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Some weak evidence that Functional Movement Screening can predict injury risk

updated

Tags: exercise, IT band pain, self-treatment, treatment, knee, leg, limbs, pain problems, overuse injury, injury, running, tendinosis

One article on PainSci cites Lehr 2013: The Complete Guide to IT Band Syndrome

PainSci notes on Lehr 2013:

Here’s some positive evidence for the power of the Functional Movement Screen to predict injury, maybe. Or … maybe it was that other test? Importantly, the study was also a test of a different screening test (Y-balance). But it’s generally good news for screening, either one or both of the tests used.

Nevertheless, my money is still on the null hypothesis — that ultimately nothing will come of this — and I don’t think any of the other evidence to date is all that persuasive yet (see Whiteside et al). But if, in the end, good evidence says FMS (or any other screening) can predict injury, then bully for FMS.

Most of my gripes with FMS concern egregious over-reaching its stated purpose as a screen, and using it as a diagnostic/prescriptive tool. If it does actually work as a screen, I will be the first in line to say, “Congratulations, FMS!” Truly. But I’m going to need some (more, better) hard data.

original abstract Abstracts here may not perfectly match originals, for a variety of technical and practical reasons. Some abstacts are truncated for my purposes here, if they are particularly long-winded and unhelpful. I occasionally add clarifying notes. And I make some minor corrections.

In athletics, efficient screening tools are sought to curb the rising number of noncontact injuries and associated health care costs. The authors hypothesized that an injury prediction algorithm that incorporates movement screening performance, demographic information, and injury history can accurately categorize risk of noncontact lower extremity (LE) injury. One hundred eighty-three collegiate athletes were screened during the preseason. The test scores and demographic information were entered into an injury prediction algorithm that weighted the evidence-based risk factors. Athletes were then prospectively followed for noncontact LE injury. Subsequent analysis collapsed the groupings into two risk categories: Low (normal and slight) and High (moderate and substantial). Using these groups and noncontact LE injuries, relative risk (RR), sensitivity, specificity, and likelihood ratios were calculated. Forty-two subjects sustained a noncontact LE injury over the course of the study. Athletes identified as High Risk (n = 63) were at a greater risk of noncontact LE injury (27/63) during the season [RR: 3.4 95% confidence interval 2.0 to 6.0]. These results suggest that an injury prediction algorithm composed of performance on efficient, low-cost, field-ready tests can help identify individuals at elevated risk of noncontact LE injury.

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