Energy Expenditure Estimated by Accelerometry and Doubly Labeled Water: Do They Agree?

LEENDERS, NICOLE Y.1, ; SHERMAN, WILLIAM MICHAEL1 ; NAGARAJA, HAIKADY N.2

Medicine & Science in Sports & Exercise:
doi: 10.1249/01.mss.0000235883.94357.95
SPECIAL COMMUNICATIONS: Methodological Advances
Abstract

Purpose: The purpose of this study was to compare energy expenditure derived from regression equations determined from accelerometry with energy expenditure obtained from the doubly labeled water method (DLW).

Methods: Thirteen subjects participated in a 7-d protocol during which total daily energy expenditure (TDEE) was measured with DLW. Simultaneously, during the 7 d, subjects wore a Tritrac-R3D and an Actigraph (ACT). Pearson and concordance correlations and one-sample t-tests were used to determine the agreement of six Tritrac and eight ACT regression equations that convert body acceleration to energy expenditure with the DLW measurements.

Results: Tritrac TDEE determined from the different Tritrac regression equations under- and overestimated TDEE determined with DLW that ranged from −10 to +101%. For ACT, the percent difference between DLW and ACT-TDEE determined with the regression equation developed by Hendelman and Swartz were not statistically significantly different from zero. The mean of the difference was −2 and −4%, but the range of the difference was large for both equations, −29 to +24%. TDEE determined with the six other ACT equations were significantly different compared with DLW.

Conclusion: Of the 14 different regression equations from the literature, only two developed for ACT compared favorably with DLW; however, the difference in TDEE between these two methods was variable and rather large. These results reemphasize the difficulty in converting body movement into energy expenditure on an individual basis from accelerometry. These results imply that researchers may want to avoid using accelerometers to predict energy expenditure in free-living conditions, instead using these instruments only to measure patterns of physical activity.

Author Information

1Sport and Exercise Sciences Section, School of Physical Activity and Educational Services, and 2Department of Statistics, Ohio State University, Columbus, OH

Address for correspondence: Nicole Y. Leenders, Ph.D., The Ohio State University, General Clinical Research Center, S2000 Davis Medical Center, 480 Medical Center Drive, Columbus, OH 43210-1228; E-mail: nicole.leenders@osumc.edu.

Submitted for publication December 2005.

Accepted for publication June 2006.

©2006The American College of Sports Medicine