Review

Br J Sports Med: first published as 10.1136/bjsports-2016-096938 on 30 March 2017. Downloaded from http://bjsm.bmj.com/ on 25 November 2018 by guest. Protected by copyright.
Do Functional Movement Screen (FMS) composite
scores predict subsequent injury? A systematic review
with meta-analysis
Robert W Moran,1,2 Anthony G Schneiders,3 Jesse Mason,4 S John Sullivan1

►► Additional material is Abstract associated with elevated injury risk have been
published online only. To view, Aim This paper aims to systematically review studies described. Of these, the Functional Movement
please visit the journal online
(http://d​ x.​doi.o​ rg/​10.​1136/​ investigating the strength of association between FMS Screen (FMS) is a movement-competency-based
bjsports-​2016-​096938). composite scores and subsequent risk of injury, taking test in widespread clinical use13 14 and has also
into account both methodological quality and clinical attracted considerable research attention.15 16
1
School of Physiotherapy, and methodological diversity. The FMS is a battery of seven movement tasks
Centre for Health, Activity
Design Systematic review with meta-analysis. and three additional clearing tests, assessed by
and Rehabilitation Research,
University of Otago, Dunedin, Data sources A systematic search of electronic visual observation using standardised criteria.11 12
New Zealand databases was conducted for the period between their Recent systematic reviews report acceptable intra-
2
Health Care, Unitec Institute inception and 3 March 2016 using PubMed, Medline, rater and inter-rater reliability for composite FMS
of Technology, Auckland, New Google Scholar, Scopus, Academic Search Complete,
Zealand scores;15 17 however, other properties are less
3
Department of Exercise and AMED (Allied and Complementary Medicine Database), well established with the use of FMS as an injury
Health Sciences, School of CINAHL (Cumulative Index to Nursing and Allied Health prevention screening tool—a particular area of
Health, Medical & Applied Literature), Health Source and SPORTDiscus. current debate.14
Sciences, Central Queensland Eligibility criteria for selecting studies Inclusion
University, Branyan,
In a recent review, Bahr18 described three
criteria: (1) English language, (2) observational research steps in the development and validation
Queensland, Australia
4
Health Care, Unitec Institute prospective cohort design, (3) original and peer-reviewed of injury prevention screening programmes. Step
of Technology, Auckland, New data, (4) composite FMS score, used to define exposure 1 involves conducting prospective cohort studies
Zealand and non-exposure groups and (5) musculoskeletal injury, to establish the strength of association between a
reported as the outcome. Exclusion criteria: (1) data putative risk factor and subsequent injury. Step 2
Correspondence to reported in conference abstracts or non-peer-reviewed
Robert W Moran, Health Care, involves validation of screening test properties,
literature, including theses, and (2) studies employing and Step 3 prescribes the use of controlled studies
Unitec Institute of Technology
Private Bag 92025, Auckland cross-sectional or retrospective study designs. to investigate effectiveness. Since Kiesel et al’s
1142, New Zealand; Results 24 studies were appraised using the Quality of
seminal ‘injury prediction’ study of American foot-
​rmoran@​unitec.​ac.​nz Cohort Studies assessment tool. In male military personnel,
ball players in 2007,19 many studies have investi-
there was ’strong’ evidence that the strength of association
Revised 17 January 2017 gated the relationship between dichotomised FMS
Accepted 3 March 2017 between FMS composite score (cut-point ≤14/21) and
composite score and injury across a variety of sports
Published Online First subsequent injury was ’small’ (pooled risk ratio=1.47,
and occupational settings.
30 March 2017 95% CI 1.22 to 1.77, p<0.0001, I2=57%). There was
Two systematic reviews have attempted to synthe-
’moderate’ evidence to recommend against the use of
sise this literature.16 20 Dorell et al20 included seven
FMS composite score as an injury prediction test in football
prospective cohort studies in their 2015 review,
(soccer). For other populations (including American football,
college athletes, basketball, ice hockey, running, police and while Bonazza et al’s16 2016 review included nine
firefighters), the evidence was ’limited’ or ’conflicting’. prospective studies but did not assess individual
Conclusion The strength of association between FMS studies for risk of bias, instead pooling all studies,
composite scores and subsequent injury does not support regardless of quality. Moreover, both previous
its use as an injury prediction tool. reviews aggregated data from studies with diverse
Trial registration number PROSPERO registration participant ages, sex, occupation and sports settings
number CRD42015025575. and injury definitions, which may bias the conclu-
sions or limit their interpretation.21 The conclusions
of Bonazza et al16 support the injury predictive
value of FMS; however, this conflicts with the
Introduction earlier review of Dorell et al,20 who concluded that
Loss of participation due to injury threatens the the diagnostic accuracy of the FMS to predict injury
health benefits of physical activity,1 and impedes was low.
competitive success for individuals and teams,2 3 Because of the emergence of several new
andare associated with socioeconomic costs and prospective cohort studies and the specific weak-
health burden.4 5 Screening tests that might identify nesses in the methodological approach of previous
modifiable intrinsic risk factors for musculoskel- reviews,16 20 we systematically and comprehen-
etal injury are appealing to applied practitioners sively reviewed studies investigating the strength
To cite: Moran RW, working in sport and exercise medicine. of association between FMS composite scores and
Schneiders AG, Mason J, Recently, several performance-based6 and move- subsequent risk of injury. We considered both
et al. Br J Sports Med ment-competency-based tests7–12 for the purpose methodological quality and clinical and method-
2017;51:1661–1669. of identifying deficits in neuromuscular ability ological diversity.

Moran RW, et al. Br J Sports Med 2017;51:1661–1669. doi:10.1136/bjsports-2016-096938    1 of 10

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(AGS) considered the full-text records and applied the selection
Table 1 Search strategy
criteria. Study characteristics were independently extracted from
Sample search syntax Database Yield* each article by two reviewers (RWM and JM), who subsequently
1. Functional Movement Screen* Google Scholar 28 met to cross-check extracted information against the original
2. Functional Movement Screen* AND Scopus (including ScienceDirect 16 articles.
(injury OR injury prediction OR injury and Embase)
prevention OR injury risk OR injury
prevention screening) Risk of bias
3. Functional Movement Screen* PubMed 23 An assessment of methodological quality for the selected
AND (preparticipation screening OR studies was undertaken using the ‘Quality of Cohort Studies’
preparticipation examination) (Q-Coh), a tool with acceptable validity and reliability specif-
4. Functional Movement Screen* EBSCO (including Academic 55 ically developed to assess risk of bias in prospective cohort
AND (flexibility OR stability OR motor Search Complete, AMED, studies.25 Risk of bias was assessed across six domains: sample
control OR athletic) CINAHL, Health Source: representativeness, comparability of groups, exposure measure,
Nursing/Academic Edition,
maintenance of comparability, outcome measures and attri-
MEDLINE, SPORTDiscus)
tion. Before commencing assessment, operational definitions
Total: 122
for interpreting Q-Coh items in the context of the topic were
*Yield after two reviewers screened titles and abstracts.
developed and agreed by the reviewers. Two reviewers (RWM
AMED, Allied and Complementary Medicine Database; CINAHL, Cumulative Index to
Nursing and Allied Health Literature. and JM) independently appraised each study before meeting to
compare findings. Disagreements in the assessment of Q-Coh
items between reviewers were resolved by consensus, and a
Methods third reviewer (AGS) was available to make a final decision,
Design if necessary. Descriptors for the overall quality of each article
A systematic review with meta-analysis was undertaken and were based on the study by Jarde et al25 and defined as ‘good’
reported based on the PRISMA (Preferred Reporting Items when ≤1 domain was not satisfied, ‘acceptable’ if 2 domains
for Systematic Reviews and Meta-Analyses) statement22 and were not satisfied and ‘low’ when >2 domains were not
MOOSE (Meta-Analysis of Observational Studies in Epidemi- satisfied.
ology) proposal for reporting.23 The study was prospectively
registered with PROSPERO (CRD42015025575). Data analysis and synthesis
When reported, we used dichotomised FMS composite scores
Search strategy based on the cut-points, as defined in each study. Meta-analysis
The search strategy was developed in consultation with a was attempted when there were at least two studies of ‘good’
specialist librarian. Databases were searched from inception, or ‘acceptable’ methodological quality, and studies shared low
and the final search was undertaken on 3 March 2016. Two methodological and clinical diversity with a sufficiently similar
reviewers (RM and JM) independently undertook initial data- design, cohort characteristics (age, sex and occupation/sport)
base search and screened search results for relevance using the and injury definitions (see online supplementary table S1). A
article title and abstract (table 1). A composite list of all arti- random-effects model, accounting for both within-study and
cles identified by each reviewer that included the term ‘func- between-study variance, was used, because it was assumed that
tional movement screen*” in the title or abstract was saved using the true effect would vary between studies.26 Statistical hetero-
reference management software, and duplicate database results geneity was explored using Cochrane χ² (Cochrane Q), with the
were removed. Subsequently, two reviewers (RM and AS) inde- statistical significance set at p<0.1. Heterogeneity was quan-
pendently screened the titles and abstracts of all articles identified tified using the I2 statistic and interpreted using the guidelines
in the search results. On the basis of the title and abstract infor- suggested in the Cochrane Handbook, with 0%–25% indi-
mation, full-text articles were retrieved for any article judged by cating that heterogeneity ‘might not be important’, 30%–60%
at least one reviewer to be investigating the association between as ‘moderate’, 50%–90% as ‘substantial’ and 75%–100% as
FMS score and injury (figure 1). The reference lists of retrieved ‘considerable’ heterogeneity.27 Review Manager (RevMan)
articles were hand-searched for additional records, and a search v5.3 (The Nordic Cochrane Centre, The Cochrane Collabora-
of the citation history of selected articles was undertaken using tion, Copenhagen, 2014) was used to undertake meta-analysis
Scopus (Elsevier, B.V.). calculations.
When meta-analysis was not appropriate, a qualitative best
Selection criteria evidence synthesis was undertaken.28 Consistent with other
Eligibility for inclusion in the review was independently assessed recent systematic reviews,15 29 we drew conclusions about the
by two reviewers (RM and JM) after considering full-text articles overall quality of evidence, using criteria adapted from the
and applying the following selection criteria. Inclusion criteria study by van Tulder et al30 (table 2). For the best evidence
were the following: (1) the language used was English; (2) the synthesis, we operationally defined the ‘smallest worthwhile
study was an observational prospective cohort design; 3) the effect’ based on the lower limit of the CI for RR ≥1.131 or
study reported original and peer-reviewed data; 4) composite OR ≥1.5. These thresholds equate to ‘small’ magnitudes of
FMS score was used to define exposure and non-exposure groups effect.32 If measures of association (RR and OR) derived from
and 5) musculoskeletal injury was reported as the outcome. dichotomised composite scores were not reported, but instead
Exclusion criteria were as follows: (1) data reported in confer- a significance test for differences in the composite FMS score
ence abstracts24 or non-peer-reviewed literature including theses between injured and non-injured participants was reported as
and (2) studies employing cross-sectional or retrospective study a continuous variable, we interpreted no statistical difference
designs. Differences between reviewers regarding selection eligi- (where p<0.05) as evidence for the absence of an effect. Simi-
bility were resolved by majority decision after a third reviewer larly, we operationally defined the smallest worthwhile effect

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Figure 1 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram of search strategy and study selection. Note
that the pooled effect derived from the meta-analysis of three studies was incorporated into the best evidence synthesis (grey arrow).

for an area under a receiver operating curve as 0.733 and a

Table 2 Levels of evidence adapted from the criteria in the study by likelihood ratio of ≥2, which equates to a change in post-test
van Tulder et al30 odds of ~15%.34
Level of evidence Criteria
Strong Consistent findings (≥75% of studies showing consistent
results)*from ≥3 high-quality† studies Results
Moderate Consistent findings from ≥1 high-quality and ≥1 low-quality† Search results
studies
Systematic database search identified 122 potential studies,
Limited Consistent findings in ≥1 low-quality study or only 1 study
which, based on the title and abstract information, appeared
available
likely to be investigating the strength of association between
Conflicting Inconsistent findings (<75% of studies showing consistent
FMS score and injury (figure 1). Following the removal of
results) in multiple studies, irrespective of study quality
duplicate records and assessment of full-text articles for eligi-
No evidence No studies found
bility, 24 articles were accepted for risk of bias assessment.
*In the case of only two or three studies, ‘consistency’ required agreement between
all studies.
Two studies35 36 reported results from the same data set; there-
†Studies rated as having ‘good’ or ‘acceptable’ quality using the Quality of Cohort fore, findings from these studies were considered concurrently
Studies risk of bias tool25 were combined into one category operationally defined as in decisions about the overall quality of evidence. The charac-
‘high quality’ for the purpose of applying these criteria. teristics of appraised studies are shown in table 3.

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 Table 3 Study characteristics (n=24)

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M Loss to follow- FMS cut-
Review

Author, year (n) F (n) n up (n) * Age (SD) (year) Participant group(s) Follow-up period point Injury type† Mechanism
85
Azzam et al, 2015 34 0 34 0 NR Professional basketball (NBA) 4 seasons ≤14 TL (≥7 days) Overuse
trauma
Bardenett et al,86 2015 88 97 185 18 15.2 High-school athletes (cc, afb, sc, sw, tn and 1 season Various TL NR
(SD NR) vb) MA
Bushman et al,45 2016 2476 0 2476 590 18–57 Light infantry brigade (US Army) 6 months ≤14 MA Overuse
trauma
Butler et al,87 2013 NR NR 108 NR NR Firefighter trainees 16 weeks ≤14 TL (≥3 days) NR
Chorba et al,88 2010 0 38 38 0 19 (1.2) Collegiate athletes 1 season ≤14 MA NR
(sc, bb and vb)
Dossa et al,89 2014 31 0 31 11 16–20 Major junior ice hockey 1 season ≤14 TL (≥1 game) NR
Garrison et al,90 2015 88 80 168 8 17–22 Collegiate athletes 1 season ≤14 MA ‘Any’
(sw/dv, rb and sc)
Hammes et al,59 2016 238 0 238 NR 44 (7) Veteran (≥32 years) football (soccer) 9 months NA TL ‘Any’
Hotta et al,41 2015 101 0 84 17 20 (1.1) College runners 6 months ≤14 TL (≥4 weeks) Excl trauma
Kiesel et al,912014 238 0 238 NR NR Prof American football 1 pre-season ≤14 TL (any) ‘Any’
Kiesel et al,922007 46 0 46 NR (0) NR Prof American football ~4.5 months ≤14 TL (≥3 weeks) ‘Any’
Knapik et al,47 2015 770 275 1045 NR 18 (0.7) US Coast Guard cadets 8 weeks ≤11 M MA ‘Any’
≤14 F
Kodesh et al,93 2015 0 158 158 NR Mdn 19 Female soldiers 3 months ≤12 MA ‘Any’
≤14 TL (≥2 days)
Letafatkar et al,352014 50 50 100 NR (0) 18–25 Students 1 season ≤17 TL (≥1 exposure) ‘Any’ lower extremity
(sc, hb and bb)
McGill et al,562012 14 0 14 NR 20.4 (1.6) University basketball 2 years NA TL ‘Any’ back injury
McGill et al,442015 53 0 53 NR (0) 38 (5) Elite task force police 5 years ≤14 NR ‘Any’ back injury, excl
accident
Mokha et al,94 2016 20 64 84 NR (0) M 20.4 (1.3); F University athletes 1 academic year ≤14 MA ‘Any’
19.1 (1.2) (rw, vb and sc) TL
O’Connor et al,462011 874 0 874 NR (0) Long course 23.0 US Marine Corp officer candidates 38 days; 68 days ≤14 MA Overuse
(2.6); Short course trauma
21.7 (2.6)
Rusling et al,40 2015 135 0 135 15 13.6 (3.3) Professional football (soccer) 8.5 months ≤14 ‘Any’ Excl contact
Schroeder et al65 2016 158 0 158 62 23.7 (3.5) Amateur football (soccer) 10 weeks NA TL(≥3 days) Non-contact lower limb
Shojaedin et al,36 2014 50 50 100 NR (11) 22.6 (3) University athletes Competitive season ≤17 NR NR
(sc, hb and bb)
Warren et al,43 2015 89 78 167 NR (0) 18–24 College athletes Competitive season ≤14 MA Only ‘non-contact’
(bb, cc, afb, gf, taf, tn, vb, sc and sw/dv)
Wiese et al,42 2014 144 0 144 NR (0) 19 (1.3) NCAA Division I (American football) 1 season ≤17 MA Overuse
TL (≥1 day) Non-contact
Zalai et al,57 2015 20 0 20 NR 23 (3) Elite male football (soccer) 6 months NA ? ?
*NR (0) = If loss to follow-up was not explicitly reported but could be inferred from results, then loss (n) is shown in brackets.
†Injury type.
afb, American football; bb, basketball; cc, cross-country; dv, diving; Excl, Excluding; F, female; gf, golf; hb, handball; M, male; MA, medical attention; Mdn, median; NA, not applicable; NBA, National Basketball Association; NCAA, National
Collegiate Athletic Association; NR, not reported; Prof, Professional; rb, rugby; rw, rowing; sc, soccer; taf, track and field; TL, time loss; tn, tennis; sw, swimming; vb, volleyball; ?, injury definition unclear from the published information.

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Table 4 Results for risk of bias assessment using the Quality of Cohort Studies tool
Sample Comparability of Maintenance of Outcome
Author, year representativeness groups Exposure measure comparability measures Attrition Overall quality
45
Bushman et al, 2016 S S S S S S Good
Hotta et al,41 2015 N S S S S S Good
O’Connor et al,46 2011 S S S S S S Good
Rusling et al,40 2015 S S S N S S Good
Warren et al,43 2015 N S S S S S Good
Wiese et al,42 2014 S S S N S S Good
Knapik et al,47 2015 S N S N S S Acceptable
McGill et al,44 2015 N S S N S S Acceptable
Azzam et al,85 2015 N S S N N S Low
Bardenett et al,86 2015 N N S N S S Low
Butler et al,87 2013 N N S N S S Low
Chorba et al,88 2010 N N S N S S Low
Dossa et al,89 2014 N N S N S S Low
Garrison et al,90 2015 N N S N S S Low
Hammes et al,59 2016 N N S S N S Low
Kiesel et al,92 2007 N N S N S S Low
Kiesel et al,91 2014 N N S N S S Low
Kodesh et al,93 2015 N N S N S S Low
Letafatkar et al,35 2014 N N S N S S Low
McGill et al,56 2012 N S S N N N Low
Mokha et al,94 2016 N N S N S S Low
Schroeder et al,65 2016 N N S N N S Low
Shojaedin et al,36 2014 N N S N S S Low
Zalai et al,57 2015 N S S N N S Low
N, domain is not satisfied; S, domain is satisfied.

Risk of bias assessment the differences in task requirements and operating environ-
Reviewers achieved initial agreement on 117 of 144 (81.3%) ment between military personnel and athletes, for the purpose
possible Q-Coh domains (κ=0.62, 95% CI 0.49 to 0.75) and of meta-analysis, two subgroups of studies were identified
achieved consensus on the remaining domains after discus- (‘Sport’ and ‘Military/Police’). The ‘Sport’ subgroup consisted
sion and consideration of the operational definitions. Of the of three studies reporting on single competitive sporting codes,
24 studies reviewed, the quality of 16 was assessed as ‘low’, 2 including football (soccer),40 running41 and American football,42
studies as ‘acceptable’ and 6 as ‘good’ (table 4). Figure 2 displays and one study of mixed codes.43 The ‘Military/Police’ subgroup
the proportion of studies satisfying each Q-Coh domain. comprised four studies and included elite task force police44
and military cohorts, including infantry,45 Marine Corps46 and
Meta-analysis Coast Guard.47 There were insufficient similarities in clinical
Of the eight studies appraised as being of ‘good’ or ‘acceptable’ (age, sex and sport) and methodological diversity (injury defini-
quality, four studies involved military/police personnel and tion) to conduct meta-analysis of studies in the ‘Sport’ subgroup;
four studies were of participants in sport. Military personnel however, there were three studies of military cohorts with suffi-
are required to complete very different physical tasks than cient similarity to conduct meta-analysis in the ‘Military/Police
those typically involved in sport,37 and both military and police subgroup (see online supplementary table S1). Data from the
personnel are also exposed to higher biomechanical loads asso- female cohort of Coast Guard cadets47 were not pooled with
ciated with body-borne tactical equipment.37–39 Thus, given data from the male cohort in the meta-analysis on the basis that
injury risk, rate and characteristics may differ between men
and women.48 Meta-analysis using a random-effects model for
the strength of association (RR) between dichotomised FMS
composite score (cut-point 14 out of 21) and subsequent muscu-
loskeletal injury resulted in a pooled RR=1.47 (95% CI 1.22 to
1.77, p<0.0001) and was associated with ‘moderate’ statistical
heterogeneity; see figure 3.

Best evidence synthesis

Results of the best evidence synthesis are displayed in table 5.
Because of the low number of studies, the level of evidence was
‘limited’ for police, firefighters, female military, middle-distance
and long-distance running, ice hockey, basketball and multiple
Figure 2 Proportion of studies (n=23) satisfying each Q-Coh domain. high-school sports. There was ‘conflicting’ evidence for Amer-
Q-Coh, Quality of Cohort Studies. ican football based on one good-quality study that is not in

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Figure 3 Forest plot of male military cohorts (Coast Guard, Marine Corps and infantry soldiers) for strength of association (risk ratio) between
dichotomised FMS composite score and subsequent musculoskeletal injury. FMS, Functional Movement Screen. M-H, Mantel-Haenszel.

favour of an association that exceeds the smallest worthwhile study and two low-quality studies not in favour an association
effect and two low-quality studies in favour of at least a ‘small’ and two low-quality studies in favour of an association that
effect. Considering collegiate-level athletes in a variety of sports, exceeds the smallest worthwhile effect. For football (soccer),
there was ‘conflicting’ evidence based on one good-quality there was ‘moderate’ evidence not in favour of an association

Table 5 Summary of best evidence synthesis for strength of association between FMS composite score and musculoskeletal injury
Sport, author, year Study quality* Effect statistic (95% CI) Descriptor for magnitude of effect† Level of evidence‡
American football
 Wiese et al,42 2014 Good OR=1.425 (0.6 to 3.2) Unclear Conflicting
 Kiesel et al,92 2007 Low OR=11.67 (2.47 to 54.52) Small
 Kiesel et al,91 2014 Low RR=1.87 (1.20 to 2.96) Small
Football (soccer)
 Rusling et al,40 2015 Good OR=1.125 (0.47 to 3.43) Unclear Moderate
 Zalai et al,57 2015 Low NSD Unclear
 Hammes et al,59 2016 Low AUC=0.55 (0.46 to 0.64) Unclear
 Schroeder et al,65 2016 Low p=0.373 Unclear
Multiple sports (collegiate)
 Warren et al,43 2015 Good OR=1.01 (0.53 to 1.91) Unclear Conflicting
 Chorba et al,88 2010 Low OR=3.85 (0.98 to 15.13) Unclear
 Mokha et al,94 2016 Low RR=0.68 (0.39 to 1.19) Unclear
 Garrison et al,90 2015 Low OR=5.61 (2.73 to 11.51) Small
 Letafatkar et al,35 2014 Low § OR=3.46 (1.36 to 8.8)¶ Trivial
 Shojaedin et al,36 2014
Multiple sports (high school)
 Bardenett et al,86 2015 Low AUC=0.50 (0.39 to 0.60) Trivial Limited
Basketball
 Azzam et al,85 2015 Low p=0.16 Unclear Limited
 McGill et al,56 2012 Low NR Unclear
Ice Hockey
 Dossa et al,89 2014 Low +LR=1.67 (0.54 to 5.17) Unclear Limited
Middle-distance and long-distance running
 Hotta et al,41 2015 Good OR=3.0 (0.8 to 11.6) Unclear Limited
Military (female)
 Knapik et al,47 2015 Good RR=1.93 (1.27 to 2.95) Small Limited
 Kodesh et al,93 2015 Low OR=0.98 (0.87 to 1.1) Unclear
Military (male)
 Bushman et al,45 2016 Good
 Knapik et al,47 2015 Good RR=1.47 (1.22 to 1.77)** Small Strong
 O’Connor et al,46 2011 Good
Firefighters
 Butler et al,87 2013 Low OR=8.31 (3.2 to 21.6) Small Limited
Police
 McGill et al,44 2015 Acceptable OR=1.25 (0.32 to 4.76)¶ Unclear Limited
*Study quality was based on the assessment of methodological quality (see table 4).
†Descriptors for the magnitude of effect were based on the study of Hopkins et al31 32.
‡Criteria for determining the level of evidence are shown in table 2.
§Two studies35 36 reported results from the same data set; therefore, findings from these studies were considered concurrently in decisions about the overall quality of evidence.
¶RR was based on the pooled effect from the meta-analysis.
**The OR and CI presented here were calculated by the authors based on raw data.
AUC, area under curve (receiver operating curve); NR, no effect statistic reported; NSD, no significant difference reported but no p value provided; +LR, positive likelihood ratio.

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based on consistent findings in one good-quality study and contradictory conclusions.16 20 Our findings align with those of
three low-quality studies. For male military personnel, there Dorrel et al,20 who, based on critical appraisal of seven studies
was ‘strong’ evidence in favour of an association that was ‘small’ using a diagnostic accuracy framework (QUADAS), concluded
in magnitude31 32 based on three good-quality studies using the that the diagnostic accuracy of the FMS to predict injury was
pooled effect from meta-analysis (figure 3). low. Bonazza et al16 reported the findings of a systematic review
and meta-analysis of nine studies for injury predictive value and
Discussion conclude that composite scores ≤14/21 were associated with
Our findings indicate that the strength of association between elevated odds of sustaining an injury (pooled OR=2.74, 95%
FMS composite scores and injury is not sufficient to support use CI 1.70 to 4.43).
as an injury prediction tool. With the exception of male military In reconciling our findings with those of Bonazza et al,16 two
personnel, where there was ‘strong’ evidence of a small associ- important differences in methodological approach need to be
ation, the overall level of evidence was ‘limited’ or ‘conflicting’ considered. First, unlike Bonazza et al,16 who pooled results
for a wide range of athletic populations, including running, ice from all studies without consideration of clinical or method-
hockey, collegiate and high school sport and professional or ological diversity, we systematically considered the appropriate-
collegiate American football. In football (soccer), the magnitude ness of pooling data in an attempt to avoid combining data from
of effect was ‘unclear’, and there was ‘moderate’ evidence to studies with obvious clinical diversity in terms of population
recommend against the use of FMS composite scores for the characteristics (age, sex and sport/occupation) and injury defini-
purpose of injury prediction. Regardless of the level of evidence tions. The use of differing injury definitions between studies is a
or the sport studied, the true magnitude of association for any well-known confounder in sports injury prevention research;50
population studied was not greater than ‘small’. thus, for meta-analysis, we pooled only studies that used similar
injury definitions. Similarly, we avoided pooling studies with
marked differences in cohort characteristics, including sex, age
Approach to the problem: diagnostic accuracy or strength of and sport, on the basis that intrinsic injury risks are likely to
association? differ by age, sex and exposure to different physical demands
The utility of a diagnostic screening tool is predicated on the in different sports. Second, unlike Bonazza et al,16 who did not
strength of association between the risk factor (ie, movement undertake appraisal of methodological quality and included all
competency) and the outcome of interest (injury). If the strength studies in their meta-analysis, we systematically assessed risk
of association is weak or unclear, then clinical utility will inev- of bias for all eligible studies and incorporated methodological
itably be poor; therefore, establishing the strength of associa- quality into decisions about the overall level of evidence.
tion between risk factor and outcome in exploratory studies
using prospective cohort designs is a fundamental first step.18
If well-controlled prospective cohort studies demonstrate suffi- Methodological issues in the studies reviewed
ciently strong estimates of the strength of association between Consistent with a previous systematic review of rater reliability
risk factor and outcome, then further studies designed to inves- for FMS composite scores that noted poor quality of study
tigate diagnostic test properties (ie, likelihood ratios) can be reporting,15 we also observed deficits in reporting quality, with
undertaken.18 essential study characteristics such as participant age and loss
In reviewing existing studies investigating the relationship to follow-up not reported in some studies. Several studies also
between FMS and subsequent injury, it is apparent that the liter- lacked precision in reporting the duration of injury surveil-
ature does not discretely align into either exploratory studies or lance, which was often limited to descriptions such as ‘one
diagnostic utility studies. This presents a dilemma for the design season’. Despite the wide availability of consensus statements
of systematic reviews because primary studies were designed, for injury definitions in many sports,51–55 several studies failed
analysed and reported using conventions of either observational to adequately define injury.36 44 56 57 Such a fundamental omis-
cohort, diagnostic accuracy studies or combinations of both. sion is surprising, given that definition of injury is a critical and
Fundamentally, the quality of studies reporting diagnostic accu- well-documented methodological issue in sports injury research
racy metrics in predicting sports injury from baseline predictors and can impact on the interpretation of both individual studies
depend on the principles of robust prospective cohort design and the synthesis of literature.50 58
because in this context, the ‘reference test’ is an injury event When considering injury causation related to modifiable risk
that has not occurred at the time of administering the index test factors, the temporal relationship between a putative risk factor
(FMS). This differs from the conventional application of diag- such as movement competency and injury occurrence needs to
nostic accuracy, where the reference and index test results are be considered. As the interval between baseline measurement
administered in close temporal proximity, and there is no need and the time of injury extends, there may be greater exposure to
to control for potential confounding effects that arise when the confounding effects that are not controlled in the study design.
index test (FMS) and reference ‘test’ (injury event) are separated This issue is less pertinent for shorter surveillance periods, such
by one or more sporting seasons. Therefore, rather than applying as a single preseason training period, but over the course of a full
a diagnostic accuracy framework such as QUADAS (Quality competitive season, the relationship between injury events and
Assessment of Diagnostic Accuracy Studies),49 we appraised all baseline risk factors is more vulnerable to confounding.
studies on the basis of the strength of association between FMS An inherent assumption in the design of many of the studies
and subsequent injury using Q-Coh,25 an appraisal tool specifi- reviewed here is that the strength of the intrinsic risk factor
cally designed to assess risk of bias in prospective observational (represented here by the FMS composite score) remains stable
cohort studies. over time. However, this design does not account for changes in
risk that may occur over time (both within and between partic-
Comparison with other studies ipants) in response to factors such as training, competition and
Two recent systematic reviews that investigated the relation- match exposure, subclinical adaptations to tissue loading and
ship between FMS composite scores and injury risk draw neuromuscular function. Although some studies addressed this

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issue (see table 4 ‘Maintenance of comparability’),41 43 45 46 59 apparent when considered in light of emerging injury aetiology
not accounting for these potential confounding factors by either models employing complex systems approaches.74 75Second, in
design or statistical analysis fails to address the recursive dynamic so far as the FMS battery might provide possible injury predictor
elements of injury aetiology described in classical60 and emerging variables for inclusion in multivariate or complex prediction
aetiological models.61 Simply put, movement competency, as models, there are several possible categorical indices that may
measured by FMS, may change over the course of a season be derived from the FMS, in addition to composite score, that
such that, at the time of injury onset, the level of movement have attracted only sparse research attention to date.76 For
competence at the time of injury is different from that recorded example, indices of pain provocation (eg, proportion of move-
at baseline, thus confounding the association. To address this ment subtests on which pain was reported) on active move-
issue, repeated administration of measures in injury prediction ment subtests,76 77 scoring discrepancies between left and right
studies has been proposed,62 although to date, very few prospec- or indices representing patterns (ie, specific subtests) of poor
tive injury prediction studies have undertaken repeated admin- movement competency could be explored further as possible
istration of measures for key predictor variables, and all of the predictor variables. This work could commence at an explor-
studies reviewed here employed a single assessment of move- atory level through secondary analysis of existing data sets from
ment competency by FMS at baseline. studies of good methodological quality.
Previous work has demonstrated that FMS scores may change
following the prescription of corrective exercise over a period of Practical implications
463 to 8 weeks.64 For studies undertaking injury surveillance over There was ‘moderate’ evidence to recommend against the use
shorter periods (eg, 6–10 weeks),46 65 the threat of bias arising of FMS composite scores as an injury prediction test in football
from temporal instability of FMS scores is probably low. Given (soccer). For other sports studied (table 5), the evidence was ‘limited’
the potential for intrinsic risk factors to change in response to or ‘conflicting’. In male military personnel, there was ‘strong’
training and competition exposures, it seems prudent for investi- evidence that the strength of association between composite score
gators to carefully evaluate the potential for repeated administra- and subsequent injury is ‘small’. The findings of this study should
tion, particularly where monitoring is planned over a prolonged be interpreted in accord with the scope of the review, which relates
period. Clearly, investigators need to make pragmatic decisions only to the strength of association between FMS composite score
related to logistic and resource constraints, and repeated admin- and subsequent injury. Beyond injury prediction, the use of FMS as
istration of measures for intrinsic risk factors may not be feasible, a standardised movement test battery that can be reliably admin-
particularly when research is embedded within pre-existing clin- istered in the field by practitioners with limited previous experi-
ical practice, as was the case in many of the studies reviewed ence15 17 may usefully inform applied practice if test limitations are
here. Notwithstanding these practical constraints, investigators acknowledged and findings are interpreted judiciously alongside
not able to account for confounding through design should at other relevant clinical information.78 79
least acknowledge these limitations in discussion and consider
the likely impact on study conclusions.66
Although employed in all studies reviewed here, the use of a Research implications
single composite score is problematic from several perspectives. Given the complexity of injury aetiology, investigators who seek
First, several studies indicate that the factor structure of the FMS to model the risk of future injury should apply multivariate anal-
battery is unlikely to be unidimensional; thus, interpretation ysis and predictor variables such as ‘movement competency’ (or
of a single composite score may not be valid.67–71Second, the similarly named constructs) need to be justified from a stronger
apparent research interest in FMS composite scores for injury theoretical basis. The theoretical construct addressed by the
risk is not commensurate with the minimal attention afforded FMS, labelled as both ‘movement competency’11 or ‘movement
to composite scores by FMS developers. Cook et al11 12 72 have quality’,80 81 has undergone limited scholarly development, and
largely focused on clinical interpretation based on 1) identifi- its relationship with similar conceptual constructs, such as phys-
cation of pain associated with each subtest, 2) the presence of ical literacy, requires explication.82
left–right asymmetrical scoring and 3) identification of poor
movement competency on each subtest (as defined by a score Limitations
of ‘1’ using the FMS scoring criteria). The FMS appears to It is possible that other studies satisfying the eligibility criteria
have been conceived in an attempt to develop a standardised exist but were not identified. We consider this to be unlikely,
and systematic approach to assessing basic movement patterns, and in order to substantially impact on conclusions regarding
with a goal of informing clinical decision making based on the the level of evidence for various sports reported here, there
interpretation of each movement subtest in the context of other would need to exist multiple, unidentified high-quality studies
clinically relevant information.11 12 72 Notwithstanding the use of with consistent findings. The exclusion of grey literature from
the word ‘screen’ in the test name, this use of the FMS battery systematic reviews can raise the risk of publication bias, although
contrasts markedly from ‘screening’ in the conventional descrip- studies reviewed here included both positive and negative find-
tion of preparticipation health screening.73 ings, indicating this risk was probably minimal. The method-
The now-substantial number of studies that have attempted ological appraisal of studies in this review was conducted using
to quantify the risk of future injury, based exclusively on the the Q-Coh, a new tool not yet in widespread use but developed
outcome of a single preparticipation administration of FMS, specifically for application to prospective observational cohort
share two notable limitations. First, an unfortunately high studies in response to limitations identified in other tools.25 66
number of studies reviewed here failed to accommodate existing The selection of critical appraisal tools in systematic reviews may
multicausal models of injury aetiology in developing research impact on review conclusions;83 84 however, based on the weak
hypotheses. The premise that a single preseason administra- magnitude of association reported in eligible studies here, we
tion of a field-based test of one intrinsic risk factor (movement consider it unlikely that differences in quality appraisal attribut-
competency) is likely to have good utility as a predictor of future able to the use of a different appraisal tool would substantially
injury may constitute causal oversimplification. This is especially impact the overall conclusions.

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Summary 9 Reid DA, Vanweerd RJ, Larmer PJ, et al. The inter and intra rater reliability of the
In summary, the level of evidence for the strength of associa- netball movement screening tool. J Sci Med Sport 2015;18:353–7.
10 Padua DA, DiStefano LJ, Beutler AI, et al. The landing error scoring system as a
tion between FMS composite scores and subsequent injury is not screening tool for an anterior cruciate ligament injury-prevention program in Elite-
sufficient to support the use of FMS composite score as an injury Youth soccer athletes. J Athl Train 2015;50:589–95.
prediction tool. 11 Cook G, Burton L, Hoogenboom BJ, et al. Functional movement screening: the use of
fundamental movements as an assessment of function - part 1. Int J Sports Phys Ther
2014;9:396–409.
What is already known? 12 Cook G, Burton L, Hoogenboom BJ, et al. Functional movement screening: the use of
fundamental movements as an assessment of function-part 2. Int J Sports Phys Ther
►► The Functional Movement Screen (FMS) is widely used by 2014;9:549–63.
clinicians as part ofpre-participation evaluation. 13 McCall A, Carling C, Davison M, et al. Injury risk factors, screening tests and
preventative strategies: a systematic review of the evidence that underpins the
►► Systematic reviews report acceptable intra-rater and inter-
perceptions and practices of 44 football (soccer) teams from various premier leagues.
rater reliability for composite FMS scores, but what are its Br J Sports Med 2015;49:583–9.
other clinimetric properties? 14 Wright AA, Stern B, Hegedus EJ, et al. Potential limitations of the functional
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15 Moran RW, Schneiders AG, Major KM, et al. How reliable are functional movement
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16 Bonazza NA, Smuin D, Onks CA, et al. Reliability, validity, and injury predictive value
►► The strength of association between FMS composite scores
of the functional movement screen: a systematic review and meta-analysis. Am
and subsequent injury was not sufficient to recommend use J Sports Med 2017;45:725–32.
as an injury prediction tool in the sports reviewed. 17 Cuchna JW, Hoch MC, Hoch JM. The interrater and intrarater reliability of the
►► In male military personnel, there was ‘strong’ evidence functional movement screen: a systematic review with meta-analysis. Phys Ther Sport
that the strength of association between composite score 2016;19:57–65.
18 Bahr R. Why screening tests to predict injury do not work— and probably never
(cut-point ≤14/21) and subsequent injury was ‘small’. will…: a critical review. Br J Sports Med 2016;50:776–80.
►► There was ‘moderate’ evidence to recommend against the 19 Kiesel K, Plisky PJ, Voight ML. Can serious injury in professional football be
use of FMS composite scores as an injury prediction test in predicted by a preseason functional movement screen? N Am J Sports Phys Ther
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20 Dorrel BS, Long T, Shaffer S, et al. Evaluation of the functional movement screen as an
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Data sharing statement All data supporting this study are provided as
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