Prediction of hypoglycaemia in subjects with type 1 diabetes during physical activity

Introduction Practicing physical activity (PA) on a regular basis is an important support for people with type 1 diabetes (T1D). However, exercise may induce in them hypoglycaemic events during or after it. One major consequence of this is that, to limit this risk, many people with T1D tend to avoid performing PA. The availability of modern continuous glucose-monitoring (CGM) devices is potentially a great asset for reducing the chances of hypoglycaemia (HP) due to PA. Several algorithms have already been proposed to predict HP in subjects with T1D. However, not many of them are specifically focused on HP induced by exercise. Among those, many involve a large number of covariates making the applicability more difficult, and none uses CGM values available during the training session. Objectives We study the problem of predicting hypoglycaemia events in subjects with T1D during PA. The final aim is to produce algorithms enabling a person with T1D to perform a planned PA session without experiencing HP. Method One of the two algorithms we developed uses the CGM data in an initial part of a PA session. A parametric model is fitted to the data and then used to predict a possible HP during the remaining part of the session. Our second algorithm uses the CGM value at the start of a session. It also relies on statistical information about the average rate of decrease of the aforementioned model, as derived from a previously measured CGM data during PA. Then, the algorithm estimates the probability of HP during the planned PA session. Both algorithms have a very simple structure and therefore are of wide applicability. Results The application of the two algorithms to a very large dataset shows their very good ability to predict HP during PA in people with T1D.