Physical Activity as a Predictor of Internet Gaming Disorder in a Swiss Male Cohort (C-SURF): L’activité physique comme prédicteur des troubles liés aux jeux vidéo en ligne dans une cohorte de jeunes hommes suisses (C-SURF)
Bibliographic record
Abstract
HISTORY AND OBJECTIVES: This study addresses the increasing global concerns surrounding Internet gaming disorder (IGD) by exploring their connection to physical activity (PA) as a potential preventive and early intervention measure. The research aims to examine the relationship between PA and the progression of IGD. METHODS: Longitudinal data from the Cohort research on Substance Use Risk Factors involving young Swiss men undergoing army conscription was employed. PA levels were assessed using the International PA Questionnaire (IPAQ), while the Game Addiction Scale (GAS) and Compulsive Internet Use Scale determined IGD presence. Analysis involved zero-inflated negative binomial regression models. RESULTS: Higher PA levels were associated with lower IGD risk. Notably, individuals engaging in high physical exercise exhibited a lower IGD prevalence compared to those with moderate or low activity levels. DISCUSSION: The study suggests that intensive physical exercise might serve as a preventive strategy against developing IGD. This protective effect could stem from various mechanisms. However, the study's limitations, such as a male-only sample and a small low-activity group, should be considered when interpreting results. CONCLUSION: The longitudinal study demonstrates the positive influence of intense physical exercise on mitigating gaming-related issues. These findings underscore the potential of PA interventions in addressing the growing problem of IGDs and their impact on health. Further research is necessary to uncover underlying mechanisms behind the PA-IGD relationship and validate these findings across diverse demographics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".