Gaming in Pandemic Times: An International Survey Assessing the Effects of Covid-19 Lockdowns on Video Gamer's Health
Bibliographic record
Abstract
<title>Abstract</title> <bold>Background</bold>: The onset of COVID-19 coincided with the peak growth of video game usage with 2.7 billion gamers in 2020. During the pandemic, gaming and streaming platforms offered an entertaining, social, and safe alternative to recreation during severe lockdowns and social isolations. This study aimed to examine the impact of the COVID-19 pandemic on health-related outcomes in self-proclaimed video gamers based on the type of lockdown experienced, and to discuss the potential role of video games during times of preventive lockdown measures. <bold>Methods: </bold>This was a cross-sectional international survey constructed by two academic institutions NYIT (NY; USA); McGill University, (Montreal, Canada) and Adamas Esports (BC, Canada). The survey consisted of questions including demographics, multiple-choice, rating, and Likert scales relating to prior and during the COVID-19 lockdowns. Respondents included 897 replies from North America (72.7%), Europe (10.9%), Asia (4.9%) and other countries (11.5%) mean age 22 years. <bold>Results:</bold> Significant increases in game time were reported in casual and competitive gamers during the first months of the pandemic. Level of gaming, type of lockdown, and physical activity level prior to the pandemic were examined as potential moderating factors. Significant increases in sedentary behaviors (video game time and sitting time) were observed, while physical activity levels remained unchanged in most participants regardless of the type of lockdown. Sleep time, but not sleep quality, increased, while mental health exhibited opposing effects, influenced by the type of lockdown and gaming competition levels. <bold>Conclusions:</bold>Video games, when played moderately, could offer a cost-effective, safe strategy to promote socialization, mental health, and improve the overall well-being of the non-gaming and gaming population during pandemic times when strict lockdowns are in place.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".