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Digital Health Communication and Health Behavior Change: Mediation, Network, and Predictive Evidence

  • Qiong Wei (魏琼) 1,   
  • Xiao Ding (丁潇) 1,   
  • Tianhui Zhao (赵天慧) 1,   
  • Ruyin Long (龙如银) 2,*,   
  • Han Huang (黄晗) 1,*

Received: 30 Jun 2026 | Revised: 11 Aug 2026 | Accepted: 25 Aug 2026 | Published: 31 Aug 2026

Abstract

Digital health communication has become an important component of contemporary health promotion, although the processes linking health-related social media use to health behavior change remain insufficiently understood. Existing research has predominantly examined construct-level explanatory relationships, with limited consideration of the psychological pathways involved, the item-level organization of these relationships, and their predictive relevance. Guided by the Knowledge, Attitude and Practice framework, the present study integrated parallel mediation analysis, item-level association network analysis, and topology-enhanced predictive modeling. Cross-sectional survey data were obtained from 204 social media users in China. Health information acquisition ability, health information beliefs, and health self-efficacy were examined as parallel mediators between health-related social media use and self-reported health behavior change. Network analysis was subsequently used to characterize associations and centrality among 29 questionnaire items, while repeated cross-validation assessed whether topology-derived features contributed predictive information beyond the original item responses. The direct association between health-related social media use and health behavior change was not significant after the mediators were included, whereas the three indirect associations were statistically significant. Health self-efficacy exhibited the largest indirect effect of 0.295, with a 95% confidence interval of [0.184, 0.401], and accounted for 50.77% of the total indirect effect. Consistent with the mediation results, items measuring health self-efficacy and health behavior change occupied central positions in the association network and formed dense cross-construct connections. The addition of topology-derived features produced a modest improvement in random forest prediction. These findings indicate that health self-efficacy represents the most prominent statistical pathway linking digital health communication to health behavior change. The results also show that item-level network analysis can reveal relational patterns that are not captured by construct-level mediation models, while network topology may provide limited and model-dependent predictive information.

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Wei (魏琼), Q.; Ding (丁潇), X.; Zhao (赵天慧), T.; Long (龙如银), R.; Huang (黄晗), H. Digital Health Communication and Health Behavior Change: Mediation, Network, and Predictive Evidence. ASTRA MAN 2026, 1 (1), 6.
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