How can we best predict energy expenditure in preschoolers? A comparison of machine learning models, METs computation and physical activity classification

Abstract

Objective. The first objective of this study was to refine previously designed machine learning models that predict energy expenditure (EE) of preschool children by modifying the method used to calculate metabolic equivalents (METs). The secondary objective was to compare estimates of time spent in different physical activity intensities across the newly developed models, previously published METs models, existing METs-based models from the literature, and models calibrated using direct observation. Approach. The model training dataset included 35 Canadian children (aged 3.0–5.99 years) equipped with GT9X accelerometers on their right hip. A portable metabolic unit was used to measure EE during a semi-structured protocol consisting of activities ranging from low- to high-intensity. The resulting models were applied to a sample of Canadian preschool children (n = 118; aged 3.0–5.99 years) to estimate time spent in sedentary (SED), light (LPA), moderate-to-vigorous (MVPA), and total physical activity (TPA). A repeated measures ANOVA was used to compare time estimates across models and according to three different configurations of METs activity thresholds. Main results. Results indicated that the newly developed models from Objective 1 produced significantly different estimates of time spent in SED, LPA, MVPA, and TPA compared to both previously published models and other existing METs-based models, highlighting the impact of different approaches to calculating METs. Significance. Model selection and METs calculation methods markedly influenced activity intensity estimates, underscoring the need for consistent methodology. Classification models yielded the most plausible free-living estimates.