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Predictors of Quitting Smoking in Cardiac Rehabilitation.

ABSTRACT: Quitting smoking and participation in cardiac rehabilitation (CR) are effective strategies in reducing morbidity and mortality. However, little is known about the predictors of quitting smoking in those who attend CR. This study aimed to determine the sociodemographic and clinical factors associated with the likelihood of CR attendees who are quitting smoking. Data from the UK National Audit of Cardiac Rehabilitation (NACR) database, between April 2013 and March 2016, were used. Smoking status is categorized as smokers and quitters, assessed by patient self-report. The study used patient demographics, cardiovascular risk factors, comorbidities, and physical and psychosocial health measures. Binary logistic regression was performed to identify the predictors of quitting smoking among CR attendees. Of the 3290 patients who started CR and were entered into the NACR database, 2052 were continued smokers (mean age 58.59 ± 10.49 years, 73.6% men) and 1238 were quitters (mean age 57.63 ± 10.36 years, 75.8% men). The median duration of CR was 9 weeks. Compared to smokers, the quitters were younger, weighed more, were less anxious and depressed, and were more likely to be employed. Single patients had 0.60 times lower odds (95% CI 0.43 to 0.82) of quitting smoking than patients with partners, and low-risk patients had 1.71 times higher odds (95% CI 1.12 to 2.62) of quitting smoking than high-risk patients. Increasing number of comorbidities and depression scores were associated with decreasing likelihood of quitting. This study highlights the routine factors that determine smoking cessation outcomes, which could inform the delivery of CR to better help patients to quit smoking.

SUBMITTER: Salman A

PROVIDER: S-EPMC7465128 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Predictors of Quitting Smoking in Cardiac Rehabilitation.

Salman Ahmad A Doherty Patrick P

Journal of clinical medicine 20200812 8

Quitting smoking and participation in cardiac rehabilitation (CR) are effective strategies in reducing morbidity and mortality. However, little is known about the predictors of quitting smoking in those who attend CR. This study aimed to determine the sociodemographic and clinical factors associated with the likelihood of CR attendees who are quitting smoking. Data from the UK National Audit of Cardiac Rehabilitation (NACR) database, between April 2013 and March 2016, were used. Smoking status i ...[more]

PMID: 32806587

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Project description:INTRODUCTION The number of cessation clinics in China have been increasing ever since the Chinese government supported the establishment of smoking cessation clinics (SCCs) in each province in 2014. Many studies have examined smoking cessation behaviors among male smokers, but few of female smokers. This study aimed to understand female smokers’ quitting behaviors in SCCs and identify predictors of successful cessation. METHODS This study used data of the SCCs Platform in China from 2018 to 2020. The self-reported 7-day point prevalence of abstinence rate (PPAR) at 1 month and at 3 months follow-up and the continuous abstinence rate (CAR) at 3 months follow-up are reported based on smokers’ characteristics and intention to treat analysis. A multiple logistic regression model was used to identify predictors of continuous abstinence at 3 months follow-up. RESULTS The 7-day PPAR of female outpatients in SCCs was 29.20% at 1 month follow-up and 28.36% at 3 months follow-up. The CAR at 3 months follow-up was 19.88%. Female smokers who were prepared to quit within 7 days (AOR=2.86; 95% CI: 1.53–5.32), today (AOR=4.01; 95% CI: 2.35–6.85), had started to quit (AOR=7.11; 95% CI: 4.12–12.27), and used a combination of counseling and drugs (AOR=2.41; 95% CI: 1.73–3.35) were more likely to quit smoking. Associated with lower quitting rates were: living in the central region of China (AOR=0.47; 95% CI: 0.31–0.73) and the west region (AOR=0.48; 95% CI: 0.31–0.73); being aged 30–39 years (AOR=0.39; 95% CI: 0.23–0.64), and 40–49 years (AOR=0.41; 95% CI:0.24–0.69); being unemployed (AOR=0.64; 95% CI: 0.45–0.91); having a fair perceived health status at the first visit (AOR=0.65; 95% CI: 0.47–0.91) and a poor one (AOR=0.37; 95% CI: 0.21–0.64); having a moderate nicotine dependence (AOR=0.64; 95% CI: 0.44–0.92) and a severe one (AOR=0.50; 95% CI: 0.34–0.72). CONCLUSIONS In our study, the region of residence, age, employment, perceived health status, Fagerström test for nicotine dependence (FTND), readiness to quit, and intervention model were independent predictors of quitting for female smokers. Improving the motivation to quit, providing intensive psychological interventions and equipping SCCs with cessation medication would assist female smokers to quit.

Project description:BackgroundQuitting support from smokers' partners can predict quit attempts and smoking abstinence but research on factors that predict such support has been limited. To add more evidence for partner support and the improved interventions for smoking cessation, we analyzed some new potential predictors of quitting support from smokers' spouses.MethodThis cross-sectional study was conducted in in 2022 and 2023, selecting the students' families in which fathers smoked and mothers didn't smoke from grade 1-5 of 13 primary schools in Qingdao, China. Parents who met the criteria completed the online questionnaires and 1018 families were included in the analysis. We measured personal information related to smokers and their spouses such as age, education and nicotine dependence, and variables related to family and marital relationship such as family functioning, perceived responsiveness and power in decision-making of quitting smoking. Quitting support from smokers' spouses was measured by Partner Interaction Questionnaire and generalized linear model was used to explore the potential predictors of partner support.ResultsIn this study, the mean age of smokers was 39.97(SD = 5.57) and the mean age of smokers' spouses was 38.24(SD = 4.59). The regression analysis showed that for smokers and their spouses, the older age groups showed the lower ratio of positive/negative support(P < 0.05) and smokers with high education showed the less positive and negative partner support(P < 0.05). Nicotine dependence was positively associated with negative support (β = 0.120, P < 0.01), and perceived responsiveness (β = 0.124, P < 0.05) as well as family functioning (β = 0.059, P < 0.05) was positively associated with positive support. These three factors were associated with ratio of positive/negative support(P < 0.05). In addition, power of smoker's spouse in decision-making of quitting smoking was positively associated with the positive (β = 0.087, P < 0.001) and negative support (β = 0.084, P < 0.001).ConclusionsNicotine dependence, family functioning, power in decision-making of quitting smoking and perceived responsiveness were found to be the predictors of quitting support from smokers' spouses. By incorporating predictors of partner support and integrating some established theories that can improve family functioning and marital relationships, smoking cessation interventions can be further improved.

Project description:Mobile health technologies are being developed for personal lifestyle and medical healthcare support, of which a growing number are designed to assist smokers to quit. The potential impact of these technologies in the fight against smoking addiction and on improving quitting rates must be systematically evaluated. The aim of this report is to identify and appraise the most promising smoking detection and quitting technologies (e.g., smartphone apps, wearable devices) supporting smoking reduction or quitting programs. We searched PubMed and Scopus databases (2008-2019) for studies on mobile health technologies developed to assist smokers to quit using a combination of Medical Subject Headings topics and free text terms. A Google search was also performed to retrieve the most relevant smartphone apps for quitting smoking, considering the average user's rating and the ranking computed by the search engine algorithms. All included studies were evaluated using consolidated criteria for reporting qualitative research, such as applied methodologies and the performed evaluation protocol. Main outcome measures were usability and effectiveness of smoking detection and quitting technologies supporting smoking reduction or quitting programs. Our search identified 32 smoking detection and quitting technologies (12 smoking detection systems and 20 smoking quitting smartphone apps). Most of the existing apps for quitting smoking require the users to register every smoking event. Moreover, only a restricted group of them have been scientifically evaluated. The works supported by documented experimental evaluation show very high detection scores, however the experimental protocols usually lack in variability (e.g., only right-hand patients, not natural sequence of gestures) and have been conducted with limited numbers of patients as well as under constrained settings quite far from real-life use scenarios. Several recent scientific works show very promising results but, at the same time, present obstacles for the application on real-life daily scenarios.

Project description:BackgroundCigarette smoking is widespread among HIV-infected patients, who confront increased risk of smoking-related co-morbidities. The effects of HIV infection and HIV-related variables on smoking and smoking cessation are incompletely understood. We investigated the correlates of smoking and quitting in an HIV-infected cohort using a validated natural language processor to determine smoking status.MethodWe developed and validated an algorithm using natural language processing (NLP) to ascertain smoking status from electronic health record data. The algorithm was applied to records for a cohort of 3487 HIV-infected from a large health care system in Boston, USA, and 9446 uninfected control patients matched 3:1 on age, gender, race and clinical encounters. NLP was used to identify and classify smoking-related portions of free-text notes. These classifications were combined into patient-year smoking status and used to classify patients as ever versus never smokers and current smokers versus non-smokers. Generalized linear models were used to assess associations of HIV with 3 outcomes, ever smoking, current smoking, and current smoking in analyses limited to ever smokers (persistent smoking), while adjusting for demographics, cardiovascular risk factors, and psychiatric illness. Analyses were repeated within the HIV cohort, with the addition of CD4 cell count and HIV viral load to assess associations of these HIV-related factors with the smoking outcomes.ResultsUsing the natural language processing algorithm to assign annual smoking status yielded sensitivity of 92.4, specificity of 86.2, and AUC of 0.89 (95% confidence interval [CI] 0.88-0.91). Ever and current smoking were more common in HIV-infected patients than controls (54% vs. 44% and 42% vs. 30%, respectively, both P<0.001). In multivariate models HIV was independently associated with ever smoking (adjusted rate ratio [ARR] 1.18, 95% CI 1.13-1.24, P <0.001), current smoking (ARR 1.33, 95% CI 1.25-1.40, P<0.001), and persistent smoking (ARR 1.11, 95% CI 1.07-1.15, P<0.001). Within the HIV cohort, having a detectable HIV RNA was significantly associated with all three smoking outcomes.ConclusionsHIV was independently associated with both smoking and not quitting smoking, using a novel algorithm to ascertain smoking status from electronic health record data and accounting for multiple confounding clinical factors. Further research is needed to identify HIV-related barriers to smoking cessation and develop aggressive interventions specific to HIV-infected patients.

Dataset Information

Predictors of Quitting Smoking in Cardiac Rehabilitation.

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Predictors of Quitting Smoking in Cardiac Rehabilitation.

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