Prevalence, Sex Differences, and Predictors of Internet Gaming Disorder Among Impoverished Rural Adolescents: Cross-Sectional and Prospective Cohort Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».