The Functional Movement Screen as an injury prediction tool for German physical education and exercise science students: a prospective cohort-study

Abstract

Background Several studies have evaluated the applicability of the Functional Movement Screen (FMS) as a screening tool for injury prediction. However, only few studies investigate gender differences for FMS as a screening tool for female and male college students.

Objective To evaluate gender differences in FMS single items and the overall score. In addition, the applicability of FMS as a diagnostic tool for injury prevention of German exercise students will be investigated.

Method N = 99 college students performed an FMS at the beginning of the semester. Injuries were recorded for the entire term. Gender differences of FMS single items were assessed using the Mann-Whitney-U-Test. Differences in injury prediction were calculated using logistic regression. If the model was statistically significant, diagnostic accuracy was calculated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). The Youden index was used to identify a cut-off score. 2 × 2 contingency tables, sensitivity and specifity, positive/negative predictive values, and likelihood ratios were assessed.

Results There were significant gender differences for Deep Squat, Shoulder Mobility, Trunk Stability Push Up, and Active Straight Leg Raise. The logistic regression showed that the composite score was statistically significant in clarifying the model for females (p = 0.005, R_N ² = 0.14), but not for males (p = 0.18, R_N ² = 0.04). The ROC curve indicated acceptable injury prediction in females (AUC: 0.66, p = 0.02) and poor injury prediction in males (AUC: 0.40, p = 0.19). The cut-off score of ≤ 16 for females resulted in a sensitivity of 63 % and specificity of 54 %. No cut-off score was calculated for males.

Conclusion Females performed better on flexibility items, while males scored higher on strength exercises. Results of the study indicate low predictive accuracy. Therefore, no solid recommendation can be made for the use of the FMS as an injury screening tool for either female or male German exercise science students.

Zusammenfassung

Hintergrund Mehrere Studien haben den Functional-Movement-Screen (FMS) als Screening-Instrument für die Vorhersage von Verletzungen untersucht. Wenige Studien gehen auf Geschlechterunterschiede des FMS als Screening-Instrument für Hochschulstudierende ein.

Ziel Evaluation von geschlechtsspezifischen Unterschieden bei FMS-Einzelitems sowie des Gesamtscore. Zudem wird die Einsetzbarkeit des FMS als Diagnostiktool zur Verletzungsprophylaxe von deutschen Sportstudierenden untersucht.

Methode N = 99 College-Studierende führten zu Beginn des Semesters einen FMS durch. Verletzungen wurden für das gesamte Semester erfasst. Geschlechterunterschiede der FMS-Einzelitems wurden mit dem Mann-Whitney-U-Test ermittelt. Unterschiede bei der Vorhersage von Verletzungen wurden mit logistischer Regression berechnet. Bei statistisch signifikanten Modellen wurde die diagnostische Genauigkeit mittels Receiver-Operating-Characteristic-Kurven (ROC-Kurven) und area under the curve (AUC) berechnet. Der Youden-Index wurde verwendet, um einen optimalen Cut-off-Score für Frauen und Männer zu schätzen. Zur Beurteilung des Verletzungsrisikos wurden Sensitivität und Spezifität, positiv und negativ prädiktive Werte, 2 × 2-Kontingenztabellen und das Risikoverhältnis berechnet.

Ergebnisse Es zeigten sich signifikante Geschlechterunterschiede in den FMS-Einzelitems Deep Squat, Shoulder Mobility, Trunk Stability Push Up und Active Straight Leg Raise. Die logistische Regression zeigte, dass der Gesamtscore statistisch signifikant zur Klärung des Modells für Frauen (p = 0,005, R_N ² = 0,14), aber nicht für Männer (p = 0,18, R_N ² = 0,04) beitragen konnte. Die ROC-Kurven bildeten eine gerade noch akzeptable Vorhersage für Verletzungen bei Frauen (AUC: 0,66, p = 0,02) und eine schlechte Vorhersage für Verletzungen bei Männern (AUC: 0,40, p = 0,19) ab. Für Frauen ergab ein Cut-off-Score von ≤ 16 eine Sensitivität von 63 % und Spezifität von 54 %. Da die AUC für Männer unter 50 % lag, wurde für diese kein Cut-off-Score berechnet.

Schlussfolgerung Frauen hatten bei Flexibilitätsübungen bessere Werte, Männer bei Kraftübungen. Die Ergebnisse der Studie deuten auf eine geringe Vorhersagegenauigkeit hin, sodass keine solide Empfehlung für den Einsatz des FMS als Screening-Instrument für Verletzungen weder für weibliche noch für männliche deutsche Studierende der Sport- und Bewegungswissenschaften gegeben werden kann.

Key words

screening - injury prevention - college student - risk factor

Schlüsselwörter

Screening - Verletzungsprävention - Studierende - Risikofaktor

Publikationsverlauf

Eingereicht: 29. Oktober 2020

Angenommen: 22. März 2021

Artikel online veröffentlicht:
24. Juni 2021

• References

• 1 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: 2740-2747 DOI: 10.1519/jsc.0000000000001838.
• 2 Evans K, Refshauge KM, Adams R. Trunk muscle endurance tests: reliability, and gender differences in athletes. J Sci Med Sport 2007; 10: 447-455 DOI: 10.1016/j.jsams.2006.09.003.
• 3 Zazulak BT, Hewett TE, Reeves NP. et al. Deficits in neuromuscular control of the trunk predict knee injury risk: a prospective biomechanical-epidemiologic study. Am J Sports Med 2007; 35: 1123-1130 DOI: 10.1177/0363546507301585.
• 4 Dallinga JM, Benjaminse A, Lemmink KA. Which screening tools can predict injury to the lower extremities in team sports? A systematic review. Sports Med 2012; 42: 791-815 DOI: 10.1007/bf03262295.
• 5 Cook G, Burton L, Hoogenboom B. Pre-participaton screening: the use of fundamental movements as an assessment of function- part 1. N Am J Sports Phys Ther 2006; 1: 62-72
• 6 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: 132-139
• 7 Tee JC, Klingbiel JF, Collins R. et al. Preseason functional movement screen component tests predict severe contact injuries in professional rugby union players. J Strength Cond Res 2016; 30: 3194-3203 DOI: 10.1519/JSC.0000000000001422.
• 8 Kiesel K, Plisky PJ, Voight ML. Can serious injury in professional football be predicted by a preaseason functional movement screen?. N Am J Sports Phys Ther 2007; 2: 147-158
• 9 O’Connor FG, Deuster PA, Davis J. et al. Functional movement screening: predicting injuries in officer candidates. Med Sci Sports Exerc 2011; 43: 2224-2230 DOI: 10.1249/MSS.0b013e318223522d.
• 10 Budnar RG, Birdwell R, Moody C. et al. FMS scores in relation to injury risk and performance. Int J Exerc Sci 2013; 2
• 11 Peate WF, Bates G, Lunda K. et al. Core strength: a new model for injury prediction and prevention. J Occup Med Toxicol 2007; 2: 1-9 DOI: 10.1186/1745-6673-2-3.
• 12 Warren M, Smith CA, Chimera NJ. Association of the functional movement screen with injuries in division I athletes. J Sport Rehabil 2015; 24: 163-170
• 13 Hotta T, Nishiguchi S, Fukutani N. et al. FMS for predicting running injuries 18–24 year old competitive male runners. J Strength Cond Res 2015; 29: 2808-2815
• 14 Letafatkar A, Hadadnezhad M, Shojaedin S. et al. Relationship between FMS score and history of injury. Int J Sports Phys Ther 2014; 9: 21-27
• 15 Garrison M, Westrick R, Johnson MR. et al. Association between the functional movement screen and injury development in college athletes. Int J Sports Phys Ther 2015; 10: 21-28
• 16 Chorba RS, Chroba DJ, Bouillon LE. et al. Use of a functional movement screening tool to determine injury risk in female collegiate athletes. N Am J Sports Phys Ther 2010; 5: 47-54
• 17 McCunn R, Aus der Fünten K, Fullagar HHK. et al. Reliability and Association with Injury of Movement Screens: A Critical Review. Sports Medicine 2016; 46: 763-781 DOI: 10.1007/s40279-015-0453-1.
• 18 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: 276-282 DOI: 10.4085/1062-6050-51.2.07.
• 19 Bring BV, Chan M, Devine RC. et al. Functional movement screening and injury rates in high school and collegiate runners: a retrospective analysis of 3 prospective observational studies. Clin J Sport Med 2018; 28: 358-363 DOI: 10.1097/jsm.0000000000000459.
• 20 Bond CW, Dorman JC, Odney TO. et al. 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; 6: 1589-1600 DOI: 10.1519/jsc.0000000000001944.
• 21 Lin CY, Casey E, Herman DC. et al. Sex Differences in Common Sports Injuries. PM & R : the journal of injury, function, and rehabilitation. 2018; 10: 1073-1082 DOI: 10.1016/j.pmrj.2018.03.008.
• 22 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: 605-617
• 23 Wang D, Chen J, Lai W. et al. Predictive Value of the Functional Movement Screen for Sports-Related Injury in NCAA Division I Athletes. Orthop J Sports Med 2017; 5 DOI: 10.1177/2325967117S00132.
• 24 Dorrel B, Long T, Shaffer S. et al. The functional movement screen as a predictor of injury in national collegiate athletic association division II Athletes. J Athl Train 2018; 53: 29-34 DOI: 10.4085/1062-6050-528-15.
• 25 Knapik JJ, Cosio-Lima LM, Reynolds KL. et al. Efficacy of the FMS for Coast Guard Cadets. J Strength Cond Res 2015; 29: 1157-1162
• 26 Loudon JK, Parkerson-Mitchell AJ, Hildebrand LD. et al. Functional movement screen scores in a group of running athletes. J Strength Cond Res 2014; 28: 909-913 DOI: 10.1097/jsc.0000000000000233.
• 27 Cohen J. Statistical power analysis. Current Directions in Psychological Science 1992; 1: 98-101
• 28 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: 132-139
• 29 Fuller CW, Ekstrand J, Junge A. et al. Consensus statement on injury definitions and data collection procedures in studies of football (soccer) injuries. Clin J Sport Med 2006; 16: 97-106
• 30 Cohen J. Statistical power analysis for the behavioral sciences. Hillsdals, NJ: Erlbaum; 1988
• 31 Kumar R, Indrayan A. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatrics 2011; 48: 277-287 DOI: 10.1007/s13312-011-0055-4.
• 32 Bahr R. Why screening tests to predict injury do not work-and probably never will: a critical review. Br J Sports Med 2016; 50: 776-780
• 33 Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 2010; 5: 1315-1316 DOI: 10.1097/JTO.0b013e3181ec173d.
• 34 Walbright PD, Walbright N, Ojha H. et al. 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: 948-959
• 35 Anderson BE, Neumann ML, Huxel Bliven KC. Functional movement screen differences between male and female secondary school athletes. J Strength Cond Res 2015; 29: 1098-1106 DOI: 10.1519/jsc.0000000000000733.
• 36 Chimera NJ, Smith CA, Warren M. Injury history, sex, and performance on the functional movement screen and Y balance test. J Athl Train 2015; 50: 475-485 DOI: 10.4085/1062-6050-49.6.02.
• 37 Kiesel K. Functional Movement Screen Updates: Rotary Stability and Ankle Clearing. FunctionalMovement.com February 14, 2020. Chatham, VA 24531
• 38 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: 276-282 DOI: 10.4085/1062-6050-51.2.07.
• 39 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: 605-617
• 40 Bender R. Interpretation of efficacy measures derived from 2 X 2 tables for the evaluation of diagnostic tests and treatment. Med Klin (Munich) 2001; 96: 116-121 DOI: 10.1007/pl00002179.
• 41 Šimundić AM. Measures of Diagnostic Accuracy: Basic Definitions. Ejifcc 2009; 19: 203-211
• 42 Gabbe BJ, Finch CF, Bennell KL. et al. How valid is a self reported 12 month sports injury history?. Br J Sports Med 2003; 37: 545-547 DOI: 10.1136/bjsm.37.6.545.
• 43 Jenkins P, Earle-Richardson G, Slingerland DT. et al. Time dependent memory decay. Am J Ind Med 2002; 41: 98-101 DOI: 10.1002/ajim.10035.
• 44 Lisman PJ, O'Connor FG, Deuster PA. et al. Functional movement screen and aerobic fitness predict injuries in military training. Med Sci Sports Exerc 2013; 45: 636-643 DOI: 10.1249/MSS.0b013e31827a1c4c.