Including Aerobic Exercise Into Data-Based Virtual Twins for Glycemic Simulation.

Abstract

Background: Data-driven models of the glucose-insulin metabolism have recently emerged as an effective framework for realistic virtual patient modeling in diabetes. The growing demand for personalized therapies requires precise and individualized models that align naturally with machine learning models trained on patient-specific data. Using deep generative models such as generative adversarial networks opens new possibilities for incorporating previously unmodeled physiological phenomena into simulations.

Methods: In this study, we developed a new extended version of our conditional Wasserstein generative adversarial network model by incorporating aerobic exercise intensity data from the T1DEXI dataset, along with insulin administration and carbohydrate consumption data. We use an aerobic physical activity model to describe the effects of immediate and prolonged exercise on glycemia from recorded discrete intensity levels. This enables the network to retain contextual information about recent aerobic physical activity. A total of 1479 days of data from 56 patients, including 308 exercise sessions, were used to train and validate our model.

Results: We evaluated the model to ensure that it replicates real-world data from the T1DEXI study in terms of mean blood glucose, time below range, time in range, time above range, and time in tight range, both in aggregate and when separated by active and sedentary days. In addition, the model reproduces aerobic exercise-induced glucose drops.

Conclusions: This new model provides a more reliable, extended framework for in silico trials that incorporate physical activity scenarios, which has the potential to be used in the design and validation of automated insulin delivery.