This study evaluated the daily, temporal associations between sleep and daytime physical activity and sedentary behavior among adolescents from the Fragile Families & Child Wellbeing Study. A sub-sample of the cohort at age 15 (N = 417) wore actigraphy monitors for one week during the school year from which we derived daily minutes in sedentary and moderate-to-vigorous physical activity (MVPA) and nighttime sleep measures. Multilevel models tested temporal associations of nightly sleep onset, offset, duration, and sleep maintenance efficiency, with daily MVPA and sedentary behavior. More MVPA than an individual’s average was associated with earlier sleep onset (p < 0.0001), longer duration (p = 0.03), and higher sleep maintenance efficiency (p < 0.0001). On days with more sedentary behavior than an individual’s average, sleep onset and offset were delayed (p < 0.0001), duration was shorter (p < 0.0001), and sleep maintenance efficiency was higher (p = 0.0005). Conversely, nights with earlier sleep onset predicted more next-day sedentary behavior (p < 0.0001), and nights with later sleep offset and longer sleep duration were associated with less MVPA (p < 0.0001) and less sedentary time (p < 0.0001, p = 0.004) the next day. These bidirectional associations between sleep and physical activity suggest that promoting MVPA may help to elicit earlier bedtimes, lengthen sleep duration, and increase sleep efficiency, critical for healthy adolescent development.
BackgroundThe current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep.PurposeTo investigate the validity of developing personalized machine learning models, trained and tested on individual-level actigraphy data, for improved prediction of sleep-wake states and reliable estimation of nightly sleep parameters.Participants and methodsWe used a dataset including 54 participants and systematically trained and tested 5 different personalized machine learning models as well as their generalized counterparts. We evaluated model performance compared to concurrent PSG through extensive machine learning experiments and statistical analyses.ResultsOur experiments show the superiority of personalized models over their generalized counterparts in estimating PSG-derived sleep parameters. Personalized models of regularized logistic regression, random forest, adaptive boosting, and extreme gradient boosting achieve estimates of total sleep time, wake after sleep onset, sleep efficiency, and number of awakenings that are closer to those obtained by PSG, in absolute difference, than the same estimates from their generalized counterparts. We further show that the difference between estimates of sleep parameters obtained by personalized models and those of PSG is statistically non-significant.ConclusionPersonalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders.
Study Objectives: Having a regular, age-appropriate bedtime and sufficient sleep from early childhood may be important for healthy weight in adolescence. This study aimed to (1) identify heterogeneous groups of children by bedtime and sleep routines and (2) test longitudinal associations of childhood bedtime and sleep routine groups with adolescent body mass index (BMI). Methods: We analyzed longitudinal data from the Fragile Families and Child Wellbeing Study, a national birth cohort from 20 US cities (N = 2196). Childhood bedtime and sleep routines were assessed by mothers' reports of their children's presence and timing of bedtimes, adherence to bedtimes, and habitual sleep duration at ages 5 and 9. At age 15, these adolescents reported their height and weight, which were used to calculate BMI z-score. Results: Latent Class Analysis revealed four groups of childhood bedtime and sleep routines: No Bedtime Routine Age 5 (Group 1), No Bedtime Routine Age 9 (Group 2), Borderline Bedtimes Ages 5 and 9 (Group 3), and Age-Appropriate Bedtime and Sleep Routines Ages 5 and 9 (Group 4, reference). Compared with adolescents in the reference group, those in the No Bedtime Routine Age 9 (Group 2) had +0.38 SD greater BMI (95% CI = [0.13 to 0.63]), above the level for overweight (1.02 SD BMI/85th percentile). Associations persisted after adjusting for age 3 BMI and sociodemographic characteristics. Conclusions: Results demonstrate heterogeneity in childhood bedtime routine groups and their associations with adolescent BMI. Future studies should focus on whether childhood sleep behavior interventions promote healthier sleep and weight in later life course stages.
Study Objectives: High school start times (SSTs) directly impact adolescents' sleep timing and duration. This study investigated the associations between SSTs and actigraphically-measured 24-hour sleep duration, sleep onset, sleep offset and sleep quality. Methods: This study included 383 adolescents (M age = 15.5, SD age = 0.6 years) participating in the age 15 wave of the Fragile Families & Child Wellbeing Study, a national birth cohort study sampling from 20 large US cities. Multilevel models used daily observations (N = 1116 school days, M days = 2.9, SD days = 1.4 per adolescent) of sleep and SSTs from concordant daily diary and actigraphy.
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