Acute to chronic workload ratio (ACWR) for predicting sports injury risk: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aimed to comprehensively and quantitatively evaluate the effectiveness of single-arm acute to chronic workload ratio (ACWR) in predicting sports injuries through an evidence-based approach and to provide references for injury prevention, physical training and training load management. METHODS: Cohort studies on ACWR were retrieved from PubMed, Web of Science, ScienceDirect, CNKI, and Wanfang, covering the period from the inception of the databases to February 15, 2025. The quality of the included literature was evaluated using the Newcastle-Ottawa Scale (NOS), and a meta-analysis was conducted using Stata (version18.0). RESULTS: A total of 22 single-arm cohort studies reporting injury incidence by ACWR category were included. Methodological quality assessment identified 16 high-quality (≥ 7 points) and 6 moderate-quality (4-6 points) indicating an overall high quality of the included research. The results of the Meta subgroup analysis showed that the injury incidence in tissue structures was 79% (95% CI [0.67; 0.89]), the injury incidence in the legs was 73% (95% CI [0.57; 0.86]). Additionally, the injury incidence in soccer players was 75% (95% CI [0.61; 0.87]), the injury incidence due to external loading was 64% (95% CI [0.53; 0.74]), or the injury incidence involving both internal and external loads was 69% (95% CI [0.45; 0.89]), and the injury incidence for individuals over the age of 25 was 73% (95% CI [0.50; 0.91]), whereas the injury incidence was minimized when the interval was kept at 0.8-1.3, with an injury incidence of 56% (95% CI [0.14; 0.94]). CONCLUSION: Although ACWR is associated with sports injury risk and may be useful in injury prevention strategies, it is necessary to use it with caution as a tool for measuring workload. Due to the heterogeneity between studies, potential publication bias, or differences in ACWR calculation methods, these factors may affect the research results. Therefore, future research should be clearer about its practical applicability. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42024615589.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.050 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".