Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis
These results indicate the value of biologically informed features and longitudinal data for identifying people with diabetes and further, suggest that these methods could make such separations possible for other chronic conditions that affect sleep and inflammation.
Authors
- Severine Soltani (ORCID: https://orcid.org/0000-0003-0894-9993)
- Benjamin L. Smarr (ORCID: https://orcid.org/0000-0003-4442-3956)
- Wendy Hartogensis (ORCID: https://orcid.org/0000-0002-4979-8661)
- Ashley E. Mason (ORCID: https://orcid.org/0000-0002-8744-0185)
- Frederick Hecht (ORCID: https://orcid.org/0000-0002-5782-1171)
- Stephan Dilchert (ORCID: https://orcid.org/0000-0001-6733-576X)
- Patrick Kasl (ORCID: https://orcid.org/0000-0002-7756-7326)
- Shreenithi Navaneethan
- Jamison H. Burks (ORCID: https://orcid.org/0000-0003-4816-5704)
- Varun K. Viswanath
- Edward J. Wang
Institutions
- Baruch College (US)
- University of California, San Francisco (US)
- University of California System (US)
- University of California San Diego (US)
- La Jolla Bioengineering Institute (US)
- Jacobs (United States) (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2026-03-16
- DOI
- https://doi.org/10.1038/s43856-026-01501-0
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 0.00