Digital health application integrating wearable data and behavioral patterns improves metabolic health
Open accessPublished version of a preprint
Abstract
The effectiveness of lifestyle interventions in reducing caloric intake and increasing physical activity for preventing Type 2 Diabetes (T2D) has been previously demonstrated. The use of modern technologies can potentially further improve the success of these interventions, promote metabolic health, and prevent T2D at scale. To test this concept, we built a remote program that uses continuous glucose monitoring (CGM) and wearables to make lifestyle recommendations that improve health. We enrolled 2,217 participants with varying degrees of glucose levels (normal range, and prediabetes and T2D ranges), using continuous glucose monitoring (CGM) over 28 days to capture glucose patterns. Participants logged food intake, physical activity, and body weight via a smartphone app that integrated wearables data and provided daily insights, including overlaying glucose patterns with activity and food intake, macronutrient breakdown, glycemic index (GI), glycemic load (GL), and activity measures. The app furthermore provided personalized recommendations based on users’ preferences, goals, and observed glycemic patterns. Users could interact with the app for an additional 2 months without CGM. Here we report significant improvements in hyperglycemia, glucose variability, and hypoglycemia, particularly in those who were not diabetic at baseline. Body weight decreased in all groups, especially those who were overweight or obese. Healthy eating habits improved significantly, with reduced daily caloric intake and carbohydrate-to-calorie ratio and increased intake of protein, fiber, and healthy fats relative to calories. These findings suggest that lifestyle recommendations, in addition to behavior logging and CGM data integration within a mobile app, can enhance the metabolic health of both nondiabetic and T2D individuals, leading to healthier lifestyle choices. This technology can be a valuable tool for T2D prevention and treatment.
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CGM provides continuous data, highlights trends in glycemic excursions, and detects impending hypoglycemia.
“Unlike traditional self-monitoring methods, CGM provides continuous data, highlights trends in glycemic excursions and detects impending hypoglycemia [6].”
From Sequential use of continuous glucose monitoring, with or without exercise trackers, significantly improves glycemic control in patients with type 2 diabetes (2025) · cites this paper as [6]
The Introduction (page not specified) describes real-time feedback about glucose trends and alarm features, while the Discussion (page not specified) says CGM reveals glycemic excursions more readily than older measurement methods. The copy does not say that CGM detects impending hypoglycemia; it reports alarms and reduced hypoglycemia, which do not establish that specific capability. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrevious research showed that a structured digital health program integrating CGM and behavioral tracking improved glycemic control and promoted weight loss.
“This study builds upon our previous research16, where we showed that a structured digital health program integrating CGM and behavioral tracking improved glycemic control and promoted weight loss.”
From Impact of digital health interventions on glycemic control and weight management. (2025) · cites this paper as [16]
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- Authors
- Zahedani, A. D., McLaughlin, T., Veluvali, A., Aghaeepour, N., Hosseinian, A., Agarwal, S., Ruan, J., Tripathi, S., Woodward, M., Hashemi, N., & Snyder, M.
- Affiliations
- Stanford University (US)
- Corresponding author
- Snyder, Michael
- Venue
- npj Digital Medicine
- Published
- 2023-11-25
- Volume
- 6
- Issue
- 1
- Pages
- 216-216
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC10673832
- OpenAlex
- W4388997595
- Cited by
- 96
- References
- 48
- Citations by year
- 2026: 42 · 2025: 37 · 2024: 17
- FWCI
- 34.00
- Type
- journal article
- Language
- en
- Update notices
- correction (2024-01-12)
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK116074)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- type 2 diabetes prevention, continuous glucose monitoring, wearable devices, mobile health apps, prediabetes, glycemic variability, hypoglycemia, hyperglycemia, dietary behavior, macronutrient intake, glycemic index, glycemic load, physical activity, weight loss, obesity, mobile application
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