Prevalence, Sex Differences, and Predictors of Internet Gaming Disorder Among Impoverished Rural Adolescents: Cross-Sectional and Prospective Cohort Study
Bibliographic record
Abstract
Background: Internet gaming disorder (IGD) is prevalent globally and linked to significant negative outcomes. Impoverished rural adolescents face unique risks due to limited supervision and unequal digital resources, with limited longitudinal research conducted in this population. Existing studies show sex differences in IGD prevalence, but their manifestations and mechanisms in rural populations remain unclear. Objective: This is the first large-sample cross-sectional and prospective cohort study targeting impoverished adolescents in rural areas. It aimed to determine the prevalence of IGD among impoverished rural adolescents, identify sex-specific risk and protective factors, and evaluate the longitudinal predictors of IGD. Methods: In this study, self-administered questionnaires were used to collect demographic characteristics, affective states, impulsivity, gaming time, and scores for IGD. First, the prevalence of IGD at baseline and follow-up, along with sex differences, was calculated. Correlation analysis was conducted to explore variables associated with IGD. Subsequently, multivariate logistic regression analysis was conducted to identify baseline and follow-up predictors of IGD. Results: The cross-sectional analysis at baseline included 13,931 valid responses (IGD prevalence: n=725, 5.2%; males: 489/7304, 6.7%; females: 236/6627, 3.6%). A 1-year longitudinal follow-up showed IGD prevalence of 5% (692/13,931; males: 511/7304, 7.0%; females: 181/6627, 2.7%; after multiple imputations). Common baseline factors were gaming time (females: odds ratio [OR] 1.11, 95% CI 1.08-1.14, P<.001; males: OR 1.11, 95% CI 1.09-1.13, P<.001), self-esteem scores (females: OR 0.95, 95% CI 0.92-0.98, P=.002; males: OR 0.95, 95% CI 0.92-0.97, P<.001), depression scores (females: OR 1.14, 95% CI 1.11-1.16, P<.001; males: OR 1.11, 95% CI 1.09-1.13, P<.001), and impulsive behavior (females: OR 1.16, 95% CI 1.10-1.22, P<.001; males: OR 1.10, 95% CI 1.06-1.14, P<.001). Companionship (OR 0.71, 95% CI 0.52-0.97; P=.03) was a protective factor for females, while age (OR 1.08, 95% CI 1.02-1.15; P=.02) and poor self-regulation (OR 1.07, 95% CI 1.03-1.11; P=.001) posed extra risk for males at baseline. Longitudinal predictors were baseline gaming time (females: OR 1.06, 95% CI 1.03-1.09, P<.001; males: OR 1.02, 95% CI 1.00-1.05, P=.03) and impulsive behavior (females: OR 1.38, 95% CI 1.30-1.46, P<.001; males: OR 1.27, 95% CI 1.22-1.31, P<.001). Baseline companionship (OR 0.32, 95% CI 0.23-0.43; P<.001) was a protective factor for females, while baseline poor self-regulation (OR 1.32, 95% CI 1.27-1.37; P<.001) was a predictive factor for males after 1 year. Conclusions: IGD prevalence was lower in rural than in urban populations and higher in males than in females. Impulsivity, gaming time, and guardian companionship showed sex differences: females relied more on companionship, whereas males were more vulnerable to poor self-regulation. Interventions should address these differences, strengthening family support and psychological adjustment. This study provides novel insights into sex-specific pathways of IGD in rural settings and offers empirical evidence for developing targeted prevention strategies, highlighting its practical significance for public health.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".