Western Australian Academy of Performing Arts (WAAPA)
Australian Research Training Program Scholarship (RTP)
Objective This field-based study aimed to determine the association between pre-professional student dancers’ movement quantity and quality with (i) pain severity and (ii) pain related disability. Methods Pre-professional female ballet and contemporary dance students (n = 52) participated in 4 time points of data collection over a 12-week university semester. At each time point dancers provided self-reported pain outcomes (Numerical Rating Scale as a measure of pain severity and Patient Specific Functional Scale as a measure of pain related disability) and wore a wearable sensor system. This system combined wearable sensors with previously developed machine learning models capable of capturing movement quantity and quality outcomes. A series of linear mixed models were applied to determine if there was an association between dancers’ movement quantity and quality over the 4 time points with pain severity and pain related disability. Results Almost all dancers (n = 50) experienced pain, and half of the dancers experienced disabling pain (n = 26). Significant associations were evident for pain related disability and movement quantity and quality variables. Specifically, greater pain related disability was associated with more light activity, fewer leg lifts to the front, a shorter average duration of leg lifts to the front and fewer total leg lifts. Greater pain related disability was also associated with higher thigh elevation angles to the side. There was no evidence for associations between movement quantity and quality variables and pain severity. Discussion Despite a high prevalence of musculoskeletal pain, dancers’ levels of pain severity and disability were generally low. Between-person level associations were identified between dancers’ movement quantity and quality, and pain related disability. These findings may reflect dancers’ adaptations to pain related disability, while they continue to dance. This proof-of-concept research provides a compelling model for future work exploring dancers’ pain using field-based, serial data collection.
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