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Based on reading the lit review in this study alone there seems to be conflicting evidence regarding the utility of FMS for prediction of injury.
My understanding: some people stay healthy because of their movement pattern, some people stay healthy despite their movement pattern, some people fail to stay healthy even in the presence of a good movement pattern.
My guess is that our ability to truly predict injuries will always remain relatively average at best. I hope not to sound pessimistic, but simply think that factors that contribute to injuries are too varied and complex and even unknown for us to have a high degree of confidence in predicting injury occurrence. I think we can be better than a blind guess, as even this study shows some relationship, I just don’t think it will ever be incredibly strong.
As opposed to merely predicting injury occurrence, I would imagine that we would have somewhat greater power preventing injury occurrence through specific training. I know we have some research regarding specific movement training and reduced ACL injury occurrence; does anyone know of other studies examining movement retraining and other injuries (e.g., PFP, ankle sprain, back/neck pain, labral injury)?
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The results of this study don’t surprise me. I have difficulty relating the FMS screening movements to the demands that these men and women are subjected to. I like the comprehensive and global movements that comprise the FMS, but to me it’s no wonder why the ability to predict injury is low because they aren’t actually screening the tasks that they need to be able to do.
I am not going to pretend like I know what specific tasks these men and women do every day (my best guesses is running, jumping, endurance training, heavy lifting, shooting, climbing, etc.). I could see how someone who has a really low score (5-9) would warrant further evaluation, but why not do biomechanical screening and analysis with those specific tasks?
“This may suggest that the quality of movement is not the primary indicator of the injury risk and that perhaps other factors such as pain occurrence are more strongly associated with the risk than the actual composite score.” I think this is definitely more of a valid conclusive statement.
Nick >> this was an interesting article on predictors for chronic ankle instability after primary ankle sprain. They speak on different movement tasks and biomechanical faults that might predict sustained instability and repeated injuries:
Recovery From a First-Time Lateral Ankle Sprain and the Predictors of Chronic Ankle Instability: A Prospective Cohort Analysis
Cailbhe Doherty, PhD, Chris Bleakley BSc, PhD, Jay Hertel PhD, ATC, Brian Caulfield, PhD, John Ryan FCEM, FRCSEd, FFSEM, DCH, DipSportsMed, and Eamonn Delahunt, PhD
The American Journal of Sports Medicine 2016; Vol. 44, No. 4: 995-1003 -
“some people stay healthy because of their movement pattern, some people stay healthy despite their movement pattern, some people fail to stay healthy even in the presence of a good movement pattern”
– A lot of impairments we treat that have a pathophysiological impact on a patient could very well make no difference in somebody else with the exact same impairment.” the ability to predict injury is low because they aren’t actually screening the tasks that they need to be able to do”
Also, the study included a vast variety of subjects with different activity levels and demands (although not clearly specified). I don’t know if a study like this can really show any specific results considering the design…
Having tools like the FMS is helpful, because it can show certain limitations, but I think it’s then the PT’s job to determine whether or not these limitations are meaningful (impairment vs. importance).
This reminds me of some of the discussions about Tim Gabbett’s work with training loads.
Considering the number of possible factors for injuries, there might be use in performing tests like the FMS, if it’s taken for what it’s worth and then used in the context of the individual’s environment, demands, activities and physical attributes etc. etc. -
A bit late on the response I know.
I agree with you guys on the limitations to this study and the lack of task-specificity in the tests that were used. The weight of our functional assessment is determined by the relationship it has on the specific task the individual is performing. This analysis is based on our logic and what we know about the laws of physics, biomechanics and exercise physiology. It appears these tests are missing a few key components with its testing. I’ll have to look more specifically into the FMS to say for sure, but from what I’m seeing so far it looks like they’re testing one’s ability, mobility and strength to perform a movement for a single repetition (correct me if I’m wrong on that). This lacks assessing a key component of endurance in this population. I’m pretty sure these guys need to be working their butts off for hours on end. So why not challenge their ability to perform the task for a longer duration. When I think of assessing Functional Movement, I’m thinking of Task Specific, just as you guys said. For instance, take Blaise’s functional testing for runners that also challenges their endurance specific to running.I think this just goes to show that we can do a better job at testing and treating function than the FMS. Thank goodness the results weren’t valid. Otherwise a robot could do our job for us.
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Thanks for posting the short critique on FMS Eric. As one who is not familiar with the variety of research there is on FMS, I feel more confident in understanding it’s limitations to date based on the research critique and summary presented in that article.
I feel more equipped at interpreting/handling some statistics than others – e.g, sensitivity/specificity, + and – LR vs. effect sizes, positive predictive value. Since working at UVA-HealthSouth and sharing some desk space with Kevin Cross (PhD researcher) I have come to appreciate more how statistics can be deceptively represented. We would probably all do well to review the presentation he gave at the beginning of the year.
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Example of misleading stats:
AJ and I just ran the stats on our current manip vs sham study on neurodynamic mobility. Interestingly enough, both groups (manip and sham) statistically improved regardless of intervention. However, the effect size for the lower quarter/slump was much stronger for the manip group than the sham. At face value, while the p value did suggest statistical significance, was this clinically meaningful? The confidence interval was very wide and almost included 0 in the sham group and was much better represented in the manip group. So, despite a statistical significant value, looking more closely at the data might lead one to propose that this finding is not clinically significant. Sometimes these numbers can remain hidden when presented without some context. It was brought to our attention that this was a good example so I thought I would pass it along.
#TexasInstruments, #TI-82required
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