Use of a Two-Regression Model for Estimating Energy Expenditure in Children

CROUTER, S. E., M. HORTON, and D. R. BASSETT Jr. Use of a Two-Regression Model for Estimating Energy Expenditure in Children. Med. Sci. Sports Exerc., Vol. 44, No. 6, pp. 1177-1185, 2012. Purpose: The purpose of this study was to develop two new two-regression models (2RM), for use in children, that estimate energy expenditure (EE) using the ActiGraph GT3X: 1) mean vector magnitude (VM) counts or 2) vertical axis (VA) counts. The new 2RMs were also compared with existing ActiGraph equations for children. Methods: Fifty-seven boys and 52 girls (mean +/- SD: age = 11 +/- 1.7 yr, body mass index = 21.4 +/- 5.5 kg.m(-2)) performed 30-min supine rest and 8 min of six different activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were split into three routines with each routine performed by 38-39 participants. Seventy-seven participants were used for the development group, and 39 participants were used for the cross-validation group. During all testing, activity data were collected using an ActiGraph GT3X, worn on the right hip, and oxygen consumption was measured using a Cosmed K4b(2). All energy expenditure valises are expressed as METRMR (activity (V) over dotO(2)/resting (V) over dotO(2)). Results: For each activity, a coefficient of variation was calculated using 10-s epochs for the VA and VM to determine whether the activity was continuous walking/running or an intermittent lifestyle activity. Separate regression equations were developed for walking/running and intermittent lifestyle activity. In the cross-validation group, the VM and VA 2RMs were within 0.8 METRMR of measured METRMR for all activities except Sportwall and running (all P > 0.05). The other existing ActiGraph equations had mean errors ranging from 0.0 to 2.6 METRMR for the activities. Conclusions: The new 2RMs for use in children with the ActiGraph GT3X provide a closer estimate of mean measured METRMR than other currently available prediction equations. In addition, they improve the individual prediction errors across a wide range of activity intensities.