Sex Differences in Cardiometabolic Health Indicators after HIIT in Patients with Coronary Artery Disease
Bibliographic record
Abstract
PURPOSE: Cardiorespiratory fitness (CRF) is an independent predictor of mortality, and females typically achieve smaller improvements in CRF than males after exercise-based cardiac rehabilitation. High-intensity interval training (HIIT) has been shown to produce superior improvements in CRF than traditional cardiac rehabilitation, but the sex differences are unknown. The purpose of this systematic review and meta-analysis was to evaluate sex differences for changes in CRF and cardiometabolic health indicators after HIIT in adults with coronary artery disease (CAD). METHODS AND RESULTS: A systemic search of five electronic databases for studies examining the effect of HIIT on measured CRF and cardiometabolic health indicators in adults with CAD was performed. Data (published and unpublished) from 14 studies were included in the meta-analyses with approximately eightfold greater male than female participation (n = 836 vs n = 103). Males with CAD achieved a near-significant absolute improvement in CRF (mean difference [MD] = 1.07, 95% confidence interval [CI] = -0.08 to 2.23 mL·kg-1⋅min-1, P = 0.07) after HIIT when compared with control; there were insufficient data to conduct such an analysis in females. Significantly smaller improvements in CRF were experienced by females than males (MD = -1.10, 95% CI = -2.08 to -0.12 mL·kg-1⋅min-1, P = 0.03); there was no sex difference for the relative (percentage) change in CRF after HIIT. Females achieved significantly smaller reductions in body mass index (MD = -0.25, 95% CI = -0.03 to -0.47 kg·m-2, P = 0.02) and fasting blood glucose (MD = -0.38, 95% CI = -0.05 to -0.72, P = 0.03); no sex differences were observed for other cardiometabolic health indicators. CONCLUSION: There are no sex differences for relative improvements in CRF after HIIT; however, females are greatly underrepresented in trials. Future studies should increase female participation and perform sex-based analyses to determine sex-specific outcomes following HIIT.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".