Impact of high-intensity interval training on cardiometabolic health in patients with diabesity: a systematic review and meta-analysis of randomized controlled trials
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.361 | 0.042 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it