Skip to main content
ARS Home » Plains Area » Houston, Texas » Children's Nutrition Research Center » Research » Publications at this Location » Publication #312447
Title: Modeling energy expenditure in children and adolescents using quantile regression

Author
item YANG, YUNWEN - Drexel University
item ADOLPH, ANNE - Children'S Nutrition Research Center (CNRC)
item PUYAU, MAURICE - Children'S Nutrition Research Center (CNRC)
item VOHRA, FIROZ - Children'S Nutrition Research Center (CNRC)
item BUTTE, NANCY - Children'S Nutrition Research Center (CNRC)
item ZAKERI, ISSA - Drexel University

Submitted to: Journal of Applied Physiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/26/2013
Publication Date: 5/2/2013
Citation: Yang, Y., Adolph, A.L., Puyau, M.R., Vohra, F.A., Butte, N.F., Zakeri, I.F. 2013. Modeling energy expenditure in children and adolescents using quantile regression. Journal of Applied Physiology. 115: 251-259.

Interpretive Summary: Complex metabolic and physiological processes that result in energy expenditure can be visualized using advanced mathematical models. In this study we used a technique called quantile regression to predict energy expenditure and to detect differences between nonobese and obese children. Minute-by-minute awake energy expenditure was predicted from heart rate and physical activity accelerometry counts, and child characteristics of age, sex, weight, and height in 109 children, aged 5-18 yr. Quantile regression provided more accurate predictions of energy expenditure compared with conventional regression and revealed different effects of weight, physical activity, and heart rate on energy expenditure in nonobese and obese children. Application of this advanced statistical method revealed differences in physiology not apparent by standard averaging techniques.

Technical Abstract: Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obese children. First, QR models will be developed to predict minute-by-minute awake EE at different quantile levels based on heart rate (HR) and physical activity (PA) accelerometry counts, and child characteristics of age, sex, weight, and height. Second, the QR models will be used to evaluate the covariate effects of weight, PA, and HR across the conditional EE distribution. QR and ordinary least squares (OLS) regressions are estimated in 109 children, aged 5-18 yr. QR modeling of EE outperformed OLS regression for both nonobese and obese populations. Average prediction errors for QR compared with OLS were not only smaller at the median t = 0.5 (18.6 vs. 21.4%), but also substantially smaller at the tails of the distribution (10.2 vs. 39.2% at t = 0.1 and 8.7 vs. 19.8% at t = 0.9). Covariate effects of weight, PA, and HR on EE for the nonobese and obese children differed across quantiles (P < 0.05). The associations (linear and quadratic) between PA and HR with EE were stronger for the obese than nonobese population (P < 0.05). In conclusion, QR provided more accurate predictions of EE compared with conventional OLS regression, especially at the tails of the distribution, and revealed substantially different covariate effects of weight, PA, and HR on EE in nonobese and obese children.