Variation in Hypoglycemia Risk During Real-World Physical Activity in Adults with Type 1 Diabetes: Insights from the Type 1 Diabetes Exercise Initiative
Bibliographic record
Abstract
Background: Physical activity (PA) poses significant challenges in glucose management for individuals with type 1 diabetes (T1D). Real-world PA is more frequent than structured PA, but remains underexplored. We analyzed 8171 real-world PA sessions comprising 45 activity types from the Type 1 Diabetes Exercise Initiative, examining hypoglycemia risk correlations with PA-level and population-level factors. Methods: Hypoglycemia risk was measured by change in continuous glucose monitoring (ΔCGM) from PA onset to end, low blood glucose index (LBGI), and hypoglycemia event occurrence. Primary analyses used analysis of variance and Tukey’s range test to measure correlations. Secondary analyses compared risk across activity types and categories (aerobic, mixed, and anaerobic). Results: Higher hypoglycemia risk was associated with longer PA duration (median [Interquartile Range (IQR)] ΔCGM −24 [−60, 11] mg/dL for 60–120 min vs. −12 [−31, 5] mg/dL for 15–30 min), lower starting glucose (90% of sessions starting <50 mg/dL had hypoglycemia), and declining glucose rates before PA (all P < 0.05). Carbohydrate (CHO) intake 2–4 h before and during PA was associated with higher hypoglycemia risk (ΔCGM −37 [−67, −14] mg/dL with rescue CHO vs. −15 [−42, 8] mg/dL without, P < 0.05), but this paradoxical effect was explained by higher insulin on board (IoB) and lower starting glucose. Males had larger glucose drops (ΔCGM −20 [−46, 4] mg/dL vs. −16 [−44, 7] mg/dL in females, P < 0.05). Closed-loop users exhibited lower LBGI compared with open-loop users ( P < 0.05). Secondary analyses showed significant glycemic variability across activity types ( P < 0.05). Aerobic activities caused the greatest glucose drop, followed by mixed and anaerobic ( P < 0.05), whereas LBGI differences were nonsignificant ( P = 0.32). Conclusions: Real-world PA has a highly variable glycemic impact, with longer duration, lower starting glucose, and higher IoB increasing hypoglycemia risk. Glycemic responses differed significantly by activity type, with aerobic activities resulting in the greatest decline. These findings highlight the need for tailored strategies to mitigate PA-related hypoglycemia in T1D.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".