Document Type
Article
Publication Date
6-22-2017
Publication Title
Measurement in Physical Education and Exercise Science
Volume
21
Abstract
This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a metabolic analyzer (EE criterion). Visit 1 (V1) involved structured, 5-min activities dictated by researchers; Visit 2 (V2) allowed participants activity choice and duration (simulated free-living). EE prediction models were developed incorporating data from one setting (V1/V2; V2/V2) or both settings (V1V2/V2). The V1V2/V2 method had the lowest root mean square error (RMSE) for EE prediction (1.04–1.23 vs. 1.10–1.34 METs for V1/V2, V2/V2), and the ankle-worn accelerometer had the lowest RMSE of all accelerometers (1.04–1.18 vs. 1.17–1.34 METs for other placements). The ankle-worn accelerometer and associated EE prediction models developed using data from both structured and simulated free-living settings should be considered for optimal EE prediction accuracy.
Issue
4
First Page
223
Last Page
234
DOI
https://doi.org/10.1080/1091367X.2017.1337638
ISSN
1091-367X
Recommended Citation
Montoye, Alexander H. K.; Conger, Scott A.; Connolly, Christopher P.; Imboden, Mary T.; Nelson, M. Benjamin; Bock, Josh M.; and Kaminsky, Leonard A., "Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings" (2017). Faculty Publications - Department of Kinesiology. 5.
https://digitalcommons.georgefox.edu/hhp_fac/5
Comments
Originally published in Measurement in Physical Education and Exercise Science, 21(4), p. 223-234.
https://doi.org/10.1080/1091367X.2017.1337638