The functional movement screen as a predictor of mechanical loading in dancers.

Introduction/aims The Functional Movement Screen (FMS)1 has been used as a predictor of performance2 and injury3. Dance requires effective functional movement, with implications for the biomechanical response to performance. This study investigated the efficacy of the FMS in predicting mechanical loading during the Dance Aerobic Fitness Test (DAFT)4. Methods Twenty-six university dancers (20 females, 6 males, age: 20.0 ±1.5 years, height: 161 ± 0.08 cm, mass: 58.40kg ± 6.2 kg) completed the FMS as directed by a physiotherapist trained in FMS assessment (Intra-rater reliability ICC = 0.98). Dancers also completed the first four stages of the DAFT, with a GPS-mounted (MinimaXx S4: Catapult Innovations, Scoresby, Australia) with a triaxial accelerometer (Kionx KX 94, Kionx, Ithaca, New York, USA) located at the cervico-thoracic junction and mid-scapulae (approximating to C7) in a standardised garment. Accelerometry data was sampled at 100Hz and used to calculate total accumulated PlayerLoad over the duration of the DAFT. Linear regression analysis was used to determine the strength of correlation between FMS and PlayerLoad. Stepwise hierarchical modelling was performed in order to establish which of the FMS elements were the primary predictors of mechanical loading. Results The mean FMS score was 17.1 ± 2.1. The mean total accumulated PlayerLoad was 146.65 ± 22.58 a.u. The linear correlation coefficient determining the strength of the relationship between total FMS and PlayerLoad was r = 0.67. Stepwise hierarchical ordering revealed that the Deep Squat element was the primary predictor of mechanical loading (r = 0.42). Conclusion Total FMS score was able to predict 67% of the variance in total PlayerLoad during a dance-specific exercise test. Individual differences in technique, levels of aerobic fitness, and the anatomical placement of the GPS unit might contribute to the unaccounted variance. Lower-limb placement of the GPS might further strengthen this predictive power, and could be integrated as a rehabilitative tool to monitor performance and loading changes post injury. The hierarchical modelling of the FMS elements revealed the Deep Squat as the best predictor, which may relate to the movement pattern of the DAFT and its high degree of neuromuscular control. References 1. Cook, G, Burton, L, Hoogenboom, B (2006a) Pre-participation screening: the use of fundamental movements as an assessment of function-part 1. North American Journal of Sports Physical Therapy 1 (2): 62-72 2. Okada, T, Hubel, KC, Neser, TW (2011) Relationship between core stability, functional movement and performance. The Journal of Strength and Conditioning Research 25(1): 252-261. 3. Chorba, R, Chorba, DJ, Bouillon, LE, Overmyer, CA, Landis, JA (2010) Use of a functional movement screening tool to determine injury risk in female collegiate athletes. North American Journal of Sports Physical Therapy 5 (2): 47-54. 4. Wyon, M, Redding, E, Grant, A, Head, Andrew, Sharp, N, Craig, C (2003) Development, Reliability, and Validity of a Multistage Dance Specific Aerobic Fitness Test (DAFT). Journal of Dance Medicine and Science 7 (3): 80-84 (5).