ERIC Number: EJ1158496
Record Type: Journal
Publication Date: 2017
Abstractor: As Provided
Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings
Montoye, Alexander H. K.; Conger, Scott A.; Connolly, Christopher P.; Imboden, Mary T.; Nelson, M. Benjamin; Bock, Josh M.; Kaminsky, Leonard A.
Measurement in Physical Education and Exercise Science, v21 n4 p223-234 2017
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.
Descriptors: Validity, Electronic Equipment, Prediction, Physical Activity Level, Accuracy, Models, Simulation, Metabolism, Human Body, Measurement Techniques, Statistical Analysis
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Authoring Institution: N/A
Identifiers - Location: Indiana
Grant or Contract Numbers: N/A