Conkin, Charise

Reece, Catherine

Orr, Rob Marc

Schram, Ben

Hinton, Benjamin

Pope, Rodney R

Ross, Kayla

Dawes, James

English

OBJECTIVE: To (1) critically review research examining the relationship between movement screens and injury, (2) to synthesize their findings, and (3) to explore any emerging differences in the use of movement screens between tactical and athletic populations on movement screens and their ability to predict injury. METHODS: Key databases (CINAHL, OVID, PubMed and SportsDiscus) were searched in September 2017 using variations on the search terms: movement screen(ing), FMS, SFMA and injur*. An inclusion and exclusion criteria were applied and the remaining studies appraised using the Downs and Blacks tool and Kennelly’s grading system. Key data was then extracted from the articles and tabulated. RESULTS This review included 8 tactical (mean age=22.4± 2.7) and 10 athletic (mean age= 21.4±2.3). With a methodological quality ranged from 14.29% to 58.93%. Within the studies the FMS was the most popular screening tool (12 of 18 studies), other studies included: Basketball Mobility Test (BMT), Y balance test, Physical Fitness Test (PFT), Movement Competency Screen (MCS) and Physical Readiness Test (PRT). Combined results showed a mean FMS score of 13.2-16.6 has been reported to predict injury. The FMS was reported to not have enough discriminative power or significance to predict injury in 6 of 18 studies. Components of the PFT, MCS, PRT also found significant relationships to predict injury. CONCLUSION: The clinical use of movement screens to predict injury should be used with caution in both tactical and athletic populations. The majority of the studies evaluated were either of poor quality or unable to provide a strong significant finding. Movement screens provide insight into quality of movement and perhaps future directions for conditioning programs. Key Words: FMS, injury, prediction, movement screen(ing), tactical, athletic.<br/

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Bond UniversityResearch RepositoryThe Ability of Movement Screening Tools to Predict Injury in Athletic and Tactical PopulationsConkin, Charise; Reece, Catherine; Orr, Rob Marc; Schram, Ben; Hinton, Benjamin; Pope,Rodney R; Ross, Kayla ; Dawes, JamesPublished: 07/04/2018Document Version:Publisher's PDF, also known as Version of recordLink to publication in Bond University research repository.Recommended citation(APA):Conkin, C., Reece, C., Orr, R. M., Schram, B., Hinton, B., Pope, R. R., ... Dawes, J. (2018). The Ability ofMovement Screening Tools to Predict Injury in Athletic and Tactical Populations. Poster session presented at2018 Rocky Mountain American College of Sports Medicine Annual Meeting, Colorado Springs, United States.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.For more information, or if you believe that this document breaches copyright, please contact the Bond University research repositorycoordinator.Download date: 10 May 2019Injury prediction using movement screens could save the economy billions of dollars each year in both athletic and tactical populations in terms of rehabilitation costs and productivity loss.1  Movement screens assist in identifying compensations, which can create poor movement patterns, alter biomechanics, and can have the potential to create micro- or macro traumatic injury.2 Introduction ResultsMovement Screens Ability to Predict Injury▪ Combined result from all studies: mean FMS score of 13.2-16.6 has been reported to predict injury.▪ Uninjured population mean FMS score: 12.6-16.3▪ 6 of 18 studies reported the FMS did not have enough discriminative power to or significance to predict injury▪ Components of the PFT, MCS, PRT found significant relationships to predict injuryDiscussionIn a quarter of the studies reviewed, movement screens were able to predict injury, however; the majority of studies were not able to draw a strong statistical conclusion. The quality of these studies were also evaluated as fair to poor. The main weaknesses and limitations of the studies reviewed included small sample sizes, no consensus of injury definition, short surveillance period and low sensitivities for FMS scores. Confounding variables that may have affected the ability to predict injury included: previous injuries, age, gender and exercise history. However these variables were poorly reported. References1. 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. The American journal of sports medicine. 2016;44(2):297-304.2. Cook G, Burton L, Hoogenboom B. Pre-participation screening: the use of fundamental movements as an assessment of function - part 1. N Am J Sports Phys Ther. 2006;1(2):62-72.3. Kiesel KB, Butler RJ, Plisky PJ. Prediction of Injury by Limited and Asymmetrical Fundamental Movement Patterns in American Football Players. Journal of Sport Rehabilitation. 2014;23(2):88-94.4. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. Journal of Epidemiology & Community Health. 1998;52(6):377-384.5. Kennelly J. Methodological approach to assessing the evidence. Reducing Racial/Ethnic Disparities in Reproductive and Perinatal Outcomes: Springer; 2011:7-19.A search of key databases (CINAHL, OVID, PubMed and SportsDiscus) using dedicated search terms was used to inform this literature review. The databases used included CINAHL, OVID, PubMEd and SportsDiscus with variation on search terms including movement screen(ing), FMS, SFMA and injur*The inclusion criteria were: a) human subjects, b) adults, c) injury sustained, and d) movement screen completed. The exclusion criteria were: comparing injured vs uninjured population, b) study population included participants under 18 years of age, c) injury obtained prior to initial assessment, d) not a research article, e) not injury specific, f) unable to be translated into English, g) provided specific training to decrease likelihood of injury.Included studies were evaluated using the modified Downs and Blacks4 tool. The Kennelly’s5 grading system was used to rate the quality of each paper.Following appraisal, key data were extracted from the articles and tabulated, including the design, demographics, movement screen and examiner, injury collection, criteria for injury, findings, injury risk factors, reported weaknesses/limitations and CAS.C. Conkin1, C. Reece1, R. Orr1,2, R. Pope3, B. Schram1,2, B. Hinton4, K. Ross4, J. Dawes 51 Faculty of Health Sciences and Medicine, Bond University: Australia,  2 Tactical Research Unit, Bond University: Australia 3 Charles Sturt University, Albury: Australia; 4New South Wales Police Force, Sydney: Australia5 University of Colorado Colorado Springs, USATHE ABILITY OF MOVEMENT SCREENING TOOLS TO PREDICT INJURY IN THE ATHLETIC AND TACTICAL POPULATIONSMETHODSConclusionSeven Movement Patterns of the Functional Movement ScreenThe clinical use of movement screens to predict injury should be used with caution in both tactical and athletic populations. The majority of studies evaluated were either of poor quality or unable to provide a strong significant finding. Movement screens provide insight into quality of movement and future directions for conditioning programs. Movement screens such as: Star Excursion Balance Test, Landing Error Scoring System, Y Balance Test and Functional Movement Screen (FMS) have attempted to predict injury. Created in 2005, the FMS™ is a popular screening tool that has been applied to multiple sporting populations in attempt to predict injury3The purpose of the critical review was to (1) critically review research examining the relationship between movement screens and injury, (2) to synthesize their findings, and (3) to explore any emerging differences in the use of movement screens between tactical and athletic populations on movement screens and their ability to predict injury. Figure 1. PRISMA flow diagram identifying literature search, screening and eligibility results.Study Characteristics8 Tactical and 10 Athletic studies▪ Tactical: firefighters, soldiers, Maritime Security, US Coast Guards, US Naval Midshipmen, Marine CorpsFindings: 18 to 30 years (mean=22.4± 2.7), with one study including 18 to 57 years ▪ Athletic: basketball, football, Soccer, track and field, and collegiate athletesFindings: 18 to 25 years (mean= 21.4±2.3) with one population >32 yearsMovement Screens▪ FMS (used in 12 of 18 articles)▪ Basketball Mobility Test (BMT), upper and lower Y balance test, Marine Corps Physical Fitness Test (PFT), Movement Competency Screen (MCS) and Physical Readiness Test (PRT)SummaryThe quality of the studies reviewed were evaluated as fair or poor utilizing the Downs and Black critical review tool. Upon comparison of athletic versus tactical populations, the percentage of male versus females, the quality of studies, and the FMS cut-off scores were relatively similar. Differences were present in sample sizes and the mean FMS scores. The average sample size was 9x larger for tactical populations. In addition, the FMS scores for athletic populations were also one point lower than the tactical population for both injured and non-injured FMS scores. Research Quality The studies ranged from 14.29% to 58.93% for methodological quality using the Downs and Blacks appraisal tool2018 RMACSM Annual MeetingApril 6-7, 2018

The Ability of Movement Screening Tools to Predict Injury in Athletic and Tactical Populations

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The Ability of Movement Screening Tools to Predict Injury in Athletic and Tactical Populations

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