Helping patients with chronic diseases quit smoking by understanding their risk perception, behaviour, and smoking-related attitudes.
Laurie Long Kwan HoWilliam Ho Cheung LiAnkie Tan CheungPublished in: PloS one (2023)
Continued smoking among patients with chronic diseases detrimentally affects their health and treatment outcomes. However, a majority of smokers with chronic diseases appear to have no intention to quit. Understanding the needs and concerns of this population is a crucial step in facilitating the design of an appropriate smoking cessation intervention. This study aimed to understand the risk perception, behaviours, attitudes, and experiences related to smoking and smoking cessation among patients with chronic diseases, including cardiovascular diseases, chronic respiratory diseases, and/or diabetes in Hong Kong. Individual semi-structured interviews with smokers with chronic diseases (n = 30) were conducted from May to July 2021. The methods and results are reported according to the COREQ. Four themes were generated: (1) perceptions of the association between chronic diseases and smoking/smoking cessation; (2) perceptions of the health/disease status; (3) quitting smoking is not the first priority; and (4) perceived barriers to quitting smoking. This study addressed a gap in the literature by gathering data concerning the perspectives of smokers with chronic diseases on smoking and smoking cessation. The deficit of knowledge among smokers with chronic diseases warrants the reinforcement of health education targeting this population. Our findings indicate the need for further efforts in designing appropriate smoking cessation interventions targeting smokers with chronic diseases, which will match the needs and concerns identified in this study.
Keyphrases
- smoking cessation
- healthcare
- replacement therapy
- mental health
- public health
- cardiovascular disease
- type diabetes
- primary care
- randomized controlled trial
- systematic review
- health information
- drug delivery
- quality improvement
- electronic health record
- depressive symptoms
- social media
- cardiovascular events
- deep learning
- big data