Machine Learning-Based Stepping Filter Improves Estimates of Moderate-to-Vigorous-Intensity Physical Activity from Wrist Actigraphy
- DOI
- 10.1159/000551306
- Published
- 2026-03-02
- Container
- Digital Biomarkers
- Publisher
- S. Karger AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1159/000551306,
title = {Machine Learning-Based Stepping Filter Improves Estimates of Moderate-to-Vigorous-Intensity Physical Activity from Wrist Actigraphy},
author = {Josh Cherian and Emily C. Hector and Shyh-Huei Chen and Stephanie M. George and John M. Jakicic and Ryan S. McGinnis and Michael E. Miller and W. Jack Rejeski and Jason Fanning},
year = {2026},
journal = {Digital Biomarkers},
doi = {10.1159/000551306},
url = {https://doi.org/10.1159/000551306}
}RIS
TY - JOUR TI - Machine Learning-Based Stepping Filter Improves Estimates of Moderate-to-Vigorous-Intensity Physical Activity from Wrist Actigraphy AU - Josh Cherian AU - Emily C. Hector AU - Shyh-Huei Chen AU - Stephanie M. George AU - John M. Jakicic AU - Ryan S. McGinnis AU - Michael E. Miller AU - W. Jack Rejeski AU - Jason Fanning PY - 2026 JO - Digital Biomarkers DO - 10.1159/000551306 UR - https://doi.org/10.1159/000551306 ER -
APA
Cherian, J., Hector, E. C., Chen, S., George, S. M., Jakicic, J. M., McGinnis, R. S., Miller, M. E., Rejeski, W. J., & Fanning, J. (2026). Machine Learning-Based Stepping Filter Improves Estimates of Moderate-to-Vigorous-Intensity Physical Activity from Wrist Actigraphy. Digital Biomarkers. https://doi.org/10.1159/000551306
Source records
- crossref · retrieved 2026-09-25T02:05:26.925Z