A novel method for using accelerometer data to predict energy expenditure

• Published in: Journal of applied physiology (1985) Vol. 100; no. 4; pp. 1324 - 1331
• Main Authors: Crouter, Scott E, Clowers, Kurt G, Bassett, David R., Jr
• Format: Journal Article
• Language: English
• Published: Bethesda, MD Am Physiological Soc 01-04-2006; American Physiological Society
• Subjects: Activities of Daily Living; Adult; Aged; Algorithms; Biological and medical sciences; Energy Metabolism; Exercise; Female; Fundamental and applied biological sciences. Psychology; Humans; Male; Metabolism; Middle Aged; Models, Biological; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Oxygen; Oxygen Consumption; Regression Analysis; Reproducibility of Results; Respiratory system; Running - physiology; Walking - physiology; Physical exercise; Motion; Vertebrata; motion sensor; Mammalia; Energetic cost; Variability; Accelerometer; Oxygen consumption; activity counts variability; physical activity
• ISSN: 8750-7587; 1522-1601
• DOI: 10.1152/japplphysiol.00818.2005

Department of Exercise, Sport, and Leisure Studies, University of Tennessee, Knoxville, Tennessee
Submitted 11 July 2005
; accepted in final form 1 December 2005
The purpose of this study was to develop a new two-regression model relating Actigraph activity counts to energy expenditure over a wide range of physical activities. Forty-eight participants [age 35 yr (11.4)] performed various activities chosen to represent sedentary, light, moderate, and vigorous intensities. Eighteen activities were split into three routines with each routine being performed by 20 individuals, for a total of 60 tests. Forty-five tests were randomly selected for the development of the new equation, and 15 tests were used to cross-validate the new equation and compare it against already existing equations. During each routine, the participant wore an Actigraph accelerometer on the hip, and oxygen consumption was simultaneously measured by a portable metabolic system. For each activity, the coefficient of variation (CV) for the counts per 10 s was calculated to determine whether the activity was walking/running or some other activity. If the CV was 10, then a walk/run regression equation was used, whereas if the CV was >10, a lifestyle/leisure time physical activity regression was used. In the cross-validation group, the mean estimates using the new algorithm (2-regression model with an inactivity threshold) were within 0.75 metabolic equivalents (METs) of measured METs for each of the activities performed ( P 0.05), which was a substantial improvement over the single-regression models. The new algorithm is more accurate for the prediction of energy expenditure than currently published regression equations using the Actigraph accelerometer.
motion sensor; physical activity; oxygen consumption; activity counts variability
Address for reprint requests and other correspondence: S. Crouter, Cornell Univ., Div. of Nutritional Sciences, 279 MVR, Ithaca, NY 14853 (e-mail: sec62{at}cornell.edu )