Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings. Issue 4 (2nd October 2017)

• Record Type: Journal Article
• Authors: Montoye, Alexander H. K.; Conger, Scott A.; Connolly, Christopher P.; Imboden, Mary T.; Nelson, M. Benjamin; Bock, Josh M.; Kaminsky, Leonard A.
• Is Part Of: Measurement in physical education and exercise science. Volume 21:Issue 4(2017)
• Page Start: 223
• Page End: 234
• Publication Date: 2017-10-02
• Subjects: ActiGraph -- artificial neural network -- machine learning -- physical activity -- validity; Physical fitness -- Testing -- Periodicals; Physical education and training -- Statistical methods -- Periodicals; Physical education and training -- Research -- Periodicals; 613.7
• DOI: 10.1080/1091367X.2017.1337638
• Languages: English
• ISSNs: 1091-367X

Abstract

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.