Effects of dynamic and static stretching within general and activity specific warm-up protocols.
Bibliographic record
Abstract
The purpose of the study was to determine the effects of static and dynamic stretching protocols within general and activity specific warm-ups. Nine male and ten female subjects were tested under four warm-up conditions including a 1) general aerobic warm-up with static stretching, 2) general aerobic warm-up with dynamic stretching, 3) general and specific warm-up with static stretching and 4) general and specific warm-up with dynamic stretching. Following all conditions, subjects were tested for movement time (kicking movement of leg over 0.5 m distance), countermovement jump height, sit and reach flexibility and 6 repetitions of 20 metre sprints. Results indicated that when a sport specific warm-up was included, there was an 0.94% improvement (p = 0.0013) in 20 meter sprint time with both the dynamic and static stretch groups. No such difference in sprint performance between dynamic and static stretch groups existed in the absence of the sport specific warm-up. The static stretch condition increased sit and reach range of motion (ROM) by 2.8% more (p = 0.0083) than the dynamic condition. These results would support the use of static stretching within an activity specific warm-up to ensure maximal ROM along with an enhancement in sprint performance. Key pointsActivity specific warm-up may improve sprint performance.Static stretching was more effective than dynamic stretching for increasing static range of motion.There was no effect of the warm-up protocols on countermovement jump height or movement time.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".