The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest
Authors' conclusion
RMRF and RMLR accurately predict hypoglycemia during exercise and identify factors that increase the risk of hypoglycemia. Lower glucose, decreasing levels of glucose before exercise, and greater pre-exercise IOB largely predict hypoglycemia risk in adults with T1D.
Study context
- Population
- Adults with T1D; structured study-specified (aerobic, interval, resistance training videos) and free-living exercise sessions from the T1D Exercise Initiative study
- Comparison
- Repeated measures random forest (RMRF) vs repeated measures logistic regression (RMLR) for predicting hypoglycemia during exercise
- Outcome
- Hypoglycemia (CGM value <70 mg/dL) during exercise; model performance by AUC and balanced accuracy
- Follow-up
- During exercise
Methodological detailsrepeated measures random forest, repeated measures logistic regression
- Repeated measures random forest
- Repeated measures logistic regression
Main finding
As reported in the abstract
Prediction of hypoglycemia during exercise
| Group | AUC | Balanced accuracy |
|---|---|---|
| RMRF | 0.833 | 77% |
| RMLR | 0.825 | 77% |
AUC
RMRF0.833
RMLR0.825
Balanced accuracy
RMRF77%
RMLR77%
Further findings
Predictors of hypoglycemia
- Higher probability of hypoglycemia with lower pre-exercise glucose, negative pre-exercise glucose rates of change, greater % time <70 mg/dL in the 24 h before exercise, and greater pre-exercise bolus insulin-on-board (IOB)
Hypoglycemia by exercise type
- Highest probability with free-living aerobic exercise, walking/hiking and physical labor; lowest with structured exercise
Original publication
The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest. Diabetes Technol Ther. Published 20 June 2023. doi:10.
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