Physical complaints and their relationships to esports activities among Japanese esports players: A cross-sectional study
Monma, T.; Matsui, T.; Koyama, S.; Ueno, H.; Kagesawa, J.; Oba, C.; Nakamura, K.; Takagi, H.; Takeda, F.
Show abstract
In the evolving landscape of electronic sports (esports), where economic and social expectations are soaring, a critical concern has emerged in physical complaints among esports players. However, empirical insights into these complaints prevalence and influencing factors are scarce. This study aimed to clarify the prevalence of physical complaints and their association with esports activities among Japanese esports players. A web-based cross-sectional survey encompassing 175 esports players from both professional and amateur teams in Japan was conducted. The analysis focused on 79 male participants (average age: 21.6 {+/-} 5.6 years) with complete responses. The survey items included the esports factors (the device mainly used, the duration of esports titles played primarily, hours of esports activities per day on weekdays and holidays, and the distance between the screen and the face during esports activities) and physical complaints (headache, neck pain, stiff or sore shoulders, wrist pain, finger pain, lower back pain, and eye fatigue). A total of 49.4% reported stiff or sore shoulders, 48.1% faced eye fatigue, and 30.4% had headaches. Professionals exhibited a significantly higher likelihood of neck, wrist, and lower back pain and eye fatigue than amateurs. Age-adjusted logistic regression analysis uncovered that using mainly mobile devices and being closer to the screen and face during esports activities were significantly associated with neck pain, stiff or sore shoulders, lower back pain, and eye fatigue. These results suggest that poor posture caused by using mobile devices and being closer to the screen during esports activities was related to various physical complaints.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Early sports specialization in Japanese young soccer players and related factors 97%
- Cognitive Costs and Gait Parameters During Single- and Dual-Task Conditions: A Comparative Study in Individuals With and Without Non-Specific Neck Pain 96%
- The Incidence and Severity of COVID-19 in Adult Professional Soccer Players 95%
Similar papers in this journal
- Gynaecological health patterns and motherhood experiences of female professional football players 95%
- Successful reboot of high-performance sporting activities by Japanese national women’s handball team in Tokyo, 2020 during the COVID-19 pandemic: An initiative by Japan Sports-Cyber Physical System (JS-CPS) of Sports Research Innovation Project (SRIP) 94%
- Practical recommendations for staying physically active during the COVID-19 pandemic: A systematic literature review 93%
Similar papers in this journal
Similar papers in this journal
- Translation, adaptation, and measurement properties of the Muscle-Strengthening Exercise Questionnaire among university students in Indonesia 94%
- The Effects of Pericapsular nerve group (PENG) block on Postoperative Recovery in Elderly Patients with Hip fracture: a study protocol for randomized, parallel controlled, double-blind trial 93%
- Comparative Computed Tomography with Stress Manoeuvres for Diagnosing Distal Isolated Tibiofibular Syndesmotic Injury in Acute Ankle Sprain: a Protocol for an Accuracy-Test Prospective Study. 93%
Similar papers in this journal
- Developing a psychological support intervention to help injured athletes get Back in the Game 93%
- Knowledge, attitude and practice toward COVID-19 among healthcare workers in public health facilities, Eastern Ethiopia 89%
- Hospital based contact tracing of COVID-19 patients and health care workers and risk stratification of exposed health care workers during the COVID-19 Pandemic in Eastern India 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.