ArticleBMC public health2024
Investigating the mechanisms of internet gaming disorder and developing intelligent monitoring models using artificial intelligence technologies: protocol of a prospective cohort.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Socially Prescribed Perfectionism, Resilience, and Internet Gaming Disorder in Adolescents: 3-Wave Longitudinal Study.JMIR serious games · 2026Article
- From monitoring to intervention: a closed-loop digital health framework for gaming disorder.Frontiers in public health · 2026Review
- Internet gaming disorder among individuals with autism spectrum disorder in the Kingdom of Saudi Arabia: a comparative study.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundInternet gaming disorder (IGD), recognized by the World Health Organization (WHO), significantly impacts adolescent mental and physical health. With a global prevalence of 3.05%, rates are higher in Asia, especially among adolescents and males. The COVID-19 pandemic has exacerbated IGD due to increased gaming time from isolation and anxiety. Vulnerable groups include adolescents with poor academic performance, introverted personalities, and comorbid mental disorders. IGD mechanisms remain unclear, lacking prospective research. Based on Skinner's reinforcement theory, the purpose of this study is to explore the mechanisms of IGD from individual and environmental perspectives, incorporating age-related changes and game features, and to develop intelligent monitoring models for early intervention in high-risk adolescents.
methodsThis prospective cohort study will investigate IGD mechanisms in middle and high school students in Shenzhen, China. Data will be collected via online surveys and Python-based web scraping, with a 3-year follow-up and assessments every 6 months. Unstructured data obtained through Python-based web scraping will be structured using natural language processing techniques. Collected data will include personal characteristics, gaming usage, academic experiences, and psycho-behavioral-social factors. Baseline data will train and test predictive models, while follow-up data will validate them. Data preprocessing, normalization, and analysis will be performed. Predictive models, including Cox proportional hazards and Weibull regression, will be evaluated through cross-validation, confusion matrix, receiver operating characteristic (ROC) curve, area under the curve (AUC), and root mean square error (RMSE). DISCUSSION: The study aims to understand the interplay between individual and environmental factors in IGD, incorporating age-related changes and game features. Active monitoring and early intervention are critical for preventing IGD. Despite limitations in geographic scope and biological data collection, the study's innovative design and methodologies offer valuable contributions to public health, promoting effective interventions for high-risk individuals.
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