Background: To determine the energy cost of common physical activities in preschoolers and to compare it with the Compendium of Energy Expenditure for Youth (CEEY). Methods: In total, 42 children [age: 4.8 (0.8) y; body mass index: 15.3 (2.0) kg/m2; 22 boys] completed 13 common physical activities covering sedentary to vigorous intensities, while energy expenditure (EE) was measured continuously by indirect calorimetry. Activity-specific metabolic equivalents (AME) were calculated as the EE observed during each single activity divided by the EE during observed rest. Independent t tests were applied to analyze differences between boys and girls and between AME and CEEY. Results: No significant differences in AME were observed between girls and boys. Except for playing hide-and-seek, all indoor activities revealed significantly higher energy costs compared with those stated in the compendium. Significant differences in outdoor activities were found for riding a tricycle [5.67 (95% confidence interval, 4.94–6.4) AME vs 6.2 metabolic equivalents, riding a bike, P < .05] and for fast walking [5.42 (95% confidence interval, 4.84–6.0) AME vs 4.6 metabolic equivalents, P < .05]. Conclusions: Applying the CEEY to preschoolers will lead to a substantial underestimation of EE. Therefore, we recommend that a CEEY for preschool children be developed if measurement of EE is not feasible.
Mirko Brandes, Berit Steenbock and Norman Wirsik
Berit Steenbock, Marvin N. Wright, Norman Wirsik and Mirko Brandes
Purpose: Study purposes were to develop energy expenditure (EE) prediction models from raw accelerometer data and to investigate the performance of three different accelerometers on five different wear positions in preschoolers. Methods: Fourty-one children (54% boys; 3–6.3 years) wore two Actigraph GT3X (left and right hip), three GENEActiv (right hip, left and right wrist), and one activPAL (right thigh) while completing a semi-structured protocol of 10 age-appropriate activities. Participants wore a portable indirect calorimeter to estimate EE. Utilized models to estimate EE included a linear model (LM), a mixed linear model (MLM), a random forest model (RF), and an artificial neural network model (ANN). For each accelerometer, model, and wear position, we assessed prediction accuracy via leave-one-out cross-validation and calculated the root-mean-squared-error (RMSE). Results: Mean RMSE ranged from 2.56–2.76 kJ/min for the RF, 2.72–3.08 kJ/min for the ANN, 2.83–2.94 kJ/min for the LM, and 2.81–2.92 kJ/min for the MLM. The GENEActive obtained mean RMSE of 2.56 kJ/min (left and right wrist) and 2.73 kJ/min (right hip). Predicting EE using the GT3X on the left and right hip obtained mean RMSE of 2.60 and 2.74 kJ/min. The activPAL obtained a mean RMSE of 2.76 kJ/min. Conclusion: These results demonstrate good prediction accuracy for recent accelerometers on different wear positions in preschoolers. The RF and ANN were equally accurate in EE prediction compared with (mixed) linear models. The RF seems to be a viable alternative to linear and ANN models for EE prediction in young children in a semi-structured setting.