Distributed lag and spline modeling for predicting energy expenditure from accelerometry in youth
Choi, 2010
Description
Five prediction models for energy expenditure were developed using ActiGraph GT1M monitors worn on the hip, wrist, and ankle while adolescents performed a variety of activities in a room calorimeter. Root Mean Square Error (RMSE) was 143.0 (hip + wrist + ankle), 155.8 (hip + wrist), 163.6 (hip), 180.6 (wrist), and 151.2 (ankle) kcal/day. When used to classify physical activity intensity, classification accuracy was 63 to 75% (sedentary), 68 to 79% (light), and 34 to 80% (moderate-to-vigorous) depending on the prediction model used.
Development/Validation
Sample: 76 (29 males) healthy adolescents, 10-17 years of age
Setting: Room calorimeter
Activities: Activities of daily living, cycling, stationary, stepping, treadmill running, walking
Criterion: Room calorimeter (VO2)
Accelerometer(s): ActiGraph GT1M on dominant hip, wrist, and/or ankle
Validation approach: Leave-one-out cross-validation
Instructions
Coefficients and equations for total energy expenditure (TEE), physical activity energy expenditure (PAEE), and METs are provided below for different combinations of monitors (hip + wrist + ankle, hip + wrist, hip, wrist, ankle). The following description of model notation is provided. ‘gender’ is coded as 0 for females and 1 for males.
TEE coefficients and equations
PAEE coefficients and equations
METs coefficients and equations
Source Information
Reference:
Choi, L., Chen, K. Y., Acra, S. A., & Buchowski, M. S. (2010). Distributed lag and spline modeling for predicting energy expenditure from accelerometry in youth. Journal of Applied Physiology, 108(2), 314-327. https://doi.org/ 10.1152/japplphysiol.00374.2009 Link to Paper
Corresponding author: Leena Choi, leena.choi@vanderbilt.edu
Contact
Kimberly Clevenger at accelerometerrepository@gmail.com