Prediction of Hypoglycemia During Aerobic Exercise in Adults With Type 1 Diabetes

Abstract

Background: Fear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise. Methods: We have developed and evaluated two separate algorithms to predict hypoglycemia at the start of exercise. Model 1 is a decision tree and model 2 is a random forest model. Both models were trained using a meta-data set based on 154 observations of in-clinic aerobic exercise in 43 adults with T1D from 3 different studies that included participants using sensor augmented pump therapy, automated insulin delivery therapy, and automated insulin and glucagon therapy. Both models were validated using an entirely new validation data set with 90 exercise observations collected from 12 new adults with T1D. Results: Model 1 identified two critical features predictive of hypoglycemia during exercise: heart rate and glucose at the start of exercise. If heart rate was greater than 121 bpm during the first 5 min of exercise and glucose at the start of exercise was less than 182 mg/dL, it predicted hypoglycemia with 79.55% accuracy. Model 2 achieved a higher accuracy of 86.7% using additional features and higher complexity. Conclusions: Models presented here can assist people with T1D to avoid exercise related hypoglycemia. The simple model 1 heuristic can be easily remembered (the 180/120 rule) and model 2 is more complex requiring computational resources, making it suitable for automated artificial pancreas or decision support systems.

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor

“…Very few studies addressed the prediction of hypoglycemic events as a classification problem. Reddy et al [26] applied ML techniques to predict the occurrence of a hypoglycemic episode while adults with T1D were performing aerobic exercise. Oviedo et al [27] considered support vector machines (SVM) to predict postprandial hypoglycemia using retrospective data from 10 adults with T1D under SAP therapy.…”

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

“…Certain works have also used BG data in combination with other data such as breath samples and camera samples, etc. Reddy et al [ 40 ] predicted hypoglycemia at the start of an aerobic exercise. Hypoglycemia was predicted from breath samples using ML techniques by Siegel et al [ 27 ].…”

“…This approach fixes the over-fitting problem of decision trees. Seo et al [ 43 ], Güemes et al [ 60 ], Vahedi et al [ 33 ], G Noaro et al [ 72 ], Vu et al [ 47 ], Reddy et al [ 40 ], Chen et al [ 30 ], Dave et al [ 52 ], Calhoun et al [ 45 ], Amar et al [ 75 ], Hidalgo et al [ 77 ], and Rodriguez et al [ 79 ] have all used RF for predicting/detecting hypoglycemia. Ruan et al [ 31 ] and Cappon et al [ 66 ] used the XGboost algorithm.…”

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

“…For example, CGM data can be merged with information provided by other medical devices (SMBG, pumps, smart pens) [ 64 ], mobile apps, clinical registries, electronic health records, as well as other wearable sensors, such as activity trackers. The data collected by activity trackers can be used to automatically detect exercise sessions [ 65 ], predict exercise-induced hypoglycemia [ 60 ], and if necessary, recommend the consumption of carbohydrates to avoid hypoglycemia. Such data integration could contribute to generating a digital ecosystem of diabetes data that can be used to extract new medical knowledge that cannot be discovered by relying on a single source of information [ 66 , 67 ].…”

“…Reddy et al [ 60 ] proposed two population algorithms for predicting the occurrence of hypoglycemia during aerobic exercise at the beginning of the exercise session. The models considered were a decision tree classifier and a random forest classifier.…”