Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes
Authors' conclusion
Incorporating factors such as historical glucose and insulin can improve prediction modeling for the acute glucose responses to exercise.
Study context
- Population
- Adolescents with T1D wearing continuous glucose monitors (CGMs) in the T1DEXIP study (mean ± SD age 14 ± 2 years; HbA1c 7.1 ± 1.3%; T1D duration 5 ± 4 years; 56% using HCL), 3738 activities logged over 10 days3,738 activities
- Participants
- 225 adolescents
- Comparison
- Repeated Measures Random Forest vs Repeated Measures Logistic Regression
- Outcome
- Composite:
- Risk of hypoglycemia (<70 mg/dL)
- Hyperglycemia (>250 mg/dL)
- Follow-up
- Within 2 h after starting exercise
Methodological detailsreal-world data, repeated measures random forest and logistic regression models
- Real-world data
- Repeated measures random forest and logistic regression models
Main finding
As reported in the abstract
Prediction of composite dysglycemia risk
| Group | AUROC |
|---|---|
| Repeated Measures Random Forest (RMRF) | 0.737 |
| Repeated Measures Logistic Regression (RMLR) | 0.661 |
AUROC
Repeated Measures Random Forest (RMRF)0.737
Repeated Measures Logistic Regression (RMLR)0.661
- RMRF vs RMLR: P < 0.001 — RMRF more accurate
Further findings
Activity-start factors with minimal composite risk
- Starting glucose
- between 132 and 160 mg/dL
- Glucose rate of change at activity start
- between −0.4 and −1.9 mg/dL/min
- Also predictive: time <70 mg/dL and >250 mg/dL in prior 24 h, HbA1c, insulin on board at activity start
Activity-end factors with minimal composite risk
- Glucose
- between 128 and 133 mg/dL
- Glucose rate of change
- between 0.4 and −0.6 mg/dL/min
- Separate models
Original publication
Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes. Diabetes Technol Ther. Published 14 May 2024. doi:10.
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