Vigorous Intervals and Hypoglycemia in Type 1 Diabetes: A Randomized Cross Over Trial
Bibliographic record
Abstract
Adding vigorous-intensity intervals (VII) to moderate-intensity exercise prevents immediate declines in blood glucose in type 1 diabetes (T1D) however the intensity required to minimize post-exercise hypoglycemia is unknown. To examine this question, ten sedentary T1D individuals completed four treadmill exercise sessions: a control session of 45 minutes of walking at 45-55% of heart rate reserve (HRR) and three sessions consisting of 60 seconds (VII) at 70%, 80%, or 90% of HRR every 4 minutes during exercise at 45-55% of HRR. We used continuous glucose monitoring (CGM) to measure time ≤3.9 mmol/L, glucose variability, hypoglycemia frequency and area under the curve (AUC) for hypoglycemia and hyperglycemia for 12 hours post-exercise. We also examined growth hormone and cortisol responses during and following exercise. In the 12 hours post-exercise, the percentage of time ≤3.9 mmol/L, glucose variability, and AUC for hypoglycemia and hyperglycemia were similar across conditions. The frequency of hypoglycemic events was highest after the 90% intervals compared to the control arm (12 vs 3 events, p = 0.03). There was a trend towards elevated growth hormone with increasing exercise intensity but cortisol levels were similar across conditions. Adding VII to moderate intensity exercise may increase hypoglycemia risk at higher intensities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".