Effects of intensity and amount of exercise on measures of insulin and glucose: Analysis of inter-individual variability
Bibliographic record
Abstract
AIM: To determine the separate effects of exercise amount and intensity on the rate of response for glucose and insulin variables, where rate of response was defined as the number of individuals with improvement in glucose and insulin values that was beyond the day-to-day variability of measurement. METHODS: Participants were 171 sedentary, middle-aged abdominally obese adults who completed a 24-week intervention. Participants were randomly assigned to (1) no-exercise control (n = 51), (2) low-amount, low-intensity exercise (LALI, n = 38), (3) high-amount, low-intensity exercise (HALI, n = 52), or (4) high-amount, high-intensity exercise (HAHI, n = 30). Two-hour glucose, insulin area under the curve (AUC), and fasting insulin were measured during a 2-hour, 75g oral glucose challenge. The day-to-day variability for these measures was calculated to be ±2.2 mmol/L, ±940.2 pmol/L, and ±38.9 pmol/L, respectively. RESULTS: At 24 weeks, the number of nonresponders for 2-hr glucose was 98.0%, 86.8%, 94.2%, 86.7% in the control, LALI, HALI, and HAHI groups, respectively. The number of nonresponders for insulin AUC was 88.0%, 75.7%, 75.0%, 80.0% in the control, LALI, HALI, and HAHI groups, respectively. The number of nonresponders for fasting insulin was 88.2%, 84.2%, 84.6%, 93.3% in the control, LALI, HALI, and HAHI groups, respectively. The rate of response was not different between control and any of the exercise groups for any measure (p>0.05). CONCLUSION: The improvement in glucose and insulin measures did not exceed the day-to-day variability of measurement for approximately 80% of the participants independent of exercise amount or intensity.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".