Association of aerobic fitness level with exercise‐induced hypoglycaemia in Type 1 diabetes
Bibliographic record
Abstract
AIM: To determine the impact of physical fitness level on hypoglycaemia risk during exercise in people with Type 1 diabetes. METHODS: A total of 44 patients [34 adults (aged 22-70 years) and 10 adolescents (aged 12-18 years)] with Type 1 diabetes, treated with insulin pump therapy, underwent a standardized exercise session. Cardiorespiratory fitness (maximum oxygen uptake) was measured and classified, based on established norms for age and sex, into either poor (< 25th percentile) or good fitness level (> 25th percentile). Plasma glucose levels were measured every 10 min, each patient performed physical activity at 60% maximum oxygen uptake either on a treadmill for 1 h or on a bicycle for 30 min. Frequency of hypoglycaemia (plasma glucose < 4 mmol/l) and decline in plasma glucose levels during exercise were assessed. RESULTS: In all, 23 patients had a good exercise fitness level. Hypoglycaemic events occurred in 17/23 patients (74.0%) in the good fitness level group compared with 8/21 patients (38.0%) in the poor fitness level group (P = 0.02). Both groups had similar pre-exercise plasma glucose levels. The plasma glucose values during exercise in the good fitness level group compared with the poor fitness level group were: plasma glucose nadir 3.9 ± 1.6 vs 5.5 ± 2.4 mmol/l (P = 0.01) and plasma glucose change -4.6 ± 3.4 vs. -2.1 ± 3.1 mmol/l (P = 0.01). The correlation between the plasma glucose nadir and maximum oxygen uptake was r = -0.38 (P = 0.01). CONCLUSIONS: Patients with good fitness level seem to be more prone to hypoglycaemia during exercise. This could be the result of better insulin sensitivity and the fact that they tend to exercise at greater work thresholds. These results are a step toward a better understanding of the association between physical fitness and exercise-induced hypoglycaemia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".