The effects of a 2 week modified high intensity interval training program on the homeostatic model of insulin resistance (HOMA-IR) in adults with type 2 diabetes.
Bibliographic record
Abstract
AIM: High intensity interval training (HIIT) induces similar metabolic adaptations to traditional steady state aerobic exercise training. Until recently, most HIIT studies have examined maximum efforts in healthy populations. The current study aimed to examine the effects of a 2 week modified HIIT program on the homeostatic model of insulin resistance (HOMA-IR) in individuals with type 2 diabetes (T2D). It was hypothesized that HIIT would improve HOMA-IR. METHODS: Nine individuals with T2D (age=40.2±9.7 y; BMI=33.9±5.3; fasting plasma glucose [FPG]=8.7±2.9 mmol/L; HbA1C=7.3±1.2%; [mean±SD]) performed 6 individualized training sessions of HIIT (4x30 seconds at 100% of estimated maximum workload followed by 4 minutes of active rest) over 2 weeks. HOMA-IR was calculated from FPG and serum insulin and compared against a prior 2 week baseline period. RESULTS: Blood glucose was reduced immediately after each HIIT session (P<0.05). Anthropometrics, FPG, serum insulin, and HOMA-IR were unchanged after training. However, 6 of the 9 individuals exhibited reduced HOMA-IR values after the training period and there was a significant negative correlation between HOMA-IR value prior to training and change in HOMA-IR after HIIT. CONCLUSION: These observations tend to support the positive health benefits of HITT for individuals with T2D reported in recently published data using a modified HIIT protocol. However, they suggest that the magnitude of the disease should be assessed when examining the effects of exercise interventions in individuals with T2D.
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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) | 0.001 | 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 | 0.001 |
| 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".