Cardiorespiratory Fitness and Performance Adaptations to High-Intensity Interval Training: Are There Differences Between Men and Women? A Systematic Review with Meta-Analyses
Bibliographic record
Abstract
Abstract Background It is important to consider biological sex as a variable that might influence exercise adaptation in order to optimize exercise prescription for men and women. Objective The aim of this study was to quantify the impact of biological sex on maximal oxygen uptake ( $$\dot{V}$$ V˙ O2max) and performance outcomes after high-intensity interval training (HIIT). Methods A systematic search and review was conducted by two independent reviewers up to 8 September 2022 using MEDLINE, SPORTDiscus, and Sports Medicine & Education Index in ProQuest. Trials including healthy adults were included if they presented data for or compared male and female $$\dot{V}$$ V˙ O2maxor performance outcomes in response to HIIT. Performance outcomes included measures of exercise performance and concurrently measured physiological adaptations. Where appropriate, a random-effects, pre-post meta-analysis was undertaken. Data were sub-grouped for men and women, baseline training level, mean age, intervention type, and intervention length. Heterogeneity was assessed using Chi2, Cochran’sQ, and HigginsI2and sensitivity analyses, where required. Study quality was assessed using the Newcastle–Ottawa Scale and publication bias was assessed through visual inspection of funnel plots. Results Thirty-three references from 28 trials were included in the review (n = 965; 462 women and 503 men). Meta-analyses included 19 studies for $$\dot{V}$$ V˙ O2max, eight for peak power output from $$\dot{V}$$ V˙ O2maxtesting (PPO), and five for threshold power (powerAT). Meta-analyses revealed similar increases in $$\dot{V}$$ V˙ O2maxin women (g = 0.57; 95% CI 0.44–0.69) and men (g = 0.57; 95% CI 0.42–0.72), and powerATin women (g = 0.38; 95% CI 0.13–0.64) and men (g = 0.38; 95% CI 0.11–0.64). Raw mean differences for change in $$\dot{V}$$ V˙ O2maxwere Δ 0.32 L·min−1and 3.50 mL·kg−1·min−1in men, versus Δ 0.20 L·min−1and 3.34 mL·kg−1·min−1for women. No significant sex differences were present for the primary analysis of any outcome. After sub-grouping, significant differences were present for PPO where the effect size was higher for well-trained women (g = 0.37) compared with well-trained men (g = 0.17), and for $$\dot{V}$$ V˙ O2maxwhere interventions with a duration of 4 weeks or less had significantly smaller effect sizes compared with those longer than 4 weeks (p < 0.001). Unweighted mean percentage change in $$\dot{V}$$ V˙ O2max, PPO, and powerATacross studies was 11.16 ± 7.39%, 11.16 ± 5.99%, and 8.07 ± 6.55% for women, and 10.90 ± 5.75%, 8.22 ± 5.09%, and 7.09 ± 7.17% for men, respectively. Significant heterogeneity was present for both $$\dot{V}$$ V˙ O2maxand PPO (I2, range: 62.06–78.80%). Sub-grouping by baseline training status and intervention length decreased heterogeneity in most groups. A qualitative synthesis of other outcomes indicated similar improvements in fitness and performance for men and women with some evidence suggesting differences in the mechanisms of adaptation. Limitations and Risk of Bias Publication bias is unlikely to have significantly influenced results for $$\dot{V}$$ V˙ O2maxor powerAT, but the meta-analysis of PPO could have benefitted from additional study data to strengthen results. The overlap in age categories and sensitivity of the analysis limits the accuracy of the results of the sub-grouping by age. Conclusions Findings indicated no sex-specific differences for any fitness or performance outcomes. Baseline training status and intervention length accounted for most variability in outcomes. PROSPERO registration number: CRD42021272615.
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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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".