title: Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers.
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 ActiGraph+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.
authors.affiliation: Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA, USA.
authors.forename: Issa F
authors.initials: IF
authors.lastname: Zakeri
authors.forename: Anne L
authors.initials: AL
authors.lastname: Adolph
authors.forename: Maurice R
authors.initials: MR
authors.lastname: Puyau
authors.forename: Firoz A
authors.initials: FA
authors.lastname: Vohra
authors.forename: Nancy F
authors.initials: NF
authors.lastname: Butte
mesh_terms: D061725:Accelerometry / Q000295:instrumentation; D015992:Body Mass Index; D002151:Calorimetry / Q000295:instrumentation; D002652:Child Behavior; D002657:Child Development; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D004734:Energy Metabolism; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D008954:Models, Biological; D018670:Monitoring, Ambulatory / Q000295:instrumentation; D009043:Motor Activity; D015999:Multivariate Analysis; D010101:Oxygen Consumption; D015203:Reproducibility of Results; D012119:Respiration; D013781:Texas