Wearable sensor discovers dancers dance through pain

Overview: A newly designed wearable sensor shows that many semi-professional and professional dancers continue to dance while experiencing a lot of pain.

Source: Curtin University

A new wearable sensor system developed by Curtin University has tracked dancers’ movement, providing valuable insights into how they adapt to dance through debilitating pain.

The study, published in PLOS ONE, analyzed the movement of 52 pre-professional ballet and contemporary dance students from the Western Australian Academy of Performing Arts (WAAPA). The wearable sensor system was developed as part of a collaboration between the Curtin School of Allied Health and the Curtin Institute for Computation.

Principal investigator Ph.D. student and former professional ballerina Danica Hendry, of the Curtin School of Allied Health, said that while nearly all dancers experienced pain, only half of them had to stop or adjust their movements.

“Fifty of the 52 dancers said they experienced pain, and 26 reported that this pain affected their training/performance. Our findings indicate that the dancers can often continue to dance when they are in pain, and do this by modifying their movements, such as reducing their load,” said Ms Hendry.

“Despite a high prevalence of musculoskeletal pain, pain severity and disability in dancers were generally low, with the lower back and ankle/foot being reported as the most common.”

Ms. Hendry said existing measures, such as activity calendars and schedules, do not capture the specific movements the dancers performed.

“Previous research on dancers’ movement amount has focused on measuring workload simply by looking at the daily time spent dancing,” Ms Hendry said.

“Recently, wearable sensors have been used to determine the exercise intensity of dancers during their daily training, showing that although dancers train for several hours a day, the majority of this time is spent on ‘low to medium intensity’ exercises. .

“Both approaches provide actionable insights; until now, however, there was no way to provide detailed information, such as the number of repetitions of movements.”

Ms. Hendry said existing measures, such as activity calendars and schedules, do not capture the specific movements the dancers performed. Image is in the public domain

Study supervisor and John Curtin Distinguished Professor Peter O’Sullivan, also of the Curtin School of Allied Health, said he was proud to have been involved in the creation of a wearable sensor system that could detect the amount of movement and quality of dancers.

“While athlete tracking systems are commonly adopted in many elite sports, it has only recently emerged in the field of dance and only assesses the amount of movement of dancers,” said Professor O’Sullivan.

“In recent years, much attention has been paid to quantifying athlete training to help understand the development and experience of pain and disability.

“The future application of wearable sensor technology offers clinicians the opportunity to gain a deeper understanding of the interlinkages between pain, disability and movement in athletic populations, in order to better inform person-centred care.

“However, there are some challenges of the wearable sensor system that need to be addressed before more advanced applications can be undertaken.”

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About this pain and neurotech research news

Author: press office
Source: Curtin University
Contact: Press Office – Curtin University
Image: The image is in the public domain

Original research: Open access.
“Motion quantity and quality: how do they relate to pain and disability in dancers?” by Danica Hendry et al. PLOS ONE


Abstract

Movement quantity and quality: how do they relate to pain and disability in dancers?

Objective

This field study aimed to determine the association between the quantity and quality of movements of pre-professional student dancers 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 gave self-reported pain outcomes (Numerical Rating Scale as measure of pain severity and patient-specific functional scale as measure of pain-related disability) and wore a wearable sensor system. This system combined wearable sensors with previously developed machine learning models capable of recording the quantity and quality of movements. A series of linear mixed models were applied to determine whether there was an association between dancers’ movement amount and quality across the 4 time points with pain severity and pain-related disability.

Results

Nearly all dancers (n = 50) experienced pain, and half of the dancers experienced disabling pain (n = 26). Significant associations were evident for pain-related limitations and variables for amount and quality of exercise. More specifically, greater pain-related disability was associated with more light activity, fewer forward leg lifts, shorter mean duration of forward leg lifts, 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 the variables quantity and quality of movement and pain severity.

Discussion

Despite a high prevalence of musculoskeletal pain, levels of pain and disability in dancers were generally low. Inter-person associations were identified between the amount and quality of dancers’ movement, and pain-related disabilities. These findings may reflect dancers’ adaptations to pain-related disabilities as they continue to dance. This proof-of-concept study provides a compelling model for future work investigating dancers’ pain using field-based, serial data collection.

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