Superiority of Dynamic Stretching over Static and Combined Stretching Protocols for Repeated Sprint Performance in Elite Male Soccer Players
Bibliographic record
Abstract
This study aimed to examine the effects of different stretching techniques on repeated sprint performance and to assess the influence of the sequence in which static and dynamic stretching are performed. Ten male Division II soccer players (age: 22.80 ± 1.13 years; height: 180.60 ± 3.59 cm; body mass: 70.60 ± 6.04 kg) completed a repeated sprint test consisting of 6 × 30 m sprints after five different warm-up protocols in a randomized, counterbalanced design: (1) general warm-up without stretching (NS), (2) static stretching (SS), (3) dynamic stretching (DS), (4) SS followed by DS (SS-DS), and (5) DS followed by SS (DS-SS). Stretching was performed during the recovery periods between sprints: ~6 min for SS and DS, and ~12 min for combined protocols. Sessions were spaced 72 h apart. Performance metrics included mean sprint time, best sprint time, and total sprint time. ANOVA and Cohen’s d were used for statistical analysis. Repeated sprint test performance was significantly enhanced after DS compared to SS, DS-SS, and SS-DS (p = 0.042–0.002; ES = 0.31–2.26), but not significantly different from NS (p > 0.05). SS had a detrimental effect when compared to DS and NS (p < 0.05; ES = 1.86–2.26). Improvements were observed in mean sprint time and total sprint time across all six sprints (p = 0.042–0.006; ES = 0.31–2.26) and in best sprint time (p = 0.006–0.002; ES = 0.89–1.86). In conclusion, DS prior to repeated sprint test improves performance compared to SS and combined methods. NS also supports strong performance but shows a slight advantage over SS and combinations. Incorporating DS into warm-up routines is recommended to optimize sprint performance, reduce injury risk, and support athlete preparation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".