Impact of high-intensity interval training on cardiometabolic health in patients with diabesity: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
AIMS: This systematic review and meta-analysis aimed to evaluate the effects of high-intensity interval training (HIIT) on cardiometabolic health-related outcomes in patients with type 2 diabetes and obesity (diabesity). METHODS: PubMed, Web of Science, Scopus, Science Direct, Cochrane Library, and Google Scholar databases were searched from inception up to November 2024. The search strategy encompassed the following keywords: diabetes, obesity, and HIIT. Randomized controlled trials (RCTs) recruiting adult participants with diabesity and comparing HIIT per se for ≥ 2 weeks in duration with non-exercise standard treatment were included. RESULTS: A total of 18 RCTs qualified involving 504 patients (52/48 women/men ratio; 55.0 ± 11.8 years; 31.0 ± 6.9 kg/m2). Body mass [standardized mean differences (SMD) -0.36 kg, 95% confidence intervals (CI) -0.71 to -0.01], body mass index (SMD -0.57 kg/m2, 95% CI -0.92 to -0.21), waist-to-hip ratio (SMD -1.68, 95% CI -2.50 to -0.86), fasting blood glucose (SMD -0.64 mmol/L, 95% CI -1.03 to -0.24), glycated hemoglobin (SMD -1.08%, 95% CI -1.68 to -0.47), fasting insulin (SMD -0.79 mIU/L, 95% CI -1.28 to -0.31), homeostatic model assessment for insulin resistance (SMD -0.95, 95% CI -1.43 to -0.47), low-density lipoprotein cholesterol (SMD -0.64 mg/dL, 95% CI -1.23 to -0.06), triglycerides (SMD -0.64 mg/dL, 95% CI -1.02 to -0.26), and total cholesterol (SMD -0.66 mg/dL, 95% CI -1.23 to -0.08) improved compared to standard treatment without exercise. CONCLUSIONS: The present findings suggest that HIIT improves several markers of metabolic health and cardiovascular risk, even without significant body composition improvements in patients with diabesity. OPEN SCIENCE FRAMEWORK REGISTRY.: https://osf.io/rtb42.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".