Pediatric Obesity Resources

Pediatric Obesity Research Update – December Review

Each month, the pediatric committee posts a pediatric-focused obesity research update to help keep you up to date about the latest findings. This month’s update was written by Marcella House, MD, FAAP.

Longitudinal associations between facets of sleep and adiposity in youth

Slaght JL, Wicklow BA, Dart AB, et al. Physical activity and cardiometabolic health in adolescents with type 2 diabetes: a LeMay-Russell, S, Schvey, NA, Kelly, NR, et al. Longitudinal associations between facets of sleep and adiposity in youth. Obesity (Silver Spring). 2021; 29: 1760– 1769. https://onlinelibrary.wiley.com/doi/10.1002/oby.23281

Shorter sleep duration has been associated with increased risk for higher BMI, overweight and obesity in youth, but the relationship between sleep duration and obesity is still poorly understood. Studies have attempted to demonstrate that restricting sleep is associated with increased energy intake, poorer diet quality and changes in physical activity and sedentary behaviors, but results have been inconsistent and many limited by self- and parent reported sleep patterns. The intent of this study was to utilize objective measures to examine other facets of sleep besides duration in order to better understand the relationship between sleep and weight.

Methods

Participants 8-17 years of age who were in good health were recruited from the greater Washington DC area and communities near Bethesda, MD. Youth were eligible if they had the cognitive ability of completing study procedures and had a BMI > 5th percentile.

Participants were seen for a preliminary visit where fasting body weight, height, body composition and percentage of fat mass (calculated from dual-energy x-ray absorption (DEXA) scan), medical histories, physical exam, pubertal staging, and screening for depression and socioeconomic status was obtained.

Wrist-worn actigraphy measured sleep behavior of participants for 14 days following their preliminary visit. Participants were required to have data for three or more weekday nights of sleep and one or more weekend nights of sleep. Sleep periods were confirmed by cross-referencing self-reported sleep logs from participants.

Actigraphy was used to collect a variety of facets of sleep including bedtime, waketime, average weekly sleep duration (hrs/night for 7 days) and weekend catch-up sleep (calculated by subtracting average hours per night of weekday (Sunday-Thursday) from average hours per night of weekend (Friday/Saturday) sleep). Within-person sleep duration variability was calculated as the intrasubject standard deviation of average sleep duration across the 14 days. Bedtime shift was calculated by determining the difference between average weekday sleep onset time and average weekend sleep onset time. Wake time shift was calculated by determining the difference between the average weekday and average weekend wake time. Sleep midpoint was calculated as the halfway point between sleep onset and wake time. Social jet lag was calculated as the difference between the mean sleep midpoint during weekdays and weekend days.

Participants returned one year later to repeat anthropometric measurements, body composition and physical examination. All data was collected prior to the COVID-19 pandemic.

Results

Data were collected from 137 youths with a mean age of 12.5 years. Fifty-four percent of participants were female, 28.4% were Non-Hispanic Black or African American, 28.5 % had overweight or obesity at baseline as measured at the preliminary visit. A mean baseline fat mass of 15.3 kg with a standard deviation of 8.9 and a mean 1-yr fat mass of 17 kg
with a standard deviation of 10 at follow-up were obtained from the 137 participants.

Average weekly sleep duration was 7.2 hrs per night (SD of 1.1hrs), weekend catch-up sleep was 0.3 hrs per night (SD of 1.0 hrs), within-person sleep duration variability was 1.2 hrs (SD 0.5 hrs), Bedtime shift was 0.9 hrs (SD 0.7), wake time shift was 1.3 hrs (SD 1.1), social jet lag was 1.1 (SD 1.0). Average bedtime was 11:31pm (SD 1.3) and average wake time was 7:53am (SD 1.1). Sleep midpoint was 3:46am (SD 1.1)

After analysis, no facet of sleep variability (weekend catch-up sleep, within-person sleep duration variability, bedtime shift, wake time shift, social jet lag) or actual bedtime was significantly associated with 1-year fat mass. Interestingly, wake time and sleep midpoint were both inversely associated with 1-year fat mass, meaning participants with earlier wake times and earlier sleep midpoints were more likely to have increased fat mass after 1 year (wake time p value = 0.03, sleep midpoint p value = 0.02).

Discussion

This study’s strengths were that it used objective measurements for sleep and adiposity with actigraphy and DEXA respectively. It was limited by a small sample size and a lack of consistency in the timing of data collection (participation occurred over various seasons, some participating during school and some during summer break).

Participants in this study had relatively short average weekly sleep duration with little variability (7.2 hrs with SD of 1.1hrs) and little variability in the other facets of sleep. This lack of variability combined with a small sample size and a wide range of ages could account for the many facets of sleep with null findings. The authors also point out that there is more evidence supporting an association between shorter sleep duration and increased weight in younger children, ages 0-5 than in the 8-17yr old population studied.

Additionally, since the social jet lag (absolute difference between the mean sleep midpoint during weekdays and midpoint of sleep on weekend days) was only 1.1 (SD 1.0) it may not have been a large enough difference to represent a true circadian rhythm misalignment. Other studies have demonstrated an association with larger shifts in sleep (2 hrs or more) and weight gain.

Further research should examine sleep timing using similar objective measures but on a larger, more varied population, over a longer period of time and with observation of more variations in sleep patterns (throughout the school year and summer break).

Additionally, future research should evaluate the application of this knowledge in the clinical setting and measure the effects of sleep questionnaires and recommendations on sleep behavior and weight to determine best practices.

Research should also take a deeper dive into earlier wake times to evaluate for contributing factors like lower socioeconomic status, home environment, screen time, chronic stress, or depression. For example, a child may need to wake up earlier due to lack of transportation (taking multiple buses), food insecurity (trying to get to school for a free breakfast program), or needing to help parents in caring for siblings.

Lastly, since the results suggest that earlier wake times and sleep midpoints are associated with greater weight gain over time, this study along with others may be useful in advocating for obesity prevention policy initiatives that delay school start times for youth.