ArticleJournal of global health2024
Socioeconomic inequalities in the relationship between internet usage patterns and depressive symptoms: Evidence from a Chinese longitudinal study.
Article in Journal of global health, 2024. 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.
- Income Differences in the Association Between Medical-Preventive Integration KAP and Depressive Symptoms: A Cross-Sectional Study Among Chinese Healthcare Workers.Healthcare (Basel, Switzerland) · 2026Article
- Joint trajectories of sleep duration and depressive symptoms and risk of incident multimorbidity: a longitudinal analysis with machine learning prediction.BMC geriatrics · 2026Article
- Associations Between Social Media Use and Anxiety and Depression Among Older Adults : Cross-Sectional Study.JMIR aging · 2025Article
- Urban-Rural Differences in the Association Between Internet Use Trajectories and Depressive Symptoms in Chinese Adolescents: Longitudinal Observational Study.Journal of medical Internet research · 2025Observational
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7 authors.
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Abstract
Background: The increasing prevalence of depressive symptoms has emerged as a critical public health issue globally, highlighting the need for analyses of the factors contributing to depressive symptoms within the Chinese population and the development of targeted recommendations for improving mental well-being. We aimed to explore the correlation between internet use and depressive symptoms and the role of socioeconomic inequalities in this association. Methods: We included data on 8019 residents aged 18 years and above, which we retrieved from the 2018 and 2020 waves of the China Family Panel Studies. We used latent profile analysis to categorise individuals' internet usage patterns and multiple linear regression to determine their association with depressive symptoms. Results: Higher socioeconomic status (SES) was associated with fewer depressive symptoms (τ = -0.08; 95% confidence interval (CI) = -0.36, -0.18). Individuals in the high-dependence group presented a greater likelihood of developing depressive symptoms (τ = 0.04; 95% CI = 0.007, 0.66). We observed no significant difference in the interaction effect between individual-level SES and the four patterns of internet usage. However, compared with urban-dwelling respondents, those in rural areas had a stronger association between internet usage patterns and depressive symptoms, especially those in the high-dependence group (τ = -0.07; 95% CI = -1.47, -0.20). Conclusions: Our findings indicate a significant association between depressive symptoms and internet usage patterns, indicating a need for interventions related to internet use, especially those targeted at reducing the risk of depressive symptoms in individuals of lower SES.
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