Commercial Off-The-Shelf Video Games for Reducing Stress and Anxiety: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Using commercial off-the-shelf video games rather than custom-made computer games could have several advantages for reducing stress and anxiety, including their low cost, advanced graphics, and the possibility to reach millions of individuals worldwide. However, it is important to emphasize that not all commercial video games are equal, and their effects strongly depend on specific characteristics of the games. OBJECTIVE: The aim of this systematic review was to describe the literature on the use of commercial off-the-shelf video games for diminishing stress and anxiety, examining the research outcomes along with critical variables related to computer game characteristics (ie, genre, platform, time of play). METHODS: A systematic search of the literature was performed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. The search databases were PsycINFO, Web of Science, Medline, IEEExplore, and the Cochrane Library. The search string was: [("video game*") OR ("computer game*")] AND [("stress") OR ("anxiety") OR ("relaxation")] AND [("study") OR ("trial") OR ("training")]. RESULTS: A total of 28 studies met the inclusion criteria for the publication period 2006-2021. The findings demonstrate the benefit of commercial off-the-shelf video games for reducing stress in children, adults, and older adults. The majority of the retrieved studies recruited young adults, and fewer studies have involved children, middle-aged adults, and older adults. In addition to exergames and casual video games, other genres of commercial off-the-shelf games helped to reduce stress and anxiety. CONCLUSIONS: Efficacy in reducing stress and anxiety has been demonstrated not only for exergames and casual video games but also for other genres such as action games, action-adventure games, and augmented reality games. Various gaming platforms, including consoles, PCs, smartphones, mobile consoles, and virtual reality systems, have been used with positive results. Finally, even single and short sessions of play had benefits in reducing stress and anxiety. TRIAL REGISTRATION: International Platform of Registered Systematic Review and Meta-analysis Protocols INPLASY202130081; https://inplasy.com/?s=INPLASY202130081.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 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".