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The most recent version of this article was published on 1 March 2008

Br J Sports Med. Published Online First: 30 August 2007. doi:10.1136/bjsm.2006.033399
Copyright © 2007 BMJ Publishing Group Ltd & British Association of Sport and Exercise Medicine.

Paper

A New 2-regression Model for the Actical Accelerometer

Scott E Crouter 1* and David R Bassett Jr.2

1 Cornell University, United States
2 The University of Tennessee, Knoxville, United States

* To whom correspondence should be addressed. E-mail: sec62{at}cornell.edu.

Accepted 9 August 2007


Abstract

Objective: The objective of this study was to develop a new 2-regression model relating Actical activity counts to METs.

Methods: Forty-eight participants ((mean±SD) age: 35±11.4 yrs) performed 10-min bouts of various activities ranging from sedentary behaviors to vigorous physical activities. Eighteen activities were split into three routines with each routine being performed by 20 individuals. Forty-five routines were randomly selected for the development of a new 2-regression model and 15 tests were used to cross-validate the new 2-regression model and compare it against existing equations. During each routine, the participant wore an Actical accelerometer on the hip and oxygen consumption was simultaneously measured by a portable metabolic system. The coefficient of variation (CV) of four consecutive 15-sec epochs was calculated for each minute. For each activity, the average CV and the counts.min-1 were calculated for minutes 4-9. If the CV was ≤ 13% a walk/run regression equation was used, and if the CV was > 13% a lifestyle/leisure time physical activity regression was used.

Results: An exponential regression line (R2=0.912; SEE=0.149) was used for activities with a CV ≤ 13%, and a cubic regression line (R2=0.884, SEE=0.804) was used for activities with a CV > 13%. In the cross-validation group the mean estimates, using the new 2-regression model with an inactivity threshold, were within 0.56 METs of measured METs for each of the activities performed (P≥0.05), except cycling (P<0.05).

Conclusion: For most activities examined the new 2-regression model predicted METs more accurate than currently available equations for the Actical accelerometer.

Key Words: Motion Sensor, activity count variability, oxygen consumption, physical activity


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This article has been cited by other articles:

  • Staudenmayer, J., Pober, D., Crouter, S., Bassett, D., Freedson, P. (2009). An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J. Appl. Physiol. 107: 1300-1307 [Abstract] [Full Text]  
  • Zakeri, I., Adolph, A. L., Puyau, M. R., Vohra, F. A., Butte, N. F. (2008). Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry. J. Appl. Physiol. 104: 1665-1673 [Abstract] [Full Text]  

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