Eight weeks of dynamic stretching during warm‐ups improves jump power but not repeated or single sprint performance
Bibliographic record
Abstract
There is abundant research involving the acute effects of stretching on subsequent performance; however, there is little information on dynamic stretch training programmes on range of motion (ROM), power and speed measures. It was the objective of this research to examine the training consequences of active dynamic stretching (ADS) and static dynamic stretching (SDS). A repeated measures design compared the effects of 8 weeks of warm-ups incorporating two dynamic stretch modalities: ADS and SDS on squat jump (SJ), countermovement jump (CMJ), 20-m sprint performances and repeated sprint ability (RSA) and hip ROM in 37 male soccer players. SJ height (SDS: 4.6%; ADS: 5.3%; p <0.05), CMJ height (SDS: 5.3%; ADS: 3.4%; p<0.05), CMJ force (SDS: 7.2%; ADS: 12.7%; p<0.001) and CMJ peak power (SDS: 3.9%; ADS: 3.3%; p<0.05) increased significantly after SDS and ADS training compared to the control group (no significant change). Sprint performance and RSA were not affected by either of the dynamic stretch training regimens. The SDS and ADS training programmes elicited similar improvements in flexibility (SDS: 57.6%; ADS: 45.1%; p<0.01) compared to the non-significant changes in the control group. The inclusion of ADS and SDS within the regular warm-up of an 8-week training programme can improve not only flexibility but also jump power measures as well.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".