Authors: Alex M. Warshaw1, David D. Peterson2, Sharon M. Henry1
1 Rehabilitation and Movement Science Department, University of Vermont, Burlington, VT, USA
2 Kinesiology and Allied Health Department, Cedarville University, Cedarville, OH, USA
Corresponding Author:
David D. Peterson, EdD, CSCS*D
Cedarville University
251 N. Main Street
Cedarville, OH 45314
ddpeterson@cedarville.edu
(937) 766-7761
Dr. Peterson is an associate professor of kinesiology at Cedarville University (CU) and currently serves as the Director of the Multi-Age Physical Education (MAPE) program at CU.
Movement Competency Screen Predicts Performance in Female Military Academy Recruits
ABSTRACT
Musculoskeletal injuries in military populations are a leading cause for reduced physical readiness (15). Utilizing a screening tool that predicts physical performance and injuries could help identify recruits who need remedial training or conditioning. The Movement Competency Screen (MCS) identifies poor movement patterns and suggests safe load levels for individuals (8). This study sought to establish the predictive ability of the MCS for injuries and performance in United States Naval Academy (USNA) recruits over four years. Fifteen female and 26 male recruits completed the MCS upon entry into the academy. The recruits’ Physical Readiness Test (PRT) scores and injury data were collected for eight semesters. Correlations between MCS scores and recruits’ number of injuries, missed “duty days”, and region of injury were identified using Pearson correlation coefficients. Additionally, correlations between MCS scores and recruits’ overall PRT score, number of push-ups, curl-ups, and their 1.5-mile run time were calculated. Within the first year at USNA recruits’ MCS scores correlated with the number of injuries and missed “duty days”; however, this correlation was not sustained. Recruits also experienced the most injuries in the first year. For female recruits, higher MCS scores correlated with better PRT scores, number of push-ups, and 1.5 mile run times. With its high inter- and intra-rater reliability (12), the MCS could be used to identify poor movement patterns and guide remedial training to help prevent future injury. Further research should focus on a larger military population to determine if the MCS’s predictive abilities go beyond a military academy population.
Keywords: reliability, mass screening, United States Armed Forces, military, movement competency, musculoskeletal injury, performance
INTRODUCTION
Musculoskeletal injuries in the United States (U.S.) military are the leading cause of outpatient medical encounters for service members. In 2016, the U.S. Army alone recorded over four million outpatient visits due to musculoskeletal injuries, which accounted for 22% of all outpatient visits (3). Approximately 82% of musculoskeletal injuries treated within the U.S. military population are classified as “overuse” injuries, likely due to poor movement patterns and training techniques (5).
Musculoskeletal injuries are not only responsible for missed duty days in the continental United States, but they are also responsible for the greatest percentage (34%) of combat theatre evacuations, which is even greater than the percent of evacuations due to combat injuries (15). The financial burden of musculoskeletal injuries is immense with half a billion dollars being spent annually on diagnosing and treating musculoskeletal injuries in service members, and a cost of $57,500 for each disability-related discharge (13,15). Thus, the need for a reliable screening tool that could help predict injuries in incoming military recruits is evident and is a top priority for the U.S. military today.
There are a variety of screening tools available that are designed to identify or predict musculoskeletal impairments and injuries. The Functional Movement Screen (FMS) is one of the most popular movement screening tools currently being used to predict injuries in athletic populations (7). Many studies have assessed the ability of the FMS to predict injury in various populations but results have been inconclusive (2). Furthermore, very few studies have used the FMS to assess military populations; those studies that have used the FMS found that there were many other confounding variables that contributed to the number of injuries received in addition to the FMS score (10). Additionally, a study that used the FMS to screen U.S. Marine Corps officer candidates indicated that the validity of the overall FMS score was poor, and that the overall FMS score should only be used with caution (6). Many other movement screening tools exist such as the Star Excursion Balance Test, the Y-Balance Test, the Drop Jump Test, the Landing Error Scoring System, and the Tuck Jump Assessment. However, all of these screening tools focus on injury prediction in only the lower extremities and as such were not considered for this study (2).
The Movement Competency Screen (MCS) is a newer screening tool that evaluates an athlete’s ability to perform five fundamental movement patterns and subsequently designate an external load level most appropriate for that individual (8,9,14,16). The overall MCS score can range from 7-21 and corresponds with either a poor (7-10), moderate (11-16), or good (17-21) rating for the subject’s overall movement competency. Ideally, the overall MCS score would identify individuals who will have an increased risk for injury and poor physical performance in the future.
The MCS and FMS are similar screening tools in both their use of “fundamental movements” and similar scoring systems; however, the MCS stands out from the FMS for multiple reasons. Researchers suggest that the MCS movements be video recorded so that the raters can view each movement pattern multiple times to assess multiple different body regions during each movement, which allows for a more thorough scoring process to occur (8). The MCS also has explicit scoring criteria associated with each individual movement pattern, allowing the raters to simply move through a checklist, which results in very high inter- (ICC= 0.88, 95% CI: 0.81 – 0.93) and intra-rater (ICC= 0.63-0.89) reliability (8,12,16,19) (12). The predictive ability of the MCS as it relates to injuries in various athletic populations over limited time periods has been examined (9,14,16,19). Lastly, the MCS is free to use with no fee imposed for training and requires no equipment to administer the tool, making it relatively simple to implement in new settings.
In addition to injury prediction, measured physical performance is a vital piece of information used in the U.S. military to determine which service members may not be capable of performing physical tasks specific to their duty roles. The United States Naval Academy (USNA) and United States Navy implement the Physical Readiness Test (PRT) to identify service members who are not able to perform at the required levels. The MCS has previously been assessed and tested as a predictor of injuries in athletic populations; however, the MCS has not been used to attempt to predict physical performance.
To our knowledge, the MCS has not yet been used to predict physical performance or injury rates for longer than one year. Furthermore, the MCS has yet to be used to assess a military population. Therefore, the purpose of this study was to establish the predictive ability of the MCS for injury and performance data in USNA Midshipmen. This study also sought to establish the predictive ability of the MCS over time given that the subjects in this study were followed over four academic years (8 semesters) while enrolled at USNA.
METHODS
Subjects and Experimental Approach to the Problem
Subjects were recruited from the USNA upon entry into the academy during the Fall semester of their 4th Class (Freshman) year. A sample of convenience (n=41, 15 females, 26 males) comprised the study sample and these subjects were followed over their four years at the USNA. Each subject was videotaped while performing the MCS and the recordings were sent to the University of Vermont (UVM) where a team of four, second year Doctor of Physical Therapy students and a physical therapist with 33 years of experience evaluated and scored the recordings of each subject’s MCS. Subjects wore face masks and matching USNA apparel in order to maintain confidentiality. All subjects signed an informed consent that was approved by USNA (IRB number: USNA.2014.0016).
Procedures
Scoring of the MCS
The MCS evaluates an athlete’s ability to perform five fundamental movement patterns and subsequently designate a load level most appropriate for that individual (8). The five movement patterns in the MCS consist of a body weight squat, a lunge and twist, a standard push-up, a bend and pull, and a single leg squat. The raters observed the subject performing each movement three times either in person or through a video recording. Using explicit screening criteria, the rater then identified primary and secondary impairments for each movement pattern using a standard scoring sheet. By adding the number of primary and secondary impairments in accordance with the MCS scoring criteria, the subject was assigned an appropriate load level ranging from 1-3 (assisted, body weight, or external mass, respectively) for each specific movement pattern. Once all five movement patterns were scored, the raters summed the load levels to create an overall MCS score. The overall MCS score corresponded with either a poor (7-10), moderate (11-16), or good (17-21) rating for the subject’s overall movement competency.
Injury Data Collection
Recruits were required to see USNA medical personnel if injured and the data collected from these visits were sent to UVM for analysis. Data included the date of the visit, the reason for the visit, and the start and end dates for any medical waivers provided by the provider. Missed “duty days” were calculated by subtracting the last date of the medical waiver from the first date of the medical waiver plus one day. Additionally, an average number of injuries for each academic year was calculated based on the total number of injuries each subject received within each academic year. For the purpose of this study, the term “injury data” is defined as the number of different injuries the recruits experienced as well as missed “duty days”.
Performance Data Collection
Each subject completed the PRT once per semester with qualified USNA personnel administering the test in accordance with USNA guidelines. The PRT at USNA consisted of the maximum number of push-ups and curl-ups completed in two minutes and a 1.5-mile run as fast as possible. The PRT data for all 41 subjects across all eight semesters were sent to UVM for analysis. The UVM team confirmed the overall PRT scores through the use of the official USNA PRT scoring algorithm based on each subject’s raw scores for maximum number of push-ups and curl-ups completed in two minutes and their 1.5-mile run time.
Statistical Analyses
Correlations between the MCS scores and injury and performance data were determined using Pearson Correlation Coefficients. Pearson Correlation Coefficients were run to compare the MCS scores to injury numbers within each academic year, cumulative missed “duty days” within each academic year, and the region of the body that sustained injuries during the subjects’ time at USNA. Pearson Correlation Coefficients were also run to compare the MCS scores to overall PRT scores for each semester as well as each individual component of the PRT (curl-ups, push-ups, 1.5-mile run). A one-way analysis of variance with post-hoc Tukey tests was run for the average number of injuries sustained in each academic year as well as the average number of missed “duty days” in each year. An alpha level for significance of P = 0.05 was used for all statistical testing.
RESULTS
Injury Data
Overall, higher numbers of injuries were correlated with lower MCS scores in the first (r= -0.32 P= 0.04,), third (P=0.03, r= -0.36), and fourth years (P=0.04, r= -0.04) at USNA (Table 1). Additionally, higher overall cumulative missed duty days were correlated with lower MCS scores in only the first year (P= 0.03, r= -0.34). The MCS scores did not predict the bodily region in which the subject’s injury occurred.
Significant differences in the number of injuries subjects experienced across the four years were demonstrated in a one-way ANOVA (P= 0.004). Subjects experienced significantly more injuries in their first year at the Academy than any other academic year as demonstrated by Post-hoc Tukey tests (Table 2).
No significant differences were found in the numbers of missed “duty days” that subjects experienced across their four years at the academy (P=0.48). When examining females and males separately, there were no significant correlations between injury data (number of injuries and missed duty days) and MCS scores.
Performance Data
Overall, higher MCS scores were correlated with lower (i.e., faster) 1.5-mile run times across all 8 semesters (P=0.002 – 0.04, r = -0.35 to -0.51) as well as greater numbers of curl-ups completed in the first two and a half years (P=0.007- 0.05, r = 0.31 to 0.43), and greater numbers of push-ups completed in the first three years (P=0.001 – 0.04, r = 0.35 to 0.49). For females alone, higher overall PRT scores showed significant correlations with higher MCS scores (P=<0.001 - 0.04, r = 0.65 to 0.91); however, males showed insignificant correlation values (Figure 1) (Table 3).
DISCUSSION
Overall, lower MCS scores were correlated with increased numbers of injuries and missed “duty days” in the first year at USNA, and on average subjects experienced significantly more injuries in their first year at USNA than any other year. Higher MCS scores were correlated with faster 1.5-mile run times over all four years, and females showed strong correlations between higher MCS scores and better overall performance on the PRT across all four years at USNA.
Gender Differences found in MCS and Performance Relationship
The MCS has previously been used to attempt to predict injury risk in athletic populations; however, no prior study has looked at the correlations between MCS scores and physical performance data. The results of this study indicated strong correlations between MCS scores and PRT performance in females; however, no correlations were found between the MCS and PRT performance in males. The clear difference between males and females is reflected in overall injury rates in U.S. military populations. A study that followed 861 U.S. Army basic trainees found that female service members experience an average of twice as many injuries and two and a half times more injuries that result in significant numbers of missed “duty days” when compared to their male counterparts. However, that same study found that when initial physical fitness was controlled there was no longer a link to gender differences. Overall, they found that females tended to enter basic training at a significantly lower level of aerobic fitness (1). The female subjects in this study had an average initial PRT score of 57.4, while their male peers had an average initial PRT score of 78.6 (P=0.03). This finding supports the assertion that females tend to enter training at a lower fitness level, which can also be associated with lower strength levels, resulting in poorer movement patterns. Therefore, our findings that the MCS was predictive of female performance on the PRT would logically be associated with the lower initial fitness level of this female cohort which, in turn, may have resulted in poor movement patterns.
Why the Military Should Consider the MCS
The results of this study suggest that the use of the MCS is feasible in a military population and may hold promise as a screening tool for larger numbers and for identification incoming recruits who demonstrate poor movement patterns. In 2007, 4% of U.S. Army recruits received a medical discharge before completing basic combat training resulting in a financial loss between $33 and $57 million (18). This study demonstrated that the MCS was predictive of injury risk and missed “duty days” in the first year at USNA, which is also when the most injuries appeared to occur. By implementing the MCS at the beginning of basic training, recruits with low MCS scores could receive interventions for their poor movement patterns with the goal preventing future overuse injuries.
Injury prevention programs have previously been shown to be effective in athletic populations, such as the FIFA 11+ injury prevention program that was able to reduce injuries in soccer players (n= 6,344) by 30% (17). The MCS could also be used to predict females’ future PRT performance, which could be beneficial, as fitness levels have been previously linked to injury rates as well (1). Additionally, the MCS would fit well with the military’s needs in that the tool provides suggested load levels for each fundamental movement pattern. Service members are frequently required to carry heavy loads during training and deployments, and these heavy loads have been shown to have negative effects on trunk and lower limb biomechanics (11). By using the MCS to assign incoming recruits with appropriate load levels, those who already exhibit poor movement patterns could receive remedial training prior to having to carry excessive loads.
Previous studies were not able to find any statistically significant correlations between MCS scores and injury rates (14,16,19). Additionally, none of these prior studies looked at the relation between MCS scores and physical performance measures with regard to the athletes’ fitness level (9,14,16,19). In contrast, this study was able to show significant correlations between the MCS and numbers of injuries as well as physical performance on the PRT. The study conducted by Worth et al. looked at movement patterns and injury incidence in cross-country skiers, and the study by Newlands et al. looked at the relationship between low back pain in rowers and the MCS. Both of these studies used monthly self-report questionnaires to collect data on injuries sustained during the study, which could be a cause for error if participants were unable to recall accurately any injuries that had occurred in the previous month. However, both of these studies collected information on previous injuries and found that injury history was a significant predictor of future injury, which our study did not assess.
This study may have been able to produce more significant results because of the unique environment in which the midshipmen live at USNA. Midshipmen at USNA must adhere to a very strict lifestyle that includes significant documentation of any injuries experienced and physical performance changes throughout their time at USNA. Instead of using a self-report method, this study was able to collect injury data directly from subjects’ medical records and physical performance data through standardized PRT scores. This method of collection allowed us to minimize any recall or reporting errors in our data.
Limitations
Due to the small sample size used in this study, it may not be appropriate to generalize these findings to a larger U.S. military population. It is also important to note that all Midshipmen at the USNA were required to participate in either intramural, club, or varsity sports while enrolled, which could have an effect on the numbers of injuries the recruits experienced. Participation in these activities could result not only in a greater number of overuse injuries, but could expose recruits to varying amounts of sports medicine intervention as well as strength and conditioning training, depending on their level of competition. History of injury prior to USNA entrance was not available for the subjects of this study, which could be an important confounding variable in this study. A subjects’ prior injury history is one of the greatest predictors of future injuries so it would be important for future studies to collect that information to create a more complete picture of each subject (4).
PRACTICAL APPLICATIONS
The MCS seems to be a promising tool that could be adopted and implemented by the U.S. military to help predict and prevent future injuries in new recruits if its use is first validated in a larger military population. The MCS was predictive of injury rates and missed “duty days” within a year of the MCS being administered, which is important given that the majority of injuries occurred within the first year of enrollment at USNA for this group of subjects. The MCS was also able to very strongly predict female performance on the PRT for at least four years after the baseline MCS was administered. Before the MCS is adopted by the U.S. military, further studies should be conducted that uses a larger sample size within a larger general military population.
ACKNOWLEDGMENTS
The authors are grateful for the USNA recruits’ participation in this study and the commitment of our USNA colleagues for collecting data from the recruits over four years. The authors would also like to thank Alan B. Howard for his assistance with statistical analyses. No grant support was used for this study.
REFERENCES
1. Bell, NS, Mangione, TW, Hemenway, D, Amoroso, PJ, and Jones, BH. High injury rates among female army trainees: a function of gender? American Journal of Preventive Medicine 18: 141–146, 2000.
2. Chimera, NJ and Warren, M. Use of clinical movement screening tests to predict injury in sport. World Journal of Orthopedics 7: 202–217, 2016.
3. Davis, J. A review of musculoskeletal injuries in the United States army: demographics, risk factors, and the role of the physical therapist in prevention and return to duty. TSAC Report 47:8, 2016.
4. Fulton, J, Wright, K, Kelly, M, Zebrosky, B, Zanis, M, Drvol, C, et al. Injury risk is altered by previous injury: a systematic review of the literature and presentation of causative neuromuscular factors. International Journal of Sports Physical Therapy 9: 583–595, 2014.
5. Hauret, KG, Jones, BH, Bullock, SH, Canham-Chervak, M, and Canada, S. Musculoskeletal injuries: description of an under-recognized injury problem among military personnel. American Journal of Preventive Medicine 38: S61–S70, 2010.
6. Kazman, JB, Galecki, JM, Lisman, P, Deuster, PA, and OʼConnor, FG. Factor structure of the functional movement screen in marine officer candidates. Journal of Strength and Conditioning Research 28: 672–678, 2014.
7. Kraus, K, Schütz, E, Taylor, WR, and Doyscher, R. Efficacy of the functional movement screen: a review. Journal of Strength and Conditioning Research 28: 3571–3584, 2014.
8. Kritz, M. Development, reliability and effectiveness of the movement competency screen (MCS). Doctoral Thesis, Auckland University of Technology, 2012.
9. Lee, L, Reid, D, Cadwell, J, and Palmer, P. Injury incidence, dance exposure and the use of the movement competency screen (MCS) to identify variables associated with injury in full-time pre-professional dancers. International Journal of Sport Physical Therapy 12: 352–370, 2017.
10. Lisman, P, O’Connor, FG, Deuster, PA, and Knapik, JJ. Functional movement screen and aerobic fitness predict injuries in military training. Medicine & Science in Sports & Exercise 45: 636–643, 2013.
11. Loverro, KL, Brown, TN, Coyne, ME, and Schiffman, JM. Use of body armor protection with fighting load impacts soldier performance and kinematics. Applied Ergonomics 46: 168–175, 2015.
12. Milbank, E, Peterson, D, and Henry, S. The reliability and predictive ability of the movement competency screen in a military population. The Sport Journal 19: 1–15, 2016.
13. Molloy, JM, Feltwell, DN, Scott, SJ, and Niebuhr, DW. Physical training injuries and interventions for military recruits. Military Medicine 177: 553–558, 2012.
14. Newlands, C, Reid, D, and Palmar, P. Low back pain incidence in New Zealand rowers and its relationship with functional movement patterns. Physiotherapy e1268–e1269, 2015.
15. Nindl, BC, Williams, TJ, Deuster, PA, Butler, NL, and Jones, BH. Strategies for optimizing military physical readiness and preventing musculoskeletal injuries in the 21st century. US Army Medicine Department Journal 5–23, 2013.
16. Reid, DA, Vanweerd, RJ, Larmer, PJ, and Kingstone, R. The inter and intra rater reliability of the netball movement screening tool. Journal of Science and Medicine in Sport 18: 353–357, 2015.
17. Sadigursky, D, Braid, JA, De Lira, DNL, Machado, BAB, Carneiro, RJF, and Colavolpe, PO. The FIFA 11+ injury prevention program for soccer players: a systematic review. BMC Sports Science, Medicine and Rehabilitation 9: 18, 2017.
18. Swedler, DI, Knapik, JJ, Williams, KW, Grier, TL, and Jones, BH. Risk factors for medical discharge from united states army basic combat training. Military Medicine; Bethesda 176: 1104–10, 2011.
19. Worth, SGA. Movement patterns and injury incidence in cross-country skiers: a prospective cohort study. Doctoral Dissertation, Auckland University of Technology, 2016.
