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Intention to quit smoking according to smoking preferences and perceptions of electronic cigarettes among university students in South Korea.

Ryoung ChoiHyun Goo Kang
Published in: Medicine (2022)
This study aimed to alleviate unhealthy smoking habits among university students and provide the basic data necessary for public health-oriented approaches such as developing regulations and policies on electronic cigarettes by analyzing the relationship between university students' smoking preferences, perceptions of electronic cigarettes, and intention to quit smoking.This study involved 567 college students and conducted frequency and chi-squared analyses of the general characteristics, smoking preferences, and perceptions of electronic cigarettes. This study also performed logistic regression analysis to analyze the relationship between intention to quit smoking stratified by smoking preferences and the perceptions about electronic cigarettes. SPSS version 25.0 was used for data analysis.This study showed that electronic cigarette smokers were approximately 6.4 to 10.8 times more likely to think that electronic cigarettes positively affect smoking cessation attitude than nonsmokers. This study showed that regular cigarette smokers were approximately 1.7 to 2.2 times and other smoker 3.3 to 3.9 times more likely to think that electronic cigarettes positively affect smoking cessation attitude than nonsmokers. Those who perceived harmless to the human body, capable of reducing the frequency of smoking, and less harmful than tobacco were approximately 2.6 to 2.9, 11.6 to 12.8, and 3.3 to 3.7 times more likely have intention to quit smoking, respectively.Regular health education, advertising awareness of health hazards, and public health science-oriented approaches and policies for smoking cessation support services are needed to create awareness on electronic cigarettes among university students.
Keyphrases
  • smoking cessation
  • public health
  • replacement therapy
  • healthcare
  • primary care
  • mental health
  • depressive symptoms
  • electronic health record
  • big data
  • health insurance
  • deep learning