The main aim of this pilot project was to introduce multimodal smoking cessation intervention in the hospital setting and to analyze users' satisfaction and efficacy of the intervention within six months post-discharge. Multimodal intervention for smoking cessation was used and it consisted of the "5 A's" model (Ask, Advice, Assess, Assist, Arrange) for behavior change, printed self-help materials for smoking cessation, and telephone counseling (one, three and six months after discharge from the hospital). The main outcome of the study was smoking status at six months. A total of 103 participants were included in this pilot project. At six-month follow-up, 49% of participants self-reported continuous non-smoking. Among the remaining participants, 20 reported smoking reduction, 19 were still smoking, and 16 participants were unable to make contact with. In the logistic regression, among all analyzed variables, only two of them were positively associated with smoking cessation after six months: participants' response that they would like to quit smoking within the next six months (B=4.688; p=0.018) and answering that they did not smoke when they were ill and bed-ridden due to illness (B=3.253; p=0.020). Satisfaction with the intervention was very high; 70% of participants rated the intervention as 'excellent'. Therefore, multimodal smoking cessation intervention can be successfully introduced at hospital setting yielding high smoking abstinence rates at six months post-discharge and high level of user satisfaction. Healthcare workers who work in hospitals should be educated so they can provide such intervention on a regular basis.
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http://dx.doi.org/10.20471/acc.2022.61.02.14 | DOI Listing |
Drug Alcohol Depend Rep
December 2024
Department of Family and Preventive Medicine, College of Medicine, The University of Oklahoma Health Sciences, Oklahoma City, OK, USA.
Background: Smoking prevalence among U.S. adults experiencing homelessness is ≥70 %.
View Article and Find Full Text PDFTob Control
January 2025
Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
BackgroundTobacco use is linked to increased cancer risk, and people who smoke represent a large proportion of newly diagnosed patients with cancer. The fact that smoking cessation at the time of diagnosis can improve the patient's life expectancy is still not broadly understood. We conducted a systematic review and meta-analysis to quantify the survival benefits obtainable by quitting smoking on diagnosis.
View Article and Find Full Text PDFEur J Public Health
January 2025
Department of Cardiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
We quantified the fraction of cardiovascular deaths attributable to smoking in Germany over time, accounting for population ageing. We calculated population-attributable fractions to quantify cardiovascular deaths attributable to smoking for 1992 to 2021, and compared actual with age-standardized figures. We found a significant decline in the number of cardiovascular deaths attributable to smoking: from about 71 900 cases in 1992 to around 42 000 cases in 2021, with a steeper decline in men.
View Article and Find Full Text PDFIntern Emerg Med
January 2025
Department of Social and Behavioral Sciences, School of Global Public Health, New York University, New York, NY, USA.
Recent data on methods used by adults to stop smoking can inform tobacco control policies. Nationally representative Centers for Disease Control and Prevention survey data from the 2022 National Health Interview Survey (N = 27,651) were used to analyze populations of US adults who self-reported having stopped smoking cigarettes for 6 months or longer in the last year and the methods they used, or who did not stop smoking but tried in the last year (N = 1735). In 2022, an estimated 2.
View Article and Find Full Text PDFPsychol Methods
January 2025
Department of Psychology, University of Pittsburgh.
Intensive longitudinal data analysis, commonly used in psychological studies, often concerns outcomes that have strong floor effects, that is, a large percentage at its lowest value. Ignoring a strong floor effect, using regular analysis with modeling assumptions suitable for a continuous-normal outcome, is likely to give misleading results. This article suggests that two-part modeling may provide a solution.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!