The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm
Open accessAbstract
We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P < 0·001) and locomotive (r 0·961, P < 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cut-off classification models. This method may be useful for field investigations as well as for self-monitoring by general users.
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Cited by
DiPietro, L., Al-Ansari, S. S., Biddle, S. J. H., Borodulin, K., Bull, F. C., Buman, M. P., Cardon, G., Carty, C., Chaput, J.-P., Chastin, S., et al. (2020) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-020-01042-2 · Cited by 387
Dowd, K. P., Szeklicki, R., Minetto, M. A., Murphy, M. H., Polito, A., Ghigo, E., van der Ploeg, H., Ekelund, U., Maciaszek, J., Stemplewski, R., et al. (2018) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-017-0636-2 · Cited by 385
Applying machine learning to predict future adherence to physical activity programs
Zhou, M., Fukuoka, Y., Goldberg, K., Vittinghoff, E., & Aswani, A. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0890-0 · Cited by 45
Different Location of Triaxial Accelerometer and Different Energy Expenditures
Kim, D. Y., Jung, Y.-S., Park, R.-W., & Joo, N.-S. (2014) · Yonsei Medical Journal · doi:10.3349/ymj.2014.55.4.1145 · Cited by 34
Matsushita, J., Okada, H., Okada, Y., Sekiyama, T., Iida, H., Shindo, A., Murata, H., & Fukui, M. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.949762 · Cited by 8
“Previous study reported the strong relationships between measured energy expenditure from tri-accelerometer and indirect calorimetry (11).”
Background
Metadata
- Authors
- Ohkawara, K., Oshima, Y., Hikihara, Y., Ishikawa-Takata, K., Tabata, I., & Tanaka, S.
- Venue
- British Journal of Nutrition
- Publisher
- Cambridge University Press (CUP)
- Published
- 2011-01-25
- Volume
- 105
- Issue
- 11
- Pages
- 1681-1691
- ISSN
- 0007-1145 (ISSN-L)
- 1475-2662
- OpenAlex
- W1964754398
- Cited by
- 458
- References
- 46
- FWCI
- 5.01
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Ohkawara, K., Oshima, Y., Hikihara, Y., Ishikawa-Takata, K., Tabata, I., & Tanaka, S. (2011). Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm. British Journal of Nutrition. https://doi.org/10.1017/s0007114510005441
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