Hamstring Strain Injury (HSI) Prevention in Professional and Semi-Professional Football Teams: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Hamstring Strain Injuries (HSIs) are the most common type of lesion in professional footballers and the leading cause of absence days from sports. However, recent studies have shown that high-level football teams apparently do not apply any HSI prevention protocol. The aim of the study was to determine the effect of preventive strategies and protocols in reducing the incidence of hamstring muscle injuries in professional and semi-professional football teams. A literature search of PubMed/MEDLINE, ISI/Web of Science and Scopus databases was conducted with the keywords “hamstring* and (injury* or strain) and prevent* and (soccer or football)”. Quality and bias assessment was completed through the Kennelly modified scale. The Injury Incidence Rate (IIR) and the Incidence Rate Ratio (IRR) were assessed in the statistical analysis. In the meta-analysis, data were extracted, pooled and analysed with “Comprehensive Meta-Analysis Version 3.3.070” software. In total, 8 of the 1017 original search studies met the inclusion criteria of this review. The total exposure of the studies was 170,221.8 h, while the number of HSIs recorded was 165 in the intervention groups and 224 in the control groups. The average score of the quality assessment was 23.6/34. The meta-analysis of six of the eight included studies provided strong evidence that interventions are effective in reducing hamstring injuries. The IRR of the effect size was 0.443, with p-value = 0.001. The studies analysed applied different preventive strategies: the Nordic hamstring exercise, the FIFA 11+ programme and exercises for core stability or balance training. All these interventions proved to have a successful effect on prevention of hamstring injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".