Efficacy of high‐intensity interval training in individuals with type 2 diabetes mellitus: An umbrella review of systematic reviews and meta‐analyses
Bibliographic record
Abstract
High-intensity interval training (HIIT) has gained attention as a potentially effective alternative to traditional exercise modalities for individuals with type 2 diabetes mellitus (T2DM). Previous studies have evaluated this exercise strategy with various regimens, comparator groups and outcomes, limiting the generalisability of findings. We performed a novel umbrella review to generate an up-to-date synthesis of the available evidence regarding the effect of HIIT on glycaemic control and other clinically relevant cardiometabolic health outcomes in individuals with T2DM, as compared with traditional moderate-intensity continuous training (MICT) and/or non-exercise control (CON). This umbrella review followed the Preferred Reporting Items for Overviews of Reviews guideline. Seven databases were searched until August 2024. Systematic reviews with meta-analyses comparing HIIT with MICT and/or CON were included. Literature search, data extraction and methodological quality assessment (A MeaSurement Tool to Assess systematic Reviews 2 [AMSTAR-2]) were conducted independently by two reviewers. Ten systematic reviews with meta-analyses, encompassing 76 primary studies and 2954 unique participants, met the inclusion criteria. The data indicated that HIIT significantly improves glycosylated haemoglobin and cardiorespiratory fitness compared with CON (weighted mean difference [WMD]: -0.83% to -0.39% and 3.35-6.38 mL/kg/min) and MICT (WMD: -0.37% to -0.07% and 1.68-4.12 mL/kg/min) in individuals with T2DM. HIIT is also effective in improving other glycaemic parameters, including fasting blood glucose, fasting blood insulin and HOMA-IR. Improvement in body composition, lipid profiles and blood pressure has also been observed following HIIT. Most systematic reviews received moderate to low AMSTAR-2 score. This umbrella review supports HIIT as an efficacious exercise strategy for improving glycaemic control and certain relevant cardiometabolic health outcomes in individuals with T2DM. Our findings offer a comprehensive basis that may potentially contribute to informing physical activity recommendations for incorporating HIIT into T2DM management strategies.
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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 | 0.033 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.026 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".