The Effects of Foam Rolling Training on Performance Parameters: A Systematic Review and Meta-Analysis including Controlled and Randomized Controlled Trials
Bibliographic record
Abstract
Foam rolling (FR) is a new and popular technique for increasing range of motion. While there are a few studies that demonstrate increased performance measures after an acute bout of FR, the overall evidence indicates trivial performance benefits. As there have been no meta-analyses on the effects of chronic FR on performance, the objective of this systematic meta-analytical review was to quantify the effects of FR training on performance. We searched PubMed, Scopus, the Cochrane library, and Web of Science for FR training studies with a duration greater than two weeks, and found eight relevant studies. We used a random effect meta-analysis that employed a mixed-effect model to identify subgroup analyses. GRADE analysis was used to gauge the quality of the evidence obtained from this meta-analysis. Egger’s regression intercept test (intercept 1.79; p = 0.62) and an average PEDro score of 6.25 (±0.89) indicated no or low risk of reporting bias, respectively. GRADE analysis indicated that we can be moderately confident in the effect estimates. The meta-analysis found no significant difference between FR and control conditions (ES = −0.294; p = 0.281; I2 = 73.68). Analyses of the moderating variables showed no significant differences between randomized control vs. controlled trials (Q = 0.183; p = 0.67) and no relationship between ages (R2 = 0.10; p = 0.37), weeks of intervention (R2 = 0.17; p = 0.35), and total load of FR (R2 = 0.24; p = 0.11). In conclusion, there were no significant performance changes with FR training and no specific circumstances leading to performance changes following FR training exceeding two weeks.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".