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Published Online: 5 July 2004

Predicting Energy Expenditure of Physical Activity Using Hip- and Wrist-Worn Accelerometers

Publication: Diabetes Technology & Therapeutics
Volume 5, Issue Number 6


To investigate the association between physical activity and health, we need accurate and detailed free-living physical activity measurements. The determination of energy expenditure of activity (EEACT) may also be useful in the treatment and maintenance of nutritional diseases such as diabetes mellitus. Minute-to-minute energy expenditure during a 24-h period was measured in 60 sedentary normal female volunteers (35.4 ± 9.0 years, body mass index 30.0 ± 5.9 kg/m2), using a state-of-the-art whole-room indirect calorimeter. The activities ranged from sedentary deskwork to walking and stepping at different intensities. Body movements were simultaneously measured using a hip-worn triaxial accelerometer (Tritrac-R3D, Hemokentics, Inc., Madison, Wisconsin) and a wrist-worn uniaxial accelerometer (ActiWatch AW64, MiniMitter Co., Sunriver, Oregon) on the dominant arm. Movement data from the accelerometers were used to develop nonlinear prediction models (separately and combined) to estimate EEACT and compared for accuracy. In a subgroup (n = 12), a second 24-h study period was repeated for cross-validation of the combined model. The combined model, using Tritrac-R3D and ActiWatch, accurately estimated total EEACT (97.7 ± 3.2% of the measured values, p = 0.781), as compared with using ActiWatch (86.0 ± 4.7%, p < 0.001) or Tritrac-R3D (90.0 ± 4.6%, p < 0.001) alone. This model was also accurate for all intensity categories during various physical activities. The subgroup cross-validation also showed accurate and reproducible predictions by the combination model. In this study, we demonstrated that movement measured using accelerometers at the hip and wrist could be used to accurately predict EEACT of various types and intensity of activities. This concept can be extended to develop valid models for the accurate measurement of free-living energy metabolism in clinical populations.

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Published In

cover image Diabetes Technology & Therapeutics
Diabetes Technology & Therapeutics
Volume 5Issue Number 6December 2003
Pages: 1023 - 1033
PubMed: 14709206


Published online: 5 July 2004
Published in print: December 2003


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    Kong Y. Chen
    Department of Medicine, Vanderbilt University, Nashville, Tennessee
    Sari A. Acra
    Department of Pediatrics, Vanderbilt University, Nashville, Tennessee
    Karen Majchrzak
    Department of Medicine, Vanderbilt University, Nashville, Tennessee
    Candice L. Donahue
    Department of Medicine, Vanderbilt University, Nashville, Tennessee
    Lemont Baker
    Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
    Linda Clemens
    Department of Consumer Science and Education, University of Memphis, Memphis, Tennessee
    Ming Sun
    Department of Medicine, Vanderbilt University, Nashville, Tennessee
    Maciej S. Buchowski
    Department of Medicine, Vanderbilt University; and Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee

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