Optimizing an automated sleep detection algorithm using wrist-worn accelerometer data for individuals with chronic pain
The Tudor-Locke algorithm provides automated sleep detection for accelerometry data; however, caution must be exercised when applying the algorithm to populations beyond its validated scope. In this manuscript, we provide an empirically optimized parameter set for applying this algorithm to adults with chronic pain.
Authors
- Enrique Alba Torres
- Suzanne Dixon (ORCID: https://orcid.org/0000-0003-4173-9103)
- Emma Fortune (ORCID: https://orcid.org/0000-0001-8704-0598)
- Omid Jahanian (ORCID: https://orcid.org/0000-0002-5379-1462)
- Chris Sletten
- Louis Faust (ORCID: https://orcid.org/0000-0002-9741-7894)
- Paul Scholten (ORCID: https://orcid.org/0000-0003-4990-8776)
- Sey V. Oloyede
- Clifford Trouard
Institutions
- Mayo Clinic (US)
- Jacksonville College (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-04-04
- DOI
- https://doi.org/10.1371/journal.pone.0319348
- Citations
- 3
- Primary Topic
- Sleep and related disorders
- Type
- article
- Field-Weighted Citation Impact
- 6.35