Continuous Glucose Monitoring Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications
New methods of continuous glucose monitoring (CGM) data analysis are emerging that are valuable for interpreting CGM patterns and underlying metabolic physiology. These new methods use functional data analysis and artificial intelligence (AI), including machine learning (ML). Compared to traditional metrics for evaluating CGM tracing results (CGM Data Analysis 1.0), these new methods, which we refer to as CGM Data Analysis 2.0, can provide a more detailed understanding of glucose fluctuations and trends and enable more personalized and effective diabetes management strategies once translated into practical clinical solutions.
Authors
- Bin Sheng (ORCID: https://orcid.org/0000-0001-8678-2784)
- Shahid N. Shah (ORCID: https://orcid.org/0000-0001-8481-6493)
- Daniel Espes (ORCID: https://orcid.org/0000-0001-8843-7941)
- Juan Espinoza (ORCID: https://orcid.org/0000-0003-0513-588X)
- Michael Snyder (ORCID: https://orcid.org/0000-0003-0784-7987)
- Mark A. Clements (ORCID: https://orcid.org/0000-0002-2368-0331)
- Richard M. Bergenstal (ORCID: https://orcid.org/0000-0002-9050-5584)
- Julia Katharina Mader (ORCID: https://orcid.org/0000-0001-7854-4233)
- David Kerr (ORCID: https://orcid.org/0000-0003-1335-1857)
- Eda Cengiz (ORCID: https://orcid.org/0000-0001-7992-9506)
- Nestoras Mathioudakis (ORCID: https://orcid.org/0000-0002-0210-655X)
- Guillermo E. Umpierrez (ORCID: https://orcid.org/0000-0002-3252-5026)
- Elizabeth Healey (ORCID: https://orcid.org/0000-0002-7307-8429)
- Boris P. Kovatchev (ORCID: https://orcid.org/0000-0003-0495-3901)
- David Charles Klonoff (ORCID: https://orcid.org/0000-0001-6394-6862)
- Ahmed A. Metwally (ORCID: https://orcid.org/0000-0002-0155-7412)
- David M. Maahs (ORCID: https://orcid.org/0000-0002-4602-7909)
- M. Shao (ORCID: https://orcid.org/0009-0004-9550-9965)
- Cindy Ho (ORCID: https://orcid.org/0009-0008-3067-1004)
- Alessandra T. Ayers
- Agatha F. Scheideman (ORCID: https://orcid.org/0009-0008-4211-4934)
Institutions
- Sutter Health (US)
- Boston Children's Hospital (US)
- Google (United States) (US)
- Lurie Children's Hospital (US)
- Children's Mercy Hospital (US)
- Harvard University (US)
- Johns Hopkins University (US)
- Cairo University (EG)
- Emory University (US)
- University of California, San Francisco (US)
- Shanghai Jiao Tong University (CN)
- Medical University of Graz (AT)
- Johns Hopkins Medicine (US)
- Science for Life Laboratory (SE)
- Park Nicollet Clinic (US)
- Mills Peninsula Health Services (US)
- Lurie Children's Hospital (US)
- Diabetes Technology Society (US)
- Ready Mixed Concrete Research and Education Foundation (US)
- University of Virginia (US)
- Stanford University (US)
Publication Details
- Journal
- Journal of Diabetes Science and Technology
- Published
- 2025-08-14
- DOI
- https://doi.org/10.1177/19322968251353228
- Citations
- 11
- Primary Topic
- Diabetes Management and Research
- Type
- article
- Field-Weighted Citation Impact
- 14.24