The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A novel method for using accelerometer data to predict energy expenditure
Abstract
The purpose of this study was to develop a new two-regression model relating Actigraph activity counts to energy expenditure over a wide range of physical activities. Forty-eight participants [age 35 yr (11.4)] performed various activities chosen to represent sedentary, light, moderate, and vigorous intensities. Eighteen activities were split into three routines with each routine being performed by 20 individuals, for a total of 60 tests. Forty-five tests were randomly selected for the development of the new equation, and 15 tests were used to cross-validate the new equation and compare it against already existing equations. During each routine, the participant wore an Actigraph accelerometer on the hip, and oxygen consumption was simultaneously measured by a portable metabolic system. For each activity, the coefficient of variation (CV) for the counts per 10 s was calculated to determine whether the activity was walking/running or some other activity. If the CV was ≤10, then a walk/run regression equation was used, whereas if the CV was >10, a lifestyle/leisure time physical activity regression was used. In the cross-validation group, the mean estimates using the new algorithm (2-regression model with an inactivity threshold) were within 0.75 metabolic equivalents (METs) of measured METs for each of the activities performed ( P ≥ 0.05), which was a substantial improvement over the single-regression models. The new algorithm is more accurate for the prediction of energy expenditure than currently published regression equations using the Actigraph accelerometer.
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Body-worn accelerometer signals can be correlated with energy expenditure.
“whose signals can be correlated with energy expenditure [e.g., 8–12]”
From Human walking in the real world: interactions between terrain type, gait parameters, and energy expenditure (2021) · cites this paper as [9]
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Metadata
- Authors
- Crouter, S. E., Clowers, K. G., & Bassett, D. R.
- Venue
- Journal of Applied Physiology
- Publisher
- American Physiological Society
- Published
- 2006-04
- Volume
- 100
- Issue
- 4
- Pages
- 1324-1331
- ISSN
- 8750-7587
- 1522-1601
- Cited by
- 345
- References
- 2
- Language
- en
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