Dancers' Perceived and Actual Knowledge of Anatomy

Abstract

Dancers are highly susceptible to musculoskeletal injuries and frequently require interaction with medical professionals. While many dancers have a finely tuned awareness of their bodies, their knowledge of the fundamentals of human anatomy is not uniform. There is a paucity of literature on the benefits of human anatomy education in dancers, though it seems intuitive that there should be a relationship. The purpose of this study was to assess dancers' perceived and actual knowledge of basic musculoskeletal anatomy and its relationship to function. Adult dancers at the undergraduate, pre-professional, and professional levels were surveyed through an anonymous online questionnaire. Questions included demographic information, dance techniques studied, anatomy training, and injury history. Subjects rated their perceived knowledge of anatomy and were tested with 15 multiple-choice questions on basic musculoskeletal anatomy. Four hundred seventy-five surveys were completed. Ordinal regression showed a correlation of perceived to actual knowledge of anatomy (p < 0.001). Factors that correlated with increases in both perceived and actual knowledge of anatomy included having taken an anatomy course of any type (p < 0.001) and increased age (p ≤ 0.001). Years of dance training and professional dancer status both significantly correlated with increased knowledge of anatomy (p < 0.001) but not perceived knowledge. Chi-square analysis showed that dancers with training in either modern or jazz dance had a significantly higher perceived, but not actual, knowledge when compared to those without training in those styles of dance (p < 0.001 and p = 0.011, respectively). In conclusion, dancers generally scored well on questions pertaining to basic musculoskeletal anatomy, and their perception correlated with their actual knowledge of anatomy. Factors that contribute to dancers' knowledge of anatomy include age, years of experience, professional dancer status, and anatomy training.

Associated factors and profile of injuries in professional ballroom dancers in Brazil: a cross-sectional study

“…There are many causes of this type of injury, such as the elevated time dedicated to the modality, the extenuating training load and the lack of structure offered. These are combined with the physical, technical and tactical demands, indicated as possible risk factors for the development of this type of injury, both in dancing and high yield sports 1,20 . The present study also made it possible to identify osteoarticular injuries as the most important among the musculoskeletal injuries, in a sex-independent manner, in agreement with a hybrid cross-sectional study concerning dancing injuries.…”

“…Professional dancers have a training routine that demands artistic excellence, sensitivity to specific physical abilities and advanced techniques 1 . Their performance combines technical and esthetical values and professional dancers are compared to elite athletes, but the training routines may lead to physical exhaustion, reaching their limit 2 .…”

“…Some factors such as the body mass index, clothing (high heeled sandals), time practicing the modality, repetitive movements, sex, and age may be associated with the prevalence of injury 6 . In addition, inadequate technique and space, the floor surface and a lack of dancing equipment may also contribute to the occurrence of injuries 1,7 .…”

“…In high yield athletes, musculoskeletal injuries recur most frequently and can be understood as damage caused to the organism as a consequence of an overload on these structures 8,9 . The prevalence of injury in amateur dancers varies from 40% to 84% 10 , and epidemiological studies estimate that this percentage may reach 90% in professional dancers 1 . Studies point out that the lower limbs are the most affected in professional classical ballet, modern and contemporary dancers 11 , but there are no data available for professional ballroom dancers.…”

Application of Posture Recognition Service System Based on Information Fusion Smart Sensor in Dance Training

“…Due to the influence of noise, etc., there are many nonfalse positive human targets in the candidate body. Then, we calculate the HOG descriptors of the candidate body coverage area in the image and input the calculated HOG features into the support vector machine for classification, so that the false positive sample points of the candidate body set obtained in the first step can be removed [25]. Use the shape context to quickly detect the human target from the inside and outside of the contour image of the shape to obtain a set of points.…”