




































    

 American Journal of Medical and Physical Education 
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1252 Columbia Rd NW, Washington DC, United States 

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GENDER DIFFERENCES IN FUNCTIONAL MOVEMENT SCREEN SCORES OF 

COLLEGIATE SOCCER PLAYERS 

 

Anthony Deldin, Michael J. Clark and Sarah A. Smith 
University of Bristol United Kingdom 

 

Abstract: The Functional Movement Screen (FMS) is a tool used to assess movement patterns and identify 

potential injury risk. This study evaluated the effectiveness of the FMS as a tool during preparticipation screening 

of asymptomatic collegiate soccer players. The results showed that the FMS was not an effective predictor of 

future injury in this population. However, there were some correlations between FMS scores and rate of injury, 

as well as differences between male and female athletes. 

Keywords: Functional Movement Screen, injury prevention, soccer players, preparticipation screening 

 

 

Acknowledgments   
The results of this study do not constitute endorsement of the product by the authors. The authors do not have 

any professional relationships with the creators or manufacturers of the Functional Movement Screen. The 

authors thank Rayanne Nguyen, MS, RD, LDN, for her editorial assistance on multiple drafts of this article and 

the teams, coaches, and athletic trainers for their willingness to participate and assist with the study. The results 

of the present study do not constitute endorsement of the Functional Movement Screen by the authors or the 

NSCA.  

  

 

 

1. Introduction  
According to the National Collegiate Athletic Association (NCAA) Injury Surveillance System, a recorded 
182,000 injuries occurred between the years of 1988 and 2004, averaging to 11,000 injuries a year (Hootman, 
Dick, & Agel, 2007). Since 2004, the number of athletes competing has risen dramatically as have the needs to 
protect the athletes’ safety. Significant research has been extended towards the study of injury prevention through 
proper screening and treatment methods. One tool, the Functional Movement Screen (FMS), has drawn particular 
attention from the athletic community for its purpose of (Cook, 2010):   
1. Identifying individuals at risk, who are attempting to maintain or increase activity level.  
2. Assisting in program design by systematically using corrective exercise to normalize or improve 
fundamental movement patterns.  
3. Providing a systematic tool to monitor progress and movement pattern development in the presence of 
changing fitness levels.  
4. Creating a functional movement baseline which will allow rating and ranking movement for statistical 
observation.  
The FMS tests an individual's movement patterns and side-to-side symmetry utilizing seven exercises that 
examine mobility, neuromuscular control, balance, and stability through specific, fundamental movement 
patterns. Most recently, the FMS has been commonly used as an indicator for potential injury. (Garrison, 
Westrick, Johnson, & Benenson, 2015; Kiesel, Plisky, & Butler, 2011; Lisman, O’Connor, Deuster, & Knapik, 
2013)  

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The purpose of this study is to evaluate the effectiveness of the FMS as a tool during preparticipation screening 
of asymptomatic collegiate soccer players for the identification of potential musculoskeletal injury. Previous 
studies have evaluated the tool in military, collegiate and elite athlete populations (Brown, 2011; Chorba et al., 
2010; Garrison, et al., 2015; Kiesel et al., 2011; Warren, Smith, & Chimera, 2015). These studies evaluated the 
use of the FMS as a predictor of injury. Many of these studies have indicated composite scores below 14 as 
correlating to increased risk of injury (Garrison et al., 2015; Kiesel et al., 2011; Lisman et al., 2013). Chorba and 
colleagues (2010) suggested such with the stipulation that the correlation was to lower body injury only. In 
contradiction to these findings, Warren et al. (2015) found that the FMS could not be used as a predictor of future 
injury. Additional analysis will be dedicated to the exploration of potential correlations between FMS scores, rate 
of injury, and differences between male and female athletes.  
To the best of the authors’ knowledge, this study will be the first to report significant differences in FMS scores 
between healthy male and female collegiate soccer players. Although significant differences in FMS scores 
between males and females have not been reported previously, the potential findings of this study may correlate 
with previously published evidence that females have deficits in intrinsic factors like muscle activation (Hart et 
al., 2007), neuromuscular control (Brophy et al., 2009), and core stability when compared with males (Brophy et 
al., 2009; Zazulak et al., 2007). Each of these intrinsic factors has the ability to contribute to overall movement 
patterns and capacity. This study may add additional evidence that female athletes may be at higher risk for injury 
than male athletes.  
Thus, the purpose of this study is to evaluate the effectiveness of the FMS as a screening tool in determining rate 
of injury in Division 1 college athletes’ predisposition to injury and whether the correlation of FMS score and 
rate of injury differ between males and females. The researchers hypothesize that an FMS composite score less 
than or equal to 14 is an effective predictor of injury for men and women (that there is a correlation between 
composite FMS score and injury occurrence), that there will be a significant difference between sex and 
musculoskeletal injury occurrence, and that males will score significantly lower on the composite FMS scores 
than their female counterparts.  

2. Methods  

2.1. Participants 
For this study, 36 student-athletes, age 18-22, from a NCAA D1 men’s and women’s soccer program were 
recruited by the research team. The inclusion criteria was clearance by the athletic training staff for participation; 
absence of a head, musculoskeletal, or spine injury within the last 3 months; and no report of vestibular, visual, 
or balance disorders. Participants were recruited from the men’s (n = 20) and women’s (n = 16) soccer programs 
prior to the start of the spring season. The study protocol was approved by the institution’s institutional review 
board, and written informed consent was obtained from all participants before any data collection. This research 
was carried out fully in accordance to the ethical standards of the International Journal of Exercise Science 
(Navalta, Stone, & Lyons, 2020).  

2.2. Protocol  
After confirmation of study eligibility, participants performed the FMS during a single session administered by 
the lead researchers/authors. The FMS involved a series of seven screening tests, each of which were used during 
data collection. The FMS tests included the deep squat, hurdle step, incline lunge, shoulder mobility, active 
straight-leg raise, trunk stability push-up, and rotary stability. All subjects were tested at the start of the 
competitive spring sports season, with subject testing occurring just before team practice sessions. Subjects were 
provided the opportunity to complete a voluntary 5-minute warm-up before FMS testing. Participants were given 
verbal instructions for task performance and allowed 3 attempts for each task. Each movement task was 36                                              
Journal of Physical Education and Sports Management, Vol. 8(2), December 2021 

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scored using standard composite scoring. If the participant was able to correctly perform the movement task 
without any compensation, a score of 3 was be awarded; completion of the movement task with compensation 
was scored a 2, and inability to complete the movement task was scored a 1.   
Any task that produced pain was scored a 0. Tasks with right and left side components were scored individually; 
the lowest score was used in the calculation of the total composite score. Total composite scores ranged from 0 
to 21 points, and individual task scores ranged from 0 to 3 points. Clearance screens were scored either positive 
or negative based on the presence of pain. Two raters, both of whom had experience using the FMS in clinical 
practice, scored participant performance on the movement tasks. FMS scores were shared with program’s soccer 
coaching staff and strength and conditioning coaches following testing.  
Injuries acquired by the athletes during the course of a recent season (10 training weeks) were diagnosed and 
recorded by the program’s athletic training staff. Injuries were recorded on a hard copy health record maintained 
by the athletic training staff. Injury records were then transferred to data collection sheets that received an 
identification number and identifiable information was removed. The recordings were used to quantify the 
number of injuries throughout the season. Data collection sheets recorded the injury anatomical location, type of 
injury, and time off from practice and play. In addition, days of treatment and history of injury were added to 
data collection sheets. At the conclusion of the sports season, data were abstracted for analysis.  

2.3.  Statistical Analyses  
For each subject, FMS data collection sheets were compiled and entered into a spreadsheet where each athlete’s 
composite score was then calculated. Data was analysed using SPSS predictive analysis software (IBM Corp.; 
version 22.0; 2013). 
Correlation between injury and composite FMS score was first ran using Pearson Correlation test. The dependent 
variable, injured or not injured, was converted to numerical code (noninjured = 0, injured = 1). The independent 
variable, composite FMS score, remained scored as a range from 0 to 21. The p value was set at 0.05.  
To determine the difference between rate of injury and sex an independent samples t-test was used with p value 
set at 0.05. The independent variable, sex, was converted to numerical code (female = 0, male = 1). The dependent 
variable, injured or not injured, was also convereted to numerical code (noninjured = 0, injured = 1). This test 
was run to determine whether respective musculoskeletal injury occurrence means from the two groups were 
statistically significantly different.  
The difference between FMS composite score and sex was determine using an independent samples t-test with a 
p value set at 0.05. The independent variable, sex, was converted to numerical code (female = 0, male = 1). The 
dependent variable, FMS composite score, remained scored as a range from 0 to 21. This test was run to determine 
whether respective FMS means from the two groups were statistically significantly different.  

3. Results 
A total of 36 student-athlete soccer players were eligible for participation at the start of the study. Nine of those 
were excluded from the study due to history of injury 3 months prior to testing (n = 3), unable to attend the test 
session (n = 5), or leaving the soccer program after starting (n = 1), leaving 27 participants in the final sample. A 
full description of the sample is available in Table 1.  
Table 1. Description of Participant Eligibility  

Group  N  Injury in Previous 3 Months  Unable to Attend Test Session  Left Soccer Program   
Female  16  2  1  0   
Male  20  1  4  1   

  
As shown in Table 2, the average FMS score for the female athletes (n = 13) was 16.769 ± 1.235. The average 
FMS score for the male athletes (n = 14) was 15.357 ± 1.447. Females scored approximately 1.4 points higher 

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than male subjects. Additional analysis using independent samples t-test reveals a significant difference (p< 0.05) 
between sex and FMS composite score. Females had a significantly higher FMS score. 
A Pearson product-moment correlation was run to determine the relationship between FMS score and injury 
occurrence. There was a small, positive correlation between these two variables, which was statistically 
significant (r = 0.195, n = 27, p< .05).   
R2, or the percentange of variance in injury occurrence accounted for by FMS score, was ~0.04. In other words 
4% of the variance in injury occurrence is accounted for by a subjects’ FMS score (or in other words, the FMS 
score reports at least 4% of why injury occurrence would vary).  
Analysis using independent samples t-test reveals a significant difference (p< 0.05) between sex and 
musculoskeletal injury occurrence. Females had a significantly higher injury occurrence (7 injuries in 13 females 
versus 2 injuries in 14 males) (Table 3).  
Table 2. Descriptive Measures of FMS Scores within Each Group  

Group   Participants  Mean   Standard Deviation  Range   

Female   13  16.8*   1.24  15-18   

Male   14  15.4*   1.45  13-18   

*denotes significant difference (p< 0.05)  

  
Table 3. Comparison of Injuries per Participants and Mean FMS Score for Each Group  

 
Group   Participants   Injuries   Mean FMS Composite   

Female   13   7   16.7692   

Male   14   2   15.3571   

 

4. Discussion  
The results of this study did not support our first hypothesis that lower FMS scores correlate with higher risk of 
injury. There was only slight correlation between FMS score and injury found in this study (r = 0.195). The slight 
correlation lends some support to the understanding that the FMS highlights compensatory movement patterns 
and increased risk for more severe musculoskeletal injury as suggested by Cook et al. (2006) Several factors may 
account for the weakness of the correlation. Similar to Warren et al. (2015) our sample sizes represented by two 
collegiate sports teams may have been too small to support association between injury and FMS. We suggest 
further analysis of collegiate soccer players, both males and females, in a larger sample size to fully study this 
correlation or lack thereof. Garrison et al. (2015) found that there was statistically significant difference between 
injured and uninjured FMS scores. To determine injury, Garrison et al. (2015) clarified musculoskeletal injury to 
include an association with athletic participation, a consultation to an athletic trainer, physical therapist, or 
physician, and modified training for a minimum of 24 hours or protective splinting or taping because of injury. 
This differed from our methods in that musculoskeletal injury was determined by an association with athletic 
participation, a consultation with the athletic trainer, but did not incorporate a minimum time commitment to 
modification of training or taping and bracing. Making this change to our methods would suggest that lower FMS 
score may correlate with musculoskeletal injury requiring 24 hours or greater of training adaptations and may be 
cause for additional testing.  

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The results did however support our hypothesis that there would be a significant difference between male and 
female occurrence of injury. During the ten-week spring competition season, seven of the females were injured 
while two men were injured (p < 0.05). This is consistent with the findings of Warren et al. (2015) and Agel, 
Arendt, & Bershadsky (2005). Warren et al. (2015) found 30 of 89 male Division II athletes were injured while 
44 of 78 female Division II athletes were injured during their respective competition seasons (p< 0.005). Agel et 
al. (2015) contend that the noncontact injury rate for female NCAA athletes is three times higher in basketball 
and one-and-a-half times higher in soccer. As suggested by Ransdell and Murray (2016), the increased rate of 
injury for females is likely due to a multitude of factors (Ransdell & Wells, 1999) including smaller muscle fibers 
and cross-sectional area of muscle (Sale et al., 1987), smaller skeletal frame (Holloway, 1994), lower levels of 
muscle activation of the gluteus medius (Hart et al., 2007), vastus medialis oblique and vastus lateralis (Kim, Y 
& Yi, 2009), lower levels of neuromuscular control (Brophy et al., 2009; Hughes, Watkins, & Owen, 2008), and 
core stability (Brophy et al., 2009; Zazulak et al., 2007).   
The results also supported our hypothesis that there would be a significantly weaker male than female FMS 
scores. We found that females averaged approximately 1.4 points higher on the FMS than males in the same 
sport, a significantly higher score (p< 0.05). To the best of the authors’ knowledge, this is the first study to find 
significantly weaker male than female FMS scores. The cause of such a correlation is not clear however. 
Anderson, Neumann, and Bliven (2015) first suggested that there is a significant difference between male and 
female athletes in their study of the FMS in secondary school athletes but indicated several limitations including 
the absence of intrinsic factor measures that have not been correlated to the FMS. Likewise, we did not measure 
intrinsic factors that differ between men and women and may affect FMS scores. The relationship between 
intrinsic factors such as muscle activation, neuromuscular control, and core stability and FMS scores needs 
additional research. Further limiting the reliability of the findings was the size of each group sample. We suggest 
further study with larger samples to verify our findings.  
The significant difference between the male and female occurrence of injury in conjunction with the significantly 
higher FMS scores in females leads us to believe a higher FMS score would be more useful as an indicator of 
injury risk than the current standard of 14. This differs from Chorba et al. (2010) previous studies involving the 
female sample that found that a score of 14 or less on the FMS was an accurate predictor of increased to risk in 
lower body injury. The significance of these findings could aid in the use of the FMS as a preparticipation 
screening for both male and female athletes. Again, our current sample size was smaller but supports further 
analysis as the implications of increasing the current cutoff score of 14 to a cutoff score of 16 or 17 could reduce 
the rate of injuries in females when utilized properly.   

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