Prediction and Prevention of Nocturnal Hypoglycemia in Persons With Type 1 Diabetes Using Machine Learning Techniques

The objective is to develop a novel system to predict and prevent nocturnal hypoglycemia in type 1 diabetic (T1D) patients, focused in patients with multiple daily injections (MDI) therapy. The general idea is to make use of previous-day information in the moment when patients go to sleep, and then predict if in the next following hours any hypoglycemic event will occur. If the system will have predicted any hypoglycemic event in that moment, it is expected that it will be able to warn the patient to take some action: such as reduce basal insulin dose or to consume a snack before sleep. 10 patients with T1D for more than five years will be included. It is a longitudinal, prospective, interventional study in which every patient will use intermittently scanned Continuous Glucose Monitoring (isCGM) and a physical activity tracker during 12 weeks. Moreover, during this period, patients will store in a mobile application (Freestyle LibreLink) or in a reader information regarding their diabetes management activities, such as insulin delivery doses and meal consumption.