Location: Children's Nutrition Research Center
Title: Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water Authors
|Butte, Nancy -|
|Wong, William -|
|Adolph, Anne -|
|Puyau, Maurice -|
|Vorhr, Firoz -|
|Zakeri, Issa -|
Submitted to: Journal of Nutrition
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 28, 2010
Publication Date: N/A
Interpretive Summary: To address the Healthy People 2010 goals for reducing the proportion of children and adolescents who are classified as overweight or obese, an understanding of national trends in food consumption and energy expenditure are needed. Energy expenditure is difficult to measure accurately in any population – especially in children and adolescents. In response, researchers at Baylor College of Medicine and Drexel University have developed, and validated the accuracy of a relatively nonintrusive, and inexpensive method of measuring energy expenditure, using a combined heart rate and accelerometer device in this group. To test their models, they enrolled healthy-weight and obese children, and measured the free-living total energy expenditure of each child using the "gold standard" doubly-labeled water method. The children were fitted with a heart rate monitor and an accelerometer device (which essentially measured body movement). Predicted total energy expenditure from heart rate monitors and accelerometers, compared favorably to those determined from doubly-labeled water. It is likely that this research will be instrumental as researchers continue to study ways to prevent and treat obesity in high-risk and vulnerable populations.
Technical Abstract: Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant characteristics, heart rate (HR), and accelerometer counts (AC) for prediction of minute-by-minute EE, and hence 24-h total EE (TEE), against a 7-d doubly labeled water (DLW) method in children and adolescents. Our secondary aim was to demonstrate the utility of CSTS and MARS to predict awake EE, sleep EE, and activity EE (AEE) from 7-d HR and AC records, because these shorter periods are not verifiable by DLW, which provides an estimate of the individual’s mean TEE over a 7-d interval. CSTS and MARS models were validated in 60 normal-weight and overweight participants (ages 5–18 y). The Actiheart monitor was used to simultaneously measure HR and AC. For prediction of TEE, mean absolute errors were 10.7 +/- 307 kcal/d and 18.7 +/- 252 kcal/d for CSTS and MARS models, respectively, relative to DLW. Corresponding root mean square error values were 305 and 251 kcal/d for CSTS and MARS models, respectively. Bland-Altman plots indicated that the predicted values were in good agreement with the DLW-derived TEE values. Validation of CSTS and MARS models based on participant characteristics, HR monitoring, and accelerometry for the prediction of minute-by-minute EE, and hence 24-h TEE, against the DLW method indicated no systematic bias and acceptable limits of agreement for pediatric groups and individuals under free-living conditions.