HIIT'ing or MISS'ing the Optimal Management of Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis of High- Versus Moderate-Intensity Exercise Prescription
Bibliographic record
Abstract
Introduction: Polycystic Ovary syndrome (PCOS) is a metabolic disorder associated with increased cardiovascular disease risk. Exercise is an effective treatment strategy to manage symptoms and reduce long-term health risk. High-intensity interval training (HIIT) has been suggested as a more efficient exercise mode in PCOS; however, it is not clear whether HIIT is superior to moderate intensity steady state exercise (MISS). Methods: We synthesized available data through a systematic review and meta-analysis to compare the effectiveness of isolated HIIT and MISS exercise interventions. Our primary outcome measures were cardiorespiratory fitness and insulin resistance, measured using V˙ O2max and HOMA-IR respectively. Results: A total of 16 studies were included. Moderate-quality evidence from 16 studies identified significant improvements in V˙ O2max following MISS (Δ = 1.081 ml/kg/min, p < 0.001, n = 194), but not HIIT (Δ = 0.641 ml/kg/min, p = 0.128, n = 28). Neither HIIT nor MISS improved HOMA-IR [(Δ = −0.257, p = 0.374, n = 60) and (Δ = −0.341, p = 0.078, n = 159), respectively]. Discussion: A significant improvement in V˙ O2max was evident following MISS, but not HIIT exercise in women with PCOS. This contrasts with previous literature in healthy and clinical cohorts that report superior benefits of HIIT. Therefore, based on available moderate-quality evidence, HIIT exercise does not provide superior outcomes in V˙ O2max compared with MISS, although larger high-quality interventions are needed to fully address this. Additional dietary/pharmacological interventions may be required in conjunction with exercise to improve insulin sensitivity.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".