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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".