24-hour movement behaviour profiles and their transition in children aged 5.5 and 8 years – findings from a prospective cohort study

Abstract

Background Time spent in movement behaviours, including physical activity (PA), sedentary behaviour (SB) and sleep, across the 24-h day may have distinct health consequences. We aimed to describe 24-h movement behaviour (24 h-MB) profiles in children and how profile membership changed from age 5.5 to 8 years. Methods Children in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort were asked to wear an accelerometer (ActiGraph-GT3X+) on their wrist for seven consecutive days at ages 5.5 and 8 years to measure 24 h-MB patterns. Time spent in night sleep, inactivity (proxy for SB), light PA, moderate PA (MPA), and vigorous PA (VPA) per day were calculated using the R-package GGIR 2.0. Using latent profile analyses (n = 442) we identified 24 h-MB profiles, which were given animal names to convey key characteristics. Latent transition analyses were used to describe the profile membership transition from ages 5.5 to 8 years. Associations with sex and ethnicity were examined. Results We identified four profiles, “Rabbits” (very high-MPA/VPA, low-inactivity and average-night-sleep), “Chimpanzees” (high-MPA, low-inactivity and average-night-sleep), “Pandas” (low-PA, high-inactivity and high-night-sleep) and “Owls” (low-PA, high-inactivity and low-night-sleep), among children at both time points. At ages 5.5 and 8 years, the majority of children were classified into profiles of “Chimpanzees” (51 and 39%, respectively) and “Pandas” (24 and 37%). Half of the sample (49%), particularly “Rabbits”, remained in the same profile at ages 5.5 and 8 years: among children who changed profile the predominant transitions occurred from “Chimpanzees” (27%) and “Owls” (56%) profiles to “Pandas”. Sex, but not ethnicity, was associated with profile membership: compared to girls, boys were more likely to be in the “Rabbits” profile (adjusted OR [95% CI]: 3.6 [1.4, 9.7] and 4.5 [1.8, 10.9] at ages 5.5 and 8 years, respectively) and less likely to be in the “Pandas” profile (0.5 [0.3, 0.9] and 0.4 [0.2, 0.6]) at both ages. Conclusions With increasing age about half the children stayed in the same of four 24 h-MB profiles, while the predominant transition for the remaining children was towards lower PA, higher inactivity and longer sleep duration. These findings can aid development and implementation of public health strategies to promote better health. Study registration This study was registered on 4th August 2010 and is available online at ClinicalTrials.gov: NCT01174875.

Association of daily and time-segmented physical activity and sedentary behaviour with mental health of school children and adolescents from rural Northeastern Ontario, Canada

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The cross-sectional and prospective associations of parental practices and environmental factors with 24-hour movement behaviours among school-aged Asian children

“…Days with ≥16 hours of activity recordings (from midnight to midnight) were considered valid, and children with at least two valid weekdays and one valid weekend day were included in the analysis. The “2015 van Hees algorithm” was applied to detect sustained inactivity and the night sleep window [ 43 – 45 ]. After visually inspecting the actigraphy data, we determined that sustained inactivity bouts of lasting at least 15-minutes likely represent naps; these bouts were classified as napping time.…”

A school-family blended multi-component physical activity program for Fundamental Motor Skills Promotion Program for Obese Children (FMSPPOC): protocol for a cluster randomized controlled trial

“…Participants were encouraged to wear the accelerometer 24 h per day (removing only for water-based activities: i.e., swimming/bathing) for at least 7 d, including two weekend days. Days with > 16 h/d of activity recordings (from midnight to midnight) were considered as valid [ 80 ], and the minimum amount of non-sleep data that was considered acceptable for inclusion was at least 4 days with at least 10 h of wake wear time per day, including at least one weekend day [ 81 ]. Data were collected at a sampling rate of 80 Hz downloaded in 1-s epochs with the low-frequency extension filter using the ActiLife software version 6.13 (ActiGraph LLC) and reintegrated to 15-s epochs for analysis.…”