Effects of chronic static stretching interventions on jumping and sprinting performance–a systematic review with multilevel meta-analysis
Bibliographic record
Abstract
When improving athletic performance in sports with high-speed strength demands such as soccer, basketball, or track and field, the most common training method might be resistance training and plyometrics. Since a link between strength capacity and speed strength exists and recently published literature suggested chronic stretching routines may enhance maximum strength and hypertrophy, this review was performed to explore potential benefits on athletic performance. Based on current literature, a beneficial effect of static stretching on jumping and sprinting performance was hypothesized. A systematic literature search was conducted using PubMed, Web of Science and Google scholar. In general, 14 studies revealed 29 effect sizes (ES) (20 for jumping, nine for sprinting). Subgroup analyses for jump performance were conducted for short- long- and no stretch shortening cycle trials. Qualitative evaluation was supplemented by performing a multilevel meta-analysis via R (Package: metafor). Significant positive results were documented in six out of 20 jump tests and in six out of nine sprint tests, while two studies reported negative adaptations. Quantitative data analyses indicated a positive but trivial magnitude of change on jumping performance (ES:0.16, p = 0.04), while all subgroup analyses did not support a positive effect (p = 0.09–0.44). No significant influence of static stretching on sprint performance was obtained (p = 0.08). Stretching does not seem to induce a sufficient stimulus to meaningfully enhance jumping and sprinting performance, which could possibly attributed to small weekly training volumes or lack of intensity.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".