Impact of 2-Month Exercise Training on Glycemic Metrics on Days with and without Exercise in Adults with Type 1 Diabetes
Bibliographic record
Abstract
AIMS: Implementing exercise programs in individuals with type 1 diabetes may precipitate glycemic fluctuations. A better understanding of these fluctuations is essential for developing appropriate glucose management strategies. We aimed to assess glycemic excursions and their progression during a 2-month training program, comparing fluctuations around exercise sessions with those of non-exercising days. METHODS: Nineteen (13 female) adults with type 1 diabetes participated in two to three supervised 90-min combined (aerobic/strength) exercise sessions per week, over 2 months. Glycemic excursions (continuous glucose monitoring) were measured during specific periods (24-h, nocturnal; periods before, during, after exercise sessions) and compared between exercise and non-exercise days (linear mixed models, logistic regressions). RESULTS: Nights following exercise sessions showed a reduced risk of hyperglycemia (>10.0 mmol·L -1 ) versus non-exercise nights. This difference diminished over the weeks of training, alongside a progressive increase in the risk of time >16.7 mmol·L -1 during the early and late recovery phases of exercise. Overall, regardless of exercise session occurrence, the risk of spending time < 70 or 54 mg/dL increased as the training program progressed. CONCLUSIONS: Initially, acute exercise sessions reduced nocturnal hyperglycemia without increasing hypoglycemia. However, over time, the risk of nocturnal hypoglycemia increased, highlighting the need for vigilant glycemic supervision, particularly at night, even on non-exercise days.
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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 | 0.001 |
| 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) | 0.001 | 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".