Injury prevention in professional soccer players: can re-warmup training be organized in the post-warmup and half-time during a soccer game in a short time?
Bibliographic record
Abstract
INTRODUCTION: Passive time, during the post-warmup and half-time, decreases athletes' performance and increases injury risk factors in the active phases of the soccer match. Objective. This narrative review aims to research and synthesize existing evidence to identify brief re-warmup strategies that may find applicability in the post-warmup and half-time of a soccer match. EVIDENCE ACQUISITION: The analysis was conducted on PubMed, Web of Science, PEDro, SPORTDiscus and Google Scholar. Due to the lack of evidence, no temporal time was established, preferring most up-to-date articles. The data were synthesized in relation to the objectives, following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. EVIDENCE SYNTHESIS: Our research yielded a total of 753 studies, 463 related to re-warmup, 136 articles on re-warmup in soccer, and 154 articles on re-warmup during half-time. Of these, 26 met the inclusion objectives and were included in this research. Our findings confirm that a re-warmup can mitigate the decremental effect of static rest on performance. From the studies considered, we have identified a work that emphasizes how a 1-minute warmup of high-intensity exercise at speed corresponding to 90% of VO2max can prevent decreases related to passive time, in sprint performance and muscle strength, as well as improve muscle temperature. These results could apply to both post-warmup and half-time scenarios of a soccer match. CONCLUSIONS: From this narrative review, it has been possible to highlight a one-minute high-intensity re-warmup that improved sprint performance, increased core temperature, and enhanced muscle activation, not leading to additional physiological or psychological fatigue.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".