Metabolic Insights: Integrating Continuous Glucose Monitoring with Behavioral AI to Predict Hypoglycemic Events in Type 2 Diabetes

Abstract

Background:
Hypoglycemia remains a significant barrier in optimizing glycemic control among patients with type 2 diabetes mellitus (T2DM). Advances in continuous glucose monitoring (CGM) and artificial intelligence (AI) have opened avenues for real-time prediction and personalized prevention of glucose excursions. This study evaluated a hybrid digital system combining CGM data with AI-driven behavioral analytics to predict and reduce hypoglycemic episodes in patients with T2DM.

Methods:
This prospective cohort study recruited 210 adults with insulin-treated T2DM. Participants were equipped with CGM devices and a companion mobile app incorporating AI-based behavioral tracking (sleep, physical activity, meal logging). The AI model was trained to identify individual glucose response patterns and predict hypoglycemic events 30–90 minutes in advance. Data were collected over a 12-week period. Primary outcomes included incidence and duration of hypoglycemic episodes (<70 mg/dL). Secondary outcomes included user satisfaction and HbA1c reduction.

Results:
The integrated CGM-AI system successfully predicted 83.6% of hypoglycemic events with a lead time of 52.3 ± 13.7 minutes. The intervention group experienced a 37% reduction in total hypoglycemic events (p < 0.001) compared to baseline. HbA1c improved from 8.4% to 7.6% (p = 0.003), and 91% of participants reported high satisfaction with system usability. No severe hypoglycemia was recorded.

Conclusion:
Integrating CGM with behavioral AI enhances the prediction and prevention of hypoglycemic events in T2DM. These findings underscore the potential of digital therapeutics to transform chronic disease management by enabling anticipatory care and patient empowerment.