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

Abstract

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.

FIG. 1

Schematic diagram of the whole-room indirect calorimetry chamber at Vanderbilt University.

FIG. 2

Mean ± 2 SD of the difference between the measured (calorimeter) and estimated 24-h EE_ACT using prediction models (ActiWatch, Tritrac-R3D, and Tritrac-R3D+ActiWatch, in Eqs. 2, 1, and 3, respectively), with respect to the mean of the EE_ACT values.

FIG. 3

Group mean EE_ACT in different intensity categories as measured by the calorimeter, modeled by the ActiWatch, Tritrac-R3D, and combined (Tritrac-R3D+ActiWatch) models. *p < 0.05 compared with the measured values.

FIG. 4

Estimated total EE_ACT from the modeled day (dotted with dashed trend line) and the cross-validation day (triangle with solid trend line), versus the measured EE_ACT in the subgroup (n = 12). The line of identity is also shown for theoretical ″perfect″ fit.