JMIR Publications

ABSTRACT

Background:

Despite advances in continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems, many individuals with type 1 diabetes (T1D) fail to achieve recommended glycemic targets. Although behavioral factors (e.g., physical activity, sleep, diet, insulin timing) influence glucose outcomes, the behavioral context under free-living conditions remains insufficiently characterized.

Objective:

To examine associations between real-world behavioral patterns and glycemic outcomes in individuals with type 1 diabetes using automated insulin delivery systems by analyzing multimodal data from wearable sensors, mobile food logs, and continuous glucose monitoring.

Methods:

We conducted a prospective observational study involving 19 adults with T1D using AID systems over a 30-day period. Participants wore a smartwatch to capture behavioral metrics, including step counts, exercise duration, and sleep duration, and used a custom mobile application to log time-stamped food intake. Wearable and mobile app data were integrated with AID system data to construct a multimodal dataset. Behavioral–glycemic relationships were analyzed using a complementary framework combining unsupervised clustering and correlation analyses across individuals.

Results:

Clustering revealed distinct groups with similar overall activity and intake patterns but different percentages of time-in-range (TIR ≈ 69–86%), indicating that comparable behavioral profiles were associated with different levels of glycemic control. Insulin timing relative to meals consistently differentiated glycemic profiles, whereas physical activity and carbohydrate intake alone showed weaker separation. Correlation analysis identified average meal–bolus delay as one of the strongest behavioral correlates of glycemic outcomes, with a negative association with TIR (ρ ≈ −0.67). Sleep duration showed a moderate positive association with TIR and lower variability, while activity- and intake-related measures were strongly interrelated but less directly associated with glycemic metrics.

Conclusions:

Glycemic differences appear to be more closely associated with how behaviors are coordinated—particularly insulin timing relative to meals—than with exercise or carbohydrate intake alone. These findings highlight the importance of incorporating behavioral context to explain heterogeneity in real-world diabetes management and support personalized, behavior-aware strategies.