Lina Guo,1,* Yuying Guo,1,* Jed Montayre,2 Wenjing Ning,2 Genoosha Namassevayam,3 Mengyu Zhang,1 Yuying Xie,4 Xinxin Zhou,4 Peng Zhao,4 Juanjuan Wang,1 Ruiqing Di5 

1Department of Neurology, National Advanced Stroke Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 2School of Nursing, the Hong Kong Polytechnic University, Hong Kong, SAR, People’s Republic of China; 3Department of Supplementary Health Sciences, Faculty of Health-Care Sciences, Eastern University, Batticaloa, Sri Lanka; 4School of Nursing and Health, Zhengzhou University, Zhengzhou, People’s Republic of China; 5Department of Nursing, the first Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Ruiqing Di, Department of Nursing, the first Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China, Email Sunshine__1227@126.com

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.

Keywords: atrial fibrillation, ischemic stroke, proactive health behavior, multi-center study, latent class analysis