Objective: To examine physical activity(PA) of post-percutaneous coronary intervention(PCI) patients and explore the demographic, clinical, and social psychological characteristics associated with PA levels. Methods: ...Objective: To examine physical activity(PA) of post-percutaneous coronary intervention(PCI) patients and explore the demographic, clinical, and social psychological characteristics associated with PA levels. Methods: A total of 246 post-PCI patients from the Peking University Third Hospital in Beijing, China, were included in this crosssectional study through convenience sampling. Data were collected from a self-repor ted questionnaire. PA was categorized into low, moderate, or high levels. The ordinal multinomial logistic regression model was used to estimate the relationship among demographic, medical, and psychosocial characteristics. Results: The overall prevalence of low, moderate, and high PA was 20%, 70%, and 10%, respectively. For the domain-specific PA patterns, most par ticipants took par t in leisure-time PA(84.5%);walking was the most common PA. Increased motivation and selfefficacy, lower monthly income, and unemployment were predictors of high PA. Conclusions: PA levels in post-PCI patients was not optimal, and leisure-time PA had the highest par ticipation rate. Analyses of influencing factors can provide medical staff and health workers information to focus on high-risk groups and introduce more tailored interventions. Future studies can explore more regions, and ecological models can be introduced to study more influencing factors.展开更多
文摘Objective: To examine physical activity(PA) of post-percutaneous coronary intervention(PCI) patients and explore the demographic, clinical, and social psychological characteristics associated with PA levels. Methods: A total of 246 post-PCI patients from the Peking University Third Hospital in Beijing, China, were included in this crosssectional study through convenience sampling. Data were collected from a self-repor ted questionnaire. PA was categorized into low, moderate, or high levels. The ordinal multinomial logistic regression model was used to estimate the relationship among demographic, medical, and psychosocial characteristics. Results: The overall prevalence of low, moderate, and high PA was 20%, 70%, and 10%, respectively. For the domain-specific PA patterns, most par ticipants took par t in leisure-time PA(84.5%);walking was the most common PA. Increased motivation and selfefficacy, lower monthly income, and unemployment were predictors of high PA. Conclusions: PA levels in post-PCI patients was not optimal, and leisure-time PA had the highest par ticipation rate. Analyses of influencing factors can provide medical staff and health workers information to focus on high-risk groups and introduce more tailored interventions. Future studies can explore more regions, and ecological models can be introduced to study more influencing factors.
