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Background:
Musculoskeletal (MSK) pain and injuries are common in endurance sports where athletes are required to perform at high intensity for long periods of time. In the short term, MSK pain may significantly impair the athletes' performance, which can lead to unwanted time-off from practice and competitive tournaments. Previous studies found an association between training load, MSK pain and performance. These results indicate that an athlete may experience MSK pain or get injured from both too low and to high training loads.
Electronic sports (E-sports), also known as competitive gaming, is defined by Hamari and Sjöblom as "a form of sports where the primary aspects of the sport is facilitated by electronic systems; the input of players and teams as well as the output of the E-sports system are mediated by human-computer interference".
There are only few data on MSK pain in E-sports, however a small study with 65 participants found that 41% suffered from back or neck pain and more than 1 in 3 had pain related to the wrist. E-sports athletes have to perform for an extended period of time, similar to athletes from traditional endurance sport. As such, MSK pain in E-sports may be associated with training load like it is seen in other sports. Therefore, MSK pain in E-sports could be an unrecognised issue. To provide health professionals with and optimal starting point for managing these issues, there is a need for well-conducted studies on the prevalence of MSK pain among E-sports athletes. In addition, it is highly relevant to investigate if training loads related to E-sports and physical activity levels are different among athletes with MSK pain compared to athletes without MSK pain.
Aims:
The aims of this questionnaire-based cross-sectional study are to; I) investigate the prevalence of MSK pain in E-sports athletes, II) assess if training loads among athletes with MSK pain are different from athletes without MSK pain, III) investigate if physical activity levels among athletes with MSK pain are different from athletes without MSK pain and IIII) descriptively present data on participant characteristics, sleep patterns, physical activity levels and utilization of health professionals and pain medication in the study population.
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Data analysis:
The prevalence of MSK pain within the study population will be presented in absolute numbers and as percentage values. To investigate if E-sport related training loads differ among athletes with and without MSK pain we will use total E-sport related training load (N hours spend on structured E-sport + N hours spend on unstructured E-sport). An independent sample t-test will be applied for this comparison if data are normally distributed. To assess if physical activity levels are different among athletes with and without MSK pain we will use the responses from the IPAQ short form. For this comparison we will use an independent sample t-test or the Wilcoxon rank-sum test. Participant characteristics, distribution and number of MSK pain sites, utilization of health professionals and pain medicine, physical activity levels, training loads and sleep patterns will be presented descriptively. The data will be presented with means, standard deviations and 95% confidence intervals. Non-parametric data will be presented with absolute numbers and as percentage values, medians and range.
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Data sourced from clinicaltrials.gov
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