TY - JOUR
T1 - Identification of Proactive Health Behavior Clusters in Atrial Fibrillation-Related Ischemic Stroke Patients
T2 - A Multi-Center Latent Class Analysis
AU - Guo, Lina
AU - Guo, Yuying
AU - Montayre, Jed
AU - Ning, Wenjing
AU - Namassevayam, Genoosha
AU - Zhang, Mengyu
AU - Xie, Yuying
AU - Zhou, Xinxin
AU - Zhao, Peng
AU - Wang, Juanjuan
AU - Di, Ruiqing
N1 - Publisher Copyright:
© 2025 Guo et al.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - atrial fibrillation
KW - ischemic stroke
KW - latent class analysis
KW - multi-center study
KW - proactive health behavior
UR - http://www.scopus.com/inward/record.url?scp=105015055114&partnerID=8YFLogxK
U2 - 10.2147/VHRM.S534357
DO - 10.2147/VHRM.S534357
M3 - Article
AN - SCOPUS:105015055114
SN - 1176-6344
VL - 21
SP - 749
EP - 758
JO - Vascular Health and Risk Management
JF - Vascular Health and Risk Management
ER -