Novel approaches to assess physical activity (PA) and predict energy expenditure (EE) are essential for quantifying the characteristically sporadic PA patterns and variable rates of EE of preschool-aged children. Because of methodological limitations, there is a paucity of comprehensive quantitative data on the habitual PA patterns and normative rates of EE in preschoolers. Accelerometers or miniaturized heart rate (HR) monitors are used to assess PA and predict EE, however, the mathematical modeling of accelerometer counts (AC) and HR has been limited to regression models that do not take into account the interdependence of the data and do not exploit all the information in the raw data. In this proposal, we will apply advanced technology (fast- response room calorimetry, doubly labeled water (DLW), accelerometers and miniaturized HR monitors) and sophisticated mathematical modeling techniques to develop and validate prediction models that capture the dynamic nature of PA and EE in preschool-aged children. Cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models will be developed in 88 preschool-aged children using 12-h room respiration calorimetry as the criterion method and validated in an independent sample (n=50) against12-h room respiration calorimetry and the 7-d DLW method.
Specific Aims : 1. For assessment of PA using unaxial and triaxial accelerometry (ActiGraph GT1M and GT3X), develop CSTS and MARS models for prediction of minute-to-minute activity energy expenditure (AEE) based on subject characteristics and the functional relationship between AC and AEE, measured by 12-h room respiration calorimetry in 88 preschool-aged children. 2. For classification of sedentary, light, moderate and vigorous levels of PA and awake/sleep periods, develop, evaluate, and compare algorithms using statistical and machine learning methods. 3. For prediction of EE using accelerometry and HR monitoring (Actiheart), develop CSTS and MARS models for prediction of minute-by-minute EE and hence TEE based on subject characteristics and the relationship between AC+HR and EE as measured by 12-h calorimetry in the same 88 preschoolers. 4. Validate the classification algorithms for PA levels and awake/sleep periods developed in Aim 2. 5. Validate the use of uniaxial and triaxial accelerometers for the prediction of AEE based on AC and subject characteristics, against 12-h calorimetry and the DLW method in an independent sample of 50 preschoolers. 6. Validate the CSTS and MARS models for the prediction of minute-by-minute EE and hence TEE, AEE, awake EE and sleep EE from AC and HR and subject characteristics against 12-h calorimetry and the DLW method in the same independent sample of 50 preschoolers.
In the US, childhood obesity has been increasing at alarming rates, particularly among preschool-aged children. Cost-effective, non-intrusive, valid and precise methods for the quantitative assessment of energy expenditure and physical activity are essential to determine patterns of physical activity, prevalence and determinants, dose-response relationships between physical activity and health outcomes, and intervention effectiveness in preschool-aged children. We will develop models to assess physical activity and to predict energy expenditure in preschoolers using advanced technology (room calorimetry, doubly labeled water, accelerometers, miniaturized heart rate monitors) and sophisticated mathematical modeling techniques that capture the dynamic nature of physical activity and energy expenditure in preschool-aged children.
|Butte, Nancy F; Wong, William W; Lee, Jong Soo et al. (2014) Prediction of energy expenditure and physical activity in preschoolers. Med Sci Sports Exerc 46:1216-26|
|Zakeri, Issa F; Adolph, Anne L; Puyau, Maurice R et al. (2013) Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers. J Nutr 143:114-22|