The effects of high intensity interval training on indices of health in type 2 diabetics
Bibliographic record
Abstract
Traditional steady state aerobic exercise training is a proven method to treat metabolic disorders, in particular diabetes. Short duration high intensity interval training (HIIT) induces similar metabolic adaptations and improvements. To date, most HIIT studies have utilized “all out” efforts; however, lower intensity HIIT has recently been demonstrated to produce similar effects to all out efforts in healthy adults. It is unknown whether a lower intensity HIIT has the capacity to improve insulin resistance in a diseased population. Nine untrained type 2 diabetics [age=40.2 ± 9.1 yr; BMI = 33.9 ± 5.31 kg/m2; VO2max = 1.95 ± .21 L/min (mean ± SD)] performed 6 training sessions of HIIT over 2 weeks. Anthropometric measures, homeostatic model assessment of insulin resistance (HOMA-IR), fasting triglycerides, and cholesterol were unchanged with training (p<0.05); however, most measures trended towards improvement, especially among those with the highest HOMA-IR values at the start of the intervention. Moreover, this short duration exercise was able to significantly reduce blood glucose after each interval bout (9.7±3.9mmol pre versus 8.3±3.4mmol post, p<0.05). These data provide novel information for future studies and give evidence that interval training programs longer than 2 weeks may be able to garner significant improvements in glucose control in a diabetic population.
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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 | 0.001 |
| 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) | 0.001 | 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".