Characterizing the Impact of Physical Activity on Patients with Type 1 Diabetes Using Statistical and Machine Learning Models

Abstract

Continuous glucose monitoring (CGM) represents a significant advancement in diabetes management, playing an important role in glycemic control for patients with type 1 diabetes (T1D). Despite their benefits, their performance is affected by numerous factors such as the carbohydrate intake, alcohol consumption, and physical activity (PA). Among these, PA could cause hypoglycemic episodes, which might happen after exercising. In this work, two main contributions are presented. First, we extend the performance evaluation of two glucose monitoring devices, Eversense and Free Style Libre (FSL), for measuring glucose concentrations during high-intensity PA and normal daily activity (NDA). The impact of PA is investigated considering (1) different glucose ranges (hypoglycemia, euglycemia, and hyperglycemia); and (2) four time periods throughout the day (morning, afternoon, evening, and night). Second, we evaluate the effectiveness of machine learning (ML) models, including logistic regression, K-nearest neighbors, and support vector machine, to automatically detect PA in T1D individuals using glucose measurements. The performance analysis showed significant differences between glucose levels obtained in the PA and NDA period for Eversense and FSL devices, specially in the hyperglycemic range and two time intervals (morning and afternoon). Both Eversense and FSL devices present measurements with large variability during strenuous PA, indicating that their users should be cautious. However, glucose recordings provided by monitoring devices are accurate for NDA, reaching similar values to capillary glucose device. Lastly, ML-based models yielded promising results to determine when an individual has performed PA, reaching an accuracy value of 0.93. The results can be used to develop an individualized data-driven classifier for each patient that categorizes glucose profiles based on the time interval during the day and according to if a patient performs PA. Our work contributes to the analysis of PA on the performance of CGM devices.

Keywords

• continuous glucose monitoring
• type 1 diabetes
• physical activity
• machine learning
• TabPFN