Intensive lifestyle intervention including high-intensity interval training program improves insulin resistance and fasting plasma glucose in obese patients
Bibliographic record
Abstract
OBJECTIVES: To analyze the effects of a long-term intensive lifestyle intervention including high-intensity interval training (HIIT) and Mediterranean diet (MedD) counseling on glycemic control parameters, insulin resistance and β-cell function in obese subjects. METHODS: The glycemic control parameters (fasting plasma glucose, glycated hemoglobin), insulin resistance, and β-cell function of 72 obese subjects (54 women; mean age = 53 ± 9 years) were assessed at baseline and upon completion of a 9-month intensive lifestyle intervention program conducted at the cardiovascular prevention and rehabilitation center of the Montreal Heart Institute, from 2009 to 2012. The program included 2-3 weekly supervised exercise training sessions (HIIT and resistance exercise), combined to MedD counseling. RESULTS: Fasting plasma glucose (FPG) (mmol/L) (before: 5.5 ± 0.9; after: 5.2 ± 0.6; P < 0.0001), fasting insulin (pmol/L) (before: 98 ± 57; after: 82 ± 43; P = 0.003), and insulin resistance, as assessed by the HOMA-IR score (before: 3.6 ± 2.5; after: 2.8 ± 1.6; P = 0.0008) significantly improved, but not HbA1c (%) (before: 5.72 ± 0.55; after: 5.69 ± 0.39; P = 0.448), nor β-cell function (HOMA-β, %) (before: 149 ± 78; after: 144 ± 75; P = 0.58). CONCLUSION: Following a 9-month intensive lifestyle intervention combining HIIT and MedD counseling, obese subjects experienced significant improvements of FPG and insulin resistance. This is the first study to expose the effects of a long-term program combining HIIT and MedD on glycemic control parameters among obese subjects.
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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 | 0.000 |
| 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".