Document Type

Article

Publication Date

6-22-2017

Publication Title

Measurement in Physical Education and Exercise Science

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.

Volume

21

Issue

4

First Page

223

Last Page

234

DOI

https://doi.org/10.1080/1091367X.2017.1337638

ISSN

1091-367X

Comments

Originally published in Measurement in Physical Education and Exercise Science, 21(4), p. 223-234.

https://doi.org/10.1080/1091367X.2017.1337638

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