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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The common assertion in strength training literature (I use that term loosely) is that compound movements must be done (and short rest intervals used) to maximize the growth hormone output associated with training to accentuate muscle hypertrophy.

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Today my good friend Bret Contreras posted a guest blog by a really brilliant guy (guess who) investigating this very notion.  You can check it out HERE.

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On a side note, ever since I sent Bret the initial article he keeps sending me emails saying something about how Jamie Eason affects his “Growth Hormone”.  Not really sure what he’s getting at, but I wish he’d keep it to himself. 

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A little while back I posted a review of a study looking at the interrater reliability of the Functional Movement Screen.  Today I’ll be looking at another study on the FMS to further elucidate the research that has been done on this popular system.

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Again, I’m not trying to rip anyone apart here.  I’m primarily looking the the research for my own benefit and in doing so have decided to share it with you.  If you care to read this study for yourself you can get the full text for free at the top right hand of the page HERE.

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Core strength: A New Model for Injury Prediction and Prevention

Peate WF, et al.   Journal of Occupational Medicine and Toxicology 2007, 2:3.

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Background

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Injuries to firefighters are among the highest of all occupations.  Research suggests that decreased core strength may contribute to injuries of the back and extremities, that training may decrease musculoskeletal damage, and that core stability can be tested using functional movement methods.  The purpose of this study was to use the Functional Movement Screen to better assess the risk of firefighter injury due to functional movement performance, and to decrease injuries by using that information.

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Methods Part 1: Screening

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Functional Movement Screen conducted on 433 active firefighters.  Analyzed correlation between FMS scores and data from fire department database including injury history, age, gender, tenure, and rank.

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Methods Part 2: Intervention

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Firefighters underwent 3 hour seminars (actual number attended by each firefighter hard to determine based on the way the study was written) covering causation and prevention of injuries.  During seminars each firefighter demonstrated proper body mechanics in sample work settings and taught how to minimize spinal load during work situations.

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“For example, firefighters were instructed to use an outstretched arm held against a firm surface as a prop to decrease mechanical load on the back when the firefighter’s spine is in lumbar flexion.”

Subjects were taught how to activate their transversus abdominis as well.

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“Participants were shown that muscle’s location in the anterior abdominal wall. Photos of various methods of recruiting and strengthening the TA with written explanations were provided, along with verbal reinforcement of the material.  Once the firefighter demonstrated competency in basic TA muscle tightening, physiotherapy balls and dowels were employed to challenge the firefighter in different positions that mimicked firefighting tasks.”

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Core exercises included three basic models which are similar to hamstring pushups, hamstring pushups with the upper back on a ball (hip thrusts?), and hamstring pushups with the feet on a ball.  Arm movement was added using weights of various amounts.  A detailed explanation of exercise variations, progressions, and how loading was used is lacking in this paper.  There are a few photos, but you’d be hard pressed to repeat this protocol by just reading the article.

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Injuries after one year were compared to injuries in the year previous to the addition of the intervention.

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Results Part 1: Screening

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Increasing age, rank, and tenure was associated with a lower functional movement score using linear regression.  After adjusting for age and using multiple regression, those with a previous injury tended to score an average of 0.24 points lower on the FMS, but this relationship was not significant (p = 0.25).  When the scoring on the FMS was observed as a pass or fail score (for some reason they said a fail was below 16) the odds of failing the FMS after having a previous injury was 1.68 times greater based on multiple logical regression (p= 0.033).
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Results Part 2: Intervention

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Compared to the previous year, lost time due to injuries was reduced by 62%!  The total number of injuries was reduced by 44%.  Injuries to the back and upper extremities were reduced, but injuries to the lower extremities were not reduced by the intervention.
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Funding
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Administration of the Tucson Fire Department
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My Thoughts

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I’ve noticed that this article is frequently used to support the Functional Movement Screen as a tool for screening for injury risk prior to physical activity.  However, when you look at the outcomes, the FMS was only able to determine that the risk of injury increases with age, tenure, and rank (the latter two are also typically related to age).  I don’t think I’m going out on a limb to suggest that most people could have told you this before even running the FMS.

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Although the authors do mention that there was a relationship between FMS score and previous injury based on linear regression, this relationship totally disappeared when corrected for age which appears to the major determinant of injury in this study.  The authors make a point of mentioning that they were indeed related but shy of statistical significance using multiple regression.  However, the p value (a measure of statistical probability) was 0.25 which is a LONG way from statistical significance at 0.05.  I would think it is fair to say that there is pretty much no relationship between FMS and previous injury in this study once you control for age.  Changing the FMS to a pass or fail criterion did make it better for determining risk after an injury.  As was mentioned earlier though, you don’t really need to do this if you just look at age as the primary predictor.
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I have also seen this study referenced to support the idea that the interventions based on the FMS are effective for injury prevention.  Taking a look at the study though, there are a few errors with this assumption.
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- The FMS was conducted at the beginning of the study and there was no follow up testing at the end to determine if there was an improvement in FMS score related to the 62% reduction in lost time due to injury.

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- The intervention had nothing at all to do with the FMS.  Everyone virtually received the same intervention regardless of score on the testing.  I’m no expert, but I’m pretty sure this isn’t how the FMS is supposed to be used.

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- There were other factors within the intervention besides the physical training.  Subjects were taught about injury mechanisms, bracing, and how to move in the work environment.  Personally I agree that this is a great way to intervene to make a larger difference in the safety of the firefighters.  However, from a scientific perspective, it introduces a whole bunch of other variables that could have lead to decreases in injuries that had nothing to do with the FMS.

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- There was no control group.  Injuries were compared to previous years which makes sense from a operations perspective, but when doing a scientific experiment a group not recieving treatment is usually run alongside the group that does.  In this case, it wasn’t done making comparisons in injury rates pretty difficult.  Again, a 62% decrease in lost time is excellent, but it could be due to factors beyond the intervention itself (i.e., less fires this year).
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Summing Up

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At the risk of pissing off a lot of people, I’m going to suggest that the FMS did not play an integral role (actually any role at all) in the improvements seen in this study.  Moreover, age was seen to be the biggest predictor of future injury which could technically be used alone instead of having to run the FMS on such a large group each year.

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As far as the Functional Movement Screen is concerned, the intervention doesn’t really matter because they weren’t really linked in any way.  That said, if you’re a firefighter, you might want to find out what these people are doing because it just may prevent you from experiencing an injury.  If you’re a firefighter and you’re a little older, you might want to consider a good accident benefits plan.

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What do you think?

I have to be honest and say that I was once swept up onto the interval training bandwagon.  But when it comes to fat loss, the research isn’t actually as strong in favor of intervals as you might think.  Check out my review on TMuscle HERE and let me know what you think.

Special thanks to Nate Green for editing my piece and making it look like I know how to write.

No thanks to the haters in the discussion after the article for the unsubstantiated personal attacks.  Damn interwebz.

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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?