Diagnostic accuracy |
Bardenett et al., 20153232. 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 FMSTM be used as a predictor of injury? Int J Sports Phys Ther. 2015;10(3):303-8.
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AUC=0.49; IG versus NIG (P=0.95); s=0.56; e=0,38, +LR=0.91; -LR=1.14 |
Bushman et al., 20152222. Bushman TT, Grier TL, Canham-Chervak M, Anderson MK, North WJ, Jones BH. The Functional Movement Screen and injury risk: association and predictive value in active men. Am J Sports Med. 2016;44(2):297-304. doi: 10.1177/0363546515614815 https://doi.org/10.1177/0363546515614815...
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FMS (cutoff=14): NTI: s=37%; e=81%; PPV=43%; NPV=77% AUC: 61%; TI: s=28%; e=77%; PPV =19%; NPV=85% AUC: 54%; AI: s=33%; e=82% VPP=52%; VPN=68% AUC: 60% |
Garrison et al., 20153838. 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.
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FMS (cutoff=14): s=0.67, e= 0.73, +LR=2.51; -LR=0.45; FMS (cutoff=14) + past injuries: s= 0.65, e=0.89, +LR=5.88; -LR=0.39 |
Hammes et al., 20163333. Hammes D, Aus der Fünten K, Bizzini M, Meyer T. Injury prediction in veteran football players using the Functional Movement ScreenTM. J Sports Sci [Internet]. 2016;34(14):1371-9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26939907. doi: doi: 10.1080/02640414.2016.1152390 https://doi.org/doi:...
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FMS (cutoff=14): AUC (any injury)=0.56; CI 95%=0.47-0.64; P=0.17; AUC (NTI)=0.55; CI 95%=0.46-0.64, P=0.30) |
Kiesel et al., 20071111. Kiesel K, Plisky PJ, Voight ML, Glaws KR, Juneau CM, Becker LC, et al. Can serious injury in professional football be predicted by a preseason functional movement screen? North Am J Sport Phys Ther. 2007;2(3):147-58.
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FMS (cutoff=14): s=0.54 (CI 95%=0.34-0.68); e=0.91 (CI 95%=0.83-0.96), +LR=5.92 (CI 95%=1.97-18.37), -LR=0.51 (CI 95%=0.34-0.79) |
Kiesel et al., 20143434. 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. doi: 10.1123/jsr.2012-0130 https://doi.org/10.1123/jsr.2012-0130...
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FMS (cutoff=14): s=0.26 (CI 95%=0.18-0.36), e= 0.87 (CI 95%=0.84-0.90; FMS (cutoff=14)+asymmetry: e=0.87 (CI 95% 0.84-0.90) |
Chorba et al., 20103535. Chorba RS, Chorba DJ, Bouillon LE, Overmyer CA, Landis JA. 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
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FMS (cutoff=14): s=0.579 (CI 95%=0.335-0.798); e=0.737 (CI 95%=0.488-0.909); +LR=2.200 (CI 95%=0.945-5.119) |
Mokha et al, 20163939. 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(2). doi: 10.4085/1062-6050-51.2.07 https://doi.org/10.4085/1062-6050-51.2.0...
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FMS (cutoff=14): s=26,3%; e=58.7% |
Dossa et al., 20143636. 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 Chiropr Assoc. 2014;58(4):421-7.
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s=0.5 (CI 95%=0.189-0.811); e=0.7 (CI 95%=0.348-0.930); +LR=1.67 (CI 95%=0.54-5.17); -LR=0.71 (CI 95%=0.34-1.50); PPV=62.50% (CI 95%=0.25-0.91); NPV=58.33% (CI 95%=0.28-0.85) |
McGill et al., 20152626. McGill SM, Frost DM, Lam T, Finlay T, Darby K, Cannon J. Can fitness and movement quality prevent back injury in elite task force police officers? A 5-year longitudinal study. Ergonomics. 2015;139:1-8. doi: 10.1080/00140139.2015.1035760 https://doi.org/10.1080/00140139.2015.10...
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s=0.28 (low back pain) e 0.42 (AI); e=0.76 (low back pain) and 0.47 (AI); P=NR |
O’Connor et al., 20112727. O'Connor FG, Deuster PA, Davis J, Pappas CG, Knapik JJ. Functional movement screening: Predicting injuries in officer candidates. Med Sci Sports Exerc. 2011;43(12):2224-30. doi: 10.1249/MSS.0b013e318223522d https://doi.org/10.1249/MSS.0b013e318223...
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s=0.452 (AI), 0.12 (severe injury) and 0.13 (NTI). e=0.782 (AI), 0.939 (severe injury) and 0.901 (NTI). AUC=0.58 (AI), 0.53 (severe injury) and 0.52 (NTI). |
Weise et al., 20143737. Weise W, Boone J, Mattacola C, McKeon P, Lee T. Determination of the functional movement screen to predict musculoskeletal injury in intercollegiate athletics - PROQUEST. Athl Train Sport Healthc. 2014;6(4):161-9. doi: 10.3928/19425864-20140717-01 https://doi.org/10.3928/19425864-2014071...
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AUC=0.491 (P=0,854), s=0.495;1- e=0.429, +LR=1.154 (P=0.819); UL injuries (FMS cutoff =17): AUC =0.483 (P=0.769); s=0.500; 1-e=0.464; +LR =1.078 (P=0.765) LL injuries (FMS cutoff =17): AUC=0.486 (p=0.766); s=0.480; 1-e=0.464; +LR =1.035(P=0.762) NTI: AUC=0.490 (P=0.846); s=0.232; 1-e=0.216; RVP=1.075(P=0.843); Injuries with time-loss > 10 days: AUC=0.422(P=0.194), s=0.996; e=0.974; +LR=0.992(P=0.187) |
Tee et al., 20162929. Tee JC, Klingbiel JFG, Collins R, Lambert M, 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. doi: 10.1519/JSC.0000000000001422 https://doi.org/10.1519/JSC.000000000000...
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s=0.83 (CI 95%=0.52-0.98); e=0.46 (CI 95%=0.35-0.48); AUC=0.68 and P=0.049 |
Warren et al., 20152828. Warren M, Smith CA, Chimera NJ. Association of the Functional Movement Screen with injuries in division I athletes. J Sport Rehabil. 2015;24:163-70. doi: 10.1123/jsr.2013-0141. https://doi.org/10.1123/jsr.2013-0141...
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s=0.54; e=0.46; AUC=0.48 |
Kodesh et al., 20152424. Kodesh E, Shargal E, Kislev-Cohen R, Funk S, Dorfman L, Samuelly G, et al. Examination of the effectiveness of predictors for musculoskeletal injuries in female soldiers. J Sport Sci Med. 2015;515-21.
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s=0.42; e=0.63; AUC=0.51 |
Odds Ratios |
Bushman et al., 20152222. Bushman TT, Grier TL, Canham-Chervak M, Anderson MK, North WJ, Jones BH. The Functional Movement Screen and injury risk: association and predictive value in active men. Am J Sports Med. 2016;44(2):297-304. doi: 10.1177/0363546515614815 https://doi.org/10.1177/0363546515614815...
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FMS (cutoff=14): OR=1.96 (P=0.01) |
Garrison et al., 20153838. 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.
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FMS (cutoff=14): OR=5.71; CI 95%=2.73-11.51 |
Kiesel et al., 20071111. Kiesel K, Plisky PJ, Voight ML, Glaws KR, Juneau CM, Becker LC, et al. Can serious injury in professional football be predicted by a preseason functional movement screen? North Am J Sport Phys Ther. 2007;2(3):147-58.
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FMS (cutoff=14): OR=11.67 (CI 95%=2.47-54.52) |
Chorba et al., 20103535. Chorba RS, Chorba DJ, Bouillon LE, Overmyer CA, Landis JA. 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
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FMS (cutoff=14): OR=3.850 (CI 95%=0.980-15.13) |
Mokha et al, 20163939. 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(2). doi: 10.4085/1062-6050-51.2.07 https://doi.org/10.4085/1062-6050-51.2.0...
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FMS (cutoff=14): OR=2.07 (P=0.15). FMS (cutoff=14)+asymmetry: OR=5.27 (CI 95%=1.93-14.40; P=0.001) |
O’Connor et al., 20112727. O'Connor FG, Deuster PA, Davis J, Pappas CG, Knapik JJ. Functional movement screening: Predicting injuries in officer candidates. Med Sci Sports Exerc. 2011;43(12):2224-30. doi: 10.1249/MSS.0b013e318223522d https://doi.org/10.1249/MSS.0b013e318223...
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OR: AI (OR=2.0; (CI 95%=1.3-3.1, P=0.002), NTI (OR=1.4; CI 95%=0.71-2.6, P=0.35); severe injuries (OR=2.0; CI 95%=1.0-4.1; P=0.05) |
Weise et al., 20143737. Weise W, Boone J, Mattacola C, McKeon P, Lee T. Determination of the functional movement screen to predict musculoskeletal injury in intercollegiate athletics - PROQUEST. Athl Train Sport Healthc. 2014;6(4):161-9. doi: 10.3928/19425864-20140717-01 https://doi.org/10.3928/19425864-2014071...
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FMS (cutoff=17): AI-OR =1.425 (P=0.392); CI 95%=NR; UL injuries-OR=1.134 (P=0.793); LL injuries-OR=1.113 (P=0.789); FMS (cutoff=18): NTI-OR=0.949 (P=0.926); FMS (cutoff=12): Injuries with “time-loss” >10 days: OR=2.154 (P=0.380). |
Lisman et al., 20132323. Lisman P, O'Connor FG, Deuster PA, Knapik JJ. Functional movement screen and aerobic fitness predict injuries in military training. Med Sci Sports Exerc. 2013;45(4):636-43. doi: 10.1249/MSS.0b013e31827a1c4c https://doi.org/10.1249/MSS.0b013e31827a...
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ST/FMS (cutoff=14): AI-OR=2.04 (CI 95%=1.32-3.15) and P=0.001; NTI-OR=1.34 (CI 95%=0.70-2.56) and P=0.382; TI-OR=1.92 (CI 95%=1.21-3.02) and P=0.005; LT/FMS (cutoff=14): AI-OR=2.10 (CI 95% =1.34-3.29) and P=0.001; TI-OR=1.80 (CI 95%=1.12-2.89) and P=0.015; NTI: NR. |
Butler et al., 20132525. Butler RJ, Contreras M, Burton LC, Plisky PJ, Goode A, Kiesel K. Modifiable risk factors predict injuries in firefighters during training academies. Work. 2013;46(1):11-7. doi: 10.3233/WOR-121545 https://doi.org/10.3233/WOR-121545...
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OR: 1.21 (CI 95%=1.01-1.42) |
Tee et al., 20162929. Tee JC, Klingbiel JFG, Collins R, Lambert M, 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. doi: 10.1519/JSC.0000000000001422 https://doi.org/10.1519/JSC.000000000000...
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OR=4,3 (CI 95%=0.9-21.0) |
Warren et al., 20152828. Warren M, Smith CA, Chimera NJ. Association of the Functional Movement Screen with injuries in division I athletes. J Sport Rehabil. 2015;24:163-70. doi: 10.1123/jsr.2013-0141. https://doi.org/10.1123/jsr.2013-0141...
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OR=1.01 (CI 95%=0.53-1.91) |
Kodesh et al., 20154141. Chen H, Cohen P, Chen S. How big is a big odds ratio? Interpreting the magnitudes of odds ratios in epidemiological studies. Commun Stat - Simul Comput. 2010;39(4):860-4. doi: 10.1080/03610911003650383 https://doi.org/10.1080/0361091100365038...
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OR=0.98 (CI 95%=0.87-1.10) |
Relative Risk |
Bushman et al., 20152222. Bushman TT, Grier TL, Canham-Chervak M, Anderson MK, North WJ, Jones BH. The Functional Movement Screen and injury risk: association and predictive value in active men. Am J Sports Med. 2016;44(2):297-304. doi: 10.1177/0363546515614815 https://doi.org/10.1177/0363546515614815...
|
FMS (cutoff=14): RR=1.86 (NTI) and RR=1.49 (AI) - P=0.01) |
Kiesel et al., 20143434. 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. doi: 10.1123/jsr.2012-0130 https://doi.org/10.1123/jsr.2012-0130...
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FMS (cutoff=14): RR=1.87 (CI 95%=1.20-2.96) |
Mokha et al, 20163939. 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(2). doi: 10.4085/1062-6050-51.2.07 https://doi.org/10.4085/1062-6050-51.2.0...
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FMS (cutoff=14): RR=2.73 (CI 95% =1.36 - 5.44; P=0.001); FMS (cutoff=18): RR=0.56 (CI 95%=0.34-0.93) |
O’Connor et al., 20112727. O'Connor FG, Deuster PA, Davis J, Pappas CG, Knapik JJ. Functional movement screening: Predicting injuries in officer candidates. Med Sci Sports Exerc. 2011;43(12):2224-30. doi: 10.1249/MSS.0b013e318223522d https://doi.org/10.1249/MSS.0b013e318223...
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ST - FMS (cutoff=14): RR (AI)= 1.91 (CI 95%=1.21-3.01; P<0.01); LT - FMS (cutoff=14): RR (AI)=1.65 (CI 95%=1.05-2.59; p=0.03); ST+LT: RR (AI)=1.5 (P=0.003) |
Azzam et al., 20153030. 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. doi: 10.1097/BCO.0000000000000296 https://doi.org/10.1097/BCO.000000000000...
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RR=0.86 (CI 95%=0.42-1.76) |
Martin et al., 20164040. Martin AC, 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. doi: 10.1123/jsr.2016-0073 https://doi.org/10.1123/jsr.2016-0073...
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RR=0.59 (CI 95%=0.16-2.20) |
Kodesh et al., 20154141. Chen H, Cohen P, Chen S. How big is a big odds ratio? Interpreting the magnitudes of odds ratios in epidemiological studies. Commun Stat - Simul Comput. 2010;39(4):860-4. doi: 10.1080/03610911003650383 https://doi.org/10.1080/0361091100365038...
|
RR*=1.49 (CI 95%=0.998-2.23) |