PainSci summary of Lehr 2013?This page is one of thousands in the PainScience.com bibliography. It is not a general article: it is focused on a single scientific paper, and it may provide only just enough context for the summary to make sense. Links to other papers and more general information are provided at the bottom of the page, as often as possible. ★★★☆☆3-star ratings are for typical studies with no more (or less) than the usual common problems. Ratings are a highly subjective opinion, and subject to revision at any time. If you think this paper has been incorrectly rated, please let me know.
Here’s some positive evidence for the power of the Functional Movement Screen™ (FMS) 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.
- “Grading the Functional Movement Screen™: A Comparison of Manual (Real-Time) and Objective Methods,” David Whiteside, Jessica M Deneweth, Melissa A Pohorence, Bo Sandoval, Jason R Russell, Scott G McLean, Ronald F Zernicke, and Grant C Goulet, Journal of Strength & Conditioning Research, 2014.
One article on PainScience.com cites Lehr 2013 as a source:
- PS Save Yourself from IT Band Syndrome! — All your treatment options for Iliotibial Band Syndrome reviewed in great detail, with clear explanations of recent scientific research supporting every key point
This page is part of the PainScience BIBLIOGRAPHY, which contains plain language summaries of thousands of scientific papers & others sources. It’s like a highly specialized blog. A few highlights:
- A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: the case of felt power. Gronau 2017 Comprehensive Results in Social Psychology.
- The neck and headaches. Bogduk 2014 Neurol Clin.
- Agreement of self-reported items and clinically assessed nerve root involvement (or sciatica) in a primary care setting. Konstantinou 2012 Eur Spine J.
- Effect of NSAIDs on Recovery From Acute Skeletal Muscle Injury: A Systematic Review and Meta-analysis. Morelli 2017 Am J Sports Med.
- Association of Spinal Manipulative Therapy With Clinical Benefit and Harm for Acute Low Back Pain: Systematic Review and Meta-analysis. Paige 2017 JAMA.