Dynamic Movement Assessment y Functional Movement Screen para la predicción de la lesión: una revisión sistemática

Bunn, Priscila dos Santos; Silva, Elirez Bezerra da

doi:10.1590/1809-2950/17004225032018

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SYSTEMATIC REVIEW • Fisioter. Pesqui. 25 (3) • Jul-Sep 2018 • https://doi.org/10.1590/1809-2950/17004225032018 copiar

Dynamic Movement Assessment y Functional Movement Screen para la predicción de la lesión: una revisión sistemática

Autoría SCIMAGO INSTITUTIONS RANKINGS

RESUMEN

Evaluación Dinámica del Movimiento^TM (DMA^TM) y Detección del Movimiento Funcional ™ (FMS™) son herramientas para predecir el riesgo de lesiones musculoesqueléticas en individuos que practican actividades físicas. Esta revisión sistemática tuvo como objetivo evaluar la asociación de DMA^TM y FMS^TM con el riesgo de lesiones musculoesqueléticas en diferentes actividades físicas y categorizarlas por análisis. En noviembre de 2016 se llevó a cabo una investigación sin filtros de idioma o de tiempo en las bases de datos MEDLINE, Google Scholar, SciELO, SCOPUS, SPORTDiscus, CINAHL y BVS, utilizando las palabras clave: predicción de lesiones, riesgo de lesiones, sensibilidad, especificidad, detección del movimiento funcional y evaluación dinámica de movimientos. Se incluyeron estudios prospectivos que analizaron la asociación entre DMA^TM y FMS^TM con el riesgo de lesiones musculoesqueléticas en actividades físicas. Los datos extraídos de los estudios fueron: perfil del participante, tamaño de la muestra, criterios de clasificación de la lesión, tiempo de seguimiento y los resultados presentados, subdivididos por el tipo de análisis estadístico. El riesgo de sesgo se realizó con la Escala Newcastle-Ottawa para estudios de cohorte. No se encontró ningún estudio con DMA^TM. Se incluyeron un total de 20 estudios FMS^TM que analizaron uno o más de los siguientes indicadores: precisión diagnóstica (VPP, VPN y ABC), odds ratios (OR) o riesgo relativo (RR). FMS^TM mostró una sensibilidad = del 12 al 99%; especificidad = del 38 al 97%; VPP = del 25 al 91%; VPN = del 28 al 85%; ABC = 0,42 a 0,68; OR = 0,53 a 54,5; y RR = 0,16-5,44. El FMS^TM ha demostrado ser un predictor de lesiones musculoesqueléticas. Sin embargo, debido a limitaciones metodológicas, se debe evitar su uso indiscriminado.

Palabras clave
Transtornos de Traumas Acumulados; Traumatismos en Atletas; Movimiento

Study	Participants	Sample	Injury’s classification criteria	Follow-up time
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...	Military personnel	n=2476 soldiers; Age=18 - 57 years old.	I	6 months
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...	Military personnel	n=447 (ST) and n=427 (LT) marine officer’s students; Age=22.4±2.7 years old.	I	6 (n=427) or 10 weeks (n=447), according with course (ST or LT).
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.	Military personnel	n=158 female soldiers from the Combat Fitness Instructor Course (CFIC) - Israel Defense Forces (IDF); Age=19.0 (18.1 - 20.2) years old.	I	Three months
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...	Military personnel	n=108 firefighter training course students; Age=NR.	III*	4 months
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...	Military personnel	n=53 men, elite police department; Age=37.9±5 years old.	I, II	5 years
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...	Military personnel	n=874 marine officer’s students; Age=18 - 30 years old.	I, III**	6 (n=427) or 10 weeks (n=447), according with course (ST or LT).
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...	Athletes	n=167 college athletes; Age=18 - 24 years old.	I	One Season
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.	Athletes	n=46 football players; Age=NR.	III*	4,5 months
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...	Athletes	n=62 rugby players; Age=NR.	III*	6 months
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...	Athletes	n=34 basketball players; Age=NR.	III#	One season
Clay et al., 2016³¹31. 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.	Athletes	n=45 Division I female collegiate rowers; Age=at least 18 years of age;	III	One season
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.	Athletes	n=185 (97 women, 88 men) high school athletes - several modalities; Age=13 - 18 years old.	II, III	One-quarter (autumn)
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:...	Athletes	n=238 football players >32 years old; Age=44±7 years old.	I, III	9 months
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...	Athletes	n=238 football players; Age=NR.	I, III	One preseason
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	Athletes	n=38 collegiate athletes, women - several modalities; Age=18 - 26 years old.	I, II	One season
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.	Athletes	n=20 hockey players; Age=16 - 20 years old.	II, III	One season (2013-2014)
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...	Athletes	N=144 collegiate athletes - football; Age=18.9±1.3 years old.	I, II, III	One season
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.	Athletes	n=168 collegiate athletes - several modalities Age=17 - 22 years old.	I, II, III	One season
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...	Athletes	n=84 (20 men) - several modalities; Age=20.4±1.3 years old (men) and 19.1±1.2 (women).	I, II, III	One season
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...	Athletes	n=27 adolescent Cricket Pace Bowlers ; Age=13 - 18 years old.	I, II, III	One season

Statistical analysis	Author	Results
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.	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...	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.	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:...	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.	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...	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	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...	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.	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...	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...	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...	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...	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...	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.	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...	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.	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.	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	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...	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...	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...	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...	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...	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...	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...	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...	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...	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...	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...	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...	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...	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)

Domain/study	Selection	Comparability	Outcome	Score	Risk
Bushman et al.²²22. 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...	***	**	*	******	Low
Garrison et al.³⁸38. 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.	**	*	*	****	High
Kiesel et al.¹¹11. 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.	**	-	*	***	High
Lisman et al.²³23. 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...	****	**	*	*******	Low
Butler et al.²⁵25. 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...	****	-	**	******	Uncertain
Chorba et al.³⁵35. 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	***	*	-	****	High
Mokha et al.³⁹39. 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...	***	-	-	***	High
O’Connor et al.²⁷27. 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...	****	**	*	*******	Low
Weise et al.³⁷37. 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...	***	-	*	****	High
Kiesel et al.³⁴34. 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...	**	-	-	**	High
Bardenett et al.³²32. 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.	***	-	**	*****	Uncertain
Dossa et al.³⁶36. 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.	**	-	*	***	High
Hammes et al.³³33. 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:...	***	**	*	******	Low
McGuill et al.²⁶26. 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...	**	-	*	***	High
Tee et al.²⁹29. 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...	***	-	*	****	High
Azzam et al.³⁰30. 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...	**	-	**	****	High
Martin et al.⁴⁰40. 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...	***	-	**	*****	Uncertain
Warren et al.²⁸28. 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...	***	-	**	*****	Uncertain
Kodesh et al.²⁴24. 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.	***	-	*	****	High
Clay et al.³¹31. 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.	***	-	*	****	High

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[1] Corresponding address: Priscila dos Santos Bunn - Universidade do Estado do Rio de Janeiro - Rua São Francisco Xavier, 524, 9th floor - Rio de Janeiro (RJ), Brazil - Zip Code: 20550-900 - E-mail: priscilabunn@yahoo.com.br