ArticleTobacco induced diseases2025
Interdisciplinary perspective-based behavioral prediction of e-cigarette use: A population-based study among Chinese college students.
Article in Tobacco induced diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prevalence and factors associated with e-cigarette use among aspiring media professionals: A cross-sectional survey at a vocational college.Tobacco induced diseases · 2026Article
- E-cigarette use among female Chinese Indonesian college students in China: A qualitative interpretive phenomenological analysis from an acculturation perspective.Tobacco induced diseases · 2026Article
- Strengthening the cognition of university students to refuse e-cigarette use: A pilot randomized controlled trial of a peer-to-peer intervention.Tobacco induced diseases · 2025Article
- A protection motivation theory-based scale for e-cigarette use assessment among Chinese college students: Development and validation.Tobacco induced diseases · 2025Article
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6 authors.
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Abstract
introductionE-cigarette use is rising among young adults globally, and college students are particularly vulnerable due to high social media engagement and targeted promotions. Understanding which factors predispose this population to initiate vaping is critical for designing effective prevention strategies.
methodsWe conducted a cross-sectional survey of 303 never-smoking, never-vaping Chinese college students (aged 18-24 years) recruited via online platforms and referrals. The 25-item questionnaire assessed six domains: demographics, parental smoking, peer e-cigarette use, 'quasi-deviant' behaviors (regular alcohol consumption and bar attendance), social media use and trust, and exposure to e-cigarette marketing across five media channels. A three-item susceptibility scale was combined into a single index via principal component analysis. An Extremely Randomized Trees classifier (n_estimators=60, max_depth=6) with grid-search and five-fold cross-validation on a 75:25 train-test split, identified the strongest predictors of high susceptibility. Model performance was evaluated by accuracy and area under the receiver operating characteristic curve (AUC).
resultsThe model achieved 81% classification accuracy. Feature importance (FI) indicated that bar attendance (FI=0.21), alcohol consumption frequency (FI=0.12), exposure to e-cigarette marketing messages (FI=0.08), social media use (FI=0.08), peer e-cigarette use (FI=0.05), and parental smoking (FI=0.05) were the most influential predictors. Among the participants, 18.8% were classified as high-susceptibility, indicating elevated risk for future vaping initiation.
conclusions'Quasi-deviant' behaviors (regular alcohol use and bar attendance), social media marketing exposure, and social influences (peer and parental smoking) are key predictors of e-cigarette susceptibility in Chinese college students. Multi-level prevention strategies - enforcing digital marketing restrictions, peer-focused education, and integrated substance-use interventions - may effectively reduce susceptibility and avert vaping initiation in this high-risk group.
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