The Effect of Warm-Ups Incorporating Different Volumes of Dynamic Stretching on 10- and 20-m Sprint Performance in Highly Trained Male Athletes
Bibliographic record
Abstract
Recently, athletes have transitioned from traditional static stretching during warm-ups to incorporating dynamic stretching routines. However, the optimal volume of dynamic drills is yet to be identified. The aim of this repeated-measures study was to examine varying volumes (1, 2, and 3 sets) of active dynamic stretching (ADS) in a warm-up on 10- and 20-m sprint performance. With a within-subject design, 16 highly trained male participants (age: 20.9 ± 1.3 years; height: 179.7 ± 5.7 cm; body mass: 72.7 ± 7.9 kg; % body fat: 10.9 ± 2.4) completed a 5-minute general running warm-up before performing 3 preintervention measures of 10- to 20-m sprint. The interventions included 1, 2, and 3 sets of active dynamic stretches of the lower-body musculature (gastrocnemius, gluteals, hamstrings, quadriceps, and hip flexors) performed approximately 14 times for each exercise while walking (ADS1, ADS2, and ADS3). The active dynamic warm-ups were randomly allocated before performing a sprint-specific warm-up. Five minutes separated the end of the warm-up and the 3 postintervention measures of 10- to 20-m sprints. There were no significant time, condition, and interaction effects over the 10-m sprint time. For the 0- to 20-m sprint time, a significant main effect for the pre-post measurement (F = 10.81; p < 0.002), the dynamic stretching condition (F = 6.23; p = 0.004) and an interaction effect (F = 41.19; p = 0.0001) were observed. A significant decrease in sprint time (improvement in sprint performance) post-ADS1 (2.56%, p = 0.001) and post-ADS2 (2.61%, p = 0.001) was observed. Conversely, the results indicated a significant increase in sprint time (sprint performance impairment) post-ADS3 condition (2.58%, p = 0.001). Data indicate that performing 1-2 sets of 20 m of active dynamic stretches in a warm-up can enhance 20-m sprint performance. The results delineated that 3 sets of ADS repetitions could induce acute fatigue and impair sprint performance within 5 minutes of the warm-up.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".