Identification of Proactive Health Behavior Clusters in Atrial Fibrillation-Related Ischemic Stroke Patients: A Multi-Center Latent Class Analysis

Lina Guo, Yuying Guo, Jed Montayre, Wenjing Ning, Genoosha Namassevayam, Mengyu Zhang, Yuying Xie, Xinxin Zhou, Peng Zhao, Juanjuan Wang, Ruiqing Di

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: This study aims to identify latent classes of proactive health behavior and to explore the predictive factors associated with various clusters of proactive health behavior among patients with atrial fibrillation-related ischemic stroke. Methods: A multi-center cross-sectional study was conducted, recruiting a total of 1,250 participants through cluster random sampling from January 2023 to May 2024. Latent class analysis was performed to identify classes of proactive health behavior within the sample of atrial fibrillation-related ischemic stroke patients. Additionally, multinomial regression analyses were utilized to investigate the predictive factors associated with the different latent classes identified. This study adhered to the STROBE checklist. Results: Out of the 1,250 participants, 1,196 (91.6%) completed the survey, including 809 males and 387 females, with 71% of them reporting moderate or lower levels of proactive health behavior. The findings revealed three latent classes: (1) low proactive health behavior with health responsibility deficiency (n=426, 35.6%); (2) moderate proactive health behavior with stress and coping disorder (n=464, 38.7%); and (3) high proactive health behavior with light physical activity (n=306, 25.5%). Factors correlated with the latent classes of proactive health behavior were identified. Protective factors included a high level of stroke knowledge, strong awareness of health beliefs, and better environmental and social support (all p < 0.05). Conversely, risk factors for the latent classes of proactive health behavior included low education, being unmarried, lack of thrombolysis, and low household income (all p < 0.05). Conclusion: This study successfully identified three different latent classes of proactive health behaviors and their related predictors in Chinese atrial fibrillation-related ischemic stroke patients. These findings provide theoretical guidance and practical insights for the development of targeted intervention programs aimed at improving proactive health behaviors in patients with atrial fibrillation-related ischemic stroke patients.

Original languageEnglish
Pages (from-to)749-758
Number of pages10
JournalVascular Health and Risk Management
Volume21
DOIs
Publication statusPublished - 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2025 Guo et al.

Keywords

  • atrial fibrillation
  • ischemic stroke
  • latent class analysis
  • multi-center study
  • proactive health behavior

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