Poster 237: The Effect of the FIFFA-11+ Warm-up Program in University Level Athletes on Drop Vertical Jump Biomechanics and Risk of ACL Injuries: a Prospective Observational Cohort Study
Bibliographic record
Abstract
Objectives: Anterior cruciate ligament (ACL) injuries can pose significant challenges for athletes, leading to morbidity, loss of playing time loss and impaired performance. Neuromuscular training programs, such as the FIFA-11+ program, have shown promise in reducing the risk of lower extremity injury in this high-risk population. The purpose of this study was to evaluate the effect of the FIFA-11+ on lower extremity biomechanics during a drop vertical jump (DVJ) and to determine whether it modifies athlete risk classification for ACL injury. We hypothesized that the FIFA-11+ program would positively impact lower limb biomechanics during DVJ in varsity athletes by decreasing initial coronal (IC) and peak coronal (PC) knee abduction angles while increasing peak sagittal (PS) knee flexion angles. Methods: Thirty-six (36) university varsity athletes (seventy-two (72) knees) were recruited for this investigation. The intervention group included female and male soccer players who incorporated the FIFA-11+ program into their pre-game 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. Pre- and post-intervention biomechanical data during DVJs, captured using the Microsoft Kinect V2 system, were compared between the FIFA-11+ and control groups. Results: The FIFA-11+ group showed significant reductions in mean PC angles from high-risk to low-risk and significant decreases in PS angles, while IC angles remained unchanged. Conversely, the control group showed a significant increase in IC angles moving them to the high-risk group, with no notable changes in PC and PS angles. Conclusions: This study demonstrates that the FIFA-11+ program effectively reduces PC knee abduction angles, with mean decreases in these parameters demonstrating an overall shift from high to low-risk biomechanics amongst the intervention group. Additionally, worsening post-season knee biomechanics in the control group highlights that injury risk increases throughout the season when targeted neuromuscular training programs are not incorporated into pre-game warm-ups. This suggests that the FIFA-11+ program mitigates key biomechanical risks factors for non-contact 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".