Does High-Intensity Interval Training Increase Muscle Strength, Muscle Mass, and Muscle Endurance? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
High-intensity/sprint interval training (HIIT/SIT) improves aerobic and anaerobic performance, but it is unknown if HIIT/SIT increases strength, muscle mass/size, and muscle endurance (ME). We aimed to determine if HIIT/SIT increases strength, muscle mass/size, and ME. Databases (Ovid Medline, Sport Discus, EMBASE, and CINAHL) and the gray literature (Google Scholar) were searched for original research articles investigating the impact of HIIT/SIT on strength, muscle mass/size, and ME (23 March 2025). The risk of bias (ROB) was assessed via the Cochrane ROB 2 Tool. Meta-analyses were performed when three or more randomized controlled trials compared HIIT/SIT to a common comparator. Fifty-four studies were included (N = 1136). Twenty-five studies had a high ROB, while twenty-nine had some concerns. Standardized mean differences (SMD) (95% CI) of 0.16; (−0.09, 0.40), 0.33; (−0.21, 0.87) were observed for meta-analyses comparing the effect of HIIT/SIT to moderate intensity continuous training (MICT) and non-exercise controls (CON) on FFM, respectively. A meta-analysis comparing the effect of HIIT/SIT to resistance training (RT) on leg press strength yielded a SMD of −0.82; 95% CI: (−1.97, 0.33). HIIT/SIT may induce slightly greater gains than MICT and CON for FFM, while RT is likely superior to HIIT/SIT for improving leg press strength. However, the certainty of evidence is low, and 95% CIs intersect zero for all analyses.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".