Capturing Real-World Habitual Sleep Patterns With a Novel User-Centric Algorithm to Preprocess Fitbit Data in the All of Us Research Program: Retrospective Observational Longitudinal Study
The user-centric algorithm captures the natural variability in sleep schedules, providing an alternative approach to preprocess and evaluate sleep metrics related to schedule, duration, and disturbances. A publicly available R package facilitates the implementation of this algorithm for clinical and translational research.
Authors
- Lide Han (ORCID: https://orcid.org/0000-0002-9077-7766)
- Logan Douglas Schneider (ORCID: https://orcid.org/0000-0001-8729-5224)
- Kelsie M Full (ORCID: https://orcid.org/0000-0002-6264-0228)
- Evan L. Brittain (ORCID: https://orcid.org/0000-0001-5843-3392)
- Neil S. Zheng (ORCID: https://orcid.org/0000-0001-8737-0773)
- John Hernandez (ORCID: https://orcid.org/0000-0002-3313-430X)
- Jack Ching (ORCID: https://orcid.org/0000-0003-0752-8628)
- Douglas M. Ruderfer (ORCID: https://orcid.org/0000-0002-2365-386X)
- Hiral Master (ORCID: https://orcid.org/0000-0003-0019-3087)
- Jeffrey Annis (ORCID: https://orcid.org/0000-0002-3097-4186)
- Peyton L. Coleman (ORCID: https://orcid.org/0000-0001-5388-6886)
- Karla Gleichauf
Institutions
- Brigham and Women's Hospital (US)
- Google (United States) (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-04-23
- DOI
- https://doi.org/10.2196/71718
- Citations
- 1
- Primary Topic
- Sleep and related disorders
- Type
- article
- Field-Weighted Citation Impact
- 2.12