logistic regression. Summary statistics came from existing genome-wide association studies of European ancestry that were focused on sleep duration, insomnia, chronotype, lifetime cannabis use, and cannabis use disorder (CUD). A target sample for PRS prediction consisted of high-risk participants and participants from twin/family community-based studies (European ancestry; n = 760, male = 64%; mean age = 26.78 years). Target data consisted of self-reported sleep (sleep duration, feeling tired, and taking naps) and cannabis use behaviors (lifetime ever use, number of lifetime uses, past 180-day use, age of first use, and lifetime CUD symptoms). Significant genetic correlation between lifetime cannabis use and an eveningness chronotype (rG = 0.24, p < 0.001), as well as between CUD and both short sleep duration (<7 h; rG = 0.23, p = 0.017) and insomnia (rG = 0.20, p = 0.020). Insomnia PRS predicted earlier age of first cannabis use (OR = 0.92, p = 0.036) and increased lifetime CUD symptom count (OR = 1.09, p = 0.012). Cannabis use is genetically associated with both sleep deficits and an eveningness chronotype, suggesting that there are genes that predispose individuals to both cannabis use and sleep deficits.

Sleep problems and mental health difficulties in older adults who endorse high autistic traits

BackgroundSleep problems and mental health difficulties are common in autistic children and young adults. However, these problems have seldom been studied in older autistic adults, or in older adults with elevated autistic traits. MethodCross-sectional data was examined from 13,897 adults aged 50–81 years taking part in the PROTECT study, who reported whether they experienced persistent socio-communicative autistic traits. Approximately 1%, 187 individuals, were identified as endorsing high autistic traits in childhood and currently, henceforth ‘Autism Spectrum Trait’ (AST) group. An age- and gender-matched comparison group was formed of 6740 individuals who endorsed no autistic traits, henceforth ‘Control Older Adults’ (COA) group. Differences between AST and COA groups were explored in self-reported sleep behaviors, and in depression and anxiety symptoms. ResultsAST and COA groups reported similar sleep duration and depth, and nighttime waking frequency. However, the AST group reported significantly more problems with falling asleep, morning drowsiness, and lower sleep quality/satisfaction than COA. More AST adults reported sleep problems past cut-off, as well as clinical levels of depression and anxiety, compared to COA. Adults in both groups who met criteria for high sleep problems experienced more mental health difficulties than those with few sleep problems. However, even amongst those without depression/anxiety, the AST group reported more sleep problems than the COA. ConclusionsThese associations suggest that older adults with high autistic traits, like diagnosed autistic children/young adults, may experience poorer sleep and more mental health difficulties than those with low autistic traits. Further work is needed to see whether these results extend to older individuals meeting diagnostic criteria for autism.

1209 Effect Of Wearables On Sleep In Healthy Individuals: A Randomized Cross-over Trial And Validation Study

Abstract Introduction To determine whether a wearable sleep-tracker improves perceived sleep quality in healthy subjects. To test whether wearables reliably measure sleep quantity and quality compared to polysomnography. Methods A single-center randomized cross-over trial of community-based participants without medical conditions or sleep disorders. Wearable device (WHOOP, Inc.) that provided feedback regarding sleep information to the participant for 1-week and maintaining sleep logs versus 1-week of maintaining sleep logs alone. Self-reported daily sleep behaviors were documented in sleep logs. Polysomnography was performed on one night when wearing the wearable. PROMIS Sleep disturbance sleep scale was measured at baseline, 7, and 14 days of study participation. Results In 32 participants (21 women; 23.8 + 5 years), wearables improved nighttime sleep quality (PROMIS sleep disturbance; B= -1.69; 95% Confidence Interval -3.11, -0.27; P=0.021) after adjusting for age, sex, baseline, and order effect. There was a small increase in self-reported daytime naps when wearing the device (B = 3.2; SE 1.4; P=0.023) but total daily sleep remained unchanged (P=0.43). The wearable had low bias (2.5 minutes) and low precision (5.6 minutes) errors for measuring sleep duration and measured dream sleep and slow wave sleep accurately (Intra-class coefficient 0.74 + 0.28 and 0.85 + 0.15, respectively). Bias and precision error for heart rate (bias -0.17%; precision 1.5%) and respiratory rate (bias 1.8%’ precision 6.7%) were very low when compared to that measured by electrocardiogram and inductance plethysmography during polysomnography. Conclusion In healthy people, wearables can improve sleep quality and accurately measure sleep and cardiorespiratory variables. Support WHOOP Inc.