The Association Between Training Load and Injury Risk in Elite Youth Soccer Players: a Systematic Review and Best Evidence Synthesis
Bibliographic record
Abstract
BACKGROUND: Injury risk in elite youth soccer players is high. Implementing an optimal training load is of utmost importance to reduce the risk of injuries. OBJECTIVE: To conduct a systematic review and best evidence synthesis to explore the effects of internal and external training load on injury risk in elite youth soccer players. METHODS: MEDLINE, Embase, Web of Science, CENTRAL, and CINAHL were searched up until 17 January 2020. Each article had to meet all of the following criteria: (1) the study population consisted of male elite youth soccer players aged between 12 and 21 years; (2) a longitudinal, prospective study design was used; (3) soccer-related injuries were registered (i.e., self-reported or by 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. RESULTS: Five studies (2 high quality, 3 low quality) were included. Best evidence synthesis highlighted that there was moderate evidence for (1) no association between 2-, 3-, and 4-week cumulative loads for total distance covered; (2) no association between 1-week workloads (sRPE × duration); and (3) no association between A:C workload ratios (4 weeks) and injury risk. For all other comparisons, only insufficient or conflicting evidence was found. CONCLUSION: There is a paucity of evidence for an association between internal and external training load parameters and injury risk in elite youth soccer players.
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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 | 0.022 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".