Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry

Abstract

Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observations on the same individual, may be advantageous for prediction of EE. CSTS models for prediction of minute-by-minute EE and, hence, total EE (TEE) from heart rate (HR), physical activity (PA) measured by accelerometry, and observable subject variables were developed in 109 children and adolescents by use of Actiheart and 24-h room respiration calorimetry. CSTS models based on HR, PA, time-invariant covariates, and interactions were developed. These dynamic models involve lagged and lead values of HR and lagged values of PA for better description of the series of minute-by-minute EE. CSTS models with random intercepts and random slopes were investigated. For comparison, likelihood ratio tests were used. Log likelihood increased substantially when random slopes for HR and PA were added. The population-specific model uses HR and 1- and 2-min lagged and lead values of HR, HR(2), and PA and 1- and 2-min lagged values of PA, PA(2), age, age(2), sex, weight, height, minimum HR, sitting HR, HR x height, HR x weight, HR x age, PA x weight, and PA x sex interactions (P < 0.001). Prediction error for TEE was 0.9 +/- 10.3% (mean +/- SD). Errors were not correlated with age, weight, height, or body mass index. CSTS modeling provides a useful predictive model for EE and, hence, TEE in children and adolescents on the basis of HR and PA and other observable explanatory subject characteristics of age, sex, weight, and height.

Incorporating an Exercise Detection, Grading, and Hormone Dosing Algorithm Into the Artificial Pancreas Using Accelerometry and Heart Rate

“…A validated linear regression model described by Zakeri et al 11 was used to estimate EE in kilocalories/minute. This model uses heart rate (HR), physical activity (PA), as well as Hardware components of the OHSU AP.…”

“…For this reason, the best algorithm to estimate EE within the context of an AP was a regression model, of which all of the parameters have been published. 11 In this article, we show results of detecting and grading EE during exercise and nonexercise time epochs in adult subjects with type 1 diabetes on sensor-augmented pump therapy.…”

Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents

“…Raw signals from a biaxial accelerometer and subject characteristics were used to develop an ANN model for the prediction of minute-by-minute EE in 102 young adults (16). Recently, we applied a parametric model, cross-sectional time series (CSTS) analysis, for the prediction of EE from HR and AC (25). This approach is transparent and accounts for the interdependence of EE, HR, and AC over time.…”

Validation of Total Daily Energy Expenditure Calculated with Actiheart against Doubly Labeled Water Method in Costa Rican Schoolchildren

“…It has been reported that AC monitors register correlations between EE and AC that range from moderate to high; on the other hand, accelerometers are not as accurate when certain physical activities, static jobs, and movements against external forces have to be measured [16]. This certainly is the limitation for the calculation of the TEE in sedentary populations.…”