Abstract
Introduction: The Functional Movement Screen (FMS) was created as a tool to determine functional mobility deficits in athletes that could potentially affect performance. The FMS consists of seven tests including: hurdle step, deep squat, in-line lunge, shoulder mobility, active straight leg raise, rotary stability, and stability push up. Each test is scored from 0-3 and the highest score one can receive is a score of 21. Objective: The purpose of this study was: to examine the relationship between injury rates and FMS scores in collegiate Division 1 softball athletes, and to examine FMS score changes that occur throughout a season. Our hypothesis was that a score of less than or equal to 14 would result in a higher injury risk. Our second hypothesis was that FMS scores would decrease throughout the season as a result of fatigue and decreased conditioning. The study examined FMS scores at pre-season, mid-season, and post-season and analyzed injury rates. Methods: 19 softball athletes at a division 1 University participated in this study and were asked to perform FMS testing at three separate times: pre-season, mid-season, and post-season. The athletes were instructed not to exercise prior to the testing, and to wear tennis shoes during the testing. One researcher performed all of the FMS testing. The athletic trainer and softball coach reported information on injury rates, mechanisms of injury, and loss of play time or practice time due to injury. Results: A logistic regression test was performed to examine the relationship between FMS scores and injury rates between two groups (above a 14 FMS score, and equal to or less than 14 FMS score). A repeated measures ANOVA test was performed to identify changes of FMS scores within an athlete during pre-season, mid-season, and post-season. An α of 0.05 was used to test both of the hypotheses. Only 17 athletes were included in data analysis due to there being two athletes who were unable to complete the mid-season and post-season testing. From the logistic regression tests, there was a significant difference in injury rates between pre to mid-season testing (p value= 0.007), but not a significant difference in injury rates from mid to post-season testing (p value= 0.469). From the repeated measures ANOVA tests, there was a significant difference between pre-season to post-season and mid-season to post- season FMS score changes (p value= 0.005, p value= 0.009, respectively). There was not a significant difference between pre-season to mid-season FMS score changes, however (p value= 0.865). Discussion: The results of this study did not support the hypothesis that a score at or below a 14 leads to higher injury risk. This study did, however, support the hypothesis that there would be a decrease in FMS scores throughout the season. This study was different from previous FMS studies because it only examined one sport. This study also was different from previous FMS studies because it examined pre-season, mid-season, and post-season scores in order to determine how FMS scores may change throughout a season. Conclusion: It appears that FMS score does not “predict” injury like previous studies had found. However, this study does point to the possibility of using FMS as a tool to highlight strengths and weaknesses in an athlete. It also appears that FMS scores may decrease throughout a season. Future research should continue to examine only one sport, and potentially examine different positions within the same sport. Future research should also perform multiple FMS tests throughout the season.