The association between 24-h movement behaviours and adiposity among Australian preschoolers: a compositional data analysis.

Introduction: The relationship between 24-h movement behaviours (i.e. sleep, sedentary behaviour and physical activity) and adiposity in preschoolers remains unclear. Therefore, this study aims to investigate the associations between 24-h movement behaviours and adiposity in preschoolers making use of compositional data analysis (CoDA).

Methods: Australian preschoolers (3-5 years) from the Early Start Baseline Study wore an ActiGraph accelerometer to assess sedentary behaviour (SB), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Their weight and height were measured using standardized protocols and converted to Body Mass Index (BMI) z-scores using the World Health Organisation growth references. Their parents completed a questionnaire to assess their level of education and the child's sleep duration, age and sex. CoDA was employed to investigate the association between 24-h movement behaviours and adiposity in R.

Results: This study included 169 preschoolers and their overall 24-h movement behaviour composition was associated with BMI z-scores (F = 5.02, p = 0.002). When examining the association between each movement behaviour relative to the others and BMI z-scores, we observed a statistically significant favourable association for sleep (p = 0.025) and unfavourable association for MVPA (p = 0.010), but not for the other behaviours. As such, reallocating 10 min from sleep or from MVPA, proportionally to all other behaviours was associated with a difference of + 0.031 (95%CI = 0.004,0.06) and -0.085 (95%CI = -0.15,-0.02) in BMI z-score, respectively.

Conclusion: Despite the association between more time spent in MVPA and higher BMI z-scores, promoting a balanced amount of time in each 24-h movement behaviour-more MVPA, less sedentary time, and sufficient sleep-remains important for overall health. Future studies should address methodological challenges (e.g. recruitment bias that may exist in the parents/children willing to participate versus the general population, recall bias in parent reported sleep duration, or other confounding variables such as diet), use larger and more diverse samples, and consider longitudinal designs. Additionally, focusing on other adiposity indicators, such as waist-to-height ratio or fat percentage, could enhance understanding of these relationships.