Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes
We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.
Authors
- Souptik Barua (ORCID: https://orcid.org/0000-0002-1675-8874)
- David Kerr (ORCID: https://orcid.org/0000-0003-1335-1857)
- Ryan Pai (ORCID: https://orcid.org/0009-0000-8501-7570)
- Bo Sung Kim (ORCID: https://orcid.org/0000-0003-0227-1923)
- Raven Wierzchowska-McNew (ORCID: https://orcid.org/0000-0003-2374-1664)
- Ashutosh Sabharwal (ORCID: https://orcid.org/0000-0003-1898-5787)
- Nicolaas E.P. Deutz (ORCID: https://orcid.org/0000-0001-5845-6447)
- Amruta Pai (ORCID: https://orcid.org/0000-0002-1689-7129)
- Maya McDonald
Institutions
- Sutter Health (US)
- New York University (US)
- Rice University (US)
- Texas A&M University (US)
Publication Details
- Journal
- Journal of Diabetes Science and Technology
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1177/19322968241274800
- Citations
- 9
- Primary Topic
- Diabetes Management and Research
- Type
- article
- Field-Weighted Citation Impact
- 3.22
Funders
- National Science Foundation