GlucoseGo: A Simple, User-Friendly, Machine Learning-Derived Tool for Predicting Exercise-Related Hypoglycaemia Risk in Type 1 Diabetes
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Aims/hypothesis
This study aims to develop an accessible, machine learning-derived tool for people with type 1 diabetes that predicts hypoglycaemia risk at the start of exercise, facilitating quick, clear risk assessment that can directly support safer exercise habits.
Methods
We integrated data from four diverse studies encompassing 16,477 exercise sessions from 834 participants aged 12-80, using various insulin delivery methods. The XGBoost algorithm was used to develop a comprehensive and simplified model to predict hypoglycaemia during exercise, determined by continuous glucose monitor readings below 3.9 mmol/L (70 mg/dL).
Results
The comprehensive model demonstrated a mean ROC AUC of 0.89, while the simplified model, relying solely on glucose levels at the start of exercise, duration of exercise and glucose rate of change arrows, achieved an ROC AUC of 0.87. This model was shown to be effective for any type of exercise and for people on a variety of insulin delivery devices. This simplified model was then translated, through collaborative efforts with type 1 diabetes participants, into “GlucoseGo,” a user-friendly, traffic-light heatmap that visually demonstrates risk of hypoglycaemia during exercise based on these three variables.
Conclusions/interpretation
The GlucoseGo heatmap offers a simple, readily available tool for predicting hypoglycaemia risk at the onset of exercise. This advancement empowers users to manage their exercise routines more safely, with potential to reduce hypoglycaemia incidents and enhancing exercise engagement among the type 1 diabetes population.
Research in context
What is already known about this subject?
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Exercise is crucial for managing type 1 diabetes, yet adherence to recommended guidelines is low.
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Exercise-induced hypoglycaemia is a major barrier to exercise for those with type 1 diabetes.
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Existing machine learning models for predicting hypoglycaemia during exercise often require complex data inputs, limiting their practical use.
What is the key question?
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Can a machine learning model using minimal data effectively predict exercise-induced hypoglycaemia in type 1 diabetes?
What are the new findings?
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We developed a simplified machine learning model using only three variables; starting glucose levels, glucose rate of change arrows and exercise duration - that nearly matches the performance of more complex models, with an ROC AUC of 0.87 versus 0.89.
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This model was transformed into “GlucoseGo,” user-friendly heatmaps, designed collaboratively with individuals with type 1 diabetes, that visually indicate exercise-induced hypoglycaemia risk.
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Subgroup analyses show consistently good performance in predicting hypoglycaemia risk across diverse patient profiles and exercise types, validating its broad applicability.
How might this impact clinical practice in the foreseeable future?
GlucoseGo offers a practical tool for safely managing exercise, potentially reducing hypoglycaemic incidents and increasing exercise participation among those with type 1 diabetes.
