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Title: Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers

Author
item ZAKERI, ISSA - 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)

Submitted to: Journal of Nutrition
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/7/2012
Publication Date: 1/1/2013
Citation: Zakeri, I.F., Adolph, A.L., Puyau, M.R., Vohra, F.A., Butte, N.F. 2013. Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers. Journal of Nutrition. 143(1):114-122.

Interpretive Summary: Prediction equations of energy expenditure (EE) using accelerometers and miniaturized heart rate (HR) monitors have been developed in older children and adults, but not in preschool-aged children. Because the relationships between accelerometer counts (AC), HR and EE are influenced by growth and maturation, age-specific EE prediction equations are required. We used advanced technology (fast-response room calorimetry, Actiheart and Actigraph accelerometers and miniaturized HR monitors) and sophisticated mathematical modeling (cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS)) to develop models for the prediction of minute-by-minute EE in preschool-aged children. Predicted awake EE values were within +/-10% for 81-87% of individuals for CSTS models and for 91-98% of individuals for MARS models. CSTS and MARS models should prove useful in predicting the EE and movement characteristic of preschool-aged children. These findings are important as we look at other EE experiments in preschool-aged children.

Technical Abstract: Prediction equations of energy expenditure (EE) using accelerometers and miniaturized heart rate (HR) monitors have been developed in older children and adults but not in preschool-aged children. Because the relationships between accelerometer counts (ACs), HR, and EE are confounded by growth and maturation, age-specific EE prediction equations are required. We used advanced technology (fast-response room calorimetry, Actiheart and Actigraph accelerometers, and miniaturized HR monitors) and sophisticated mathematical modeling [cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS)] to develop models for the prediction of minute-by-minute EE in 69 preschool-aged children. CSTS and MARS models were developed by using participant characteristics (gender, age, weight, height), Actiheart (HR+AC_x) or ActiGraph parameters (AC_x, AC_y, AC_z, steps, posture) [x, y, and z represent the directional axes of the accelerometers], and their significant 1- and 2-min lag and lead values, and significant interactions. Relative to EE measured by calorimetry, mean percentage errors predicting awake EE (-1.1 +/- 8.7%, 0.3 +\- 6.9%, and -0.2 +/- 6.9%) with CSTS models were slightly higher than with MARS models (-0.7 +/- 6.0%, 0.3 +/- 4.8%, and -0.6 +/- 4.6%) for Actiheart, ActiGraph, and ctiGraph+HR devices, respectively. Predicted awake EE values were within +/-10% for 81-87% of individuals for CSTS models and for 91-98% of individuals for MARS models. Concordance correlation coefficients were 0.936, 0.931, and 0.943 for CSTS EE models and 0.946, 0.948, and 0.940 for MARS EE models for Actiheart, ActiGraph, and ActiGraph+HR devices, respectively. CSTS and MARS models should prove useful in capturing the complex dynamics of EE and movement that are characteristic of preschool-aged children.