Data-Driven Insights for Improved Diabetes Management

Abstract

Effective diabetes management requires understanding the complex interplay between multiple physiological and behavioral factors affecting glycemic control. This study presents a comprehensive analysis of diabetes management data from six distinct datasets containing continuous glucose monitoring (CGM) data, insulin administration records, carbohydrate intake, physical activity, sleep quality, and stress measurements. We employed time series analysis techniques such as STL decomposition, Dynamic Time Warping, LSTM neural networks, and XGBoost modeling to identify patterns in glucose variability and their relationships with modifiable factors. Our findings revealed significant time-of-day variations in glucose levels, with highest variability in late afternoon and evening (CV = 0.41 vs. 0.34, p < 0.01). Integrated prediction models achieved 83% sensitivity for hypoglycemia and 81% for hyperglycemia detection. The insulin-to-carbohydrate ratio varied by time of day, with morning ratios (1:8) differing from evening (1:12). Physical activity demonstrated intensity-dependent effects, with moderate-intensity exercise reducing glucose levels by 42 mg/dL for approximately 12 hours post-activity. Sleep quality below rating 3 (on a 5-point scale) was associated with a 37% increase in next-day glucose variability. These findings provide evidence-based insights for developing personalized diabetes management strategies that account for chronological variations in insulin sensitivity, meal timing, physical activity scheduling, and lifestyle interventions targeting sleep and stress management.