A comparison of a single bout of stretching or foam rolling on range of motion in healthy adults
Bibliographic record
Abstract
PURPOSE: Stretching and foam rolling are common warm-up exercises and can acutely increase the range of motion (ROM) of a joint. However, possible differences in the magnitude of change on ROM between these two interventions on the immediate and prolonged effects (e.g., 10 min after the intervention) are not yet well understood. Thus, the purpose of this review was to compare the immediate and prolonged effects of a single bout of foam rolling with a single bout of stretching on ROM in healthy participants. METHODS: In total, 20 studies with overall 38 effect sizes were found to be eligible for a meta-analysis. For the main analysis, subgroup analysis, we applied a random-effect meta-analysis, mixed-effect model, respectively. The subgroup analyses included age groups, sex, and activity levels of the participants, as well as the tested muscles, the duration of the application, and the study design. RESULTS: Meta-analyses revealed no significant differences between a single stretching and foam rolling exercise immediately after the interventions (ES = 0.079; P = 0.39) nor a difference 10 min (ES = - 0.051; P = 0.65), 15 min (ES = - 0.011; P = 0.93), and 20 min (ES = - 0.161; P = 0.275) post-intervention. Moreover, subgroup analyses revealed no other significant differences between the acute effects of stretching and foam rolling (P > 0.05). CONCLUSION: If the goal is to increase the ROM acutely, both interventions can be considered as equally effective. Likely, similar mechanisms are responsible for the acute and prolonged ROM increases such as increased stretch tolerance or increased soft-tissue compliance.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.019 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".