Factors Affecting Health-Promoting Behaviors in Patients with Cardiovascular Disease.
Hwan-Cheol ParkJihyun OhPublished in: Healthcare (Basel, Switzerland) (2021)
Cardiovascular disease is the leading cause of death globally and the second most common cause of death in South Korea. Health-promoting behaviors recommended for patients with cardiovascular disease include control of diet, physical activity, cessation of smoking, medication adherence, and adherence to medical recommendations. This study aimed to determine the relationship between depression, anxiety, perception of health status, and health-promoting behavior in patients from South Korea who have suffered from cardiovascular disease. The study population comprised 161 patients at the cardiovascular center at H Hospital who were diagnosed with cardiovascular disease. Descriptive statistics and stepwise multiple regression were employed to analyze the data. Negative correlations existed between depression, perception of health status, and health-promoting behavior. By contrast, a positive correlation existed between the perception of health status and health-promoting behavior. The main factors affecting health-promoting behaviors were alcohol consumption, duration of diagnosis, perception of health status, and depression. These variables explained 15.8% of the variance. To prevent adverse cardiac events, patients who suffer from cardiovascular disease should be assessed as soon as possible to identify psychiatric symptoms, thereby developing a potential intervention aimed at decreasing negative illness consequences.
Keyphrases
- cardiovascular disease
- healthcare
- public health
- mental health
- physical activity
- health information
- type diabetes
- depressive symptoms
- randomized controlled trial
- sleep quality
- end stage renal disease
- health promotion
- heart failure
- cardiovascular risk factors
- cardiovascular events
- emergency department
- magnetic resonance imaging
- body mass index
- ejection fraction
- left ventricular
- human health
- newly diagnosed
- coronary artery disease
- machine learning
- peritoneal dialysis
- big data
- prognostic factors
- artificial intelligence
- drug induced
- data analysis