In this study, the investigators will apply advanced technology (fast-response room calorimetry, doubly labeled water (DLW), accelerometers and miniaturized HR monitors) and sophisticated mathematical modeling techniques (cross-sectional time series, CSTS and multivariate adaptive regression splines, MARS) to develop and validate prediction models that capture the dynamic nature of physical activity (PA) and energy expenditure (EE) in preschool-aged children. CSTS and MARS models for the assessment of PA based on activity energy expenditure (AEE) and for the prediction of minute-by-minute EE will be developed in 100 preschool-aged children using 12-h room respiration calorimetry as the criterion method and validated in an independent sample (n=50) against 12-h room respiration calorimetry and the 7-d DLW method. In addition, the investigators will develop algorithms for the classification of PA levels and sleep/awake periods using statistical and machine learning methods and incorporate the results into our prediction models.

The purpose of this study is to calibrate the Actigraph, Respironics and CamNtech monitors, in a wide range of children using energy expenditure measured by respiration calorimetry. Energy expenditure will be predicted from the combination of heart rate and activity measured by accelerometry. Prediction equations for energy expenditure will be tested and validated against a stable isotope method called doubly labeled water for the measurement of free-living total energy expenditure.