Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings |
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Authors: | Alexander H K Montoye Scott A Conger Christopher P Connolly Mary T Imboden M Benjamin Nelson Josh M Bock |
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Institution: | 1. Clinical Exercise Physiology Program, Ball State University, Muncie, Indiana;2. Department of Integrative Physiology and Health Science, Alma College, Alma, Michigan;3. Department of Kinesiology, Boise State University, Boise, Idaho;4. Department of Educational Leadership, Sports Studies, &5. Educational/Counseling Psychology, Washington State University, Pullman, Washington |
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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. |
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Keywords: | ActiGraph artificial neural network machine learning physical activity validity |
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