514 MEP036 – The relationship between workload and injury in elite soccer players : a systematic review and best evidence synthesis
Bibliographic record
Abstract
Injuries in football are still the number one concern for strength and conditioning coaches in football, especially at the highest level. To address this, an analysis of the links between training load and injuries is becoming increasingly popular. The aim of the study is to explore relationships between the internal and external workload and the injury in elite male soccer players. To do this, relevant literature was electronically searched on PubMed, Web of Science and SportDiscus up until July 2023. Each article had to meet all of the following criteria: (1) the study population consisted of male elite soccer players aged upper 17 years; (2) a longitudinal, cohorte study design was used; (3) soccer-related injuries were registered (medical staff); (4) external and/or internal load parameters were described; and (5) the article was published in an English peer-reviewed scientific journal. The quality of the included articles was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). A best evidence synthesis was performed to rank the level of evidence. The results of this research regroup 523 articles. After full text screening, 39 articles remained. Nineteen studies were of high quality and twenty were of low quality. Different load parameters (internal and external), at different moments (training on pitch, training at gym, game during competitive match or friendly/pre-season match) were used to establish relationships between workload and injury in elite soccer players. Several high-quality studies shown an association between duration and risk of injury. In conclusion, the nature of the injury is complex and multifactorial, that is why it’s difficult to have strong evidence between a metric of workload and an injury risk. Only training load time showed strong evidence with the injury risk.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".