A personalized diet and exercise recommender system for type 1 diabetes self-management: An in silico study

Abstract

Management of diet and exercise levels needs to be personalized for patients with Type 1 Diabetes (T1D) to reduce the number of hypoglycemia events and to achieve a good glycemic control. This study developed a model-based Recommender system that could provide an (optimal) personalized intervention on diet and exercise for T1D patients, which could be potentially implemented as a mobile application (app) for self-management of T1D in the future work. At each intervention time, the Recommender makes prediction of blood glucose based on a patient-specific model of glucose dynamics, and then provides optimal interventions, which could be a meal/snack size or a target heart rate during exercise, by minimizing a risk function with constraints under a future time horizon. Simulations were conducted to evaluate the Recommender through 30 virtual subjects generated from a modified UVa/Padova simulator with an added exercise-glucose subsystem. The performance of the Recommender was compared to two self-management schemes: the Starter scheme and the Skilled scheme, where the Skilled represents an off-line optimal scheme providing a lower bound on the risk index. Compared to the Starter, the Recommender reduced the mean Low Blood Glucose Index by 84% and reduced the Blood Glucose Risk Index by 49% (P < 0.05), and it had comparable performance as the Skilled. The Recommender also reduced the number of hypoglycemia events during and post-exercise compared to the Starter and the Skilled.