Upper-Body Pain in Gamers: An Analysis of Demographics and Gaming Habits on Gaming-Related Pain and Discomfort
Bibliographic record
Abstract
With the rapid growth of both the gaming and esports industries, millions of individuals are now playing games as a hobby or career. The intense and repetitive nature of gaming can likely increase an individual’s susceptibility to musculoskeletal injuries and pain. The purpose of this study was to assess demographic information and gaming habits of gamers and determine any association with upper-body, gaming-related pain. An online survey was used to obtain demographic information and gaming habits of individuals, as well as the location and description of upper-body pain experienced when gaming. Of the 522 respondents, 77.8% (n = 406) reported experiencing gaming-related pain in the upper body. The most prevalent areas of pain were the neck (43.9%), lower back (41.4%), and the distal upper limb (37.9%). Few strong correlations were found between any demographics or gaming habits and the presence or intensity of pain in the upper body. The results of this study demonstrate that gaming-related pain is a problem; however, due to its complex nature, it is likely that a multifaceted interaction of both gaming habits and unaccounted lifestyle factors contributes to individualized pain development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".