The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The use of uniaxial accelerometry for the assessment of physical-activity-related energy expenditure: a validation study against whole-body indirect calorimetry
Open accessAbstract
Assessing the total energy expenditure (TEE) and the levels of physical activity in free-living conditions with non-invasive techniques remains a challenge. The purpose of the present study was to investigate the accuracy of a new uniaxial accelerometer for assessing TEE and physical-activity-related energy expenditure (PAEE) over a 24 h period in a respiratory chamber, and to establish activity levels based on the accelerometry ranges corresponding to the operationally defined metabolic equivalent (MET) categories. In study 1, measurement of the 24 h energy expenditure of seventy-nine Japanese subjects (40 (SD 12) years old) was performed in a large respiratory chamber. During the measurements, the subjects wore a uniaxial accelerometer (Lifecorder; Suzuken Co. Ltd, Nagoya, Japan) on their belt. Two moderate walking exercises of 30 min each were performed on a horizontal treadmill. In study 2, ten male subjects walked at six different speeds and ran at three different speeds on a treadmill for 4 min, with the same accelerometer. O 2 consumption was measured during the last minute of each stage and was expressed in MET. The measured TEE was 8447 (SD 1337) kJ/d. The accelerometer significantly underestimated TEE and PAEE (91·9 (SD 5·4) and 92·7 (SD 17·8) % chamber value respectively); however, there was a significant correlation between the two values (r 0·928 and 0·564 respectively; P<0·001). There was a strong correlation between the activity levels and the measured MET while walking (r2 0·93; P<0·001). Although TEE and PAEE were systematically underestimated during the 24 h period, the accelerometer assessed energy expenditure well during both the exercise period and the non-structured activities. Individual calibration factors may help to improve the accuracy of TEE estimation, but the average calibration factor for the group is probably sufficient for epidemiological research. This method is also important for assessing the diurnal profile of physical activity.
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Cites
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965
Calibration of the Computer Science and Applications, Inc. accelerometer
FREEDSON, P. S., MELANSON, E., & SIRARD, J. (1998) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-199805000-00021 · Cited by 3,825
Esparza, J., Fox, C., Harper, I., Bennett, P., Schulz, L., Valencia, M., & Ravussin, E. (2000) · International Journal of Obesity · doi:10.1038/sj.ijo.0801085 · Cited by 184
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Nitrogen metabolism in the rumen
Bach, A., Calsamiglia, S., & Stern, M. D. (2005) · Journal of Dairy Science · doi:10.3168/jds.s0022-0302(05)73133-7 · Cited by 1,311
Strath, S. J., Kaminsky, L. A., Ainsworth, B. E., Ekelund, U., Freedson, P. S., Gary, R. A., Richardson, C. R., Smith, D. T., & Swartz, A. M. (2013) · Circulation · doi:10.1161/01.cir.0000435708.67487.da · Cited by 1,029
WILSON, R. P., WHITE, C. R., QUINTANA, F., HALSEY, L. G., LIEBSCH, N., MARTIN, G. R., & BUTLER, P. J. (2006) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2006.01127.x · Cited by 797
Physical Activity Assessment With Accelerometers: An Evaluation Against Doubly Labeled Water
Plasqui, G., & Westerterp, K. R. (2007) · Obesity · doi:10.1038/oby.2007.281 · Cited by 654
Hurling, R., Catt, M., De Boni, M., Fairley, B. W., Hurst, T., Murray, P., Richardson, A., & Sodhi, J. S. (2007) · Journal of Medical Internet Research · doi:10.2196/jmir.9.2.e7 · Cited by 536
Metadata
- Authors
- Kumahara, H., Schutz, Y., Ayabe, M., Yoshioka, M., Yoshitake, Y., Shindo, M., Ishii, K., & Tanaka, H.
- Venue
- British Journal of Nutrition
- Publisher
- Cambridge University Press (CUP)
- Published
- 2004-02
- Volume
- 91
- Issue
- 2
- Pages
- 235-243
- ISSN
- 0007-1145 (ISSN-L)
- 1475-2662
- OpenAlex
- W2145821003
- Cited by
- 534
- References
- 4
- FWCI
- 8.55
- Type
- journal article
- Language
- en
- Open access
- bronze
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