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Predicting energy expenditure of physical activity using hip- and wrist-worn accelerometers.

Chen KY, Acra SA, Majchrzak K, Donahue CL, Baker L, Clemens L, Sun M, Buchowski MS
Diabetes Technol Ther. 2003 5 (6): 1023-33

PMID: 14709206 · PMCID: PMC2901160 · DOI:10.1089/152091503322641088

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.

MeSH Terms (13)

Algorithms Calorimetry, Indirect Diabetes Mellitus Energy Metabolism Female Humans Life Style Monitoring, Physiologic Motor Activity Reference Values Regression Analysis Seasons Walking

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