TY - JOUR
T1 - Dynamics of sleep, sedentary behavior, and moderate-To-vigorous physical activity on school versus nonschool days
AU - Armstrong, Bridget
AU - Beets, Michael W.
AU - Starrett, Angela
AU - Brazendale, Keith
AU - Turner-Mcgrievy, Gabrielle
AU - Saelens, Brian E.
AU - Pate, Russell R.
AU - Youngstedt, Shawn D.
AU - Maydeu-Olivares, Alberto
AU - Weaver, R. Glenn
N1 - Publisher Copyright:
© 2020 Sleep Research Society. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - Study Objectives: Studies examining time-use activity behaviors (sleep, sedentary behavior, and physical activity) on school days compared with nonschool days have examined these behaviors independently, ignoring their interrelated nature, limiting our ability to optimize the health benefits of these behaviors. This study examines the associations of school-day (vs. nonschool day) with time-use activity behaviors. Methods: Time series data (6,642 days) from Fitbits (Charge-2) were collected (n = 196, 53% female, 5-10 years). We used a variable-centered dynamic structural equation modeling approach to estimate day-To-day associations of time-use activity behaviors on school days for each child. We then used person-centered cluster analyses to group individuals based on these estimates. Results: Within-participant analysis showed that on school days (vs. nonschool days), children (1) slept less (β =-0.17, 95% CI =-0.21,-0.13), (2) were less sedentary (β =-0.05, 95% CI =-0.09,-0.02), and (3) had comparable moderate-To-vigorous physical activity (MVPA; β =-0.05, 95% CI =-0.11, 0.00). Between-participant analysis showed that, on school days, children with higher sleep carryover experienced greater decreases in sleep (β = 0.44, 95% CI = 0.08, 0.71), children with higher body mass index z-score decreased sedentary behavior more (β =-0.41, 95% CI =-0.64,-0.13), and children with lower MVPA increased MVPA more (β =-0.41, 95% CI-0.64,-0.13). Cluster analysis demonstrated four distinct patterns of connections between time-use activity behaviors and school (High Activity, Sleep Resilient, High Sedentary, and Dysregulated Sleep). Conclusions: Using a combination of person-centered and more traditional variable-centered approaches, we identified patterns of interrelated behaviors that differed on school, and nonschool days. Findings can inform targeted intervention strategies tailored to children's specific behavior patterns.
AB - Study Objectives: Studies examining time-use activity behaviors (sleep, sedentary behavior, and physical activity) on school days compared with nonschool days have examined these behaviors independently, ignoring their interrelated nature, limiting our ability to optimize the health benefits of these behaviors. This study examines the associations of school-day (vs. nonschool day) with time-use activity behaviors. Methods: Time series data (6,642 days) from Fitbits (Charge-2) were collected (n = 196, 53% female, 5-10 years). We used a variable-centered dynamic structural equation modeling approach to estimate day-To-day associations of time-use activity behaviors on school days for each child. We then used person-centered cluster analyses to group individuals based on these estimates. Results: Within-participant analysis showed that on school days (vs. nonschool days), children (1) slept less (β =-0.17, 95% CI =-0.21,-0.13), (2) were less sedentary (β =-0.05, 95% CI =-0.09,-0.02), and (3) had comparable moderate-To-vigorous physical activity (MVPA; β =-0.05, 95% CI =-0.11, 0.00). Between-participant analysis showed that, on school days, children with higher sleep carryover experienced greater decreases in sleep (β = 0.44, 95% CI = 0.08, 0.71), children with higher body mass index z-score decreased sedentary behavior more (β =-0.41, 95% CI =-0.64,-0.13), and children with lower MVPA increased MVPA more (β =-0.41, 95% CI-0.64,-0.13). Cluster analysis demonstrated four distinct patterns of connections between time-use activity behaviors and school (High Activity, Sleep Resilient, High Sedentary, and Dysregulated Sleep). Conclusions: Using a combination of person-centered and more traditional variable-centered approaches, we identified patterns of interrelated behaviors that differed on school, and nonschool days. Findings can inform targeted intervention strategies tailored to children's specific behavior patterns.
KW - children
KW - intensive longitudinal
KW - person-centered
KW - physical activity
KW - school
KW - sedentary
KW - sleep
UR - http://www.scopus.com/inward/record.url?scp=85102098879&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102098879&partnerID=8YFLogxK
U2 - 10.1093/sleep/zsaa174
DO - 10.1093/sleep/zsaa174
M3 - Article
C2 - 32893864
AN - SCOPUS:85102098879
SN - 0161-8105
VL - 44
JO - Sleep
JF - Sleep
IS - 2
M1 - zsaa174
ER -