Timothy T Bushman1, Tyson L Grier2, Michelle Canham-Chervak2, Morgan K Anderson2, William J North3, Bruce H Jones2. 1. Directorate of Epidemiology and Disease Surveillance, Army Institute of Public Health, Aberdeen Proving Ground, Maryland, USA Timothy.t.bushman.ctr@mail.mil ttbushman@gmail.com. 2. Directorate of Epidemiology and Disease Surveillance, Army Institute of Public Health, Aberdeen Proving Ground, Maryland, USA. 3. Henry Jackson Foundation, Fort Carson, Colorado, USA.
Abstract
BACKGROUND: The Functional Movement Screen (FMS) is a series of 7 tests used to assess the injury risk in active populations. PURPOSE: To determine the association of the FMS with the injury risk, assess predictive values, and identify optimal cut points using 3 injury types. STUDY DESIGN: Cohort study; Level of evidence, 2. METHODS: Physically active male soldiers aged 18 to 57 years (N = 2476) completed the FMS. Demographic and fitness data were collected by survey. Medical record data for overuse injuries, traumatic injuries, and any injury 6 months after the FMS assessment were obtained. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated along with the receiver operating characteristic (ROC) to determine the area under the curve (AUC) and identify optimal cut points for the risk assessment. Risks, risk ratios (RRs), odds ratios (ORs), and 95% CIs were calculated to assess injury risks. RESULTS: Soldiers who scored ≤14 were at a greater risk for injuries compared with those who scored >14 using the composite score for overuse injuries (RR, 1.84; 95% CI, 1.63-2.09), traumatic injuries (RR, 1.26; 95% CI, 1.03-1.54), and any injury (RR, 1.60; 95% CI, 1.45-1.77). When controlling for other known injury risk factors, multivariate logistic regression analysis identified poor FMS performance (OR [score ≤14/19-21], 2.00; 95% CI, 1.42-2.81) as an independent risk factor for injuries. A cut point of ≤14 registered low measures of predictive value for all 3 injury types (sensitivity, 28%-37%; PPV, 19%-52%; AUC, 54%-61%). Shifting the injury risk cut point of ≤14 to the optimal cut points indicated by the ROC did not appreciably improve sensitivity or the PPV. CONCLUSION: Although poor FMS performance was associated with a higher risk of injuries, it displayed low sensitivity, PPV, and AUC. On the basis of these findings, the use of the FMS to screen for the injury risk is not recommended in this population because of the low predictive value and misclassification of the injury risk.
BACKGROUND: The Functional Movement Screen (FMS) is a series of 7 tests used to assess the injury risk in active populations. PURPOSE: To determine the association of the FMS with the injury risk, assess predictive values, and identify optimal cut points using 3 injury types. STUDY DESIGN: Cohort study; Level of evidence, 2. METHODS: Physically active male soldiers aged 18 to 57 years (N = 2476) completed the FMS. Demographic and fitness data were collected by survey. Medical record data for overuse injuries, traumatic injuries, and any injury 6 months after the FMS assessment were obtained. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated along with the receiver operating characteristic (ROC) to determine the area under the curve (AUC) and identify optimal cut points for the risk assessment. Risks, risk ratios (RRs), odds ratios (ORs), and 95% CIs were calculated to assess injury risks. RESULTS: Soldiers who scored ≤14 were at a greater risk for injuries compared with those who scored >14 using the composite score for overuse injuries (RR, 1.84; 95% CI, 1.63-2.09), traumatic injuries (RR, 1.26; 95% CI, 1.03-1.54), and any injury (RR, 1.60; 95% CI, 1.45-1.77). When controlling for other known injury risk factors, multivariate logistic regression analysis identified poor FMS performance (OR [score ≤14/19-21], 2.00; 95% CI, 1.42-2.81) as an independent risk factor for injuries. A cut point of ≤14 registered low measures of predictive value for all 3 injury types (sensitivity, 28%-37%; PPV, 19%-52%; AUC, 54%-61%). Shifting the injury risk cut point of ≤14 to the optimal cut points indicated by the ROC did not appreciably improve sensitivity or the PPV. CONCLUSION: Although poor FMS performance was associated with a higher risk of injuries, it displayed low sensitivity, PPV, and AUC. On the basis of these findings, the use of the FMS to screen for the injury risk is not recommended in this population because of the low predictive value and misclassification of the injury risk.
Authors: Christopher J Hadley; Somnath Rao; Fotios P Tjoumakaris; Michael G Ciccotti; Christopher C Dodson; Paul A Marchetto; Sommer Hammoud; Steven B Cohen; Kevin B Freedman Journal: Orthop J Sports Med Date: 2022-04-18
Authors: Sarah M Coogan; Catherine S Schock; Jena Hansen-Honeycutt; Shane Caswell; Nelson Cortes; Jatin P Ambegaonkar Journal: Int J Sports Phys Ther Date: 2020-12
Authors: Daniel I Rhon; Deydre S Teyhen; Scott W Shaffer; Stephen L Goffar; Kyle Kiesel; Phil P Plisky Journal: Inj Prev Date: 2016-11-24 Impact factor: 2.399
Authors: Jason Brumitt; Victor Wilson; Natalie Ellis; Jordan Petersen; Christopher John Zita; Jordon Reyes Journal: Int J Sports Phys Ther Date: 2018-06