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Progress in Orthopedic Science
DOI: 10.5455/pos.20150803113054
www.scopemed.org
Prog Orthop Sci ● 2015 ● Vol 1 ● Issue 2 41
e Functional Movement Screening Tool Does
Not Predict Injury In Football
Christopher Rusling1, Kimberley L. Edwards2, Archan Bhattacharya3,
Adam Reed1, Stuart Irwin1, Andrew Boles1, Alex Potts1, Lisa Hodgson4
ABSTRACT
Background: The Functional Movement Screen (FMS) is used to predict individuals at heightened risk of injury within
sports such as American Football. However, the relationship between the FMS and injury within football (a.k.a. soccer) has
yet to be quantified. The current literature does not allude to whether the FMS has a role in predicting injury within this sport
specific group. Objective: To evaluate the association between the 7 FMS tasks and the incidence of non-contact injury
amongst football players from a professional English football club over one season. Methods: 135 footballers between
the ages of 8 and 21 years from one professional football club’s academy were used. Players performed the FMS and
then were observed throughout the study period to record and establish injury incidence in rates per 1000 training and
match hours. Results: The deep squat (p=0.0128) and trunk stability push-up (p=0.0621) were significant predictors
of non-contact injury. Players with a trunk stability push up score of 3 had a statistically significant lower risk of injury than
those with a score of 1. There was a similar trend for players with a trunk stability push up score of 2, but this was not
statistically significant. Total FMS score was not statistically significantly related to injury. Conclusion: There appears to
be only statistically significant associations between 2 of the 7 FMS components and non-contact injury incidence within
youth players from one professional football clubs’ academy. Further investigations need to be conducted to see whether
these results reflect the academy football population within the English academy programme.
KEY WORDS: Functional movement screen, injury, Football, adolescent, injury prevention, Physiotherapy, screening.
1Middlesborough FC, Darlington, UK.
2Rheumatology, Orthopaedics and
Dermatology, School of Medicine,
Academic Orthopaedics, Trauma
and Sports Medicine, University of
Notitngham, UK.
3Arthritis Research UK Centre for
Sport Exercise and Osteoarthritis,
Rheumatology, Orthopaedics and
Dermatology, School of Medicine,
Academic Orthopaedics, Trauma
and Sports Medicine, University of
Nottingham, UK
4Rheumatology, Orthopaedics and
Dermatology, School of Medicine,
Academic Orthopaedics, Trauma and
Sports Medicine, Centre for Sports
Medicine, University of Nottingham,
UK
Address for correspondence:
Lisa Hodgson,
Rheumatology, Orthopaedics and
Dermatology, School of Medicine,
Academic Orthopaedics, Trauma and
Sports Medicine, Centre for Sports
Medicine, University of Nottingham,
UK.
lisa.hodgson@nottingham.ac.uk
Received: May 06, 2015
Accepted: July 10, 2015
Published: September 16, 2015
Original Research
INTRODUCTION
Football has a high incidence of injuries that require
medical treatment and leads to time loss from training and
competition [1, 2]. Injury has large financial implications
for clubs in lost wages, increased insurance premiums
and the associated medical costs [3]. These financial
implications extend further if key players are unavailable
to play, potentially reducing match attendance and prize
money from cup competitions or poor league position [3].
More specifically at academy level, preventing injury is
fundamental in ensuring the health and wellbeing of the
players is maintained. Although there is a cost associated with
injuries sustained within an academy setting, safeguarding
the players to protect their welfare is paramount. In extreme
cases, injury may affect a player’s ability to pursue a career
within the game. Therefore, the need to prevent time loss
injuries at an academy level is an important role of the club’s
medical department.
Epidemiological research in English football has shown that
professional footballers experience an average of 1.3 injuries
per player per season [1]. Of all the injuries sustained, 38%
were the result of contact mechanisms and 58% of non-
contact mechanisms [1]. More recently, a 7 year European
study found teams suffered an average of 2.0 injuries per
player per season, which equates to an injury incidence
of 8.0 injuries/1,000 training and match hours [4]. Injury
incidence for English youth academy footballers was initially
indicated to be lower, with data indicating teams to suffer
0.40 injuries per player per season [2]. However, much of
the available data is over 14 years old and based upon an old
academy model. Comprehensive data from a more recent 6
year study based within the English Premier League indicated
Rusling, et al.: FMS does not predict injury in football
42 Prog Orthop Sci ● 2015 ● Vol 1 ● Issue 2
players to sustain 2.23 injuries per player per 1000 hours of
total exposure [5].
Contact based injuries result from uncontrollable extrinsic
factors, e.g. a tackle, and are thus unpredictable. Conversely,
non-contact injuries, such as low back pain or injuries due to
running, are theoretically predictable but their occurrence
may be multi-factorial; for instance, such an injury may
result from muscle imbalances, gender, baseline fitness
levels, past history, activity and even ethnicity [6, 7]. More
recently published literature support a growing consensus
view that movement patterns and efficiency are a significant
contributing factor [8]. The research implies that adopting
less efficient and effective compensatory movement patterns
predisposes an athlete to injury [9].
First developed by Gray Cook and Lee Burton, the FMS
attempts to break down individual’s movement patterns to
highlight inefficiencies and deficiencies that may contribute
to injury or poor performance [8, 10]. Using the FMS as an
injury prediction tool therefore, provides health professionals
with an opportunity to implement preventative measures
aimed at reducing injury occurrence [10]. There are 7
tasks that constitute the FMS which are said to be basic
movements that occur during many sporting activities [11].
Each task is scored from 0 to 3, with 3 constituting normal
and efficient movement, creating a final score out of 21. To
be completed successfully, the tasks demand a variety of
attributes including strength, flexibility, range of movement
(ROM), co-ordination, balance and proprioception [8].
Individuals with these attributes demonstrate a balance of
stability, mobility and motor control, which is postulated to
protect the body from injury [12].
There has been a large emphasis on movement screening
following the Premier Leagues overhaul of the academy
system within England. The recent implementation of the
Elite Player Performance Plan (EPPP) includes a stipulation
for clubs to conduct movement screening of players [13].
However, the evidence base that assesses the application
of the FMS in determining injury risk within individuals
specifically from football is limited.
Data from previously published literature based within
American Football has demonstrated player’s with a lower
FMS score to have a greater risk of injury. Players with a score
of 14 or less had an eleven-fold increase in the risk of injury
when followed over a season within the National Football
League (NFL) [8]. Other authors supported the use of the
FMS within injury prediction but to a much lesser extent.
Stating that individuals with a score less than 14 being
associated with a one-fold [14], two-fold [15], four-fold [6]
and eight-fold [16] increase in the risk of injury. Again, the
populations used within these studies were not specific to
football and varied from fire-fighters to female collegiate
athletes.
There is limited evidence surrounding the FMS system in
highlighting those individuals at heightened risk of injury
within football. Evidence centres on American football or
non-sports-persons without providing statistically significant
results representative of the academy football community.
This study is the first to be conducted upon a population
of academy football players and helps to establish a more
comprehensive analysis of the FMS system in football and
its relationship with injury. Previous studies conducted
have investigated the relationship between the FMS and
performance in football [17]. Such evidence significantly
correlates FMA to athletic performance. However, there is
a body of evidence that contradicts these findings, albeit on
a non-football population [18].
The aim of the study was to establish whether an association
existed between the 7 FMS tasks and the incidence of
non-contact injury amongst academy players from one
professional English football club over one season. The null-
hypothesis was that there would be no association between
FMS scores and incidence of non-contact injury.
METHODS
A prospective cohort study of professional academy
footballers during the 2012/2013 season (3rd September
2012 and 10th May 2013). All signed players (n=140)
from one professional football academy (aged 8-21 years)
were invited to participate. Players were excluded if they
were injured at the start of the season, left the club during
the season, or opted out of the study (n=20). Players were
assessed performing the FMS at the beginning of the season
by the lead strength and conditioning coach using the FMS’
assessment criteria [12].
An injury was defined as “Any physical complaint sustained
by a player that resulted from a football match or football
training, irrespective of the need for medical attention or
time loss from football activities. An injury that results
in a player receiving medical attention is referred to as a
‘medical attention’ injury”. This is in line with the consensus
statement on injury definitions in studies of football injuries
[19]. Contact injuries and illnesses were excluded because of
their unpredictable nature when compared to non-contact
mechanisms of injury.
Players were categorised in to the Foundation, Youth
or Professional phase in relation to the EPPP academy
programme. The Foundation phase consists of players from
the under 9 – 12 age groups (8 to 12 year olds). The Youth
Development phase consists of players from the under 13 to
16 age groups (12 to 16 year olds). Finally, the Professional
Development phase consists of players from the under 18
to 21 age groups (16 to 21 year olds). Age groups were
determined by the player’s age as of the 31st August.
Individual player exposure (training and match hours) were
collected through club records, recorded by the academy
coaching staff and logged upon the Premier Leagues online
Rusling, et al.: FMS does not predict injury in football
Prog Orthop Sci ● 2015 ● Vol 1 ● Issue 2 43
Player Management Application (PMA) [20]. Training
exposure was defined as “team based and individual physical
activities under the control or guidance of the team’s
coaching or fitness staff that are aimed at maintaining or
improving players’ football skills or physical condition” [19].
Match exposure was defined as “play between teams from
different clubs” [19]. Time off play due to injury was also
recorded. The injury incidence in rates per 1000 training
and match hours were calculated.
Players had to be registered with the football club before
the start of the study. Players joining the club after the
beginning of the study were not included to avoid skewed
results. Informed consent was obtained via an ‘opt out’
policy where player’s data were automatically entered into
the study, as it was purely observational. Also, players had to
be fully fit prior to the start of the study so that the cohort
shared similar baseline characteristics.
Statistical Analyses: The characteristics (age, height, weight,
FMS score) of the cohort were reported, as was injury status
(number and type of injuries, dichotomised into contact and
non-contact injuries). Total exposure (match and training
hours) were summarised. Incidence of injury per 1000
player hours was calculated. Injury in rates per 1000 training
and match hours were calculated as the number of cases,
multiplied by 1000, divided by the total exposure in hours. A
logistic regression model was utilised (outcome: non-contact
injury status yes/no) to investigate the relationship with
individual FMS score and age. A paired comparison of means
post hoc power calculation with standard assumptions of 80%
power and α=0.05 was conducted to determine whether the
study was sufficiently powered. All statistical analyses were
undertaken in SAS® 9.3. The study was approved by the
University of Nottingham Faculty of Medicine and Health
ethics committee (Ref: E11042013 SCS)
RESULTS
One hundred and forty players were invited to participate
in the study (n=140). Five were excluded due to injury at
the start of the season; 15 were withdrawn from the study
due to leaving the club (whether on loan or end of contract).
A paired comparison of means post hoc power calculation
with standard assumptions of 80% power and α=0.05
determined that sufficient participants were included in this
study to determine a difference of 0.6 in total FMS score.
When the individual components of the FMS score are
considered, shoulder mobility had the highest variance, but
the study includes enough participants, has sufficient power
to determine a difference of 0.2 points (range 0-3 points).
This advocates that the study is sufficiently powered.
The mean age of the cohort was 13.6 years (range 8 to 20); the
mean height was 178.4 cm (range 130.4 to 186.1) and weight
was 55.3 kg (range 27.3 to 96.9) (see Table 2). The data were
positively skewed, i.e. more younger, taller, heavier players.
The mean total FMS score was 12.1 (range 8 to 17, SD =
2.3). The 7 component scores of the FMS varied, with the
active straight leg raise score having the highest mean and
the Inline Lunge the lowest mean (see Table 1).
Table 1. Ummary of FMS scores (0-3)
FMS Task Mean
Deep Squat 1.63
Hurdle Step 1.73
In-Line Lunge 1.53
Shoulder Mobility 1.71
Active Straight Leg Raise 2.03
Trunk Stability Push-up 1.83
Rotary Stability 1.64
There were 133 total injuries during the season, affecting 54
players. Players who were injured during the season were
slightly older (p=0.678), shorter (p=0.435) and heavier
(p=0.335) than the non-injured players (see Table 2). 72
injuries (54%) occurred during training activities, 46 (35%)
during match play, and 15 (11%) were the result of other
reasons, for example, developmental groin pathology. The
most common injury was to muscle (41.4%) followed by
contusions (20.3%) and ligament injuries (15%) (see Table
3).
The total time loss due to injury was 2923 days (mean 22
days per injury). There were 1870 days (64%) lost as a result
of 86 non-contact injuries, whilst 1053 days (36%) were the
result of 47 contact based injuries.
Table 2. Summary of cohort characteristics, stratified by injury status (any injury).
Injury Status Age (years) Height (cm) Body mass (kg)
Non-contact Injury (n=54) 13.7 ± 2.85 167.2 ± 16.87 57.0 ± 16.32
Not Injured (n=66) 13.4 ± 3.63 187.6 ± 209.97 53.9 ± 18.71
Overall (n=120) 13.6 ± 3.29 178.4 ± 155.92 55.3 ± 17.67
Rusling, et al.: FMS does not predict injury in football
44 Prog Orthop Sci ● 2015 ● Vol 1 ● Issue 2
Table 3. Summary of injury type.
Type of Injury Total Number Non-Contact
Fracture 4 0
Other bony injury 4 4
Haematoma/contusion/bruise 27 1
Muscle injury 55 55
Tendon injury 6 6
Dislocation/Subluxation 1 0
Sprain/Ligament Injury 20 18
Meniscus/Cartilage Lesion 1 0
Overuse 12 0
Total exposure was 25397.3 hours (mean exposure was 211.6
hours per player). There was an average of 237.3 training
hours each, with wide variation (range 4 to 479.7 hours; SD
= 144.17 hours). Similarly, there was an average of 19.7
match hours per player; range 2.9 to 50.2; SD = 9.4 hours).
There was little difference in exposure between those players
injured or not (mean total exposure was 211 hours for injured
players and 213 hours for uninjured players, p=0.930).
Incidence of non-contact injury was 3.4 injuries per 1000
player hours [86 / 25397.3 x 1000]; and contact injury was 1.9
[47 / 25397.3 x 1000]; Incidence of all injury was 5.2 injuries
per 1000 player hours [133 / 25397.3 x 1000].
Fifty four of one hundred and twenty players experienced
a non-contact injury during the season. The logistic
regression model comparing the individual components of
the FMS score and age (youth, foundation or professional),
showed that there is a significant difference between level
2 and level 1 in deep squat (p=0.0128) with its upper 95%
confidence limit for odds ratio just touching one while the
95% confidence interval of odds ratio for trunk stability push
up does not include unity when level 3 and 1 are compared
in predicting outcome (i.e. non-contact injury, see Figure
1). Similarly, youth and professional players had a higher
risk of injury (2.5x and 1.8x respectively) than foundation
players (p>0.05). The ROC area under the curve was 0.7346,
suggesting a good model fit. The logistic regression model
comparing individuals total FMS score and outcome was
not significant in the presence or absence of age. Therefore,
total FMS score was not statistically significant in predicting
outcome.
DISCUSSION
Statistically significant interactions were found between
2 of the 7 FMS tasks and noncontact injury incidence
within youth academy footballers. There was a statistically
significant interaction between the deep squat and non-
contact injury incidence (p=0.0128). This may be because
of its relationship with neuromuscular control and kinetic
chain mechanics. Muscles do not work in isolation and are
required to work together in order to produce movement
[21]. This multi muscle pattern of recruitment within the
posterior chain requires high level neuromuscular control
in order to be performed effectively. If there is an absence
of co-ordinated firing patterns within the muscular chain
then abnormal demands are placed on structures. If these
abnormal loads or compensations exceed the normal
biomechanical and structural role of the tissue, resulting
in injury [22]. An example of this association between
neuromuscular control and injury would be how altered
neuromuscular firing within the posterior chain (e.g. reduced
gluteal activation) would place higher demands on the
hamstrings leading to a predisposition to injury [23]. This is
because the hamstrings are required to work harder in order
to achieve a required movement.
Figure 1. Odds ratios for individual FMS score and non-contact injury
status
In addition, the squat also requires sufficient anterior core
stability, which is documented to protect from injury [24].
The multi-joint movement also requires a combination of
triple flexion and extension, which challenges individual’s
mobility [11]. Sufficient thoracic, hip and ankle mobility
is all needed simultaneously in order to achieve a higher
FMS score. The combination of stability achieved by
synchronised neuromuscular firing patterns and multijoint
mobility may be a reason as to why an association is present.
It is important to note that the other FMS tasks consist of
a strong neuromuscular element, especially the hurdle step
and in-line lunge. However, it appears that the deep squat
is superior to these in relation to injury.
Criticism of the deep squat may stem from it being a bilateral
exercise [25]. Mechanisms of injury rarely result from player’s
having two feet on the ground (apart from some jumping/
contact mechanisms), which questions the football specific
nature of the deep squat. Alternatives, such as some single leg
screening measures e.g. single leg squat may be more specific
to the game of football and may show enhanced associations
with injury. Further studies would need to be implemented
to compare the two and establish whether the deep squat is
inferior when compared to single leg alternatives.
Rusling, et al.: FMS does not predict injury in football
Prog Orthop Sci ● 2015 ● Vol 1 ● Issue 2 45
A statistically significant interaction was also found between
the trunk stability push-up and non-contact injury incidence
as those individuals who scored a 3 had a statistically
significant lower risk of injury than those with a score of 1
(p=0.0621). Scores on the remaining individual FMS tasks
seem to be unrelated to injury.
The trunk stability push up assesses an individual’s core
stability and may be associated with injury because of
the relationship between core stability and kinetic chain
mechanics [8]. An individual’s core is strongly documented
to be associated with movement efficiency [26]. Therefore,
altered core stability may elicit dysfunction within the kinetic
chain. This dysfunction is thought to contribute toward
compensatory movement patterns that exceed structures
normal physical and structural capabilities, leading to such
structures breaking down.
Screening procedures need to be efficient and succinct as
time may be limited within the elite sporting environment.
In this current study only 2 of the FMS tasks have a
statistically significant role in predicting injury. Exclusion
of the remaining 5 tests would allow for more useful tests
to be added to a medical department’s injury screen. It is
important to note that these results are only applicable to
injury prevention. Other authors have indicated the FMS
to have a role in predicting performance, but that would be
the subject of a separate study. There is existing research
that illustrates a strong correlation between the FMS
and performance within football [17]. However, there is
conflicting evidence within alternative populations [18].
Therefore, as this pilot study shows, if the FMS has no
relationship with injury or athletic performance, particularly
within football, then there may be no justification for its
implementation clinically due to its lack of external validity.
To date, there have been no comparable studies conducted
that assesses the ability of the FMS to determine injury risk
within academy footballers. In a smaller cohort of 46 National
Football League (NFL) players, an FMS score of 14 or less
prior to the start of a season had an eleven-fold increase in the
risk of injury [8]. However, the injury definition used within
the study did not enable comprehensive analysis, future
studies should be based upon a definition that includes any
injury leading to a time loss from training or competitive play.
Analysis within this study indicated that player’s scoring 14 or
less had a one-fold increase in the risk of injury, compared to
those scoring over 14. Adjusted odds ratio (TFMS<=14 vs
TFMS>14) = 1.125 (0.47, 3.43). A one-fold increase in the
risk of injury is 10 times smaller than previous research [8],
which suggests limited application to the academy football
setting. Despite this, the data indicating a higher risk of getting
injured for those scoring less than 14, it was not statistically
significant. Therefore, the score of 14 not being significantly
related to injury means that it may not be a relevant marker
for highlighting those players’s at heightened risk of injury
within an academy football setting.
This study mirrors a large proportion of the published material
in that it has only been able to highlight those players who
were injured during one sporting season. It may be more
appropriate to interpret player’s FMS scores with more long
term goals in mind. This lack of longitudinal follow-up over
several competitive football seasons means that the diagnostic
value of the FMS is based upon the assumption that players
will become injured within that particular season. Although,
the season long study may not have been long enough to draw
appropriate conclusions, many injury screening and prevention
programmes operate season to season, which makes long term
follow-up very difficult within professional sport. One caveat
of conducting long term studies within professional sport is
the likelihood of a very small sample size at the completion
of the study.
The findings may also differ from previously work because
of the population and sport used. The physical demands of
the sport may result in a variety of injuries and mechanisms
underlying them [27]. For example among many differences,
English football is predominantly continuous, whilst the
NFL has large breaks in play and evidence indicates that
intermittent involvement in play increases the risk of injury
due to the altered ability of soft tissues to change and adapt
[28]. The continuous nature of football may also predispose
athletes to a higher proportion of pathology that result from
repetitive load e.g. tendinopathy or fatigue related muscle
injury.
Previous epidemiological research within youth football
indicated an average of 2.23 injuries per 1000 player hours,
with 51% and 36% of injuries occuring during competition
and training respectively. The data from this study (5.2
injuries per 1000 player hours) indicates a large increase in
the injury incidence compared to the epidemiological research
conducted 10 years ago. These contrasting results may reflect
the modern game of football, where previous research is dated
and based upon an old academy model. The heightened injury
incidence and occurrence during training activities could also
be associated with the increased exposure associated with
the EPPP.
The prospective injury surveillance incorporated within this
study used a more robust definition of injury, ensuring that all
injuries were captured, however minor. This allowed for more
comprehensive reporting and therefore, a more comprehensive
analysis of the association between FMS scores and non-
contact injury incidence. This is compared to previous studies
whose definitions only included injuries that resulted in a time
loss of at least 3 weeks from normal training and competition
and as a result, missed a substantial number of injuries [8]. In
fact, time loss injuries are documented to be unrepresentative
of a sample as non–time-loss injuries represent the largest
proportion of injuries within athletes
[29].
The mean FMS score of 12.3 is lower than previously
published literature. Previous studies have produced similar
protocols and generated reference data and inter/intra
Rusling, et al.: FMS does not predict injury in football
46 Prog Orthop Sci ● 2015 ● Vol 1 ● Issue 2
rater reliability based upon various smaller populations
such as Fire-fighters and American Footballers, over much
shorter study periods. This absence of research into the
academy football setting means that the normative values
documented within the literature are not representative of
the players observed within this study.
It is undoubted that future research is required in order to
expand on the current available research associated with the
FMS and further establish its role within injury prevention.
Future studies should be conducted in a prospective
manner using a similar definition of injury that is robust
enough to ensure that all injuries are captured, however
minor. Following this pilot, it appears that the observed
association between players FMS scores and injury incidence
is representative to academy populations on a national
level. Therefore, clinicians may not be able to justify the
implementation of the FMS system within an academy
football system to highlight those players at heightened
risk of injury.
In summary, the FMS is not associated with non-contact
injury incidence within youth footballers from one
professional football academy over one season. The deep
squat and trunk stability push up appear to be the only tasks
from the FMS that appear to have a statistically significant
association with non-contact injury incidence. Based on this,
it would be appropriate to use these two tasks within medical
screening. Further research that incorporates these two tasks
with alternative screening measures e.g. single leg options
may highlight better option for any injury prediction with
in academy football settings. It appears that using the full
FMS may not be appropriate in determining individual’s
injury risk within youth academy football.
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