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As many of my readers already know, I have previously reviewed a few of the studies on the Functional Movement Screen including:

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Interrater Reliability of the Functional Movement Screen

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Core strength: A New Model for Injury Prediction and Prevention

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Research Review: Can Serious Injury in Professional Football be Predicted by a Preseason Functional Movement Screen?

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Today I’ve decided to start wrapping up my series by reviewing one final study on the FMS.  Tomorrow I’ll share more comment on the FMS that I think is worth addressing and that will be it for my discussion of the research piece.   However, I’m going to withhold my final thoughts on the system as a whole until I’ve finished reading and absorbing Gray Cook’s new book Movement as I feel this will contribute a lot more to the bigger picture than just reviewing the studies themselves.

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Let’s get to it!

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Functional Movement Test Scores Improve Following a Standardized Off-Season Intervention Program in Professional Football Players

Kiesel K, et al.  Scand J Med Sci Sports, 2009. 

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Background

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Previous research has shown that players scoring lower than a 14 on the FMS were more likely to be injured than those scoring above 14.  However, no studies have assessed whether changes in an individual’s FMS score can be achieved with a training program.

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Purpose

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1)  To determine if an offseason intervention program is effective in improving FMS scores in professional American football players.

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2)  To examine if there was a greater percentage of players above the injury threshold of a score of 14 at the after training than before.

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3)  Because, right and left asymmetry in the FMS has also been related to injury subjects were examined to see if more players were free of asymmetry at the end of the study compared with the beginning of the study.

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4)  To determine if it was possible to predict who would not improve their score above the injury threshold score of 14 using data from the pre-screen.

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Methods

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FMS scores were obtained prior to the start of an off-season training program for 62 professional American football players and each subject was prescribed an individualized training program based on their FMS score.  “Movement preparation” and “corrective exercises” were selected to improve movement scores and decrease assymetries.  After the 7 week training program FMS scores were collected and compared with the scores from prior to training.

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Results

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Linemen and linebackers tended to have slightly lower FMS scores both at the start and end of the study when compared to the other “skill positions” (my words not theirs). 

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As a whole, linemen and other players from ”skill positions” were able to increase their FMS scores from 11.8 to 14.8 and 13.3 to 16.3 respectively. 

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At the start of the study only 7 subjects had a score above the injury threshold of 14 on the FMS.  At the end of the intervention 30 subjects had a score above 14 on the Functional Movement Screen.  Keep in mind that this means that 32 people still failed to improve their FMS score above 14 over the training period.

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Prior to training 31 players were determined to have at least one assymetry on the FMS.  After training only 20 players had a remaining assymetry.

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The only significant predictor of whether a subject failed to improve their score above the injury threshold of 14 on the FMS was a low score on the deep squat.

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Funding

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None declared.

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My Thoughts

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I think there are a few things that need to be addressed when looking at this study, but the main thing that stands out to me is the complete lack of a control group.  Although the football players had some quite impressive improvements in their scores, it is hard to determine whether these were due to the FMS specific interventions, any off-season training program in general, or just becoming more proficient on the tests as a result of repeated practice.

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Moreover, there may have been other treatments (massage, chiropractic, etc) taking place during the same time from so it is possible that these contributed to the improvments in scores.  That is not to say that I don’t think the improvements are there, but I think a control group is one of the most important elements of the study design when evaluating the effectiveness of an intervention.  As a result, the results of this study (while promising) should be taken with a grain of salt until similar results are produced in the presence of a control group.

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The other thing that leaps out at me is that while the FMS score below 14 has been related to an increased risk of injury we must be cautious in interpreting the results of this study as they pertain only to the FMS and not to actual injury risk.  In other words, “It has yet to be determined if prospective improvements in the FMS actually reduce injury risk.”

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I should note that the authors of this study did acknowledge both of the limitations above so perhaps future research that addresses them will turn up eventually.

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Finally, I think it is interesting that the deep squat is such a powerful predictor of success or failure when it comes to reaching a score of 14 on the FMS.  I find this especially interesting because (as far as I know) this is not one of the patterns that is typically immediately addressed with an FMS specific intervention.

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Given the relatively large number of subjects who failed to reach the injury threshold of 14 on the FMS, perhaps this indicates a need for more intensive intervention for those who score low on the deep squat from the very beginning.  I’m also wondering if the lack of improvement in the total score (and assymetry) in some of the players is simply a result of a short intervention. 

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Summing Up

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I think what we can take away from this study as a definite is that FMS scores can be improved and assymetries can be eliminated.  While it is likely that this is due to a specific exercise protocol related to the FMS score, the results of this study should be taken as tentative or hypothesis generating until an intervention trial with a control group is performed.

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Again, I think it is important to reiterate that FMS scores are an injury prevention tool and there is no research yet to suggest that increasing these scores or eliminating assymetries will reduce injury.  I think it is pretty safe to say that this is possible…or even likely…but it certainly isn’t guaranteed. 

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Finally, I think it highlights the need to look at the deep squat when assessing our clients whether we use the FMS or not.  This movement is obviously related to global improvements in movement patterns and it should be carefully investigated if there are any limitations.

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What you you think?

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Speaking of injuries, don’t forget to grab your FREE copy of Mike Robertson’s Bulletproof Knees by dropping a comment on THIS POST before midnight tonight.

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And while you’re here, don’t forget to grab your FREE core audio interviews with Mike and other industry giants like McGill, Myers, Tumminello, and more HERE.

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Recently I’ve undertaken the task of reviewing some of the research on the very popular Functional Movement Screen.  Previously I’ve reviewed the Interrater Reliability of the Functional Movement Screen and Core strength: A New Model for Injury Prediction and Prevention.

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Today I’ll be taking on the 3rd of 4 studies I hope to review.  After the final review I’ll talk a little bit more about my overall impression of the FMS and how I believe it should be used.

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Can Serious Injury in Professional Football be Predicted by a Preseason Functional Movement Screen?

Kiesel K, et al.  North American Journal of Sports Physical Therapy Aug 27, 2:3

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Background

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Risk factors for injuries in high school and collegiate football include previous injury, body mass index, body fat percentage, playing experience, femoral intercondylar notch width, cleat design, playing surface, muscle flexibility, ligamentous laxity, and foot biomechanics.  However, injury risk is likely a combination of many of the above.

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Further, evaluation of isolated risk factors does not take into consideration how the athlete performs functional movement patterns required for sport.  The goal of this study was to examine functional movement scores (assessed by the FMS) and to determine the relationship between professional football players’ score on the FMS and the likelihood of serious injury.

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Methods

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FMS scores were obtained prior to the start of the season for 46 professional football players.  A receiveroperator characteristic curve the FMS score was used to predict injury during one complete football season.  For the sake of this study, injury was defined as membership on the injured reserve for at least 3 weeks.

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A dependent t-test was used to determine if a difference existed between of the FMS scores of those who were injured versus those who were not.

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Sidebar – Definitions

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To have a clear understanding of the methods and the results of this study a brief discussion is needed to definte sensitivity, specificity, and how these are used to create a ROC curve (receiveroperator characteristic curve).

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Sensitivity is basically the power to detect a true positive.  For example, if you were to go through a scanner at the airport to detect for metal it would be very sensitive to decrease the likelihood that someone were able to slip onto an airplane with a weapon.  On the other hand, the scanner doesn’t have very high specificity in that it will sound for almost any piece of metal not just weapons.  In this case, a high sensitivity is most important because it is important that weapons do not sneak aboard the aircraft.

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A dog trained to sniff for narcotics would have a high specificity since only those carrying drugs would need to be stopped.  If the dog didn’t have a high specificity for a specific substance, but was highly sensitive it would possibly alert people needlessly to any scent and make the purpose of having the dog useless (since every bag would have to be checked anyway).

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In a perfect world every test would have 100% sensitivity and 100% specificity (i.e., identifying every weapon at the airport without going off for every other piece of metal), but this is rarely the case.  There is usually a tradeoff between one and the other and the ROC curve plots sensitivity against specificity to determine the ideal cutoff number to use to maximize both.

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With the FMS the the cutoff was chosen using the ROC curve such that the test correctly identifies the greatest number of athletes at risk of injury (true positives) while minimizing incorrectly identifying athletes not at risk of injury (false positives).

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Results

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A score of 14 or less on the FMS was able to predict injury with specificity of 0.91 and sensitivity of 0.54. 

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The test had a very high specificity indicating that the majority of people with a score below 14 had a greater chance of injury.

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Those with a score under 14 that got an injury = 7

Those with a score under 14 that didn’t get an injury = 3

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Unfortunately, the test had a only a moderate sensitivity so it did not detect those with a score over 14 who did experience an injury.

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Those with a score over 14 that got an injury = 6

Those with a score over 14 that didn’t get an injury = 30

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In other words, the bulk of the people with an FMS score over 14 did not get an injury and the bulk of those with a score under 14 did.  Using something called an odds ratio the authors determined that the likelihood of injury was 11 times more likely if the player had a score below 14 on the FMS.

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However, 6 people that did have a score higher than 14 did end up getting injured.  These ones were missed by the screen.  In fact, it failed to identify almost as many people as it did identify as being at risk.

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Funding

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None declared, but I believe at least two of the authors have affiliation with the FMS.  (Not that there is anything wrong with that, but I would declare this as a possible conflict of interest.)

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My Thoughts

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All in all I think that the FMS did a great job of determining that those with a score less than 14 were at risk for injury.  In terms of practical application, these players could have been flagged for specific work with a fitness/rehab professional.  On the other hand, the test wasn’t sensitive enough to detect risk of injury such that 6 athletes slipped through the cracks and ended up being injured without this being detected by the screen.

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Unfortunately, there was no differentiation between the types of injuries that landed people on the reserve list so it is possible that some of the injured athletes suffered from contact injuries that could not have been predicted by any test or screen.  Perhaps if contact injuries were ruled out (since you can’t really test for these) the FMS would have proven to be more sensitive.  Then again, maybe some of those with a score below 14 suffered contact injuries as well.  It would have been interesting to see if the results were different if these types of injuries were excluded.

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It is also possible that one reason the FMS predicted injuries so well below the score cutoff of 14 with this group is because it is the same group whose results were used to create the cutoff in the first place.  Only future research will tell if this pass/fail cutoff is equally as effective for other groups.

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Summing Up

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The FMS indicated correctly that those with a score less than 14 were more prone to injury.  However, the results of the present study indicate that the FMS may also miss equally as many people as it detected (which may be the reason why the pass/fail score for the FMS when it is typically used is actually lower than this).

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It would also have been interesting to see which of the individual scores within the FMS were most related in injury.  Since the FMS typically suggests that side to side imbalances are most important to address, it would have been nice to see this data to see if this hypothesis holds true.

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In the end though, the FMS does appear to effectively predict injury in this below a value of 14 in the group studied.  It does not catch all injuries and as such is not a perfect screen, but effective at picking out some who are at risk.  And since the results are those of professional football players, we should be careful when generalizing them to other populations.

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What do you think?

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Anyone who reads this blog regularly knows that I’m a big fan of assessments prior to training and repeat assessments along the way to ensure results are coming as desired.  Recently though, it seems that the Functional Movement Screen which was created by Lee Burton and Gray Cook has become extremely popular among my colleagues in the fitness industry.

If you’re not familiar with the Functional Movement Screen, here’s a little excerpt from their website describing the system:

“Put simply, the FMS is a ranking and grading system that documents movement patterns that are key to normal function. By screening these patterns, the FMS readily identifies functional limitations and asymmetries. These are issues that can reduce the effects of functional training and physical conditioning and distort body awareness.

The FMS generates the Functional Movement Screen Score, which is used to target problems and track progress. This scoring system is directly linked to the most beneficial corrective exercises to restore mechanically sound movement patterns.

Exercise professionals monitor the FMS score to track progress and to identify those exercises that will be most effective to restore proper movement and build strength in each individual.”

In short, you do 7 movements, get scored, and based on the outcome your potiential issues are identified and your training program can be designed.

 
Being the skeptic I am, I had to consult the research to determine what has and hasn’t been studied about the FMS.  And while I do believe that not everything worth doing is necessarily validated by science (yet), I’m also cautious when I see a pendulum swinging in one direction and when a great number of people are on board. It seems to me that the FMS has been accepted with little discussion as to whether it is valid or not. The more people that jump on board, the less people are apt to question it (especially when those people are big names).

So I’m not saying the FMS isn’t valid or useful or trying to discredit Lee, Gray, or anyone else who uses the system.  The next few posts are simply meant to be an examination of the existing body of scientific study on the FMS.  More to the point, I’ll be reviewing only the peer reviewed studies that have appeared in journals and not the unpublished stuff that is available on the internet (including at least one doctoral dissertation and a couple conference poster presentations).

Without further ado, let’s get to the first study.

Interrater Reliability of the Functional Movement Screen

Minik KI, et al.  J Strength Cond Res. 2010. 24(2): 479-486

While this isn’t actually the first published study on the movement screen, I thought it was important to present this first as it is indicative of the reliability of the scoring.  In a setting where different individuals would be scoring the FMS, you would need for them to be scoring the same way or the tool would be a lot less useful.

Background

To reduce injury risk, sports medicine professionals have begun to focus on improving movement patterns as opposed to focusing on rehabilitation of a specific joint.  The Functional Movement Screen has been put forth as a potential screening tool for these movement patterns.  The goal of this study is to establish interrater reliability of the FMS by comparing expert raters (who took part in the development of the FMS) with novice raters (who have completed the standardized FMS training program).

Methods

Forty students were filmed performing each of the 7 movements in the Functional Movement Screen.  Each of the subjects’ videos were then viewed by two expert and two novice raters and each of the 7 movements was independently scored as a 0, 1, 2, or 3 using the FMS criteria.  The scores were then compared using the weighted Kappa statistic.

Results

The pair of novice raters demonstrated excellent agreement on 6 of the 17 test components, including the deep squat and shoulder mobility tests, and portions of the trunk stability push-up and ASLR tests. Substantial agreement was evident on 8 of the 17 test components. The right and left components of the lunge and the final component of the rotary stability test each demonstrated moderate agreement.

The pair of expert raters varied more in scoring, with excellent agreement on 4 of the 17 test components, including the shoulder mobility test and the final component of the ASLR. Substantial agreement was seen in 9 of the 17 test components. Two components of the lunge and 2 components of the rotary stability tests demonstrated moderate agreement.

When comparing the average scores of the paired novice and expert raters, 14 of the 17 tests demonstrated excellent agreement. Substantial agreement was evident in 1 component of the rotary stability test and 2 components of the in-line lunge.

Funding

University of Evansville Honor’s Program grant and the University of Evansville’s College of Education and Health Science.

My Thoughts

The kappa statistic is actually a good choice in this case because it is fairly conservative and takes into account possible agreement of the raters due to chance.  They also used a weighted kappa which allowed them to rate larger disagreements between the raters less favorably.   However, it should be noted that the categories listed as excellent, substantial, and moderate are pretty aribitrary and are based pretty much on the personal opinions of some other researchers.  Other arbitrary guidelines exist that rate kappa values differently.   I’m not sure how much this really matters in this case, but it is important to note that these ratings aren’t universally accepted.

The most surprising thing to me was that the novice raters tended to have more agreement with each other than the expert raters.  You’d expect that over time your ratings would become more similar than different.  But why did the novice and expert rater’s results tend to agree with each other when they compared them?  My guess is that some of the differences disappeared when they averaged the two novice and two advanced raters before comparing them to each other which could possibly have masked some of the variability.

The authors did suggest that since the tests were only filmed from two angles there was a third dimension missing and this could have influenced the degree of agreement between raters.  Having evaluated people in person, I can definitely see how this could have an impact.  In person you can move around and get a better view of each movement.  Perhaps one reason the expert raters had more disagreement is because they’re more used to looking at movements in three dimensions that novice raters who are less likely to be able to make as much use of this additional information.

All in all, I think the study was fairly well conducted and the results indicate that the scoring of the FMS is relatively reliable.  It did highlight that perhaps work needs to be done to ensure expert raters are scoring similarly.  Another important point to take home is that both sets of raters tended to struggle with agreement on the lunge and rotary stability tests so if you’re using the FMS you might want to pay extra attention when scoring these movements.

I personally would have liked to have seen the same raters score the same subject numerous times to see if the same rater would come up with the same scores each time.  In personal training settings I believe this scenario would be more likely.  Perhaps this could be a route for future investigation.

One final thing I want to mention is that two of the people conducting the study have a personal stake in the outcome since they are involved with the FMS.  This is not to suggest that they would deliberately alter the outcome, but the possibility is always something to be congizant of.  Testing by independent researchers always carries more weight (at least in my mind).

What are your thoughts?