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Factors Influencing the Relationship Between the Functional Movement Screen and Injury Risk in Sporting Populations: A Systematic Review and Meta-analysis

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Abstract

Background

Studies investigating the association between the Functional Movement Screen (FMS) and sports injury risk have reported mixed results across a range of athlete populations.

Objectives

The purpose of this systematic review was to identify whether athlete age, sex, sport type, injury definition and mechanism contribute to the variable findings.

Study design

Systematic review and meta-analysis.

Methods

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.

Results

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.

Conclusion

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.

Protocol registration

CRD42018092916.

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References

  1. Bahr R, Krosshaug T. Understanding injury mechanisms: a key component of preventing injuries in sport. Br J Sports Med. 2005;39(6):324–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    PubMed  PubMed Central  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.

    PubMed  PubMed Central  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.

    PubMed  PubMed Central  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.

    PubMed  PubMed Central  Google Scholar 

  6. Kiesel KB, Butler RJ, Plisky PJ. Prediction of injury by limited and asymmetrical fundamental movement patterns in American football players. J Sport Rehabil. 2014;23(2):88–94.

    Article  PubMed  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.

    PubMed  PubMed Central  Google Scholar 

  8. Dorrel B, Long T, Shaffer S, Myer GD. The functional movement screen as a predictor of injury in National Collegiate Athletic Association Division II athletes. J Athl Train. 2018;53(1):29–34.

    Article  PubMed  PubMed Central  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.

    PubMed  PubMed Central  Google Scholar 

  10. Tee JC, Klingbiel JF, Collins R, Lambert MI, Coopoo Y. Preseason Functional Movement Screen component tests predict severe contact injuries in professional rugby union players. J Strength Cond Res. 2016;30(11):3194–203.

    Article  PubMed  Google Scholar 

  11. Wiese BW, Boone JK, Mattacola CG, McKeon PO, Uhl TL. Determination of the Functional Movement Screen to predict musculoskeletal injury in intercollegiate athletics. Athl Train Sports Health Care. 2014;6(4):161–9.

    Article  Google Scholar 

  12. Dorrel BS, Long T, Shaffer S, Myer GD. Evaluation of the Functional Movement Screen as an injury prediction tool among active adult populations: a systematic review and meta-analysis. Sports Health. 2015;7(6):532–7.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bonazza NA, Smuin D, Onks CA, Silvis ML, Dhawan A. Reliability, validity, and injury predictive value of the Functional Movement Screen: a systematic review and meta-analysis. Am J Sports Med. 2016;45(3):725–32.

    Article  PubMed  Google Scholar 

  14. Moran RW, Schneiders AG, Mason J, Sullivan SJ. Do Functional Movement Screen (FMS) composite scores predict subsequent injury? A systematic review with meta-analysis. Br J Sports Med. 2017;51(23):1661–9.

    Article  PubMed  Google Scholar 

  15. Chalmers S, Fuller JT, Debenedictis TA, Townsley S, Lynagh M, Gleeson C, et al. Asymmetry during preseason Functional Movement Screen testing is associated with injury during a junior Australian football season. J Sci Med Sport. 2017;20(7):653–7.

    Article  PubMed  Google Scholar 

  16. Chalmers S, Debenedictis TA, Zacharia A, Townsley S, Gleeson C, Lynagh M, et al. Asymmetry during Functional Movement Screening and injury risk in junior football players: a replication study. Scand J Med Sci Sports. 2018;28(3):1281–7.

    Article  CAS  PubMed  Google Scholar 

  17. Moher D, Liberati A, Tetzlaff J, Altman D. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg. 2010;8(5):336–41.

    Article  PubMed  Google Scholar 

  18. Orchard J, Hoskins W. For debate: consensus injury definitions in team sports should focus on missed playing time. Clin J Sport Med. 2007;17(3):192–6.

    Article  PubMed  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 

  20. McHugh ML. Interrater reliability: the kappa statistic. Biochem Med. 2012;22(3):276–82.

    Article  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 

  23. Fritz JM, Wainner RS. Examining diagnostic tests: an evidence-based perspective. Phys Ther. 2001;81(9):1546–64.

    Article  CAS  PubMed  Google Scholar 

  24. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Br Med J. 2003;327(7414):557–60.

    Article  Google Scholar 

  25. Avery M, Wattie N, Holmes M, Dogra S. Seasonal changes in functional fitness and neurocognitive assessments in youth ice hockey players. J Strength Cond Res. 2018;32(11):3143–52.

    Article  PubMed  Google Scholar 

  26. Azzam MG, Throckmorton TW, Smith RA, Graham D, Scholler J, Azar FM. The Functional Movement Screen as a predictor of injury in professional basketball players. Curr Orthop Pract. 2015;26(6):619–23.

    Article  Google Scholar 

  27. Bond CW, Dorman JC, Odney TO, Roggenbuck SJ, Young SW, Munce TA. Evaluation of the Functional Movement Screen and a novel basketball mobility test as an injury prediction tool for collegiate basketball players. J Strength Cond Res. 2017;. https://doi.org/10.1519/JSC.0000000000001944.

    Article  PubMed  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.

    PubMed  PubMed Central  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 

  31. Duke SR, Martin SE, Gaul CA. Preseason Functional Movement Screen predicts risk of time-loss injury in experienced male rugby union athletes. J Strength Cond Res. 2017;31(10):2740–7.

    Article  PubMed  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.

    PubMed  PubMed Central  Google Scholar 

  33. Hotta T, Nishiguchi S, Fukutani N, Tashiro Y, Adachi D, Morino S, et al. Functional Movement Screen for predicting running injuries in 18-to 24-year-old competitive male runners. J Strength Cond Res. 2015;29(10):2808–15.

    Article  PubMed  Google Scholar 

  34. Kolodziej M, Jaitner T. Single Functional Movement Screen items as main predictors of injury risk in amateur male soccer players. Ger J Exerc Sport Res. 2018;48(3):349–57.

    Article  Google Scholar 

  35. Lee C-L, Hsu M-C, Chang W-D, Wang S-C, Chen C-Y, Chou P-H, et al. Functional Movement Screen comparison between the preparative period and competitive period in high school baseball players. J Exerc Sci Fit. 2018;16(2):68–72.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Martin C, Olivier B, Benjamin N. The Functional Movement Screen in the prediction of injury in adolescent cricket pace bowlers: an observational study. J Sport Rehabil. 2017;26(5):386–95.

    Article  PubMed  Google Scholar 

  37. Mokha M, Sprague PA, Gatens DR. Predicting musculoskeletal injury in National Collegiate Athletic Association Division II athletes from asymmetries and individual-test versus composite Functional Movement Screen scores. J Athl Train. 2016;51(4):276–82.

    Article  PubMed  PubMed Central  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 

  39. Philp F, Blana D, Chadwick EK, Stewart C, Stapleton C, Major K, et al. Study of the measurement and predictive validity of the Functional Movement Screen. BMJ Open Sport Exerc Med. 2018;4(1):e000357.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Rusling C, Edwards KL, Bhattacharya A, Reed A, Irwin S, Boles A, et al. The functional movement screening tool does not predict injury in football. Prog Orthop Sci. 2015;1(2):41–6.

    Article  Google Scholar 

  41. Schroeder J, Wellmann K, Stein D, Braumann KM. The Functional Movement Screen for injury prediction in male amateur football. Dtsch Z Sportmed. 2016;67(2):39–43.

    Article  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 

  43. Smith PD, Hanlon MP. Assessing the effectiveness of the Functional Movement Screen in predicting non-contact injury rates in soccer players. J Strength Cond Res. 2017;31(12):3327–32.

    Article  PubMed  Google Scholar 

  44. Walbright PD, Walbright N, Ojha H, Davenport T. Validity of functional screening tests to predict lost-time lower quarter injury in a cohort of female collegiate athletes. Int J Sports Phys Ther. 2017;12(6):948–59.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Warren M, Smith CA, Chimera NJ. Association of the Functional Movement Screen with injuries in Division I athletes. J Sport Rehabil. 2015;24(2):163–70.

    Article  PubMed  Google Scholar 

  46. Zalai D, Panics G, Bobak P, Csaki I, Hamar P. Quality of functional movement patterns and injury examination in elite-level male professional football players. Acta Physiol Hung. 2015;102(1):34–42.

    Article  PubMed  Google Scholar 

  47. Armstrong R, Greig M. Injury identification: the efficacy of the Functional Movement Screen in female and male rugby union players. Int J Sports Phys Ther. 2018;13(4):605–17.

    Article  PubMed  PubMed Central  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.

  49. Svensson K, Alricsson M, Olausson M, Werner S. Physical performance tests–a relationship of risk factors for muscle injuries in elite level male football players. J Exerc Rehabil. 2018;14(2):282–8.

    Article  PubMed  PubMed Central  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.

  51. McCall A, Fanchini M, Coutts AJ. Prediction: the modern-day sport-science and sports-medicine “quest for the holy grail”. Int J Sports Physiol Perf. 2017;12(5):704–6.

    Article  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.

    PubMed  PubMed Central  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.

  54. Brooks JH, Fuller C, Kemp S, Reddin DB. Epidemiology of injuries in English professional rugby union: part 1 match injuries. Br J Sports Med. 2005;39(10):757–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Chalmers DJ, Samaranayaka A, Gulliver P, McNoe B. Risk factors for injury in rugby union football in New Zealand: a cohort study. Br J Sports Med. 2012;46(2):95–102.

    Article  PubMed  Google Scholar 

  56. Gabbett TJ, Jenkins DG, Abernethy B. Physical collisions and injury in professional rugby league match-play. J Sci Med Sport. 2011;14(3):210–5.

    Article  PubMed  Google Scholar 

  57. Fuller JT, Chalmers S, Debenedictis TA, Townsley S, Lynagh M, Gleeson C, et al. High prevalence of dysfunctional, asymmetrical, and painful movement in elite junior Australian Football players assessed using the Functional Movement Screen. J Sci Med Sport. 2017;20(2):134–8.

    Article  PubMed  Google Scholar 

  58. Portas MD, Parkin G, Roberts J, Batterham AM. Maturational effect on Functional Movement Screen™ score in adolescent soccer players. J Sci Med Sport. 2016;19(10):854–8.

    Article  PubMed  Google Scholar 

  59. Shimokochi Y, Shultz SJ. Mechanisms of noncontact anterior cruciate ligament injury. J Athl Train. 2008;43(4):396–408.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Hodgson L, Gissane C, Gabbett TJ, King DA. For debate: consensus injury definitions in team sports should focus on encompassing all injuries. Clin J Sport Med. 2007;17(3):188–91.

    Article  PubMed  Google Scholar 

  61. Cholewicki J, Silfies SP, Shah RA, Greene HS, Reeves NP, Alvi K, et al. Delayed trunk muscle reflex responses increase the risk of low back injuries. Spine. 2005;30(23):2614–20.

    Article  PubMed  Google Scholar 

  62. Burgess D, Naughton G, Norton K. Quantifying the gap between under 18 and senior AFL football: 2003 and 2009. Int J Sports Physiol Perf. 2012;7(1):53–8.

    Article  Google Scholar 

  63. Romiti M, Finch CF, Gabbe B. A prospective cohort study of the incidence of injuries among junior Australian football players: evidence for an effect of playing-age level. Br J Sports Med. 2008;42(6):441–6.

    Article  CAS  PubMed  Google Scholar 

  64. O’Sullivan P. Diagnosis and classification of chronic low back pain disorders: maladaptive movement and motor control impairments as underlying mechanism. Man Ther. 2005;10(4):242–55.

    Article  PubMed  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 

  66. Bonacci J, Chapman A, Blanch P, Vicenzino B. Neuromuscular adaptations to training, injury and passive interventions. Sports Med. 2009;39(11):903–21.

    Article  PubMed  Google Scholar 

  67. Ozmun JC, Mikesky AE, Surburg PR. Neuromuscular adaptations following prepubescent strength training. Med Sci Sports Exerc. 1994;26(4):510–4.

    Article  CAS  PubMed  Google Scholar 

  68. Ramsay JA, Blimkie C, Smith K, Garner S, Macdougall JD, Sale DG. Strength training effects in prepubescent boys. Med Sci Sports Exerc. 1990;22(5):605–14.

    Article  CAS  PubMed  Google Scholar 

  69. Myer GD, Ford KR, Brent JL, Hewett TE. Differential neuromuscular training effects on ACL injury risk factors in” high-risk” versus” low-risk” athletes. BMC Musculoskelet Disord. 2007;8(1):39–45.

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. International Centre for Allied Health Evidence (iCAHE), University of South Australia, GPO Box 2471, Adelaide, SA, 5000, Australia

    Emma Moore & Steve Milanese

  2. Exercise and Sport Science, Faculty of Health Sciences, The University of Sydney, Sydney, NSW, Australia

    Samuel Chalmers

  3. Sport and Exercise Science, School of Science and Health, Western Sydney University, Sydney, NSW, Australia

    Samuel Chalmers

  4. Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, Australia

    Joel T. Fuller

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  1. Emma Moore
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  2. Samuel Chalmers
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  3. Steve Milanese
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  4. Joel T. Fuller
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Contributions

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.

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Correspondence to Emma Moore.

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Funding

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.

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Moore, E., Chalmers, S., Milanese, S. et al. Factors Influencing the Relationship Between the Functional Movement Screen and Injury Risk in Sporting Populations: A Systematic Review and Meta-analysis. Sports Med 49, 1449–1463 (2019). https://doi.org/10.1007/s40279-019-01126-5

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