PURPOSE: The purpose of this study was to refine the 2006 Crouter two-regression model to eliminate the misclassification of walking or running when starting an activity in the middle of a minute on the ActiGraph clock. METHODS: Forty-eight participants (mean [SD] age = 35 [11.4] yr) performed 10-min bouts of various activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were divided into three routines, and 20 participants performed each routine. Participants wore an ActiGraph accelerometer on the hip, and a portable indirect calorimeter was used to measure energy expenditure. Forty-five routines were used to develop the refined two-regression model, and 15 routines were used to cross validate the model. Coefficient of variation (CV) was used to classify each activity as continuous walking or running (CV < or = 10) or intermittent lifestyle activity (CV > 10). RESULTS: An exponential regression equation and a cubic equation using the natural log of the 10-s counts were developed to predict METs every 10 s for walking or running and intermittent lifestyle activities, respectively. The refined method examines each 10-s epoch and all combinations of the surrounding five 10-s epochs to find the lowest CV. In the cross-validation group, the refined method was not significantly different from measured METs for any activity (P > 0.05), except cycling (P < 0.05). In addition, the 2006 and the refined two-regression models had similar accuracy and precision for estimating energy expenditure during structured activities. CONCLUSION: The refined two-regression model should eliminate the misclassification of transitional minutes when changing activities that start and stop in the middle of a minute on the ActiGraph clock, thus improving the estimate of free-living energy expenditure.
PURPOSE: The purpose of this study was to refine the 2006 Crouter two-regression model to eliminate the misclassification of walking or running when starting an activity in the middle of a minute on the ActiGraph clock. METHODS: Forty-eight participants (mean [SD] age = 35 [11.4] yr) performed 10-min bouts of various activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were divided into three routines, and 20 participants performed each routine. Participants wore an ActiGraph accelerometer on the hip, and a portable indirect calorimeter was used to measure energy expenditure. Forty-five routines were used to develop the refined two-regression model, and 15 routines were used to cross validate the model. Coefficient of variation (CV) was used to classify each activity as continuous walking or running (CV < or = 10) or intermittent lifestyle activity (CV > 10). RESULTS: An exponential regression equation and a cubic equation using the natural log of the 10-s counts were developed to predict METs every 10 s for walking or running and intermittent lifestyle activities, respectively. The refined method examines each 10-s epoch and all combinations of the surrounding five 10-s epochs to find the lowest CV. In the cross-validation group, the refined method was not significantly different from measured METs for any activity (P > 0.05), except cycling (P < 0.05). In addition, the 2006 and the refined two-regression models had similar accuracy and precision for estimating energy expenditure during structured activities. CONCLUSION: The refined two-regression model should eliminate the misclassification of transitional minutes when changing activities that start and stop in the middle of a minute on the ActiGraph clock, thus improving the estimate of free-living energy expenditure.
Authors: Søren Brage; Niels Wedderkopp; Paul W Franks; Lars Bo Andersen; Karsten Froberg Journal: Med Sci Sports Exerc Date: 2003-08 Impact factor: 5.411
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Authors: Kelley Pettee Gabriel; James J McClain; Kendra K Schmid; Kristi L Storti; Robin R High; Darcy A Underwood; Lewis H Kuller; Andrea M Kriska Journal: Int J Behav Nutr Phys Act Date: 2010-06-15 Impact factor: 6.457