Foam rolling and stretching do not provide superior acute flexibility and stiffness improvements compared to any other warm-up intervention: A systematic review with meta-analysis
Bibliographic record
Abstract
BACKGROUND: Acute improvement in range of motion (ROM) is a widely reported effect of stretching and foam rolling, which is commonly explained by changes in pain threshold and/or musculotendinous stiffness. Interestingly, these effects were also reported in response to various other active and passive interventions that induce responses such as enhanced muscle temperature. Therefore, we hypothesized that acute ROM enhancements could be induced by a wide variety of interventions other than stretching or foam rolling that promote an increase in muscle temperature. METHODS: After a systematic search in PubMed, Web of Science, and SPORTDiscus databases, 38 studies comparing the effects of stretching and foam rolling with several other interventions on ROM and passive properties were included. These studies had 1134 participants in total, and the data analysis resulted in 140 effect sizes (ESs). ES calculations were performed using robust variance estimation model with R-package. RESULTS: Study quality of the included studies was classified as fair (PEDro score = 4.58) with low to moderate certainty of evidence. Results showed no significant differences in ROM (ES = 0.01, p = 0.88), stiffness (ES = 0.09, p = 0.67), or passive peak torque (ES = -0.30, p = 0.14) between stretching or foam rolling and the other identified activities. Funnel plots revealed no publication bias. CONCLUSION: Based on current literature, our results challenge the established view on stretching and foam rolling as a recommended component of warm-up programs. The lack of significant difference between interventions suggests there is no need to emphasize stretching or foam rolling to induce acute ROM, passive peak torque increases, or stiffness reductions.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".