An Aerobic Cooldown After Morning, Fasted Resistance Exercise Has Limited Impact on Post-exercise Hyperglycemia in Adults With Type 1 Diabetes: A Randomized Crossover Study
Bibliographic record
Abstract
OBJECTIVES: Expert guidelines recommend an aerobic cooldown to lower blood glucose for the management of post-exercise hyperglycemia. This strategy has never been empirically tested. Our aim in this study was to compare the glycemic effects of performing an aerobic cooldown vs not performing a cooldown after a fasted resistance exercise session. We hypothesized that the cooldown would lower blood glucose in the 30 minutes after exercise and would result in less time in hyperglycemia in the 6 hours after exercise. METHODS: Participants completed 2 identical resistance exercise sessions. One was followed by a low-intensity (30% of peak oxygen consumption) 10-minute cycle ergometer cooldown, and the other was followed by 10 minutes of sitting. We compared the changes in capillary glucose concentration during these sessions and continuous glucose monitoring (CGM) outcomes over 24 hours post-exercise. RESULTS: Sixteen participants completed the trial. Capillary glucose was similar between conditions at the start of exercise (p=0.07). Capillary glucose concentration decreased by 0.6±1.0 mmol/L during the 10-minute cooldown, but it increased by 0.7±1.3 mmol/L during the same time in the no-cooldown condition. The resulting difference in glucose trajectory led to a significant interaction (p=0.02), with no effect from treatment (p=0.7). Capillary glucose values at the end of recovery were similar between conditions (p>0.05). There were no significant differences in CGM outcomes. CONCLUSIONS: An aerobic cooldown reduces glucose concentration in the post-exercise period, but the small and brief nature of this reduction makes this strategy unlikely to be an effective treatment for hyperglycemia occurring after fasted exercise.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".