GlucoseGo: a simple tool to predict hypoglycaemia during exercise in type 1 diabetes
Catherine L Russon1, Michael J Allen2, Emma Cockcroft2
1University of Exeter Medical School, University of Exeter, Exeter, UK. c.russon@exeter.ac.uk.
Diabetologia
|February 25, 2026
Summary
A new machine learning tool, GlucoseGo, accurately predicts exercise-induced hypoglycaemia (low blood sugar) risk in people with type 1 diabetes using simple inputs. This empowers safer physical activity and reduces dangerous low blood sugar events.
Area of Science:
- Diabetes management and technology
- Exercise physiology
- Machine learning in healthcare
Background:
- Hypoglycaemia during exercise poses a significant risk for individuals with type 1 diabetes.
- Accurate, real-time risk assessment is crucial for safe physical activity participation.
Purpose of the Study:
- To develop a machine learning tool for predicting exercise-induced hypoglycaemia risk.
- To create an accessible and rapid risk assessment method for safer exercise participation.
Main Methods:
- Combined data from 16,430 exercise sessions across 834 participants (aged 12-80).
- Utilized the XGBoost algorithm to develop comprehensive and simplified predictive models.
- Validated model performance using ROC AUC, achieving 0.89 and 0.87 respectively.
Main Results:
- A simplified model using starting glucose, exercise duration, and glucose trend arrows achieved high accuracy (ROC AUC 0.87).
- The model demonstrated consistent performance across various exercise types and insulin delivery methods.
- The simplified model was translated into the user-friendly GlucoseGo heatmap tool.
Conclusions:
- GlucoseGo offers a practical, accessible tool for immediate pre-exercise hypoglycaemia risk prediction.
- This tool can empower individuals with type 1 diabetes to exercise more safely.
- Increased safe exercise engagement may lead to reduced hypoglycaemic episodes and improved physical activity levels.
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