IJSPT
ORIGINAL RESEARCH
RELATIONSHIP BETWEEN FUNCTIONAL MOVEMENT SCREENING SCORE AND HISTORY OF INJURY Amir Letafatkar1 Malihe Hadadnezhad1 Sadredin Shojaedin1 Elham Mohamadi2
ABSTRACT Background and Aim: The Functional Movement Screen (FMSTM) is a screening instrument that evaluates selective fundamental movement patterns. The main aim of this study was to investigate the relationship between the FMSTM score and history of injury, and attempt to determine which active students are prone to injury. Methods: One hundred physically active (50 females and 50 males) students, between 18 and 25 years of age, with no recent (.80 for all tests that comprise the FMSTM. The average inter-tester reliability between tester was high for FMSTM tests (ICC =.877-.932). The FMSTM, developed by Cook and Burton, was used in the study. The subjects tested in the study were evaluated on the FMSTM using the standard 0-3 ordinal system. A score of 3 was given for performing the specific movement perfectly, a 2 was given when the movement was completed with some compensatory movements observed, a score of 1 was given when the subject could not complete the movement, and a score of 0 was given if pain was present during the movement. The FMSTM includes seven movement tests: the deep squat, hurdle step, in-line lunge, shoulder mobility, active straight leg raise, trunk stability push-up, and rotary stability tests. The composite score for all seven movements of the FMSTM was recorded and then compared with the injury documentation and tracking of the lower
extremity that occurred throughout the season, which was achieved by the teams’ specific athletic trainer and sports medicine staff. The injury documentation was completed after each team exposure, where an exposure was considered one athlete per practice or game (based on the time of session/ practice/game). Any acute lower extremity injury that occurred and kept the athlete out of participation for one or more full consecutive exposures was counted as an injury. If an athlete suffered multiple or repeated acute injuries during the competition season, only the first injury incident was included in this analysis. Therefore, an athlete could not appear more than once in the “injured” group’s analysis. Data analysis To determine if there was a significant difference in FMSTM scores between athletes that were injured and athletes that were not injured during the regular competitive seasons, independent t-tests were performed. To determine if there was a significant difference between sports, body parts of injured subjects, and mechanism of injury, one-way analyses of variance were used. To determine cut-off scores, a receiveroperator characteristic (ROC) curve was used to plot sensitivity (true positives) versus 1-specificity (false positives) for the screening test.A A 2x2 contingency table was produced in order to dichotomize the athletes that suffered an injury and those who did not, as well as those who were above or below the specified cutoff score. From the table, odds ratios, likelihood ratios, sensitivity and specificity were calculated. Chi-square tests were used to evaluate if there were any significant differences between males and females in the distribution of scores for the different tests. The Intra-class Correlation Coefficient (ICC model 3,1) was used to establish the inter-rater reliability for the FMSTM composite score,
A
Receiver operator characteristic curves are a plot of false positives against true positives for all cut-off values. The area under the curve of a perfect test is 1.0 and that of a useless test, no better than tossing a coin, is 0.5. Many clinical tests are used to confirm or refute the presence of a disease or further the diagnostic process. Ideally such tests correctly identify all patients with the disease, and similarly correctly identify all patients who are disease free. In other words, a perfect test is never positive in a patient who is disease free and is never negative in a patient who is in fact diseased. Most clinical tests fall short of this ideal.
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 23
and the unweighted Kappa statistic was used to establish the inter-rater reliability measurement for each item. The inter-rater reliability data were interpreted according to the categories defined by Landis and Koch. A Kappa value over 86% represents excellent agreement. All calculations were performed using SPSS (version 16.0) and the a priori level of significance was set at p ≤ 0.05.
Table 2. Inter-rater reliability of individual FMSTM tests.
RESULTS One hundred subjects participated in the study, 50 females and 50 males. Table 1 presents the subject’s demographic information. Table 2 presents the interrater reliability results for the individual FMSTM tests, with levels of agreement ranging from substantial to excellent. The inter-rater reliability (ICC) of the composite score for both testers was .92, which indicates excellent reliability. The composite mean scores on the FMSTM for females, males and the entire sample were 16.3±1.2, 16.9±1.9, and 16.7±1.8 respectively. These scores are presented in Table 3. Differences observed between males and females in trunk stability push-up, the rotary stability, active SLR, and shoulder mobility tests were significant. There were significant differences between football, handball, and basketball sport groups. Basketball players had lower scores in all seven FMSTM tests.
Table 3. FMSTM individual test scores for males and females.
For all subjects, a cut-off score of 17 was used that maximized sensitivity (0.645) and specificity (0.780). These findings resulted in a positive likelihood ratio (Sensitivity/1--Specificity) of 2.46 and a negative likelihood ratio (1-Sensitivity/Specificity) of 0.621 (Table 4). An overall odds ratio was calculated at 4.70, meaning that an athlete has an approximately 4.7 time greater chance of suffering a lower extremity injury during a regular season by scoring less than 17 on the FMS™. By using the cut-off score of 17 Table 1. Demographic characteristics of subjects.
Table 4. Odds Ratio and Sensitivity/Specificity calculations by FMS Scores.
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 24
Table 5. 2x2 Contingency Table for FMSTM score data
a 2×2 contingency table was created to dichotomize the subjects by their FMS™ score and injury status after the regular competitive season (Table 5). There was a statistically significant difference between the pre-season FMSTM scores of the injured and the non-injured groups (t100=3.60; p=.005). A one-way ANOVA revealed a statistically significant difference between the ankle injury group, knee injury group, and no injury group (F2,99= 3.43; p=.030). The Bonfferoni post hoc testing demonstrated that the differences existed between the ankle injury group and no injury group (p=.021), as well as between the knee injury group and no injury group (P=.030); but not between the ankle injury group and knee injury group (p=.101). The one-way ANOVA did reveal statistically significant differences between the groups with a contact injury, non-contact injury or no injury (F2,99=2.11; p=0.010). The Bonfferoni post hoc testing demonstrated that differences existed between the noncontact injury and no injury groups (p=.032), as well as between the contact injury and no injury groups (p=.013); but not between the contact and non-contact injury groups (p=.217). DISCUSSION This study was designed to determine if a battery of functional assessment tests relating to athletic performance could be used to predict lower extremity injury risk in a select group of subjects. Because of compensations which may occur along the kinetic chain during movements, isolation of individual body parts may be necessary to determine if the subject is at, above, or below average in a certain area. The most difficult challenge will be to determine which tests are the most appropriate to use during the screening process. The composite score for all seven components of the FMSTM test was recorded and then compared with
the injury documentation and tracking of the lower extremity injuries that occurred throughout the season by the teams’ specific athletic trainer and sports medicine staff. The mean composite score reported in this study is lower than that reported for a group of professional male football players2. It might be expected that professional football players score better than the average athlete due to their intensive training regimens, however, in a subsequent study on a similar cohort the mean pre-intervention composite score was 11.8 for “lineman” and nonlineman.5 The difference may relate to the cohort studied, the specific training regimens undertaken by each team or familiarity with the FMSTM testing procedures. Cowen14 studied male and female firefighters whose mean baseline FMSTM score was also lower than the current study at 13.25. In the latter two studies the composite FMSTM score significantly increased following an exercise-based intervention. Based on this study, males were on average better on the trunk stability push-up and the rotary stability tests than females, and females performed better on the active straight leg raise and the shoulder mobility items. The trunk stability push-up is associated with upper body strength and stability (including core stability in the sagittal plane), the rotary stability test with transverse plane (rotational) core stability, the active straight leg raise with flexibility in the hamstring muscles, and the shoulder mobility test with range of motion in the shoulder complex and thoracic spine.12 The sex differential finding was supported by Kibler et al in a study that investigated 2107 athletes from a variety of sports inclusive of junior high to college levels.13 The rotary stability test demands trunk stability in the sagittal and transverse planes during asymmetric movement of the upper and lower extremities.12 The FMSTM training manual comments that it is difficult to obtain a score of 3 (only 1 subject did so in the present study) but it is included to capture elite performance. The authors believe that the proper and perfect implementation of this test is not applicable for all, and maybe only some professional athletes are able to perform this test without error. The rotary stability test does however provide the potential to measure change following a specific exercise-training program targeted at asymmetric or multi-planar trunk stability.
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 25
In this study 27% of the participants had a score of 14 or less which might indicate a potentially higher risk of injury. This is in comparison to the 22% of the professional football players in the Kiesel et al study2 and 89% in the subsequent study by Kiesel et al5 who scored below a 14 and were statistically deemed to be more likely to be injured. The cutoff score of 14 was determined in a study on 46 professional football players but, because of the small sample size and the fact that the target group didn’t represent a general athletic population, the authors of the current study suggest that this cutoff value should be used with caution. Further studies need to be conducted on other athletic and occupational groups before determining a substantiated cutoff value.6,15,16 Preliminary studies with the FMSTM have attempted to examine risk of injury in a small number of NFL football players.2,13 Kiesel et al retrospectively analyzed the relationship between FMSTM scores for National Football League (NFL) football players and the likelihood of serious injury.2 FMS scores were obtained before the start of the season for 46 NFL players, and a score of ≤14 was found to positively predict serious injury with a specificity of 0.91 and sensitivity of 0.54; the odds of sustaining a serious injury was 11.7 times higher in those with an FMS score ≤14 compared with those with a score >14. Kiesel et al also noted lower scores among those who had been injured compared to those without injury.2 In the present study, when the authors compared entry FMSTM scores by no injury versus any injury, the scores were the same and the odds ratio for sustaining a serious injury was 2.0; the sensitivity and specificity were 0.67 and 0.90, respectively. Interestingly, two groups have reported that FMSTM did not predict injury: one study was with 60 marathon runners17 and another was on 112 basketball players.18 Hoover et al.17 reported 8.3% sensitivity and 94.5% specificity for marathon runners, whereas Sorenson’s18 data yielded a sensitivity and specificity of 53.8% and 52.3%, respectively, for basketball players. The low sensitivity is problematic because sensitivity above 50% is desirable so those predisposed to injury can be identified early and potentially rehabilitated before injury. Although some reports of specificity are high, this is in large part explained by the small proportion of the cohort with scores ≤14.2,4,6,17-19
The current study displays the need for additional research of this nature to be conducted. Athletics has transformed into a business at the collegiate and professional levels. There seem to be unlimited possibilities as far as pre-season screening tests and collection of injury data in multiple sports at the collegiate and professional level. CONCLUSION The results of the current study demonstrated that pre-season FMSTM scores show a relationship with injury in Kharazmi University athletes. Furthermore, those who scored less than 17 on the FMS were 4.7 times more likely to sustain an injury of the lower extremity. More research is still necessary before implementing the FMSTM into a PPE for athletics, but due to the low cost and simplicity of implementation, it should be considered as a screening tool by clinicians and researchers in the future. As more evidence becomes available on the FMSTM, it could be an effective tool to use when screening athletes and determining potential risk for injury. REFERENCES 1. Brown MT. The Ability of the Functional Movement Screen in Predicting Injury Rates in Division I Female Athletes. The University of Toledo, 2011. 2. Kiesel K, Plisky PJ, Voight ML. 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. 3. 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. 4. Narducci E, Waltz A, Gorski K, Leppla L, Donaldson M. The clinical utility of functional performance tests within one year post ACL reconstraction: a systematic review. Int J Sports Phys The.r 2011, 6(4): 333-42. 5. Kiesel K, Plisky P, Butler R. Functional movement test scores improve following a standardized offseason intervention program in professional football players. Scand J Med Sci Sports. 2011; 21(2):287-92. 6. Peate WF, Bates G, Lunda K, Francis S, Bellamy K. Core strength: a new model for injury prediction and prevention. J Occup Med Toxicol. 2007; 2: 3. 7. Hewett TE, Ford KR, Hoogenboom BJ, Myer GD. Understanding and preventing ACL injuries: Current Biomechanical and Epidemiologic Considerations. N Am J Sports Phys Ther.
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 26
8. Cook G, Burton L, Fields K, Kiesel K. The Functional Movement Screen. Danville, VA: Athletic Testing Services, Inc; 1998. 9. Nadler SF, Moley P, Malanga GA. Functional deficits in athletes with a history of low back pain: A pilot study. Arch Phys Med Rehabil. 2002;88: 1753-58. 10. Battie MC, Bigos SJ, Fisher LD. Isometric lifting strength as a predictor of industrial back pain reports. Spine. 1989;14:851-856. 11. Cook G, Burton L, Hoogenboom B. The use of fundamental movements as an assessment of function- Part I. N Am J Sports Phys Ther. 2006; 2:6272. 12. Cook G, Burton L, Hoogenboom B. Pre-participation screening: The use of fundamental movements as an assessment of function - Part 2. N Am J Sports Phys Ther. 2006;1(3):132-139. 13. Kibler WB, Chandler TJ, Uhl T, Maddux RE. A musculoskeletal approach to the preparticipation physical examination: Preventing injury and improving performance. Am J Sports Med. 1989; 17(4): 525-531. 14. Cowen VS. Functional fitness improvements after a worksite-based yoga initiative. Journal of Body work and Movement Therapy. 2010; 14: 50-54.
15. Teyhen DS, Shaffer SW, Lorenson CL, Halfpap JP, Donofry DF, Walker MJ. The functional movement screen: A Reliability Study. J Orthop Sports Phys Ther. 2012; 42(6): 530-540. 16. Schneiders A, Davidsson A, Horman E, Sullivan J. Functional movement Screen normative values in a young, active population. Int J Sports Phys Ther. 2011, 6(2): 76-82. 17. Hoover D, Killian CB, Bourcier B, Shannon L, Jenny T, Willis R. Predictive validity of the Functional Movement Screeni in a population of recreational runners training for a half marathon. Med Sci Sports Exerc. 2008; 40(5): 219. 18. Sorenson EA. Functional movement screen as a predictor of injury in high school basketball athletes [dissertation]. Eugene (OR): University of Oregon; 2009. 89. 19. O’Connor FG, Deuster PA, Davis J, Pappsa CG, and Knapik JJ. Functional Movement Screening: Predicting Injuries in Officer Candidates. Med Sci Sports Exerc. 2011, 43(12): 2224-30,
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 27
ORIGINAL RESEARCH
RELATIONSHIP BETWEEN FUNCTIONAL MOVEMENT SCREENING SCORE AND HISTORY OF INJURY Amir Letafatkar1 Malihe Hadadnezhad1 Sadredin Shojaedin1 Elham Mohamadi2
ABSTRACT Background and Aim: The Functional Movement Screen (FMSTM) is a screening instrument that evaluates selective fundamental movement patterns. The main aim of this study was to investigate the relationship between the FMSTM score and history of injury, and attempt to determine which active students are prone to injury. Methods: One hundred physically active (50 females and 50 males) students, between 18 and 25 years of age, with no recent (.80 for all tests that comprise the FMSTM. The average inter-tester reliability between tester was high for FMSTM tests (ICC =.877-.932). The FMSTM, developed by Cook and Burton, was used in the study. The subjects tested in the study were evaluated on the FMSTM using the standard 0-3 ordinal system. A score of 3 was given for performing the specific movement perfectly, a 2 was given when the movement was completed with some compensatory movements observed, a score of 1 was given when the subject could not complete the movement, and a score of 0 was given if pain was present during the movement. The FMSTM includes seven movement tests: the deep squat, hurdle step, in-line lunge, shoulder mobility, active straight leg raise, trunk stability push-up, and rotary stability tests. The composite score for all seven movements of the FMSTM was recorded and then compared with the injury documentation and tracking of the lower
extremity that occurred throughout the season, which was achieved by the teams’ specific athletic trainer and sports medicine staff. The injury documentation was completed after each team exposure, where an exposure was considered one athlete per practice or game (based on the time of session/ practice/game). Any acute lower extremity injury that occurred and kept the athlete out of participation for one or more full consecutive exposures was counted as an injury. If an athlete suffered multiple or repeated acute injuries during the competition season, only the first injury incident was included in this analysis. Therefore, an athlete could not appear more than once in the “injured” group’s analysis. Data analysis To determine if there was a significant difference in FMSTM scores between athletes that were injured and athletes that were not injured during the regular competitive seasons, independent t-tests were performed. To determine if there was a significant difference between sports, body parts of injured subjects, and mechanism of injury, one-way analyses of variance were used. To determine cut-off scores, a receiveroperator characteristic (ROC) curve was used to plot sensitivity (true positives) versus 1-specificity (false positives) for the screening test.A A 2x2 contingency table was produced in order to dichotomize the athletes that suffered an injury and those who did not, as well as those who were above or below the specified cutoff score. From the table, odds ratios, likelihood ratios, sensitivity and specificity were calculated. Chi-square tests were used to evaluate if there were any significant differences between males and females in the distribution of scores for the different tests. The Intra-class Correlation Coefficient (ICC model 3,1) was used to establish the inter-rater reliability for the FMSTM composite score,
A
Receiver operator characteristic curves are a plot of false positives against true positives for all cut-off values. The area under the curve of a perfect test is 1.0 and that of a useless test, no better than tossing a coin, is 0.5. Many clinical tests are used to confirm or refute the presence of a disease or further the diagnostic process. Ideally such tests correctly identify all patients with the disease, and similarly correctly identify all patients who are disease free. In other words, a perfect test is never positive in a patient who is disease free and is never negative in a patient who is in fact diseased. Most clinical tests fall short of this ideal.
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 23
and the unweighted Kappa statistic was used to establish the inter-rater reliability measurement for each item. The inter-rater reliability data were interpreted according to the categories defined by Landis and Koch. A Kappa value over 86% represents excellent agreement. All calculations were performed using SPSS (version 16.0) and the a priori level of significance was set at p ≤ 0.05.
Table 2. Inter-rater reliability of individual FMSTM tests.
RESULTS One hundred subjects participated in the study, 50 females and 50 males. Table 1 presents the subject’s demographic information. Table 2 presents the interrater reliability results for the individual FMSTM tests, with levels of agreement ranging from substantial to excellent. The inter-rater reliability (ICC) of the composite score for both testers was .92, which indicates excellent reliability. The composite mean scores on the FMSTM for females, males and the entire sample were 16.3±1.2, 16.9±1.9, and 16.7±1.8 respectively. These scores are presented in Table 3. Differences observed between males and females in trunk stability push-up, the rotary stability, active SLR, and shoulder mobility tests were significant. There were significant differences between football, handball, and basketball sport groups. Basketball players had lower scores in all seven FMSTM tests.
Table 3. FMSTM individual test scores for males and females.
For all subjects, a cut-off score of 17 was used that maximized sensitivity (0.645) and specificity (0.780). These findings resulted in a positive likelihood ratio (Sensitivity/1--Specificity) of 2.46 and a negative likelihood ratio (1-Sensitivity/Specificity) of 0.621 (Table 4). An overall odds ratio was calculated at 4.70, meaning that an athlete has an approximately 4.7 time greater chance of suffering a lower extremity injury during a regular season by scoring less than 17 on the FMS™. By using the cut-off score of 17 Table 1. Demographic characteristics of subjects.
Table 4. Odds Ratio and Sensitivity/Specificity calculations by FMS Scores.
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 24
Table 5. 2x2 Contingency Table for FMSTM score data
a 2×2 contingency table was created to dichotomize the subjects by their FMS™ score and injury status after the regular competitive season (Table 5). There was a statistically significant difference between the pre-season FMSTM scores of the injured and the non-injured groups (t100=3.60; p=.005). A one-way ANOVA revealed a statistically significant difference between the ankle injury group, knee injury group, and no injury group (F2,99= 3.43; p=.030). The Bonfferoni post hoc testing demonstrated that the differences existed between the ankle injury group and no injury group (p=.021), as well as between the knee injury group and no injury group (P=.030); but not between the ankle injury group and knee injury group (p=.101). The one-way ANOVA did reveal statistically significant differences between the groups with a contact injury, non-contact injury or no injury (F2,99=2.11; p=0.010). The Bonfferoni post hoc testing demonstrated that differences existed between the noncontact injury and no injury groups (p=.032), as well as between the contact injury and no injury groups (p=.013); but not between the contact and non-contact injury groups (p=.217). DISCUSSION This study was designed to determine if a battery of functional assessment tests relating to athletic performance could be used to predict lower extremity injury risk in a select group of subjects. Because of compensations which may occur along the kinetic chain during movements, isolation of individual body parts may be necessary to determine if the subject is at, above, or below average in a certain area. The most difficult challenge will be to determine which tests are the most appropriate to use during the screening process. The composite score for all seven components of the FMSTM test was recorded and then compared with
the injury documentation and tracking of the lower extremity injuries that occurred throughout the season by the teams’ specific athletic trainer and sports medicine staff. The mean composite score reported in this study is lower than that reported for a group of professional male football players2. It might be expected that professional football players score better than the average athlete due to their intensive training regimens, however, in a subsequent study on a similar cohort the mean pre-intervention composite score was 11.8 for “lineman” and nonlineman.5 The difference may relate to the cohort studied, the specific training regimens undertaken by each team or familiarity with the FMSTM testing procedures. Cowen14 studied male and female firefighters whose mean baseline FMSTM score was also lower than the current study at 13.25. In the latter two studies the composite FMSTM score significantly increased following an exercise-based intervention. Based on this study, males were on average better on the trunk stability push-up and the rotary stability tests than females, and females performed better on the active straight leg raise and the shoulder mobility items. The trunk stability push-up is associated with upper body strength and stability (including core stability in the sagittal plane), the rotary stability test with transverse plane (rotational) core stability, the active straight leg raise with flexibility in the hamstring muscles, and the shoulder mobility test with range of motion in the shoulder complex and thoracic spine.12 The sex differential finding was supported by Kibler et al in a study that investigated 2107 athletes from a variety of sports inclusive of junior high to college levels.13 The rotary stability test demands trunk stability in the sagittal and transverse planes during asymmetric movement of the upper and lower extremities.12 The FMSTM training manual comments that it is difficult to obtain a score of 3 (only 1 subject did so in the present study) but it is included to capture elite performance. The authors believe that the proper and perfect implementation of this test is not applicable for all, and maybe only some professional athletes are able to perform this test without error. The rotary stability test does however provide the potential to measure change following a specific exercise-training program targeted at asymmetric or multi-planar trunk stability.
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 25
In this study 27% of the participants had a score of 14 or less which might indicate a potentially higher risk of injury. This is in comparison to the 22% of the professional football players in the Kiesel et al study2 and 89% in the subsequent study by Kiesel et al5 who scored below a 14 and were statistically deemed to be more likely to be injured. The cutoff score of 14 was determined in a study on 46 professional football players but, because of the small sample size and the fact that the target group didn’t represent a general athletic population, the authors of the current study suggest that this cutoff value should be used with caution. Further studies need to be conducted on other athletic and occupational groups before determining a substantiated cutoff value.6,15,16 Preliminary studies with the FMSTM have attempted to examine risk of injury in a small number of NFL football players.2,13 Kiesel et al retrospectively analyzed the relationship between FMSTM scores for National Football League (NFL) football players and the likelihood of serious injury.2 FMS scores were obtained before the start of the season for 46 NFL players, and a score of ≤14 was found to positively predict serious injury with a specificity of 0.91 and sensitivity of 0.54; the odds of sustaining a serious injury was 11.7 times higher in those with an FMS score ≤14 compared with those with a score >14. Kiesel et al also noted lower scores among those who had been injured compared to those without injury.2 In the present study, when the authors compared entry FMSTM scores by no injury versus any injury, the scores were the same and the odds ratio for sustaining a serious injury was 2.0; the sensitivity and specificity were 0.67 and 0.90, respectively. Interestingly, two groups have reported that FMSTM did not predict injury: one study was with 60 marathon runners17 and another was on 112 basketball players.18 Hoover et al.17 reported 8.3% sensitivity and 94.5% specificity for marathon runners, whereas Sorenson’s18 data yielded a sensitivity and specificity of 53.8% and 52.3%, respectively, for basketball players. The low sensitivity is problematic because sensitivity above 50% is desirable so those predisposed to injury can be identified early and potentially rehabilitated before injury. Although some reports of specificity are high, this is in large part explained by the small proportion of the cohort with scores ≤14.2,4,6,17-19
The current study displays the need for additional research of this nature to be conducted. Athletics has transformed into a business at the collegiate and professional levels. There seem to be unlimited possibilities as far as pre-season screening tests and collection of injury data in multiple sports at the collegiate and professional level. CONCLUSION The results of the current study demonstrated that pre-season FMSTM scores show a relationship with injury in Kharazmi University athletes. Furthermore, those who scored less than 17 on the FMS were 4.7 times more likely to sustain an injury of the lower extremity. More research is still necessary before implementing the FMSTM into a PPE for athletics, but due to the low cost and simplicity of implementation, it should be considered as a screening tool by clinicians and researchers in the future. As more evidence becomes available on the FMSTM, it could be an effective tool to use when screening athletes and determining potential risk for injury. REFERENCES 1. Brown MT. The Ability of the Functional Movement Screen in Predicting Injury Rates in Division I Female Athletes. The University of Toledo, 2011. 2. Kiesel K, Plisky PJ, Voight ML. 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. 3. 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. 4. Narducci E, Waltz A, Gorski K, Leppla L, Donaldson M. The clinical utility of functional performance tests within one year post ACL reconstraction: a systematic review. Int J Sports Phys The.r 2011, 6(4): 333-42. 5. Kiesel K, Plisky P, Butler R. Functional movement test scores improve following a standardized offseason intervention program in professional football players. Scand J Med Sci Sports. 2011; 21(2):287-92. 6. Peate WF, Bates G, Lunda K, Francis S, Bellamy K. Core strength: a new model for injury prediction and prevention. J Occup Med Toxicol. 2007; 2: 3. 7. Hewett TE, Ford KR, Hoogenboom BJ, Myer GD. Understanding and preventing ACL injuries: Current Biomechanical and Epidemiologic Considerations. N Am J Sports Phys Ther.
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 26
8. Cook G, Burton L, Fields K, Kiesel K. The Functional Movement Screen. Danville, VA: Athletic Testing Services, Inc; 1998. 9. Nadler SF, Moley P, Malanga GA. Functional deficits in athletes with a history of low back pain: A pilot study. Arch Phys Med Rehabil. 2002;88: 1753-58. 10. Battie MC, Bigos SJ, Fisher LD. Isometric lifting strength as a predictor of industrial back pain reports. Spine. 1989;14:851-856. 11. Cook G, Burton L, Hoogenboom B. The use of fundamental movements as an assessment of function- Part I. N Am J Sports Phys Ther. 2006; 2:6272. 12. Cook G, Burton L, Hoogenboom B. Pre-participation screening: The use of fundamental movements as an assessment of function - Part 2. N Am J Sports Phys Ther. 2006;1(3):132-139. 13. Kibler WB, Chandler TJ, Uhl T, Maddux RE. A musculoskeletal approach to the preparticipation physical examination: Preventing injury and improving performance. Am J Sports Med. 1989; 17(4): 525-531. 14. Cowen VS. Functional fitness improvements after a worksite-based yoga initiative. Journal of Body work and Movement Therapy. 2010; 14: 50-54.
15. Teyhen DS, Shaffer SW, Lorenson CL, Halfpap JP, Donofry DF, Walker MJ. The functional movement screen: A Reliability Study. J Orthop Sports Phys Ther. 2012; 42(6): 530-540. 16. Schneiders A, Davidsson A, Horman E, Sullivan J. Functional movement Screen normative values in a young, active population. Int J Sports Phys Ther. 2011, 6(2): 76-82. 17. Hoover D, Killian CB, Bourcier B, Shannon L, Jenny T, Willis R. Predictive validity of the Functional Movement Screeni in a population of recreational runners training for a half marathon. Med Sci Sports Exerc. 2008; 40(5): 219. 18. Sorenson EA. Functional movement screen as a predictor of injury in high school basketball athletes [dissertation]. Eugene (OR): University of Oregon; 2009. 89. 19. O’Connor FG, Deuster PA, Davis J, Pappsa CG, and Knapik JJ. Functional Movement Screening: Predicting Injuries in Officer Candidates. Med Sci Sports Exerc. 2011, 43(12): 2224-30,
The International Journal of Sports Physical Therapy | Volume 9, Number 1 | February 2014 | Page 27
