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Park MH, An B. Comparison of the Predictors of Smoking Cessation Plans between Adolescent Conventional Cigarette Smokers and E-Cigarette Smokers Using the Transtheoretical Model. CHILDREN (BASEL, SWITZERLAND) 2024; 11:598. [PMID: 38790593 PMCID: PMC11119963 DOI: 10.3390/children11050598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024]
Abstract
Recently, there has been a shift in smoking patterns among adolescents, with a decrease in the prevalence of conventional cigarette smoking and an increase in the use of electronic cigarettes (e-cigarettes). The harmful effects of e-cigarettes are remarkable, highlighting the need for proactive interventions for adolescent users and smoking cessation that consider the characteristics of both conventional cigarette smokers and e-cigarette users. This study aims to investigate the smoking status of adolescent conventional cigarette and e-cigarette smokers and to analyze the predictors of their smoking cessation plans (SCPs) based on the transtheoretical model. Self-rated health, prior smoking cessation education, consciousness-raising, and dramatic relief as types of experiential processes of change, and formation of helping relationships as a type of behavioral process of change significantly differed according to the type of cigarette behavior among adolescents. The predictors of SCP among adolescents were perceived pros of smoking and academic performance among conventional cigarette smokers and behavioral process of change, perceived pros of smoking, and economic status among e-cigarette users. This study identified differences in the characteristics and predictors of SCP. Strategies tailored to each specific adolescent smoking population are further required to promote smoking cessation.
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Affiliation(s)
- Min-Hee Park
- Department of Nursing, Wonkwang University, Iksan 54538, Republic of Korea;
| | - Bomi An
- Department of Nursing, Hannam University, Daejeon 34430, Republic of Korea
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Cheng HG, Lizhnyak PN, Richter N. Mutual pathways between peer and own e-cigarette use among youth in the United States: a cross-lagged model. BMC Public Health 2023; 23:1609. [PMID: 37612711 PMCID: PMC10463603 DOI: 10.1186/s12889-023-16470-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 08/07/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND Electronic cigarettes (e-cigarettes) have become the most common tobacco product used among adolescents in the United States (US). Prior research has shown that peer e-cigarette use was associated with increased risk of own e-cigarette use. Nonetheless, there is little empirical evidence on the directionality of these associations-if peer use predicts own use (peer influence) or if own use predicts peer use (peer selection). METHODS We estimated the association between peer and own e-cigarette use among US adolescents 12-17 years of age. We used the cross-lagged model to investigate the mutual relationship between peer and own e-cigarette use over time using data from a population-based longitudinal study, Population Assessment of Tobacco and Health. Stratified analyses were conducted by sex and age subgroups. RESULTS Results from a cross-lagged model showed a statistically significant predicting path leading from peer use at the prior time point to own use at the following time point, but not vice versa. CONCLUSIONS We found strong relationships between peer e-cigarette use and own e-cigarette use at within-individual levels. Peer influence paths were more robust than peer selection paths for e-cigarette use. Incorporating peers into prevention and intervention programs may help enhance these strategies.
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Affiliation(s)
- Hui G Cheng
- Altria Client Services LLC, 601 E. Jackson, Richmond, VA, 23219, USA.
| | - Pavel N Lizhnyak
- Altria Client Services LLC, 601 E. Jackson, Richmond, VA, 23219, USA
| | - Nadja Richter
- Altria Client Services LLC, 601 E. Jackson, Richmond, VA, 23219, USA
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Liu W, Yang H, Lv L, Song J, Jiang Y, Sun X, Ye D, Mao Y. Genetic predisposition to smoking in relation to the risk of frailty in ageing. Sci Rep 2023; 13:2405. [PMID: 36765104 PMCID: PMC9918446 DOI: 10.1038/s41598-023-28780-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 01/24/2023] [Indexed: 02/12/2023] Open
Abstract
Frailty causes emerging global health burden due to its high prevalence and mortality. In this study, we used Mendelian randomization (MR) approach to examine the potential causal relationship between smoking and frailty in ageing. Using inverse-variance weighted (IVW) method, genetically predicted smoking initiation was associated with an increased risk of frailty in ageing (odd ratio (OR) 1.23, 95% confidence interval (CI) 1.19-1.27, P = 3.21 × 10-39). Similarly, per year increase in age of initiation of regular smoking was associated with a 25% decrease in the risk of frailty (95% CI 7-39%, P = 7.79 × 10-3, per year), while higher number of cigarettes per day was associated with a 12% increased risk (95% CI 4-20%, P = 1.76 × 10-3). Compared with former smokers, current smokers were associated with an increased risk of frailty (OR 1.12, 95% CI 1.02-1.22, P = 0.01). Lifetime smoking was associated with a 46% higher risk of frailty (95% CI 37-56%, P = 2.63 × 10-29). Sensitivity analysis using alternative MR methods yielded similar results. Our study indicates that genetic predisposition to smoking is associated with the risk of frailty in ageing. Further studies are warranted to examine the exact role of smoking in the development of frailty.
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Affiliation(s)
- Wei Liu
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Hong Yang
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Linshuoshuo Lv
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jie Song
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yuqing Jiang
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Xiaohui Sun
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Ding Ye
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yingying Mao
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
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