Guo Lina, Guo Yuying, Montayre Jed, Ning Wenjing, Namassevayam Genoosha, Zhang Mengyu, Xie Yuying, Zhou Xinxin, Zhao Peng, Wang Juanjuan, Di Ruiqing
Department of Neurology, National Advanced Stroke Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
School of Nursing, the Hong Kong Polytechnic University, Hong Kong, SAR, People's Republic of China.
Vasc Health Risk Manag. 2025 Sep 4;21:749-758. doi: 10.2147/VHRM.S534357. eCollection 2025.
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.
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.
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).
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.
本研究旨在识别主动健康行为的潜在类别，并探讨与房颤相关性缺血性卒中患者不同类别主动健康行为相关的预测因素。
进行了一项多中心横断面研究，于2023年1月至2024年5月通过整群随机抽样共招募了1250名参与者。进行潜在类别分析以识别房颤相关性缺血性卒中患者样本中的主动健康行为类别。此外，使用多项回归分析来研究与所识别的不同潜在类别相关的预测因素。本研究遵循STROBE清单。
在1250名参与者中，1196名（91.6%）完成了调查，其中包括809名男性和387名女性，71%的参与者报告其主动健康行为水平为中等或较低。研究结果揭示了三个潜在类别：（1）健康责任缺失的低主动健康行为（n = 426，35.6%）；（2）伴有压力和应对障碍的中等主动健康行为（n = 464，38.7%）；以及（3）伴有轻度体育活动的高主动健康行为（n = 306，25.5%）。确定了与主动健康行为潜在类别相关的因素。保护因素包括高水平的卒中知识、强烈的健康信念意识以及更好的环境和社会支持（均p < 0.05）。相反，主动健康行为潜在类别的风险因素包括低教育水平、未婚、缺乏溶栓治疗以及低家庭收入（均p < 0.05）。
本研究成功识别了中国房颤相关性缺血性卒中患者主动健康行为的三种不同潜在类别及其相关预测因素。这些发现为制定旨在改善房颤相关性缺血性卒中患者主动健康行为的针对性干预计划提供了理论指导和实践见解。
