A model for predicting factors affecting health information avoidance on WeChat

作者：Minghong Chen, Xiumei Huang, Yongjian Wu, Shijie Song, Xianjun Qi · 发表于：Digital Health · 年份：2025 · DOI：10.1177/20552076251314277 · 被引用次数：9 · 研究领域：Health Literacy and Information Accessibility、Electronic Health Records Systems、Mobile Health and mHealth Applications

Objective: WeChat serves as a crucial source of health information, distinguished by its highly personalized nature. Avoidance of such personalized health information has a direct impact on individuals' health decision-making. This study aims to identify the factors influencing personalized health information avoidance on WeChat and to construct a hierarchical framework illustrating the relationships among these factors. Methods: A hybrid method was utilized. Semi-structured interviews and grounded theory were used to identify the influencing factors. The interpretive structural modeling (ISM) method was adopted to develop a hierarchical model of the identified factors, followed by matrice d'impacts croises-multiplication appliqué a un classemen (MICMAC) to analyze the dependence and driving power of each factor. Results: The 20 predictors of personalized health information avoidance were broadly categorized into three groups: personal, informational, and social factors. These factors collectively form a three-tier explanatory framework, consisting of the top, middle and bottom layers. At the root layer, health characteristics and cognition exerted a strong driving force, while negative emotions and affective factors at the top layer showed a high degree of dependence. In contrast, the decision-making cognition, informational factors, and social factors in the middle layer exhibited relatively weaker driving force and dependence power. Conclusion: This study bridged the resea...