Meal and physical activity behavior in people with type 1 diabetes. A retrospective analysis.

Type 1 diabetes (T1D) is a lifelong chronic metabolic disorder with a high economic, physical, social, and mental toll on both people with the condition and their caregivers. This autoimmune condition results in absolute insulin deficiency and a lifelong need for external insulin (injections) to regulate blood glucose concentration. Effective management of diabetes becomes challenging when confronted with external factors, such as meals, physical activity, and stress, which significantly impact blood glucose levels. Despite promising results on glucose management achieved from existing treatments, mechanisms to mitigate disruptions caused by these events remain elusive. Current findings estimate that almost 70% of people with T1D (80% in the US) do not reach recommended glycemic targets according to the American Diabetes Association (ADA). As such, we need to work tirelessly to offer reliable and affordable technological solutions to improve public health.

This project aims to develop an engineering methodology to digest a wealth of data in the T1D population (continuous glucose monitors -CGM-, insulin injections, activity trackers -heart rate, steps, and accelerometry data-, meal intake, and demographics) to recognize behavioral patterns with respect to meal intake and physical activity. Such information will allow us to tailor better treatments given the characteristics and behavior of the user.

We will accomplish the above by integrating mathematical models (models predicting glucose concentration) with sensor data to reconstruct the magnitude and duration of such glycemic disturbances (effects of meals and physical activity over glucose concentration). This will hold potential for personalized treatment strategies, facilitating timely interventions based on real-time data and paving the way for improved treatments on diabetes technologies.