# From awareness to action: exploring health information seeking behavior in coronary heart disease patients: a cross-sectional study

## TL;DR

This study explores how patients with coronary heart disease seek health information and finds that different groups have distinct behaviors influenced by factors like education and self-efficacy.

## Contribution

The study introduces a novel approach to subgroup CHD patients based on risk perception and self-efficacy, revealing subgroup-specific factors influencing health information seeking behavior.

## Key findings

- Four distinct subgroups of CHD patients were identified based on risk perception and self-efficacy.
- Subgroup-specific factors like education, income, and medical insurance significantly influence health information seeking behavior.
- Uniform health education approaches are insufficient; tailored strategies are needed for different patient groups.

## Abstract

Coronary heart disease (CHD) is a major global health burden requiring long-term management. Despite the essential role of health information seeking behavior (HISB) in disease self-management, current levels among CHD patients remain low, and research on its influencing factors is limited.

This study aimed to explore HISB among patients with CHD and to identify factors associated with variations in HISB using the Risk Perception Attitude (RPA) framework.

A cross-sectional study of 330 CHD patients was conducted in China, using convenience sampling method. Data were collected through validated questionnaires assessing sociodemographic and clinical characteristics, HISB, risk perception, and self-efficacy. K-means clustering based on the RPA framework was employed to empirically identify distinct patient subgroups. Multivariate linear regression identified factors associated with of HISB within each subgroup.

Four distinct subgroups were identified based on risk perception and self-efficacy: Responsive (6.7%), Proactive (41.2%), Indifference (8.2%), and Avoidance (43.9%). Multivariate regression revealed subgroup-specific factors: for Responsive, physical diagnosis and treatment risk was significant [β = 2.049, 95%CI (0.528,3.570)]; For Proactive, higher education [β = 4.725, 95%CI (2.272,7.178)], per capita monthly household income and self-efficacy were positively associated, while type of medical insurance [β = −5.814, 95%CI (−8.800, −2.828)], number of other diseases, and economic risk were negative predictors; For Indifference, only type of medical insurance was significant [β = −6.447, 95%CI (−12.503, −0.391)]; For Avoidance, older age was linked to lower HISB [β = −4.757, 95%CI (−8.525, −0.989)], whereas higher education increased it [β = 5.432, 95%CI (2.353, 8.511)].

This study validates the heterogeneity of CHD patients through RPA-based subgrouping, revealing that health information seeking behaviors are driven by distinct psychological and socioeconomic mechanisms across different groups. These findings underscore the limitation of uniform health education approaches and highlight the necessity of implementing subgroup-tailored strategies. By aligning clinical and public health interventions with the specific psychographic profiles of patient groups, healthcare providers can significantly enhance the precision and effectiveness of chronic disease management.

www.chictr.org.cn, identifier: ChiCTR2300069238.