If FMS score 14 then probability of suffering a time loss injury increased from 15% pretest probabil

1
FMS Injury Prediction

• If FMS score 14 then probability of suffering a
  time loss injury increased from 15 (pre-test
  probability) to just over 50 (when using the FMS
  as the test with a LR of 5.0).
• This is based on 1 NFL team through 1 season

2
Functional Movement Screen (FMS) as a Screening
Tool

• Chesterfield, VA Recruit School
• 23 recruits for 16 week academy.
• Looked at Fitness and Functional Movement as
  injury predictor and performance predictor.

• Findings
• FPCC(obstacle course) only injury predictor
• Functional Movement Screen didnt show
  relationship to injuries or Fitness parameters
• Asymmetry FMS score did have relationship to FPCC

3
FMS and Performance Tests

• Vertical Jump and Squat have a positive
  relationship with total score
• In-Line Lunge has a positive relationship with
  Power Clean and Vertical Jump
• Deep Squat has a positive relationship with Power
  Clean

4
MOVEMENT QUALITY ASSESSMENT A PRECURSOR TO
INJURY PREVENTION? Peter E. Pidcoe, Ph.D., PT,
Department of Physical Therapy, Virginia
Commonwealth University, Richmond, VA
Results Observable variables like body alignment
and joint excursion predict FMS rank membership.
An example of this is illustrated in Figures 1
and 2. These represent the FMS pushup task for a
subject scoring a 1 and a subject scoring a 3.
Note the difference in segment flexions at the
apex of the activity. These differences were
statistically significant. Less observable
movement derivatives like COM parameters and
muscle activation show a similar trend in the
majority of the FMS tests.

Purpose The current approach to injury prevention
for athletes is the pre-participation physical
exam followed by standardized or sports-specific
performance testing. It follows that the athlete
with the highest score is the best conditioned
and is expected to be able to avoid non-contact
injury throughout the season. This method of
screening overlooks an important component of
performance, the quality of the movement.
Athletes typically sacrifice quality of motion to
maintain quantity of motion. They may develop
compensatory movement patterns to overcome
functional deficits allowing them to maintain
high performance levels, but also potentially
increasing their risk for non-contact injuries.
The Functional Movement ScreenTM (FMS) is a tool
designed to assess mechanical factors of
functional mobility and stability that are not
well addressed during pre-participation physical
exams or standardized tests. The FMS consists of
seven tests each with three levels of ordinal
ranking based on operationally defined observed
performance variables. The purpose of this study
was to evaluate the validity of these rankings in
a quantitative manner using biomechanical
measures to first determine what variables best
predict rank membership, and second to determine
if the ranks differ significantly and in a
hierarchical fashion.
Methods Trained examiners using the FMS screened
sixty-five subjects. Their scores were used to
select a subgroup of thirty subjects (9 male, 21
female, age 21-33) that maximized the
distribution of scores in each of the three
ordinal ranks. These subjects were retested in a
biomechanics lab to evaluate biomechanical and
motor control differences in five of the seven
FMS tests. Kinematic data (6 DOF) was collected
at 100Hz using an electromagnetic system (Motion
Monitor). Surface EMG data was collected at
2000Hz from the erector spinae and abdominal
oblique muscles bilaterally. Performance measures
were extracted from these data. These included
joint excursion, COM position, COM velocity, and
muscle activation timing.
Conclusion These results suggest that FMS can
separate the performance of these tests into
three ranked levels. The significance of these
rankings is yet to be determined, but will be
further evaluated in a proposed longitudinal
injury study.
Figure 1 Trunk stability pushup task rated with
a score of 1 (poorest performance).
Clinical Relevance Establishing a foundational
measurement of movement quality for fundamental
movement patterns in an individual may help to
elucidate why some athletes with excellent
standardized performance scores are consistently
plagued by non-contact injuries. This study is
the first step in determining the validity of
such a tool.
Analysis Extracted performance data were analyzed
using a linear regression model to determine what
variables predict rank membership. The included
variables were FMS test dependent and were a
function of the operational definition of each
test. As an example, during the performance of
the deep squat test, LE joint excursion, torso
and UE alignment, and COM parameters constituted
the dependent variables. Identified predictor
variables were then used in an ANOVA to determine
if the ranks differed significantly (? 0.05).

• References
• Keppel, G. Design and analysis a researchers
  handbook. (2nd edition). Englewood Cliffs, NJ.,
  Prentice Hall, Inc, 1982.
• Radin, EL., Role of muscle in protecting athletes
  from injury. Acta Med Scand Suppl 1986, 711,
  143-147.
• Winter, D. Biomechanics and Motor Control of
  Human Movements (2nd edition). New York Wiley,
  1990.

Figure 2 Trunk stability pushup task rated with
a score of 3 (best performance).