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Record W4416572937 · doi:10.2196/83522

Prevalence, Sex Differences, and Predictors of Internet Gaming Disorder Among Impoverished Rural Adolescents: Cross-Sectional and Prospective Cohort Study

2025· article· en· W4416572937 on OpenAlexvenueno aff
Chenhan Wang, Yifan Li, Shuhong Lin, Qiuping Huang, Yongyan Shi, Guangxian Yang, Erjia Huang, Xicheng Deng, Jinwen Luo

Bibliographic record

VenueJMIR Serious Games · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionThe InternetGuardianRural areaInterpersonal relationshipCohort studyEmpirical research

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.289
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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