Video gaming linked to unhealthy diet, poor sleep quality and lower physical activity levels in Australian university students
Bibliographic record
Abstract
OBJECTIVES: Video gaming is highly prevalent among university students, yet its health associations remain poorly understood. This study examined relationships between video gaming frequency and health in Australian university students. METHODS: A cross-sectional survey of 317 Western Australian university students assessed gaming habits, diet quality (Diet Quality Tool), physical activity (International Physical Activity Questionnaire), sleep quality (Pittsburgh Sleep Quality Index), eating behaviors (Three-Factor Eating Questionnaire), and perceived stress (Perceived Stress Scale). Participants were categorized into tertiles based on gaming frequency (low [0-5 h/wk], moderate [6-10 h/wk], and high [>10 h/wk]). RESULTS: High-frequency gamers had significantly poorer diet quality scores (median 45.0 versus 50.0, P < 0.001), higher BMI (median 26.3 versus 22.2 kg/m², P < 0.001), and worse sleep quality (PSQI score 7.0 versus 6.0, P < 0.001) compared to low-frequency gamers. Correlation analyses confirmed these associations in which gaming frequency negatively correlated with diet quality (r = -0.26, P < 0.001) and physical activity (r = -0.13, P = 0.03) and positively correlated with BMI (r = 0.38, P < 0.001). Multiple regression analysis revealed total gaming hours independently predicted poorer diet quality (β = -0.16, P = 0.02) after controlling for demographic and lifestyle factors. CONCLUSIONS: Higher video gaming frequency was associated with poorer diet quality and increased BMI among university students. These findings suggest excessive gaming may contribute to adverse health outcomes. Public health interventions targeting excessive or high gaming levels and promoting healthy lifestyle habits in university populations are warranted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".