Key words
construct validity - functional movement screen - measurement - mobility - range-of-motion
- stability
Highlights
-
There was substantial overlap in joint ROM measurements between groups of
athletes with different FMS task scores.
-
FMS task scores should not be interpreted as direct evidence that joint ROM
deficits exist.
-
Using FMS scores to individualize exercise is not recommended.
-
Functional movement screening is an intriguing concept, but its validity
remains uncertain.
Introduction
The Functional Movement Screen (FMS) is promoted as a tool to identify painful and
dysfunctional movement patterns. Some preliminary studies have found associations
between FMS scores and measures of pain and musculoskeletal injuries in some
athletic and occupational populations [1]. However,
the underlying nature of the relationship between FMS scores and health or
functional status is unknown because little is known about the construct validity
of
the FMS [2].
Without understanding what is measured by the FMS, it is difficult to know if
or how it should be used to design interventions. Nonetheless, interventions have
been recommended in the literature based on FMS task scores [3]
[4]
[5],
under the assumption that failure to perform FMS tasks in a standard way is
indicative of joint mobility and stability deficits [6]
[7]. Although it seems biologically
plausible that such deficits should influence FMS task performance, there is very
limited evidence that the FMS is a valid instrument to measure joint mobility and
stability. Performance on one of its seven component tasks – the deep squat
– appears constrained by hip and ankle joint range-of-motion (ROM) capacity
[8]
[9]
[10]
[11]
[12], and composite FMS scores (i. e., sum of
all component task scores) are generally higher in performers who have the greatest
ankle dorsiflexion ROM [9]. However, it cannot be
assumed that joint mobility status, as a hypothetical construct, is accurately
characterized by composite scores across all FMS tasks [13]
[14]. In fact, it has been shown that
FMS task scores are affected by other underlying processes or constructs unrelated
to joint mobility and stability (e. g., performers’ knowledge of
grading criteria [15]). Before using the FMS to
predict injury or prescribe exercise, further evaluation of its validity and
measurement properties is needed.
Our objective was to assess the construct validity of the FMS by estimating
associations between FMS task scores and joint ROM measures among varsity
student-athletes.
Materials and Methods
Study design and hypotheses
This was a construct validity study guided by the COSMIN checklist
(COnsensus-based Standards for the selection of health Measurement INstruments)
[16] for assessing the methodological quality
of studies on construct validity (hypotheses testing) measurement properties. We
examined two pre-specified hypotheses: (1) On FMS tasks that demand large body
segment and joint angular displacements (i. e., deep squat, hurdle step,
in-line lunge, active straight-leg raise, and shoulder mobility), athletes with
lower FMS task scores would have lower joint ROM measures than would athletes
with higher task scores; (2) On FMS tasks with minimal ROM requirements
(e. g., trunk stability push-up and rotary stability), ROM measures
would be unclearly related to FMS task scores.
Participants
We invited men and women’s teams of four university intercollegiate
sports (volleyball, basketball, ice hockey, and soccer) at a single university
between January 2014 and February 2014 to participate in this study. Eligible
were all men and non-pregnant women student-athletes from the above varsity
sports rosters available to participate in ROM measurements and FMS testing in a
university sports medicine clinical setting. Intercollegiate varsity sport is
considered a high-performance sport level in Canada. Depending on the structure
for the specific sport, athletes participating at the varsity sport level are on
a pathway for national and international competition or have experience playing
semi-professionally. Many of these athletes would also have experience within
the provincial and national sport development systems.
Athletes were eligible to participate irrespective of previous injury and
pre-existing musculoskeletal issues if they were cleared to participate in all
team related activities (i. e., practices, strength and conditioning,
games, etc.). For our primary analysis, we excluded participants that expressed
painful movement with a specific FMS task (i. e., an FMS score of zero)
because pain is known to alter movement independently of joint ROM capacity.
General procedures
FMS and ROM data were collected from an athlete on the same day. Order of
exposure to the ROM and FMS data collection sessions was assigned in a balanced
and pragmatic manner due to scheduling constraints across athletes, therapists,
and teams. A minimum of 10 minutes of passive recovery was provided
between ROM and FMS data collections. The observer responsible for scoring FMS
movement tasks was blinded to the ROM data for the participants, and vice-versa,
the therapists responsible for collecting ROM measurements were blinded to the
FMS scores. These data were gathered and filed separately and the participants
and assessors in both data collection phases were not aware of the scores and
measurements of the other assessment.
FMS data collection
A single member of the research team, with over six months of experience and
training in using the FMS, administered the FMS in accordance with standard
protocols [6]
[7] and
conducted all FMS scoring. FMS scores can be reliably assigned by raters with a
range of background experiences and training [2].
Participants were blind to the scoring criteria as they were performing the FMS
tasks. They were provided with only the standard instructions. They were also
blinded to their FMS scores throughout the entire data collection procedures for
the study.
Synchronized video recordings (Dartfish, Fribourg, Switzerland) were made from
both frontal and sagittal plane perspectives to permit FMS tasks to be graded
offline. Athletes performed the deep squat (squat), hurdle step (hurdle),
in-line lunge (lunge), and rotary stability (rotary) tasks twice while facing
the frontal camera and twice while facing away (i. e., four repetitions
of the squat, hurdle, lunge, and rotary tasks were performed in total, with the
frontal camera capturing anterior and posterior views). The shoulder mobility
(shoulder) task was performed three times while athletes faced away from the
frontal camera (i. e., posterior view). Athletes performed the active
straight leg raise (slr) task three times per side with the top of their head
facing away from the frontal camera (i. e., inferior/caudal
view). The trunk stability push-up (pushup) task was performed twice with the
plantar surface of the feet facing the frontal camera (i. e.,
inferior/caudal view), and twice while facing away (i. e.,
superior/cranial view). Each pain-clearing test was recorded while
facing the frontal camera (i. e., posterior view), and the left side of
all bilateral tasks was performed first. Raw video recordings of the FMS tasks
were cropped, compressed, and coded by research assistants before creating
synchronized split-screen (frontal and sagittal plane view) output files
(Dartfish, Fribourg, Switzerland).
Standard criteria [6]
[7] were used to grade FMS tasks offline using a four-point ordinal
scale, with possible scores of 0, 1, 2 and 3. The researcher who administered
and graded the FMS observed all the video recordings for each movement task and
assigned the specific FMS task score based on visual observation of video
recordings. This same procedure was followed for each movement task across each
subject. For the bilateral tasks (hurdle, lunge, shoulder, slr, rotatory), the
left and right sides were evaluated separately using the same procedure as
above. To obtain the composite score for the bilateral tasks, the minimum value
between the left and right side was used as per the scoring criteria of the
FMS.
Range-of-motion data collection
Passive ankle dorsiflexion, hip extension, hip flexion, and shoulder flexion ROM
availability were measured bilaterally both with and without imposing
multi-articular restraints on motion, for a total of eight ROM measurements (see
Appendix Fig A1 for full details on joint ROM measurements). All ROM
measurements were made on each athlete in the same order, with left side
measured before the right, and repeated twice. Two trained research assistants
helped with body positioning and data recording, while 6 licensed therapists
made measurements using a manual goniometer. In preliminary work, where
intraclass correlation coefficients (ICCs) were calculated for the ROM measures
used in the current study [17], the inter-rater
reliability of these ROM measurements ranged between good (ICC=0.61) and
excellent (ICC=0.86), with the exception of multi-articular ankle
dorsiflexion measurement, which was fair (ICC=0.53). Repeated ROM
measurements were averaged for each side individually; the lower of left- and
right-side average values was used to represent the ROM available at each
joint.
Statistical analysis
Descriptive summaries of participants’ characteristics and FMS task
scores were provided for the study population and by sex subgroups. As our
interest was in examining all eligible participants of the four targeted sports,
we did not calculate a pre-study sample size. Using multivariable linear
regression analysis, we estimated the associations between FMS task scores (as a
3-level categorical factor) and ROM measurements (as continuous term), with ROM
measurements as the outcome of interest and FMS task scores as the main
explanatory variable, while adjusting for age, sex, and body mass index (BMI) as
potential confounders. Age and BMI were included as continuous terms and sex as
a binary factor in every model. Every combination of FMS task scores and joint
ROM measurements were modelled separately to investigate whether relationships
were present when hypothesised as plausible and not present when hypothesised as
implausible. Observations with an FMS score of zero – assigned when a
participant expresses painful movement – were excluded from the
regression analyses because pain is known to alter movement independently of
joint ROM [18]. The distributions of ROM
measurements were then plotted for each of the FMS tasks by the task scores
(from 1 to 3; higher scores indicate better performance). For the 3-level FMS
variable, we first examined whether the three adjusted mean ROM values were all
equal; this hypothesis has a single p value, which we called the overall
p-value. Then, we calculated p values for specific pairs of
comparisons of adjusted mean ROM values between different FMS levels
(e. g., 2 vs. 1, 3 vs. 1, 3 vs. 2). As a visual aid to the
identification of the most notable associations, where the overall
p-values from the regressions were less than 0.1, the individual plots
were highlighted in colour. Given the exploratory nature of our analysis, we did
not adjust for multiple comparisons – this is preferable as it leads to
fewer errors of interpretation [19]. All
p-values and 95% confidence intervals (CIs) were two-sided, and
analyses were performed in R version 3.6.0 [20].
Ethical approval and patient and public involvement
Our study was conducted in accordance with recognised ethical standards in sport
and exercise science research [21]. All
participants gave written informed consent. The study was approved by the
independent Office of Research Ethics at the University of Toronto. Patients or
the public were not involved in the design, conduct, reporting, or dissemination
plans of this study.
Results
Participant characteristics
We recruited 101 university varsity student-athletes (out of 156 eligible;
64% participation): 26 from volleyball (84% participation), 21
from basketball (68% participation), 32 from ice hockey (64%
participation), and 22 from soccer (48% participation) ([Table 1]). There were 52 female participants (mean
age 19.9 years) and 49 male participants (mean age 20.8 years). With respect to
the sport training and competition experience of the study population, the four
varsity teams included in this study typically trained 3–5 times per
week, had 2–3 strength and conditioning sessions per week, and
1–2 games per week for those sports in their competitive seasons. Soccer
was “out-of-season” and did not have any intercollegiate games
during the data collection period.
Table 1 Characteristics of study population.
|
Characteristic
|
All (N=101)
|
Women (n=52)
|
Men (N=49)
|
|
Age (y) — mean (SD)
|
20.4 (1.9)
|
19.9 (1.9)
|
20.8 (1.9)
|
|
Height (cm) — mean (SD)
|
177.2 (10.2)
|
170.3 (7.4)
|
184.5 (7.1)
|
|
Mass (kg) — mean (SD)
|
74.8 (12.6)
|
66.2 (8.0)
|
83.9 (10.1)
|
|
BMI (kg/m2) — mean (SD)
|
23.7 (2.1)
|
22.8 (2.0)
|
24.6 (2.0)
|
|
Sport — N (%)
|
|
|
|
|
Hockey
|
32 (31.7)
|
20 (38.5)
|
12 (24.5)
|
|
Volleyball
|
26 (25.7)
|
14 (26.9)
|
12 (24.5)
|
|
Soccer
|
22 (21.8)
|
8 (15.4)
|
14 (28.6)
|
|
Basketball
|
21 (20.8)
|
10 (19.2)
|
11 (22.4)
|
FMS scores
The mean total FMS score (out of 21) for the whole group was 13.1 (95%
CI, 12.7 to 13.5). A total of 25 participants were assigned at least one FMS
task score of 0, yielding an estimated point prevalence of painful movement
elicited by the FMS in our study population of 25% (95% CI, 17
to 35%). [Table 2] details the frequency
distribution of FMS task scores for the seven FMS tasks. Of note, the FMS hurdle
and rotary tasks were poor differentiating tasks – no student-athlete in
our study was assigned a score of 3 for the rotary task, and no participant
scored 1 on the hurdle task ([Fig. 1] and [Table 2]).
Fig. 1 Range-of-motion measurements by FMS task scores in
university intercollegiate student-athletes (higher scores indicate
better task performance and coloured boxplots indicate notable
relationships with overall p<0.1).
Table 2 Summary of FMS Scores.
|
FMS score
|
All (N=101)
|
Women (n=52)
|
Men (N=49)
|
|
Total score out of 21 — mean (95% CI)
|
13.1 (12.7–13.5)
|
13.2 (12.7–13.7)
|
13.0 (12.4–13.6)
|
|
Squat — N (%)
|
|
|
|
|
0
|
1 (1.0)
|
0
|
1 (2.0)
|
|
1
|
57 (56.4)
|
28 (53.8)
|
29 (59.2)
|
|
2
|
38 (37.6)
|
23 (44.2)
|
15 (30.6)
|
|
3
|
5 (5.0)
|
1 (1.9)
|
4 (8.2)
|
|
Hurdle — N (%)
|
|
|
|
|
0
|
0
|
0
|
0
|
|
1
|
0
|
0
|
0
|
|
2
|
87 (86.1)
|
43 (82.7)
|
44 (89.8)
|
|
3
|
14 (13.9)
|
9 (17.3)
|
5 (10.2)
|
|
Lunge — N (%)
|
|
|
|
|
0
|
3 (3.0)
|
1 (1.9)
|
2 (4.1)
|
|
1
|
4 (4.0)
|
0
|
4 (8.2)
|
|
2
|
84 (83.2)
|
48 (92.3)
|
36 (73.5)
|
|
3
|
10 (9.9)
|
3 (5.8)
|
7 (14.3)
|
|
Shoulder — N (%)
|
|
|
|
|
0
|
14 (13.9)
|
5 (9.6)
|
9 (18.4)
|
|
1
|
6 (5.9)
|
2 (3.8)
|
4 (8.2)
|
|
2
|
42 (41.6)
|
23 (44.2)
|
19 (38.8)
|
|
3
|
39 (38.6)
|
22 (42.3)
|
17 (34.7)
|
|
SLR — N (%)
|
|
|
|
|
0
|
0
|
0
|
0
|
|
1
|
38 (37.6)
|
19 (36.5)
|
19 (38.8)
|
|
2
|
50 (49.5)
|
22 (42.3)
|
28 (57.1)
|
|
3
|
13 (12.9)
|
11 (21.2)
|
2 (4.1)
|
|
Push-up — N (%)
|
|
|
|
|
0
|
9 (8.9)
|
3 (5.8)
|
6 (12.2)
|
|
1
|
38 (37.6)
|
32 (61.5)
|
6 (12.2)
|
|
2
|
19 (18.8)
|
3 (5.8)
|
16 (32.7)
|
|
3
|
35 (34.7)
|
14 (26.9)
|
21 (42.9)
|
|
Rotary — N (%)
|
|
|
|
|
0
|
3 (3.0)
|
1 (1.9)
|
2 (4.1)
|
|
1
|
6 (5.9)
|
3 (5.8)
|
3 (6.1)
|
|
2
|
92 (91.1)
|
48 (92.3)
|
44 (89.8)
|
|
3
|
0
|
0
|
0
|
Construct validity outcomes – FMS scores and ROM availability
There were several notable (i. e., p<0.1) differences in
ankle, hip, and shoulder joint ROM measures between athletes who scored 1, 2, or
3 on FMS tasks ([Fig. 1], [Table 3] and Appendix Table A1). Squat and
lunge task scores were positively associated with ankle dorsiflexion ROM; lunge
task scores positively associated with hip extension ROM; and squat and shoulder
task scores positively associated with shoulder flexion ROM. There was
considerable variation in individual ROM measures within FMS task score levels
of 1, 2 or 3 ([Fig. 1]). For athletes who scored
1 on the FMS squat task, for example, the range of uni-articular ankle
dorsiflexion ROM measurements was more than 30-degrees ([Fig. 1]). There was overlap in joint ROM
availability between groups of athletes with different FMS task scores.
Table 3 Multivariable linear regression analysis results
for notable associations (overall p<0.1) between FMS
task scores (levels 1 to 3) and range-of-motion measurements in
university student-athletes.
|
Joint ROM*
|
FMS task
|
FMS score comparison
|
Mean ROM difference in degrees (95% CI)
|
p
|
overall p
|
|
ankle.uni
|
squat
|
2v1
|
+4 (+1 to+7)
|
0.009
|
0.002
|
|
3v1
|
+10 (+4 to+17)
|
0.003
|
|
3v2
|
+6 (−1 to+13)
|
0.076
|
|
ankle.uni
|
lunge
|
2v1
|
+7 (0 to+15)
|
0.060
|
0.019
|
|
3v1
|
+12 (+3 to+21)
|
0.007
|
|
3v2
|
+5 (0 to+10)
|
0.053
|
|
ankle.mul
|
squat
|
2v1
|
+2 (−1 to+6)
|
0.144
|
0.079
|
|
3v1
|
+7 (0 to+14)
|
0.051
|
|
3v2
|
+5 (−3 to+12)
|
0.202
|
|
ankle.mul
|
lunge
|
2v1
|
+6 (−2 to+14)
|
0.152
|
0.037
|
|
3v1
|
+11 (+2 to+20)
|
0.018
|
|
3v2
|
+5 (0 to+10)
|
0.046
|
|
hipflx.uni
|
shoulder
|
2v1
|
+12 (+1 to+23)
|
0.031
|
0.007
|
|
3v1
|
+17 (+6 to+28)
|
0.003
|
|
3v2
|
+5 (−0 to+10)
|
0.072
|
|
hipflx.uni
|
rotary
|
2v1
|
+15 (+5 to+26)
|
0.004
|
0.004
|
|
hipflx.mul
|
squat
|
2v1
|
+7 (+2 to+12)
|
0.009
|
0.031
|
|
3v1
|
+4 (−7 to+16)
|
0.471
|
|
3v2
|
−3 (−15 to+9)
|
0.633
|
|
hipflx.mul
|
hurdle
|
3v2
|
−7 (−14 to 0)
|
0.066
|
0.066
|
|
hipflx.mul
|
slr
|
2v1
|
+9 (+4 to+14)
|
<0.001
|
<0.001
|
|
3v1
|
+17 (+9 to+24)
|
<0.001
|
|
3v2
|
+8 (+1 to+15)
|
0.032
|
|
hipext.uni
|
lunge
|
2v1
|
+6 (−1 to+13)
|
0.083
|
0.097
|
|
3v1
|
+8 (+1 to+16)
|
0.031
|
|
3v2
|
+2 (−2 to+7)
|
0.260
|
|
hipext.mul
|
lunge
|
2v1
|
+9 (+1 to+16)
|
0.028
|
0.060
|
|
3v1
|
+10 (+2 to+19)
|
0.021
|
|
3v2
|
+2 (−3 to+7)
|
0.489
|
|
hipext.mul
|
pushup
|
2v1
|
+8 (+4 to+13)
|
<0.001
|
0.003
|
|
3v1
|
+5 (+1 to+9)
|
0.011
|
|
3v2
|
−3 (−7 to+1)
|
0.106
|
|
shdflx.uni
|
squat
|
2v1
|
+6 (−1 to+13)
|
0.0816
|
0.039
|
|
3v1
|
+16 (+1 to+31)
|
0.0315
|
|
3v2
|
+10 (−5 to+25)
|
0.178
|
|
shdflx.uni
|
shoulder
|
2v1
|
+10 (−4 to+23)
|
0.165
|
0.009
|
|
3v1
|
+18 (+4 to+32)
|
0.0109
|
|
3v2
|
+8 (+2 to+15)
|
0.0167
|
|
shdflx.mul
|
squat
|
2v1
|
+9 (+1 to+16)
|
0.0194
|
0.016
|
|
3v1
|
+17 (+1 to+33)
|
0.035
|
|
3v2
|
+8 (−8 to+25)
|
0.301
|
|
shdflx.mul
|
shoulder
|
2v1
|
+10 (−5 to+25)
|
0.192
|
0.038
|
|
3v1
|
+17 (+2 to+33)
|
0.0278
|
|
3v2
|
+7 (0 to+15)
|
0.0636
|
*ankle.uni is uni-articular ankle dorsiflexion
ROM; ankle.mul is multi-articular ankle dorsiflexion ROM;
hipflx.uni is uni-articular hip flexion ROM;
hipflx.mul is multi-articular hip flexion ROM;
hipext.uni is uni-articular hip extension ROM;
hipext.mul is multi-articular hip extension ROM;
shdflx.uni is uni-articular shoulder flexion ROM;
shdflx.mul is multi-articular shoulder flexion ROM; All
multivariable linear regression models were adjusted for age, sex and
BMI.
We hypothesized that on FMS tasks with large ROM requirements (squat, hurdle,
lunge, slr and shoulder tasks) athletes scoring higher on the FMS task would
have greater ROM (Hyp1). Athletes rated as having higher FMS squat scores had
greater uni- and multi-articular ankle dorsiflexion ROM than did athletes who
scored lower on the squat ([Fig. 1], [Table 3] and Appendix Table A1). For
instance, athletes who scored 2 on the FMS squat task had 4°
(95% CI, 1° to 7°; p=0.009) more
uni-articular ankle dorsiflexion ROM compared with those who scored 1, while
those who scored 3 on the FMS squat task had 10° (4° to
17°; p=0.003) more uni-articular ankle dorsiflexion ROM
compared with those who scored 1. Although not statistically significant at an
alpha level of 0.05, there was a tendency for athletes with higher FMS squat
scores to have greater multi-articular ankle dorsiflexion ROM than those with
lower squat scores (FMS score 2 vs 1:+2° [−1°
to+6°] multi-articular ankle dorsiflexion ROM,
p=0.144; FMS score 3 vs 1:+7° [0°
to+14°], p=0.051; FMS score 3 vs
2:+5° [−3° to+12°],
p=0.202; [Table 3]).
Higher FMS lunge task scores were associated with greater ankle dorsiflexion ROM,
both uni- and multi-articularly, and greater uni-articular and multi-articular
hip extension ROM ([Table 3]). Both uni-articular
and multi-articular shoulder ROM was greater in athletes with higher FMS
shoulder task scores compared to those with lower FMS shoulder scores. Athletes
who scored highest on the FMS slr task had the greatest multi-articular hip
flexion ROM (FMS score 2 vs 1:+9° [+4°
to+14°], p<0.001; FMS score 3 vs
1:+17° [+9° to+24°],
p<0.001; FMS score 3 vs 2:+8°
[+1° to+15°], p=0.032; [Table 3]). Although there were several expected
relationships between FMS task scores and joint ROM, there was still substantial
unexplained variability in ROM, with the largest R2 in any of our
models being 0.33 for the FMS slr task and multi-articular hip flexion ROM.
We also hypothesized that on FMS tasks with minimal ROM requirements (pushup and
rotary tasks), ROM would be unclearly related to FMS task scores (Hyp2). This
was the case for most FMS task scores and ROM comparisons for the pushup and
rotary FMS tasks ([Fig. 1], [Table 3] and Appendix Table A1).
Unexpectedly, we observed that athletes rated as 3 for the FMS shoulder task had
greater uni-articular hip flexion ROM than did athletes rated at 1 for the
shoulder task (overall p=0.007). We also observed an unexpected
positive association between the FMS pushup scores and multi-articular hip
extension ROM (overall p=0.003).
Discussion
To date, there is limited evidence to suggest that the FMS is a valid instrument
indicative of joint mobility and stability deficits. Consistent with some of our
pre-specified hypotheses, we found a few differences in joint ROM among
student-athletes rated as scoring 1, 2, or 3 on FMS tasks. However, not all ROM
measurements differed between athletes with different FMS task scores, and
associations between FMS scores and ROM were not always in same direction. At the
level of individual participants, we found substantial overlap in joint ROM between
groups of athletes with different FMS task scores.
Key findings and implications
We found higher FMS squat and lunge task scores to be generally associated with
more ankle dorsiflexion ROM, which is consistent with prior research for the
squat task [9]
[10]
[12] but not for the lunge task
[9]. Athletes with the most hip extension ROM
tended to be those with the highest lunge scores in the current study –
this differs from previous findings [8]
[22]. Shoulder flexion ROM was generally greater in
those athletes with higher shoulder and squat task scores; however, previous
research failed to find relationships between shoulder scores and glenohumeral
joint ROM measurements [23]. Our finding that
athletes who achieved higher squat and slr task scores typically had greater hip
flexion ROM (with multi-articular restraints imposed) also contrasts with prior
research. Specifically, it was previously reported that hip flexion ROM measures
made with only uni-articular motion restraints imposed were related to squat
task scores [8], but we found no such
relationship. In fact, the only statistically notable associations we found
between FMS task scores and uni-articular hip flexion ROM were for shoulder and
rotary tasks. It is reasonable that hip flexion ROM could influence rotary task
performance, but its relationship to shoulder scores is likely a statistical
anomaly. Finally, we found that athletes who scored lower, compared to higher,
on the pushup task had more shoulder flexion ROM, which may imply that ability
to stabilize the shoulder complex may be compromised by having more shoulder
mobility.
Functional movement screening is an intriguing concept; however, critical
evaluation of the concept, constructs and associated measurement properties is
warranted and needed. The FMS may be reliable, but its validity is uncertain
[2]. Similar conclusions have been drawn about
other functional movement screens [24]. As an
injury prediction tool, the evidence for the utility of the FMS is equivocal at
best [25]
[26]
[27]
[28], as are the
associations between FMS scores and movement kinematics [29] or function [30]
[31]. Furthermore, a convincing argument has been
made against using functional movement screens for predicting injury [32]. As a means to identify joint mobility and
stability deficits, results of our current and previous studies [15] challenge whether this can be done based solely
on visual observation of complex whole-body movements. Movement patterns are
inherently variable within and between performers because coordination and
control processes are governed by non-linear interactions between personal,
task, and environmental constraints [33].
Therefore, visually observed deviations from an assumed “ideal”
or “optimal” movement pattern are not – in themselves
– evidence of dysfunction. In their book [34], the FMS creators advised against using the FMS for diagnostic
purposes, recommending instead the Selective Functional Movement Assessment
(SFMA) to identify sources of pain and dysfunction. Ultimately, validated
assessments are required to confirm the presence of joint mobility and stability
deficits.
Our results – when taken together with previous research – offer
partial support for using a subset of FMS task scores as a crude indicator of
sagittal plane joint ROM capacity. However, it must be emphasized that the body
of evidence is based on data collected from different populations
(i. e., athletes, recreationally active young adults, firefighters, and
police officers) and involves different active, passive, and active-assisted ROM
measurements. Therefore, any inferences made about joint ROM based on FMS task
scores should be informed by kinesiological theory, account for equivocal
findings across studies, and be confirmed on an individual basis using validated
ROM assessments.
Practical advantages
Our results show that some FMS task scores may be used to screen for potential
joint ROM deficits at a group-level. This is beneficial to identify individual
athletes from large teams in a time-efficient and cost-effective manner who may
be candidates for further assessment. With that said, our data do not support
using FMS task scores by themselves to make personalized exercise
recommendations. For example, [Figure 1] showed
that an individual with an FMS squat score of 1 did not necessarily have ankle
dorsiflexion ROM limitations, and that an individual with a squat score of 3 was
unlikely to have ankle dorsiflexion ROM limitations. More formally, when used as
a screen to detect ankle dorsiflexion ROM deficits, the squat had high
sensitivity (i. e., low false-negative rate) and low specificity
(i. e., high false-positive rate) [10].
Altogether, these findings imply that performance on a subset of FMS tasks may
be used to rule out joint ROM restrictions, but should not be used to
rule-them-in. To confirm the presence of genuine joint ROM deficits, validated
assessments are needed. If such assessments confirm that ROM deficits are
present, further evaluation would be required to determine if ROM-constraining
factors are potentially modifiable (e. g., soft tissue compliance) or
non-modifiable (e. g., bony anatomy) before prescribing corrective
exercises.
Methodological limitations
First, our statistical analyses incorporated the lower average values of
unilateral ROM measurements and lower scores of unilateral FMS tasks. This
limited our ability to assess direct relationships between specific FMS scores
and ROM measures, but was a necessary simplification, given the vast number of
statistical tests that would be required to investigate bilateral asymmetries.
Second, we used manual goniometry to make passive ROM measurements. Using
kinematic recording systems to measure joint ROM may have resulted in more
reliable and accurate measurements. However, we carried out preliminary
reliability work [17] that supported our use of
manual goniometry by experienced therapists for our study purposes. Third, we
acknowledge that our ROM measures may not directly and fully represent the
notion of “mobility” suggested by FMS proponents, especially
given that only relatively small numbers and types of ROM measures were
incorporated. When observing whole-body movements like FMS tasks, the joint
motion exhibited is constrained by factors other than the amount available
(e. g., ability to position and control motion of remote joints, open-
vs. closed-chain movements, etc.). This makes it difficult to operationally
conceptualize “mobility”, which is why we used a deducible
component of mobility in our study – amount of joint ROM available with
and without multi-articular motion restraints imposed.
Conclusion
Our findings generally support using FMS squat task scores to screen for ankle
dorsiflexion, and hip and shoulder flexion ROM limitations. Support was also found
for using the slr and shoulder tasks to screen for potential hip and shoulder
flexion ROM limitations, respectively. However, low FMS task scores should not be
interpreted as evidence that joint ROM deficits exist – the FMS was not
designed for this purpose. Rather, FMS task scores may be used to screen for
potential joint ROM deficits before using a validated assessment for
diagnostic and intervention purposes.
Contributorship
All authors made a significant contribution to the work reported, whether that is
in
the conception, study design, execution, acquisition of data, analysis, and
interpretation, or in all these areas; took part in drafting, revising or critically
reviewing the article; gave final approval of the version to be published; have
agreed on the journal to which the article has been submitted; and agree to be
accountable for all aspects of the work.
Data Sharing Statement
The datasets used and analysed during the current study are available from the
corresponding author on reasonable request.
Ethical Approval Information
Ethical Approval Information
All participants gave written informed consent. The study was approved by the
independent Office of Research Ethics at the University of Toronto.
