Literature DB >> 25162646

Grading the Functional Movement Screen: A Comparison of Manual (Real-Time) and Objective Methods.

David Whiteside1, Jessica M Deneweth, Melissa A Pohorence, Bo Sandoval, Jason R Russell, Scott G McLean, Ronald F Zernicke, Grant C Goulet.   

Abstract

Although intertester and intratester reliability have been common themes in Functional Movement Screen (FMS) research, the criterion validity of manual grading is yet to be comprehensively examined. This study compared the FMS scores assigned by a certified FMS tester to those measured by an objective inertial-based (IMU) motion capture system. Eleven female division I collegiate athletes performed 6 FMS exercises and were manually graded by a certified tester. Explicit kinematic thresholds were formulated to correspond to each of the grading criteria for each FMS exercise and then used to grade athletes objectively using the IMU data. The levels of agreement between the 2 grading methods were poor in all 6 FMS exercises and implies that manual grading of the FMS may be confounded by vague grading criteria. Evidently, more explicit grading guidelines are needed to improve the uniformity and accuracy of manual FMS grading and also facilitate the use of objective measurement systems in the grading process. Contrary to the approach that has been adopted in several previous studies, the potential for subjective and/or inaccurate FMS grading intimates that it may be inappropriate to assume that manual FMS grading provides a valid measurement tool. Consequently, the development and criterion validation of uniform grading procedures must precede research attempting to link FMS performance and injury rates. With manual grading methods seemingly susceptible to error, the FMS should be used cautiously to direct strength and/or conditioning programs.

Mesh:

Year:  2016        PMID: 25162646     DOI: 10.1519/JSC.0000000000000654

Source DB:  PubMed          Journal:  J Strength Cond Res        ISSN: 1064-8011            Impact factor:   3.775


  8 in total

Review 1.  Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review.

Authors:  Martin O'Reilly; Brian Caulfield; Tomas Ward; William Johnston; Cailbhe Doherty
Journal:  Sports Med       Date:  2018-05       Impact factor: 11.136

2.  Movement Competency Screens Can Be Reliable In Clinical Practice By A Single Rater Using The Composite Score.

Authors:  Kerry J Mann; Nicholas O'Dwyer; Michaela R Bruton; Stephen P Bird; Suzi Edwards
Journal:  Int J Sports Phys Ther       Date:  2022-06-01

3.  Comparison of Lower Extremity Kinematics during the Overhead Deep Squat by Functional Movement Screen Score.

Authors:  Caitlyn Heredia; Robert G Lockie; Scott K Lynn; Derek N Pamukoff
Journal:  J Sports Sci Med       Date:  2021-10-01       Impact factor: 2.988

Review 4.  Utility of FMS to understand injury incidence in sports: current perspectives.

Authors:  Meghan Warren; Monica R Lininger; Nicole J Chimera; Craig A Smith
Journal:  Open Access J Sports Med       Date:  2018-09-07

5.  Study of the measurement and predictive validity of the Functional Movement Screen.

Authors:  Fraser Philp; Dimitra Blana; Edward K Chadwick; Caroline Stewart; Claire Stapleton; Kim Major; Anand D Pandyan
Journal:  BMJ Open Sport Exerc Med       Date:  2018-05-07

6.  Functional movement screen comparison between the preparative period and competitive period in high school baseball players.

Authors:  Chia-Lun Lee; Mei-Chich Hsu; Wen-Dien Chang; Szu-Chieh Wang; Chao-Yen Chen; Pei-Hsi Chou; Nai-Jen Chang
Journal:  J Exerc Sci Fit       Date:  2018-07-04       Impact factor: 3.103

7.  Associations between Inter-Limb Asymmetries in Jump and Change of Direction Speed Tests and Physical Performance in Adolescent Female Soccer Players.

Authors:  Elena Pardos-Mainer; Chris Bishop; Oliver Gonzalo-Skok; Hadi Nobari; Jorge Pérez-Gómez; Demetrio Lozano
Journal:  Int J Environ Res Public Health       Date:  2021-03-27       Impact factor: 3.390

8.  Functional movement screen dataset collected with two Azure Kinect depth sensors.

Authors:  Qing-Jun Xing; Yuan-Yuan Shen; Run Cao; Shou-Xin Zong; Shu-Xiang Zhao; Yan-Fei Shen
Journal:  Sci Data       Date:  2022-03-25       Impact factor: 6.444

  8 in total

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