Performance Evaluation of Algorithms to Estimate Daily Sedentary Time Using Wrist-Worn Sensors in Free-Living Adults
Fifteen of 21 (71%) algorithms produced estimates of sedentary time that were moderate-strongly correlated with the reference measure, but only five (24%) were within 10% of the reference. Free-living benchmarking studies like this can identify more accurate and precise algorithms to estimate sedentary time and identify characteristics of algorithm development studies that yield better results.
Authors
- Sarah Kozey Keadle (ORCID: https://orcid.org/0000-0002-9569-9306)
- Alaina H. Shreves (ORCID: https://orcid.org/0000-0002-0127-4391)
- Havaughnia Hayes
- Eric T. Hyde (ORCID: https://orcid.org/0000-0002-5896-0894)
- Charles Edwards Matthews (ORCID: https://orcid.org/0000-0001-8037-3103)
- Pedro F. Saint‐Maurice (ORCID: https://orcid.org/0000-0002-8752-2340)
- Rena R. Jones (ORCID: https://orcid.org/0000-0003-1294-1679)
- Aiden Doherty (ORCID: https://orcid.org/0000-0003-1840-0451)
- Joshua R. Freeman (ORCID: https://orcid.org/0000-0002-5493-6352)
- Katie Ylarregui
Institutions
- Champalimaud Foundation (PT)
- National Institutes of Health (US)
- California Polytechnic State University (US)
- University of California San Diego (US)
- University of Oxford (GB)
- Human Longevity (United States) (US)
- National Cancer Institute (US)
Publication Details
- Journal
- Journal for the Measurement of Physical Behaviour
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1123/jmpb.2024-0051
- Citations
- 1
- Primary Topic
- Physical Activity and Health
- Type
- article
- Field-Weighted Citation Impact
- 1.16