The comparison between foam rolling either combined with static or dynamic stretching on knee extensors’ function and structure
Bibliographic record
Abstract
Static stretching (SS) and dynamic stretching (DS) in combination with foam rolling (FR) have been attracting attention as warm-up routines in sports. However, the combined and intervention order effects of SS or DS and FR on flexibility, muscle strength, and jump performance are still unclear. Therefore, this study aimed to compare the combined effects of FR and SS or DS with the various intervention orders (i.e., SS + FR, DS + FR, FR + SS, DS + FR) on the function and properties of the knee extensors. Using a crossover, random allocation design, 17 male university students (21.0 ± 1.1 y) performed four conditions combining FR and SS or DS. The measurement included knee flexion range of motion (ROM), pain pressure threshold (PPT), tissue hardness, maximum voluntary isometric contraction (MVC-ISO), maximum voluntary concentric contraction (MVC-CON) torque, and single-leg countermovement jump (CMJ) height of the knee extensors. All interventions significantly (p < 0.01) increased knee flexion ROM (SS + FR: d = 1.29, DS + FR: d = 0.45, FR + SS: d = 0.95, FR + DS: d = 0.49), and significantly (p < 0.01) decreased tissue hardness (SS + FR: d = -1.11, DS + FR: d = -0.86, FR + SS: d = -1.29, DS + FR: d = -0.65). There were no significant changes in MVC-ISO, MVC-CON, and CMJ height in all conditions, but a near significant, small magnitude (p = 0.056, d = -0.31) decrease of MVC-ISO was observed in the FR + SS condition. Our results showed that all the combinations of SS or DS and FR effectively decreased tissue hardness and increased ROM without decreasing muscle strength. Also, effect sizes indicated the largest increase in ROM and decrease in tissue stiffness after SS + FR without decreasing muscle strength and jump performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".