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Objectives: Sleep promotion is not specifically recommended as a target for hypertension management. We examined associations of sleep duration and timing with blood pressure parameters in patients referred to pediatric nephrology clinic for elevated blood pressure evaluation., Methods: This is a retrospective study of initial ambulatory blood pressure monitoring data and self-report sleep data collected from patients referred to nephrology clinic for the evaluation of elevated blood pressure. Linear and logistic regression modeling determined associations between sleep exposures (duration and timing) and continuous and dichotomous blood pressure outcomes, respectively, adjusted for age, sex, body mass index, and weekday versus weekend status., Results: The study sample included 539 patients with mean age 14.6 years and 56% meeting hypertension criteria. Sleep duration averaged 9.1 hours per night. Average timing of sleep onset and offset were 11:06 pm and 8:18 am, respectively. Longer sleep duration was associated with better daytime blood pressure parameters (eg, every extra hour of sleep duration was associated with a reduced odds of wake hypertension [odds ratio, 0.88; 95% CI, 0.79-0.99]). Later sleep onset was associated with worse daytime blood pressure parameters (eg, each additional hour of later sleep onset was associated with higher wake systolic blood pressure index [mean wake blood pressure/95th percentile]) (β = 0.07; 95% CI, 0.02-0.13). Associations were consistent across sex, age, body mass index, and weekday status., Conclusions: Longer sleep duration and earlier sleep onset were associated with lower blood pressure. This suggests that sleep optimization may be an important target for intervention in hypertension management., (Copyright © 2024 by the American Academy of Pediatrics.)

Objective: Neighborhood-level factors, including education, health and environment, and socioeconomic exposures, are important contextual determinants of child health. We explored whether these factors, measured via the Childhood Opportunity Index 2.0, were associated with sleep health in adolescents., Methods: Actigraphy was used to assess sleep duration, timing, and efficiency among 110 adolescents in eighth (13.9 (0.4)) and ninth (14.9 (0.4)) grade. Home addresses were geocoded and linked to Childhood Opportunity Index 2.0 scores (including 3 subtype scores and the 29 individual factor Z-scores). Mixed-effects linear regression was used to determine associations between the Childhood Opportunity Index 2.0 scores and the sleep outcomes, adjusting for sex, race, parent education, household income, school grade and weeknight status. Interactions were also tested by school grade, weeknight status, sex, and race., Results: No associations were observed between overall or subtype scores with sleep outcomes in adolescents. However, we detected associations between select individual Childhood Opportunity Index 2.0 Z-scores, spanning health & environment and education domains, and sleep outcomes. For example, greater fine particulate matter was associated with later timing of sleep onset and offset; ozone concentration was associated with earlier sleep onset and offset; greater exposure to extreme temperature was associated with later sleep onset and offset and increased odds of optimal sleep efficiency., Conclusions: Specific neighborhood factors indexed by the Childhood Opportunity Index 2.0 were associated with sleep health among adolescents. In particular, neighborhood air quality measures were associated with sleep timing and efficiency, warranting further investigation., Competing Interests: Declaration of conflict of interest The authors have no conflicts of interest to report., (Copyright © 2023 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Study Objectives: Over 75% of US high school students obtain insufficient sleep, placing them at risk for adverse health outcomes. Identification of modifiable determinants of adolescent sleep is needed to inform prevention strategies, yet little is known about the influence of the built environment on adolescent sleep., Methods: In this prospective study, actigraphy was used to assess sleep outcomes among 110 adolescents for 14 days each in eighth and ninth grades: duration (hours/night), onset and offset, and sleeping ≥8 hours. Home addresses were linked to built environment exposures: sound levels, tree canopy cover, street density, intersection density, population density, and housing density. Mixed-effects regression estimated associations of built environment measures with sleep outcomes, adjusting for sex, race, parent education, household income, household size, grade, weeknight status, and neighborhood poverty., Results: A 1-standard deviation (SD) increase in neighborhood sound was associated with 16 minutes later sleep onset (β = 0.28; 95% confidence interval (CI): 0.06, 0.49) and 25% lower odds of sleeping for ≥8 hours (odds ratio (OR) = 0.75, 95% CI: 0.59, 0.96). A 1-SD increase in neighborhood tree canopy was associated with 18 minutes earlier sleep onset (β = -0.31, 95% CI: -0.49, -0.13) and 10 minutes earlier sleep offset (β= -0.17, 95% CI: -0.28, -0.05). No associations were observed for density-based exposures., Conclusions: Higher neighborhood sound level was associated with lower odds of sufficient sleep, while higher tree canopy cover was associated with more favorable sleep timing. Neighborhood sound levels and tree canopy cover are potential targets for policies and interventions to support healthier sleep among adolescents., (© Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Purpose: The purpose of the study was to quantify changes in sleep during the middle-to-high school transition and determine if changes in sleep differ by sociodemographic characteristics., Methods: Adolescents were enrolled in eighth grade and followed into ninth grade (N = 110; 2,470 nights observed). The outcomes were actigraphy-estimated sleep duration, sleep onset, sleep offset, and sleep sufficiency (≥8 hours of sleep). The exposures were school grade (eighth or ninth), school night status (school or nonschool), sex (female or male), and race (white, black, or other)., Results: On school nights, sleep duration declined by 25.8 minutes per night (p < .001) from eighth to ninth grade. There was no change in sleep duration on nonschool nights. Timing of sleep onset was 22.2 minutes later on school nights (p < .001) and 17.4 minutes later on nonschool nights (p < .001) in ninth grade. Timing of sleep offset did not change on school mornings but was 22.2 minutes later on nonschool mornings (p < .001) in ninth grade. The proportion of school nights (and nonschool nights) with sleep duration ≥8 hours was 9.4% (38.3%) in eighth grade and 5.7% (35.9%) in ninth grade. The odds of sleeping ≥8 hours per night was 42% lower in ninth grade, compared toeighth grade (odds ratio = .58; 95% confidence interval: .37, .91). Males were 59% less likely to sleep ≥8 hours per night. Black adolescents were 51% less likely to sleep ≥8 hours per night., Conclusions: Insufficient sleep is highly prevalent, especially on school nights and among male and black adolescents, and this problem worsens with the transition to high school., (Copyright © 2020 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.)

Objectives: Independently, physical activity (PA), sedentary behavior (SB), and sleep are related to the development and progression of chronic diseases. Less is known about how rest-activity behaviors cluster within individuals and how rest-activity behavior profiles relate to health. In this study we aimed to investigate if adult women cluster into profiles based on how they accumulate rest-activity behavior (including accelerometer-measured PA, SB, and sleep), and if participant characteristics and health outcomes differ by profile membership., Methods: A convenience sample of 372 women (mean age 55.38 + 10.16) were recruited from four US cities. Participants wore ActiGraph GT3X+ accelerometers on the hip and wrist for a week. Total daily minutes in moderate-to-vigorous PA (MVPA) and percentage of wear-time spent in SB was estimated from the hip device. Total sleep time (hours/minutes) and sleep efficiency (% of in bed time asleep) were estimated from the wrist device. Latent profile analysis (LPA) was performed to identify clusters of participants based on accumulation of the four rest-activity variables. Adjusted ANOVAs were conducted to explore differences in demographic characteristics and health outcomes across profiles., Results: Rest-activity variables clustered to form five behavior profiles: Moderately Active Poor Sleepers (7%), Highly Actives (9%), Inactives (41%), Moderately Actives (28%), and Actives (15%). The Moderately Active Poor Sleepers (profile 1) had the lowest proportion of whites (35% vs 78-91%, p < .001) and college graduates (28% vs 68-90%, p = .004). Health outcomes did not vary significantly across all rest-activity profiles., Conclusions: In this sample, women clustered within daily rest-activity behavior profiles. Identifying 24-hour behavior profiles can inform intervention population targets and innovative behavioral goals of multiple health behavior interventions., Competing Interests: The authors have declared that no competing interests exist.

Background: This study tested if the timing of meals, physical activity, light exposure, and sleep cluster within individuals and are associated with body mass index (BMI) in a sample of free-living adults (N = 125)., Methods: Data were collected between November 2015 and March 2016 at the University of California, San Diego, Children's Hospital of Philadelphia, and Washington University in St Louis. Height and weight were measured, and BMI (kg/m 2 ) was calculated. Sleep timing was estimated using actigraphy, and timing of meals, physical activity, and light exposure were self-reported using a smartphone application. General linear models estimated the mean BMI across time categories of behaviors, adjusting for covariates. A latent class analysis was used to identify patterns of timing variables that clustered within individuals and test for associations between identified patterns and BMI., Results: Later exposure to outdoor light was associated with a lower BMI (P trend < .01). The timing of other behaviors was not independently associated with BMI. The latent class analysis identified 2 distinct groups related to behavioral timing, reflecting an "early bird" and "night owl" phenotype. These phenotypes were not associated with BMI (P > .05)., Conclusion: Timing of exposures to light, meals, sleep, and physical activity were not strongly associated with BMI in this sample.

Study Objectives: To examine associations between 24-hour rest-activity patterns and body mass index (BMI) among community-dwelling US adults. Rest-activity patterns provide a field method to study exposures related to circadian rhythms., Methods: Adults (N = 578) wore an actigraph on their nondominant wrist for 7 days. Intradaily variability and interdaily stability (IS), M10 (most active 10-hours), L5 (least active 5-hours), and relative amplitude (RA) were derived using nonparametric rhythm analysis. Mesor, acrophase, and amplitude were calculated from log-transformed count data using the parametric cosinor approach., Results: Participants were 80% female and mean (standard deviation) age was 52 (15) years. Participants with higher BMI had lower values for magnitude, RA, IS, total sleep time (TST), and sleep efficiency. In multivariable analyses, less robust 24-hour rest-activity patterns as represented by lower RA were consistently associated with higher BMI: comparing the bottom quintile (least robust) to the top quintile (most robust 24-hour rest-activity pattern) of RA, BMI was 3-kg/m2 higher (p = .02). Associations were similar in magnitude to an hour less of TST (1-kg/m2 higher BMI) or a 10% decrease in sleep efficiency (2-kg/m2 higher BMI), and independent of age, sex, race, education, and the duration of rest and/or activity., Conclusions: Lower RA, reflecting both higher night activity and lower daytime activity, was associated with higher BMI. Independent of the duration of rest or activity during the day or night, 24-hour rest, and activity patterns from actigraphy provide aggregated measures of activity that associate with BMI in community-dwelling adults., (© Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Physical activity and time spent outdoors may be important non-pharmacological approaches to improve sleep quality and duration (or sleep patterns) but there is little empirical research evaluating the two simultaneously. The current study assesses the role of physical activity and time outdoors in predicting sleep health by using objective measurement of the three variables. A convenience sample of 360 adult women (mean age = 55.38 ±9.89 years; mean body mass index = 27.74 ±6.12) was recruited from different regions of the U.S. Participants wore a Global Positioning System device and ActiGraph GT3X+ accelerometers on the hip for 7 days and on the wrist for 7 days and 7 nights to assess total time and time of day spent outdoors, total minutes in moderate-to-vigorous physical activity per day, and 4 measures of sleep health, respectively. A generalized mixed-effects model was used to assess temporal associations between moderate-to-vigorous physical activity, outdoor time, and sleep at the daily level (days = 1931) within individuals. There was a significant interaction (p = 0.04) between moderate-to-vigorous physical activity and time spent outdoors in predicting total sleep time but not for predicting sleep efficiency. Increasing time outdoors in the afternoon (versus morning) predicted lower sleep efficiency, but had no effect on total sleep time. Time spent outdoors and the time of day spent outdoors may be important moderators in assessing the relation between physical activity and sleep. More research is needed in larger populations using experimental designs.

Rest-activity patterns provide an indication of circadian rhythmicity in the free-living setting. We aimed to describe the distributions of rest-activity patterns in a sample of adults and children across demographic variables. A sample of adults (N = 590) and children (N = 58) wore an actigraph on their nondominant wrist for 7 days and nights. We generated rest-activity patterns from cosinor analysis (MESOR, acrophase and magnitude) and nonparametric circadian rhythm analysis (IS: interdaily stability; IV: intradaily variability; L5: least active 5-hour period; M10: most active 10-hour period; and RA: relative amplitude). Demographic variables included age, sex, race, education, marital status, and income. Linear mixed-effects models were used to test for demographic differences in rest-activity patterns. Adolescents, compared to younger children, had (1) later M10 midpoints (β = 1.12 hours [95% CI: 0.43, 1.18] and lower M10 activity levels; (2) later L5 midpoints (β = 1.6 hours [95% CI: 0.9, 2.3]) and lower L5 activity levels; (3) less regular rest-activity patterns (lower IS and higher IV); and 4) lower magnitudes (β = -0.95 [95% CI: -1.28, -0.63]) and relative amplitudes (β = -0.1 [95% CI: -0.14, -0.06]). Mid-to-older adults, compared to younger adults (aged 18-29 years), had (1) earlier M10 midpoints (β = -1.0 hours [95% CI: -1.6, -0.4]; (2) earlier L5 midpoints (β = -0.7 hours [95% CI: -1.2, -0.2]); and (3) more regular rest-activity patterns (higher IS and lower IV). The magnitudes and relative amplitudes were similar across the adult age categories. Sex, race and education level rest-activity differences were also observed. Rest-activity patterns vary across the lifespan, and differ by race, sex and education. Understanding population variation in these patterns provides a foundation for further elucidating the health implications of rest-activity patterns across the lifespan.

Purpose: This study aimed to determine whether physical activity patterns are associated with sleep later at night and if nighttime sleep is associated with physical activity patterns the next day among adult women., Methods: Women (N = 353) living throughout the United States wore a wrist and a hip accelerometer for 7 d. Total sleep time (TST, hours per night) and sleep efficiency (SE, %) were estimated from the wrist accelerometer, and moderate to vigorous physical activity (MVPA, >1040 counts per minute, h·d) and sedentary behavior (SB, <100 counts per minute, h·d) were estimated from the hip accelerometer. Mixed-effects models adjusted for age, race, body mass index, education, employment, marital status, health status, and hip accelerometer wear time were used to analyze the data. Follow-up analyses using quantile regression were used to investigate associations among women with below average TST and MVPA and above average SB., Results: The average age of our sample was 55.5 yr (SD = 10.2 yr). The majority of participants were White (79%) and married (72%), and half were employed full time (49%). The participants spent on average 8.9 and 1.1 h·d in SB and MVPA, respectively, and 6.8 h per night asleep. No associations were observed between MVPA and SB with nighttime TST or SE. There were no associations between nighttime TST and SE with MVPA or SB the next day. The findings were the same in the quantile regression analyses., Conclusion: In free-living adult women, accelerometry-estimated nighttime sleep and physical activity patterns were not associated with one another. On the basis of our observational study involving a sample of adult women, higher physical activity will not necessarily improve sleep at night on a day-to-day basis (and vice versa)., Competing Interests: The results of the present study do not constitute endorsement by ACSM

Emerging evidence suggests that physical activity may be associated with improved sleep in autistic children. We aimed to determine whether physical activity associated with sufficient sleep duration in children and whether this association was modified by reported autism spectrum disorder (ASD) status. We analyzed existing data of children 6-17 years old whose caregivers completed the 2018 National Survey of Children's Health (nonautistic N = 20,845; ASD N = 681). Logistic regression determined whether physical activity (days active in the past week) associated with sufficient sleep duration. Physical activity-by-ASD interactions were included to determine whether the association differed for autistic children. Physical activity-by-ASD-by-sex and physical activity-by-ASD-by-age-group interactions were also modeled. Physical activity was associated with increased odds of sufficient sleep duration (e.g. 0 days vs 4-6 days: odds ratio (OR) = 1.85; 95% confidence interval (CI): 1.48-2.32). We did not observe an overall statistically significant interaction between physical activity and reported ASD status; however, the positive association between physical activity and sufficient sleep duration was weaker in autistic children, especially those with more severe ASD, female autistic children, and autistic children ages 6-12 years old. In conclusion, physical activity is a promising approach to improve sufficient sleep duration but with nuanced findings in autistic children.

Autistic children and adults often have sleep disturbances, which may affect their and their family's quality of life. Yet, the relationship between sleep–wake patterns and autism spectrum traits is understudied. Identifying such relationships could lead to future research elucidating common mechanistic underpinnings. Thus, we aimed to determine whether sleep–wake patterns, specifically related to sleep, physical activity, and the daily sleep–wake rhythm (i.e., circadian rhythm), are associated with autism spectrum‐related traits. Accelerometer‐derived sleep–wake parameters were estimated in individuals with autistic spectrum traits and their family members (N = 267). We evaluated autism spectrum traits using the Social Responsiveness Scale (SRS) to assess the presence and severity of social impairment and the Behavior Rating Inventory of Executive Function (BRIEF) to assess executive function. The linear multivariate regression analysis (using SOLAR‐Eclipse) showed that in adults, increased core autism spectrum traits and executive dysfunction were associated with disruption of several sleep–wake parameters, particularly related to the daily sleep–wake rhythm, and that executive dysfunction was associated with disrupted sleep quality and level of physical activity. We highlight the interplay between daytime function and disrupted sleep–wake patterns, specifically related to the daily sleep–wake rhythm, that could guide future research into common mechanisms. Lay Summary: Autistic children and adults often report sleep disturbances. To dissect the relationship between a range of autism spectrum traits and sleep–wake patterns, we assessed social interaction and executive function in participants who also wore actimetry watches on their wrists to assess their sleep–wake patterns. We found that increased impairments in social and executive function occurred with increased sleep–wake disturbances, particularly those related to the circadian rhythm, suggesting that these perturbations/disruptions in the sleep–wake cycle could be connected to autism spectrum traits. [ABSTRACT FROM AUTHOR]

Sleep is critical for optimal childhood metabolic health and neurodevelopment. However, there is limited knowledge regarding childhood sex differences in sleep, including children with neurodevelopmental disorders, and the impact of such differences on metabolic health. To evaluate if sex differences in childhood sleep exist and if sleep associates with metabolic health outcomes equally by sex. Using autism spectrum disorder (ASD) as a case study, we also examine sleep sex differences in children with a neurodevelopmental disorder. A narrative review explored the literature focussing on sex differences in childhood sleep. Sex differences in sleep were not detected among pre-adolescents. However, female adolescents were more likely to report impaired sleep than males. Childhood obesity is more common in males. Shorter sleep duration may be associated with obesity in male pre-adolescents/adolescents; although findings are mixed. ASD is male-predominant; yet, there was an indication that pre-adolescent female children with ASD had more impaired sleep. Sex differences in sleep appear to emerge in adolescence with more impaired sleep in females. This trend was also observed among pre-adolescent female children with ASD. Further research is needed on sex differences in childhood sleep and metabolic health and the underlying mechanisms driving these differences. [ABSTRACT FROM AUTHOR]

Study Objectives: Pediatricians lack tools to support families at home for the promotion of childhood sleep. We are using the Multiphase Optimization Strategy (MOST) framework to guide the development of a mobile health platform for childhood sleep promotion. The objective of this study is to demonstrate feasibility of a mobile health platform towards treating children with insufficient sleep. Methods: Children aged 10-12 years were enrolled (Study #1: N = 30; Study #2: N = 43). Participants wore a sleep tracker to measure sleep duration. Data were retrieved by a mobile health platform, programmed to send introductory messages during run-in (2 weeks) and goal achievement messages during intervention (7 weeks) periods. In study #1, participants were randomized to control, gain-framed incentive or loss-framed incentive arms. In study #2, participants were randomized to control, loss-framed incentive, normative feedback or loss-framed incentive plus normative feedback arms. Results: In study #1, 1514 nights of data were captured (69%) and sleep duration during the intervention was higher by an average of 21 (95% CI: -8, 51) and 34 (95% CI: 7, 61) minutes per night for the gain-framed and loss-framed arms, respectively, compared to controls. In study #2, 2,689 nights of data were captured (81%), with no major differences in average sleep duration between the control and the loss-framed or normative feedback arms. Conclusions: We have developed and deployed a mobile health platform that can capture sleep data and remotely communicate with families. Promising candidate intervention components will be further investigated under the optimization phase of the MOST framework. [ABSTRACT FROM AUTHOR]

Zeitgebers such as light, eating and physical activity provide input to the circadian clock. Chronic circadian misalignment is associated with significant adverse health effects. An improved understanding of the impact of the timing of zeitgebers on the stability of 24-hour rest-activity rhythm in free-living settings may identify behavioural and environmental intervention targets. A total of 133 healthy adults, aged 21-60 years, wore a wrist actigraph for 7 consecutive days. We applied a non-parametric analysis to activity counts to derive rest-activity patterns. We administered a questionnaire through a smartphone app to collect self-reported timing of light exposure, eating episodes and physical activity. To assess the relationship between timing exposures (first and last exposure to outdoor light, first exposure to indoor light, last eating episode, first eating episode, morning physical activity proportion, evening physical activity proportion) and rest-activity or sleep outcomes (bedtimes, total sleep time, inter-daily stability, intra-daily variability, L5 and M10 midpoint), we first calculated Spearman correlations, using the false discovery rate method to control for multiple comparisons. From those significant associations, we then fit regression models adjusting for age, sex, race, household income, education level, study site, body mass index, as well as physical activity. Finally, we tested for interaction between chronotype and each timing-related exposure and stratified the analysis by morning type. All zeitgebers, except for evening physical activity proportion, were correlated with at least four of the seven sleep and rest-activity outcomes. In adjusted analysis, later timing of first (after 6:30 to 7:45 AM versus earlier) and last exposure to indoor light (after 11:00 PM versus earlier) and first (after 7:45-9:45 AM versus earlier) and last eating episode (after 8:00-09:00 PM versus earlier) were associated with a shift of 0.60-1.39 hours to later bedtimes, M10 and L5 midpoints (i.e. timing of peak activities or inactivities). Later timing of first exposure to outdoor light (after 09:30 AM versus earlier) was also associated with 0.51 (95% CI: 0.19 to 0.83) hours longer total sleep time. Higher morning physical activity proportion (> 33%) was associated with 0.95 (95% CI: −1.38 to −0.53) hours earlier in-bed time and 0.69 (95% CI: −1.14 to −0.24) hours earlier out-of-bed time, 0.92 (95% CI: −1.41 to −0.42) hours earlier M10 and 0.96 (95% CI: −1.42 to −0.49) min earlier L5 midpoint. The results did not change substantially with further adjustment for total activity. There was a significant interaction between morning chronotype and first eating episode with rest-activity patterns (p < 0.05), with first eating episode associating with timing of activities only in non-morning type adults. Timing of zeitgebers was associated with sleep and rest-activity patterns, including bedtimes, L5 and M10 midpoint. Future research should evaluate the impact of manipulating zeitgebers on both circadian rhythms and health outcomes. [ABSTRACT FROM AUTHOR]

Sensors, including accelerometer-based and electronic adherence monitoring devices, have transformed health data collection. Sensors allow for unobtrusive, real-time sampling of health behaviors that relate to psychological health, including sleep, physical activity, and medicationtaking. These technical strengths have captured scholarly attention, with far less discussion about the level of human touch involved in implementing sensors. Researchers face several subjective decision points when collecting health data via sensors, with these decisions posing ethical concerns for users and the public at large. Using examples from pediatric sleep, physical activity, and medication adherence research, we pose critical ethical questions, practical dilemmas, and guidance for implementing health-based sensors. We focus on youth given that they are often deemed the ideal population for digital health approaches but have unique technology-related vulnerabilities and preferences. Ethical considerations are organized according to Belmont principles of respect for persons (e.g., when sensor-based data are valued above the subjective lived experiences of youth and their families), beneficence (e.g., with sensor data management and sharing), and justice (e.g., with sensor access and acceptability among minoritized pediatric populations). Recommendations include the need to increase transparency about the extent of subjective decision making with sensor data management. Without greater attention to the human factors involved in sensor research, ethical risks could outweigh the scientific promise of sensors, thereby negating their potential role in improving child health and care. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to determine whether baseline sedentary behavior was associated with changes in body mass index (BMI) over 9 yr. Methods: Participants were enrolled into the National Institutes of Health American Association of Retired Persons (NIH-AARP) Diet and Health study in 1995-1996 (median age, 63 yr), and BMI was reported at baseline and 9 yr later (n = 158,436). Sitting time (<3 (referent), 3-4, 5-6, 7-8, or ≥9 h•d-1), television viewing (none, <1, 1-2, 3-4, 5-6, 7-8, or ≥9 h•d-1), and the covariates (age, sex, race, education, smoking, moderate-to-vigorous physical activity, caloric intake, and sleep duration) were reported at baseline. We used longitudinal quantile regression to model changes at the 10th, 25th, 50th, 75th, and 90th BMI percentiles. Results: More sitting at baseline was associated with additional increases in BMI over time, and the association was stronger at the upper BMI percentiles (e.g., <3 (referent) vs 5-6 h•d-1 of sitting additional increases: 50th percentile = 0.41 kg•m-2 and 95% confidence interval (CI) = 0.34-0.48; 90th percentile = 0.85 kg•m-2 and 95% CI = 0.72-0.98). Similar associations were observed between more television viewing at baseline and additional increases in BMI over time (e.g., no television (referent) vs 3-4 h•d-1 of television: 50th percentile = 1.96 kg•m-2 and 95% CI = 1.77-2.15; 90th percentile = 2.11 kg•m-2 and 95% CI = 1.49-2.73). Conclusions: Reducing sedentary behavior could help prevent an increase in BMI in adulthood especially at the upper percentiles of the BMI distribution and thereby reduce the prevalence of obesity. [ABSTRACT FROM AUTHOR]

Understanding salient environmental determinants of pediatric sleep is essential for informing interventions and public health initiatives. Emerging evidence suggests that the neighborhood environment can impact pediatric sleep, but this evidence has not yet been systematically reviewed. We conducted a systematic review of the scientific literature on associations between neighborhood environments and sleep in young children (0-5 y), school-aged children (6-12 y) and adolescents (13-18 y). We reviewed 85 articles published between 2003 and 2020. The most commonly examined neighborhood exposure was low socioeconomic status (40 studies), which was associated with sleep outcomes in 58% of studies (primarily shorter sleep duration, later sleep timing, or obstructive sleep apnea). Evidence was stronger for neighborhood safety/crime/violence (21 studies), with 86% of studies reporting associations with sleep outcomes (primarily self- or caregiver-reported sleep problems). Fewer studies examined associations of neighborhood physical environment exposures, including noise (15 studies), the built environment (seven studies), and air pollution (six studies). Limitations of the current body of evidence include 1) limited examination of neighborhood exposures other than socioeconomic status or safety, 2) use of primarily cross-sectional observational study designs, 3) lack of objective sleep outcome assessment, and 4) limits of current exposure assessment methods. [ABSTRACT FROM AUTHOR]