Acute Effects of Different Warm-Up Protocols on Anaerobic Performance in Teenage Athletes
Bibliographic record
Abstract
Although pre-event static stretching (SS) is an accepted practice in most youth programs, pre-event dynamic exercise (DY) is becoming popular. The purpose of this study was to examine the acute effects of pre-event SS, DY, and combined SS and DY (SDY) on vertical jump (VJ), medicine-ball toss (MB), 10-yard sprint (SP), and pro-agility shuttle run (AG) in teenage athletes (15.5 ± 0.9 years). Thirty athletes participated in three testing sessions in random order on three nonconsecutive days. Before testing, participants performed 5 min of walking/jogging followed by one of the following 10 min warm-up protocols: a) five static stretches (2 × 30 s), b) nine moderate-to-high-intensity dynamic movements (2 × 10 yards), or c) five static stretches (1 × 30 s) followed by the same nine dynamic movements (1 × 10 yards). Statistical analysis of the data revealed that performance on the VJ, MB, and SP were significantly (p < .05) improved after DY and SDY as compared with SS. There were no significant differences in AG after the 3 warm-up treatments. The results of this study indicate that pre-event dynamic exercise or static stretching followed by dynamic exercise might be more beneficial than pre-event static stretching alone in teenage athletes who perform power activities.
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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 | 0.000 |
| 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) | 0.001 | 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".