横断面研究
健康促进
心理学
健康行为
老年学
冲程(发动机)
公共卫生
医学
环境卫生
护理部
机械工程
工程类
病理
作者
Xiao Wei,Mengfan Xu,Yang Li,Zihan Gao,Jinke Kuang,Kexin Zhou
标识
DOI:10.1177/10901981231160149
摘要
Background Health-promoting behaviors and positive lifestyle changes are crucial for effective stroke prevention. However, individuals at high risk of stroke exhibit poor health behavior due to a deficiency of individual motivation. Moreover, there are only a few studies on health-promoting behaviors that have applied behavior change theories in individuals at high risk of stroke. Objective This study aimed to use the theory of the planned behavior (TPB) model to investigate determinants of health-promoting behaviors for stroke prevention and control. Method In this cross-sectional study, 263 participants were recruited from five community health centers in Qingdao. Confirmatory factor analysis was performed to assess the reliability and validity of the constructs, and structural equation modeling was used to analyze the proposed relationships between the TPB-related variables. Results The attitudes, subjective norms, and perceptions of behavioral control positively influenced behavioral intention. The behavioral intention had a positive effect on health-promoting behaviors. Attitudes, subjective norms, and perceived behavioral control were influenced primarily by the mediating variable behavioral intention to affect health-promoting behaviors. Stroke knowledge was an influential facilitator of behavioral attitudes, subjective norms, and perceived behavior control. Conclusion The TPB-based model is suitable for explaining health-promoting behaviors in individuals at risk of stroke and for guiding the development of effective health management programs. A comprehensive person-centered motivation behavior strategy that is based on health education and complemented by social support and health resource optimization is critical in promoting health behavior motivation and health promotion behaviors in stroke high-risk groups.
科研通智能强力驱动
Strongly Powered by AbleSci AI