Acute Exercise-Induced Glucose Change During an Exercise Program in Type 2 Diabetes
Bibliographic record
Abstract
PURPOSE: Supervised exercise programs have been demonstrated to improve overall glycemic control but less well characterized is the evolution of glucose response to exercise during an exercise program. We addressed this issue, using an observational cohort design, among overweight adults with type 2 diabetes. We hypothesized that during the course of the program, glucose levels during exercise would become more stable, as insulin sensitivity improved. Among adults with type 2 diabetes, glucose levels often decline acutely during exercise. METHODS: Thirty-five adults with type 2 diabetes underwent capillary blood glucose (CBG) testing before and after supervised exercise during a 24-week program (48 sessions). After-exercise CBG values were subtracted from before-exercise values (CBG difference). Through repeated measures analysis, we examined CBG difference, before-exercise values, and after-exercise values during the program. Assuming that some initial period of exercise training is necessary to impact CBG difference, in exploratory analyses, we varied the time period analyzed (eg. Weeks 2-24, Weeks 3-24, etc). RESULTS: CBG difference appeared stable throughout the program when all available data were considered. In models that examined periods following Week 11, however, the magnitude of CBG difference declined progressively, as did before-exercise values. After-exercise values remained stable for all time periods examined. CONCLUSIONS: Our exploratory analyses suggest that following 11 weeks of exercise supervision, before-exercise CBG values decline progressively but after-exercise values remain stable, resulting in a progressive decline in CBG difference.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".