Department of Kinesiology
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Browsing Department of Kinesiology by Subject "ACCELEROMETER"
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Item A Comparison of Energy Expenditure Estimation of Several Physical Activity Monitors(Lippincott Williams & Wilkins, 2013) Dannecker, Kathryn L.; Sazonova, Nadezhda A.; Melanson, Edward L.; Sazonov, Edward S.; Browning, Raymond C.; Colorado State University; University of Alabama Tuscaloosa; University of Colorado DenverIntroduction: Accurately and precisely estimating free-living energy expenditure (EE) is important for monitoring energy balance and quantifying physical activity. Recently, single and multisensor devices have been developed that can classify physical activities, potentially resulting in improved estimates of EE. Purpose: This study aimed to determine the validity of EE estimation of a footwear-based physical activity monitor and to compare this validity against a variety of research and consumer physical activity monitors. Methods: Nineteen healthy young adults (10 men, 9 women) completed a 4-h stay in a room calorimeter. Participants wore a footwear-based physical activity monitor as well as Actical, ActiGraph, IDEEA, DirectLife, and Fitbit devices. Each individual performed a series of postures/activities. We developed models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices. Results: Estimated EE using the shoe-based device was not significantly different than measured EE (mean T SE; 476 T 20 vs 478 +/- 18 kcal, respectively) and had a root-mean-square error of 29.6 kcal (6.2%). The IDEEA and the DirectLlife estimates of EE were not significantly different than the measured EE, but the ActiGraph and the Fitbit devices significantly underestimated EE. Root-mean-square errors were 93.5 (19%), 62.1 kcal (14%), 88.2 kcal (18%), 136.6 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for Actical, DirectLife, IDEEA, ActiGraph, and Fitbit, respectively. Conclusions: The shoe-based physical activity monitor provides a valid estimate of EE, whereas the other physical activity monitors tested have a wide range of validity when estimating EE. Our results also demonstrate that estimating EE based on classification of physical activities can be more accurate and precise than estimating EE based on total physical activity.Item Development of a Cadence-based Metabolic Equation for Walking(Lippincott Williams & Wilkins, 2021) Moore, Christopher C.; Aguiar, Elroy J.; Ducharme, Scott W.; Tudor-Locke, Catrine; University of North Carolina; University of North Carolina Chapel Hill; University of Alabama Tuscaloosa; California State University Long Beach; University of North Carolina CharlottePurpose: This study aimed to develop cadence-based metabolic equations (CME) for predicting the intensity of level walking and evaluate these CME against the widely adopted American College of Sports Medicine (ACSM) Metabolic Equation, which predicts walking intensity from speed and grade. Methods: Two hundred and thirty-five adults (21-84 yr of age) completed 5-min level treadmill walking bouts between 0.22 and 2.24 m.s(-1), increasing by 0.22 m.s(-1) for each bout. Cadence (in steps per minute) was derived by dividing directly observed steps by bout duration. Intensity (oxygen uptake; in milliliters per kilogram per minute) was measured using indirect calorimetry. A simple CME was developed by fitting a least-squares regression to the cadence-intensity relationship, and a full CME was developed through best subsets regression with candidate predictors of age, sex, height, leg length, body mass, body mass index (BMI), and percent body fat. Predictive accuracy of each CME and the ACSM metabolic equation was evaluated at normal (0.89-1.56 m.s(-1)) and all (0.22-2.24 m.s(-1)) walking speeds through k-fold cross-validation and converted to METs (1 MET = 3.5 mL.kg(-1).min(-1)). Results:On average, the simple CME predicted intensity within similar to 1.8 mL.kg(-1).min(-1) (similar to 0.5 METs) at normal walking speeds and with negligible (<0.01 METs) bias. Including age, leg length, and BMI in the full CME marginally improved predictive accuracy (<= 0.36 mL.kg(-1).min(-1) [<= 0.1 METs]), but may account for larger (up to 2.5 mL.kg(-1).min(-1) [0.72 MET]) deviations in the cadence-intensity relationships of outliers in age, stature, and/or BMI. Both CME demonstrated 23%-35% greater accuracy and 2.2-2.8 mL.kg(-1).min(-1) (0.6-0.8 METs) lower bias than the ACSM metabolic equation's speed-based predictions. Conclusions: Although the ACSM metabolic equation incorporates a grade component and is convenient for treadmill-based applications, the CME developed herein enables accurate quantification of walking intensity using a metric that is accessible during overground walking, as is common in free-living contexts.