Musculoskeletal disorders in video gamers – a systematic review
Bibliographic record
Abstract
BACKGROUND: Video gaming is a recreational activity with yearly increasing popularity. It is mostly a sedentary behavior combined with repetitive movements of the upper limbs. If performed excessively, these movements may promote strain injuries and a sedentary lifestyle is one of the contributing factors to musculoskeletal disorders. Therefore, a systematic review was conducted to evaluate if video gaming negatively affects the musculoskeletal system of video gamers. METHODS: PubMed, Web of Science and The Cochrane Library were systematically searched in order to identify relevant peer reviewed original articles in English published between 2000 and 2021. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used for the analysis. Studies were included when they contained investigations of changes of the musculoskeletal system due to video gaming in healthy individuals. Studies with participants older than 60 years or solely psychological, social or cardiovascular outcomes were excluded. An adapted version of the Newcastle-Ottawa Scale was used for the risk of bias analysis. RESULTS: Sixteen observational studies involving a total of 62,987 participants met the inclusion criteria. A majority (11) of the studies reported statistical negative musculoskeletal changes due to video game playtime. Four studies did not report changes and one study found no effect of video game playtime on the musculoskeletal system. Out of the eleven studies, which demonstrated a negative impact of video game playtime on the musculoskeletal system, the most reported painful body parts were the neck (n = 4), shoulder (n = 4) and back (n = 3). Ten studies reported odds ratios (OR) for the dependence of the appearance of musculoskeletal disorders on video game playtime. In eight studies OR were significantly increased (1.3-5.2). CONCLUSION: Eleven out of twelve studies demonstrated a negative impact of video game playtime on the musculoskeletal system. In particular, excessive video game playtimes (> 3 h/day) seemed to be a predictor for the appearance of musculoskeletal disorders. Due to their great popularity across multiple generations, specific and tailored prevention and health promotion programs for video gamers need to be developed to counteract this important public health issue.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".