Effect of Warm-Ups Involving Static or Dynamic Stretching on Agility, Sprinting, and Jumping Performance in Trained Individuals
Bibliographic record
Abstract
The objective of the present study was to investigate the effects of static and dynamic stretching alone and in combination on subsequent agility, sprinting, and jump performance. Eight different stretching protocols: (a) static stretch (SS) to point of discomfort (POD); (b) SS less than POD (SS<POD); (c) dynamic stretching (DS); (d) SS POD combined with DS (SS POD + DS); (v) SS<POD combined with DS (SS<POD + DS); (vi) DS combined with SS POD (DS + SS POD); (vii) DS combined with SS<POD (DS + SS<POD); and (viii) a control warm-up condition without stretching were implemented with a prior aerobic warm-up and followed by dynamic activities. Dependent variables included a 30-m sprint, agility run, and jump tests. The control condition (4.2 +/- 0.15 seconds) showed significant differences (p = 0.05) for faster times than the DS + SS<POD (4.28s +/- 0.17) condition in the 30-m (1.9%) sprint. There were no other significant differences. The lack of stretch-induced impairments may be attributed to the trained state of the participants or the amount of time used after stretching before the performance. Participants were either professional or national level elite athletes who trained 6-8 times a week with each session lasting approximately 90 minutes. Based on these findings and the literature, trained individuals who wish to implement static stretching should include an adequate warm-up and dynamic sport-specific activities with at least 5 or more minutes of recovery before their sport activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".