Development of an Abbreviated Model for Predicting Functional Movement Screen Score Within Tactical Populations

Author:

Thompson Megan B.12,Johnson Quincy R.1,Lindsay Keston G.3,Dawes J. Jay12

Affiliation:

1. School of Kinesiology, Applied Health and Recreation, Oklahoma State University, Stillwater, Oklahoma;

2. Tactical Fitness and Nutrition Lab, Oklahoma State University, Stillwater, Oklahoma; and

3. Department of Human Physiology and Nutrition, University of Colorado, Colorado Springs, Colorado

Abstract

Abstract Thompson, MB, Johnson, QR, Lindsay, KG, and Dawes, JJ. Development of an abbreviated model for predicting functional movement screen score within tactical populations. J Strength Cond Res 38(3): 607–611, 2024—The Functional Movement Screen (FMS) is a tool commonly used to identify compensations when performing 7 specific movement patterns. Timely administration of the full FMS is largely dependent on the practitioner's familiarity and experience with the screening battery. When working in populations that are time-poor (i.e., tactical professionals), administration of the full movement pattern battery is not always feasible. The purpose of this study was to determine which, if any, combination of movement patterns that comprise the FMS could be used to predict total score on this screen among first responders. Functional Movement Screen scores for 99 male subjects (mean ± SD; age: 37.55 ± 9.83 years; height: 180.38 ± 6.59 cm; and body mass: 97.87 ± 15.32 kg) and 9 female subjects (age: 33.22 ± 3.99 years; height: 172.11 ± 8.19 cm; and body mass: 83.99 ± 14.09 kg) from a single law enforcement and fire agency were used for this analysis. Subjects performed the full FMS, which consisted of the following movement patterns: deep squat (DS), hurdle step (HS), in-line lunge (ILL), shoulder mobility (SM), active straight leg raise (ASLR), trunk stability push-up (PU), and rotary stability (RS). A stepwise regression was used to determine the best predictors of the FMS 7-pattern model based on the model's R 2. Cronbach's alpha and Guttman's lambda-2 were used to determine the reliability of the proposed models. The regression indicated that a 4-pattern model consisting of DS, ILL, SM, and PU was sufficient to predict approximately 84% of the full model (adjusted R 2 = 0.84, p ≤ 0.001). This 4-pattern model was shown to be reliable with the 7-pattern model (α = 0.93, λ = 0.93). Using this modified version of the FMS may allow practitioners working in tactical populations a time-efficient method of identifying dysfunctional movement and determine whether the full 7-pattern model of the FMS should be considered.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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