Factors Influencing the Relationship Between the Functional Movement Screen and Injury Risk in Sporting Populations: A Systematic Review and Meta-analysis
Studies investigating the association between the Functional Movement Screen (FMS) and sports injury risk have reported mixed results across a range of athlete populations.
The purpose of this systematic review was to identify whether athlete age, sex, sport type, injury definition and mechanism contribute to the variable findings.
Systematic review and meta-analysis.
A systematic search was conducted in October 2018 using PubMed, EBSCOhost, Scopus, EmBase and Web of Science databases. Studies were included if they were peer reviewed and published in English language, included athletes from any competition level, performed the FMS at baseline to determine risk groups based on FMS composite score, asymmetry or pain, and prospectively observed injury incidence during training and competition. Study eligibility assessment and data extraction was performed by two reviewers. Random effects meta-analyses were used to determine odds ratio (OR), sensitivity and specificity with 95% confidence intervals. Sub-group analyses were based on athlete age, sex, sport type, injury definition, and injury mechanism.
Twenty-nine studies were included in the FMS composite score meta-analysis. There was a smaller effect for junior (OR = 1.03 [0.67–1.59]; p = 0.881) compared to senior athletes (OR = 1.80 [1.17–2.78]; p = 0.008) and for male (OR = 1.79 [1.08–2.96]; p = 0.024) compared to female (OR = 1.92 [0.43–8.56]; p = 0.392) athletes. FMS composite scores were most likely to be associated with increased injury risk in rugby (OR = 5.92 [1.67–20.92]; p = 0.006), and to a lesser extent American football (OR = 4.41 [0.94–20.61]; p = 0.059) and ice hockey (OR = 3.70 [0.89–15.42]; p = 0.072), compared to other sports. Specificity values were higher than sensitivity values for FMS composite score. Eleven studies were included in the FMS asymmetry meta-analysis with insufficient study numbers to generate sport type subgroups. There was a larger effect for senior (OR = 1.78 [1.16–2.73]; p = 0.008) compared to junior athletes (OR = 1.21 [0.75–1.96]; p = 0.432). Sensitivity values were higher than specificity values for FMS asymmetry. For all FMS outcomes, there were minimal differences across injury definitions and mechanisms. Only four studies provided information about FMS pain and injury risk. There was a smaller effect for senior athletes (OR = 1.28 [0.33–4.96]; p = 0.723) compared to junior athletes (OR = 1.71 [1.16–2.50]; p = 0.006). Specificity values were higher than sensitivity values for FMS pain.
Athlete age, sex and sport type explained some of the variable findings of FMS prospective injury-risk studies. FMS composite scores and asymmetry were more useful for estimating injury risk in senior compared to junior athletes. Effect sizes tended to be small except for FMS composite scores in rugby, ice hockey and American football athletes.
All authors contributed to the conception and design of the review and completion of the search strategy. Joel T. Fuller was responsible for the meta-analysis. Emma Moore drafted the manuscript. All authors edited and revised the manuscript and approved the final version of the manuscript.
Compliance with ethical standards
This research received no specific grant from any funding agency.
Conflict of interest
Emma Moore, Joel T. Fuller, Steve Milanese and Samuel Chalmers declare that they have no conflict of interest.
Data availability statement
The datasets generated during and/or analysed during the current systematic review are available in the Online Supplementary Material.
- 2.Cook G, Burton L, Hoogenboom B, Voight M. Pre-participation screening: the use of fundamental movements as an assessment of function-part 1. N Am J Sports Phys Ther. 2006;1(2):62–72.Google Scholar
- 3.Cook G, Burton L, Hoogenboom B, Voight M. Functional movement screening: the use of fundamental movements as an assessment of function-part 2. Int J Sports Phys Ther. 2014;9(4):549–63.Google Scholar
- 4.Kiesel K, Plisky P, Voight M. Can serious injury in professional football be predicted by a preseason Functional Movement Screen? N Am J Sports Phys Ther. 2007;2(3):147–58.Google Scholar
- 5.Chorba R, Chorba D, Bouillon L, Overmyer C, Landis J. Use of a functional movement screening tool to determine injury risk in female collegiate athletes. N Am J Sports Phys Ther. 2010;5(2):47–54.Google Scholar
- 7.Bardenett SM, Micca JJ, DeNoyelles JT, Miller SD, Jenk DT, Brooks GS. Functional Movement Screen normative values and validity in high school athletes: can the FMS™ be used as a predictor of injury? Int J Sports Phys Ther. 2015;10(3):303–8.Google Scholar
- 9.Letafatkar A, Hadadnezhad M, Shojaedin S, Mohamadi E. Relationship between functional movement screening score and history of injury. Int J Sports Phys Ther. 2014;9(1):21–7.Google Scholar
- 19.Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, et al. Chapter 7: Systematic reviews of etiology and risk. In: Aromataris E, Munn Z, editors. Joanna Briggs Institute Reviewer’s Manual: the Joanna Briggs Institute. Adelaide: Joanna Briggs Institute; 2017.Google Scholar
- 21.Hopkins WG. A scale of magnitudes for effect statistics. Sports Sci. 2002. http://www.sportsci.org/resource/stats/effectmag.html. Accessed 3 May 2018.
- 22.Hopkins WG. Statistics used in observational studies. Sports Injury Research. 1st ed. Oxford: Oxford University Press; 2010.Google Scholar
- 28.Bring BV, Chan M, Devine RC, Collins CL, Diehl J, Burkam B. Functional Movement Screening and injury rates in high school and collegiate runners: a retrospective analysis of 3 prospective observational studies. Clin J Sport Med. 2017;21:21.Google Scholar
- 29.Clay H, Mansell J, Tierney R. Association between rowing injuries and the Functional Movement Screen™ in female collegiate Division I rowers. Int J Sports Phys Ther. 2016;11(3):345–9.Google Scholar
- 30.Dossa K, Cashman G, Howitt S, West B, Murray N. Can injury in major junior hockey players be predicted by a pre-season Functional Movement Screen—a prospective cohort study. J Can Chiropractic Assoc. 2014;58(4):421–7.Google Scholar
- 32.Garrison M, Westrick R, Johnson MR, Benenson J. Association between the Functional Movement Screen and injury development in college athletes. Int J Sports Phys Ther. 2015;10(1):21–8.Google Scholar
- 38.Moran S, Booker H, Staines J, Williams S. Rates and risk factors of injury in CrossFit™: a prospective cohort study. J Sports Med Phys Fit. 2017;57(9):1147–53.Google Scholar
- 42.Slodownik R, Ogonowska-Slodownik A, Morgulec-Adamowicz N. Functional Movement Screen™ and history of injury in assessment of potential risk of injury among team handball players. J Sports Med Phys Fit. 2018;58(9):1281–6.Google Scholar
- 48.Hammes D, Aus der Funten K, Bizzini M, Meyer T. Injury prediction in veteran football players using the Functional Movement Screen™. J Sports Sci. 2016;34(14):1371–9.Google Scholar
- 50.Newton F, McCall A, Ryan D, Blackburne C, aus der Fünten K, Meyer T et al. Functional Movement Screen (FMS™) score does not predict injury in English Premier League youth academy football players. Sci Med Football. 2017;1(2):102–6.Google Scholar
- 52.Ristolainen L, Heinonen A, Waller B, Kujala UM, Kettunen JA. Gender differences in sport injury risk and types of injuries: a retrospective twelve-month study on cross-country skiers, swimmers, long-distance runners and soccer players. J Sports Sci Med. 2009;8(3):443–51.Google Scholar
- 53.Ristolainen L, Heinonen A, Turunen H, Mannström H, Waller B, Kettunen JA et al. Type of sport is related to injury profile: A study on cross country skiers, swimmers, long‐distance runners and soccer players. A retrospective 12‐month study. Scand J Med Sci Sports. 2010;20(3):384–93.Google Scholar
- 65.Balyi I, Hamilton A. Long-term athlete development: trainability in childhood and adolescence. Victoria: National Coaching Institute British Columbia & Advanced Training and Performance Ltd; 2004.Google Scholar