The FMS and Injury Prediction
Written by Dr. Kyle Kiesel Tuesday, April 11, 2017 FMS Screening
Multiple factors can help indicate whether someone is in a high risk category for injury. Learn how the FMS and other markers play into the findings.
In the video above, I referred to several research studies around the relationship between injury prediction and the FMS that were conducted over the last decade.
In 2007, we performed a pilot study in professional football, establishing a relationship between injury risk and FMS sum scores above and below 14. The sample size was small and the results were far from definitive, but establishing a cut score of 14 merited further investigation.
Years later, we completed a follow up study in professional football which found that the cut score of 14 was consistent and, perhaps more importantly, asymmetry was predictive as well.
We used the results of the first two studies to establish the variables for a more comprehensive study of NCAA athletes. The variables included the FMS, assymmetry, YBT, previous injury and pain with movement. The results? Multiple factors indicate a high injury risk.
We expanded the concept of categorizing injury risk based on multiple variables to a larger military study. Similiar to our study of collegiate athletes, we found that more risk factors increase likelihood of injury. Specifically, we found a strong relationship with individual factors such as asymmetry in ankle dorsiflexion, upper quarter and lower quarter YBT, previous injury and recovery from injury.
This study was the first to take into consideration the multiple risk factors to estimate injury risk. The move2perform algorithm was created to categorize individual risk. The algorithm calculated and weighted the composite FMS score, individual FMS test scores, results of FMS clearing tests, presence of asymmetry on any of the 5 bilateral FMS movements, pain during testing, previous injury, YBT-LQ asymmetry, and YBT-LQ composite score less than the risk threshold for the individual athletes. Here's an example of the reports that are created (see right, also). This study demonstrated that those in the high risk group were 3.4 times more likely to be injured. Additionally, the study found that the athletes that had none of risk factors were not injured. We will cover how to apply these findings in a forthcoming whiteboard talk about Practical Injury Prediction.
Our investigation into the relationship of injury risk and movement pattern/asymmetry is far from complete. However, we hope this provides additional insight into our approach to injury prevention.