Abstract
Objective
Traditional intensity-based physical activity measures and variable-centered statistics may not fully capture the complex associations between sitting time, physical activity, and obesity indices. This study investigates the associations between device-measured sitting, standing and different modes of physical activity (i.e., slow walking, brisk-walking, cycling and high-intensity activity) and measured body mass index (BMI) in children using person-based latent profile analyses and Partial Least Squared-structural equation modeling (PLS-SEM).
Methods
A total of 344 children (11.5 ± 0.81 years, boys n = 139) wore a triaxial accelerometer (Fibion®) on their thigh for eight days, and their weight and height were measured at school. Latent profile analysis formed profiles including BMI, total sitting time, and physical activities, and their associations were further studied with PLS-SEM.
Results
The latent profile analysis indicates that high levels of physical activity always coincide with low sitting time. Both normal weight and overweight/obesity can coexist with low physical activity and prolonged sitting. The PLS-SEM results highlight a cascade-like sequence in the relationship between various types of physical activity, sitting time, and BMI. This sequence begins with light-intensity activities, such as standing, progresses to higher-intensity activities, and ultimately through reduced sitting time, mediates a decline in BMI. The most positive effects on sitting time and BMI occur when this pattern is adhered to consistently, suggesting that omitting steps could negatively impact the associations.
Conclusion
These findings suggest that persuading children to increase physical activity incrementally, starting from low-intensity activities such as standing and slow walking to activity types with higher intensities, possibly influence BMI by mediating reduced sitting time. This approach is particularly inclusive for overweight and obese children, taking into account the potential challenges they may encounter when performing activity types with high intensity. These cross-sectional associations need to be verified with longitudinal and experimental designs.