Video games and their associations with physical health: a scoping review
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to investigate the possible links between the practice of video games and physical health. It seeks to answer the following question: What are the physical health consequences of playing video games in healthy video game player? and How is it currently investigated?. METHODS: A scoping review was conducted to identify observational and experimental studies pertaining to our research question. Retrieved papers were screened using a two-phase method first involving a selection based on titles and abstracts. Then, potentially relevant studies were read and triaged. The final set of included studies was analysed, and data were subsequently extracted. Observational studies and experimental studies were assessed using the appropriate Cochrane Risk of Bias Tool and data were synthetised according to specific physical health and related health behaviours. RESULTS: Twelve peer-reviewed articles were retained for further analyses. Results of this scoping review suggest preliminary evidence that time spent gaming is associated with some health outcomes indicators. Our results indicate preliminary evidence that increased gaming time is associated with higher body mass index and lower self-reported general health status. There is insufficient evidence to conclude on a possible association between gaming time and physical activity or sedentary behaviours, sleep or fatigue, musculoskeletal pain or dietary behaviours. CONCLUSION: The results of this sopping review suggest an association between increased video game playing time and a deterioration in some physical health indicators but available evidence is scarce, precluding from any strong conclusion.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".