The Effect of the FIFA-11+ ACL Injury Prevention Program on Drop Vertical Jump Biomechanics in Varsity Athletes: A Prospective Observational Cohort Study
Bibliographic record
Abstract
Background: Anterior cruciate ligament (ACL) injuries can pose significant challenges for athletes, leading to significant morbidity, loss of playing time, and impaired performance. Neuromuscular training programs, such as the FIFA 11+, have shown promise in reducing the risk of lower extremity injuries in this high-risk population. Purpose/Hypothesis: The purpose of this study was to evaluate the effect of the FIFA 11+ program on lower extremity biomechanics during a drop vertical jump (DVJ) and to determine whether it modifies the athlete's risk of an ACL injury. It was hypothesized that the FIFA 11+ program would positively affect lower limb biomechanics during a DVJ in varsity athletes by decreasing initial coronal (IC) and peak coronal (PC) knee abduction angles while increasing peak sagittal (PS) knee flexion angles. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 36 collegiate varsity athletes (72 knees) were recruited for this investigation. The intervention group included female and male soccer players who incorporated the FIFA 11+ program into their pregame warm-up. The control group consisted of female hockey players who participated in a "standard" warm-up. The FIFA 11+ program was conducted twice weekly over 10 weeks. Biomechanical data before and after the intervention during DVJs, captured using a motion sensor, were compared between the intervention and control groups. Results: The intervention group showed a significant reduction in PC angles from high risk to low risk and a significant decrease in PS angles, while IC angles remained unchanged. Conversely, the control group showed a significant increase in IC angles from low risk to high risk, with no notable changes in PC and PS angles. Conclusion: This study demonstrated that the FIFA 11+ program effectively reduced PC knee abduction angles, with a decrease in this parameter indicating an overall shift from high- to low-risk biomechanics in the intervention group. Additionally, worsening postseason knee biomechanics in the control group highlighted that the injury risk increased throughout the season when a targeted neuromuscular training program was not incorporated into pregame warm-ups. This suggests that the FIFA 11+ program mitigates key biomechanical risk factors for noncontact ACL injuries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".