Accurate, nonintrusive and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) for prediction of EE using room calorimetry and doubly labeled water (DLW), and established accelerometry cut-points for PA levels.
Fifty preschoolers, mean±SD age 4.5±0.8 y, participated in room calorimetry for minute-by-minute measurements of EE, accelerometer counts (AC) (Actiheart and ActiGraph GT3X+) and HR (Actiheart). Free-living, 105 children, aged 4.6±0.9 years, completed the 7-d DLW procedure while wearing the devices. AC cut-points for PA levels were established using smoothing splines and receiver operating characteristic curves.
Based on calorimetry, mean percent errors for EE were -2.9±10.8% and -1.1±7.4% for CSTS models, and -1.9±9.6 and 1.3±8.1% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. Based on DLW, mean percent errors were -0.5±9.7% and 4.1±8.5% for CSTS models and 3.2±10.1% and 7.5±10.0% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. Applying activity EE thresholds, final accelerometer cut-points were determined: 41, 449, and 1,297 cpm for Actiheart x-axis; 820, 3,908, and 6,112 cpm for ActiGraph vector magnitude; and 240, 2,120, and 4,450 cpm for ActiGraph x-axis for sedentary/light, light/moderate, and moderate/vigorous PA (MVPA). Based on confusion matrices, correctly classified rates were 81–83% for sedentary PA, 58–64% for light PA and 62–73% for MVPA.
The lack of bias and acceptable limits of agreement affirm the validity of the CSTS and MARS models for the prediction of EE in preschool-aged children. Accelerometer cut-points are satisfactory for classification of sedentary, light and moderate-vigorous levels of PA in preschoolers.