Predictors of successful and unsuccessful quit attempts among smokers motivated to quit

Smit, Eline Suzanne; Hoving, Ciska; Schelleman-Offermans, Karen; West, Robert A.; Vries, Hein de

doi:10.1016/j.addbeh.2014.04.017

Cited by 110 publications

(82 citation statements)

References 34 publications

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“…While studies show that different factors play a role in predicting quitting attempts and their success, especially motivation (Smit, Hoving, Schelleman-Offermans, West, & de Vries, 2014), the results of our analyses showed no relationship between them. Although participants were motivated to stop smoking, we found no correlation between motivation and previous attempts to quit smoking.…”

Section: Discussioncontrasting

confidence: 52%

Depression among smokers of a web-based intervention to quit smoking: a cross-sectional study

Gomide

Carvalho

Menezes

et al. 2017

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Introduction. Web-based interventions for smoking cessation are an innovative strategy to reduce the burden of smoking. Although many web-based interventions are freely available in many languages and have proven to be effective, so far no study has covered in detail the association between depression and smoking. Objective. The aim of this study was to evaluate the prevalence of depression among users of the Viva sem Tabaco, a web-based intervention for smoking cessation. Method. This was a retrospective cross-sectional study. In the internet-based intervention participated 1 433. Inclusion criteria were: being 18 years or older and a smoker; exclusion criteria were: omitting to fill out two questions of the screening depression questionnaire PHQ-2 and having made multiple accesses within a limited time span, characterizing invalid access. At the end, the sample had 461 participants. Participants answered questions related to sociodemographic characteristics, tobacco history, depression (PHQ-2 and PHQ-9), alcohol use, and intervention use. Results. Participants average age was 42.3 years (SD = 12.1). Most participants were female (67%), and 70% were employed during the time of the study. From the total sample, 36.4% of the participants presented depression according to PHQ-2. Being screened with depression was associated with tobacco dependence (OR = 1.10; 95% CI = 1.00, 1.20), and associated with not having a job (OR = .53; 95% CI = .29, .97). Discussion and conclusion. Depression may be a factor to be considered in programs that offer support to quit smoking through the internet for Portuguese Speakers.

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Section: Discussioncontrasting

confidence: 52%

Depression among smokers of a web-based intervention to quit smoking: a cross-sectional study

Gomide

Carvalho

Menezes

et al. 2017

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“…For example, only 33% of apps asked users to set a quit date, and only 9% of the apps provided any support or feedback for a quit attempt. Planning a quit attempt is an active ingredient in achieving success in smoking cessation, 38 including in achieving smoking cessation with the help of an app. 39 Yet currently, most apps remained static throughout the quit attempt.…”

Section: Discussionmentioning

confidence: 99%

How Smart are Smartphone Apps for Smoking Cessation? A Content Analysis

Hoeppner

Seaboyer

et al. 2015

NICTOB

149

139

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IntroductionCurrent evidence has demonstrated the usefulness of mobile technology in supporting smoking cessation. 1 The most recent Cochrane review, based on 20 studies and a total sample size of 9100 smokers, indicated significant benefit of mobile phone-based smoking cessation interventions on long-term outcomes, with a relative risk estimate of 1.71, compared to no intervention or less intensive intervention via mobile.1 These effects were achieved with fully-automated, highly cost-effective programs of unprecedented reach. They were also achieved with a relatively low level of technological sophistication, as up to this point, mobile technology approaches to smoking cessation have largely used text messaging. AbstractIntroduction: Smartphone technology is ideally suited to provide tailored smoking cessation support, yet it is unclear to what extent currently existing smartphone "apps" use tailoring, and if tailoring is related to app popularity and user-rated quality. Methods: We conducted a content analysis of Android smoking cessation apps (n = 225), downloaded between October 1, 2013 to May 31, 2014. We recorded app popularity (>10 000 downloads) and user-rated quality (number of stars) from Google Play, and coded the existence of tailoring features in the apps within the context of using the 5As ("ask, " "advise, " "assess, " "assist, " and "arrange follow-up"), as recommended by national clinical practice guidelines. Results: Apps largely provided simplistic tools (eg, calculators, trackers), and used tailoring sparingly: on average, apps addressed 2.1 ± 0.9 of the 5As and used tailoring for 0.7 ± 0.9 of the 5As. Tailoring was positively related to app popularity and user-rated quality: apps that used two-way interactions (odds ratio ), proactive alerts .38]), responsiveness to quit status ), addressed more of the 5As (OR = 1.53 [1.10-2.14]), used tailoring for more As (OR = 1.67 [1.21-2.30]), and/or used more ways of tailoring 5As content (OR = 1.35 [1.13-1.62]) were more likely to be frequently downloaded. Higher star ratings were associated with a higher number of 5As addressed (b = 0.16 [0.03-0.30]), a higher number of 5As with any level of tailoring (b = 0.14 [0.01-0.27]), and a higher number of ways of tailoring 5As content (b = 0.08 [0.002-0.15]). Conclusions: Publically available smartphone smoking cessation apps are not particularly "smart": they commonly fall short of providing tailored feedback, despite users' preference for these features.

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“…Finally, quit intentions play an important role in getting smokers to make a quit attempt, which is a key step in the process of smoking cessation. However, it is worth noting that while the intention to quit smoking is associated with making a quit attempt, the success of a quit attempt is predicted by other factors such as nicotine dependence, selfefficacy and genotypes of the nAChR gene [60][61][62][63] . Moreover, there is evidence that intentions do not always translate into behaviours 64,65 .…”

Section: Research Papermentioning

confidence: 99%

Predictors of quit intentions among adult smokers in Mauritius: Findings from the ITC Mauritius Survey

Kaai¹,

Chung‐Hall²,

Sun³

et al. 2016

Tob. Prev. Cessation

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INTRODUCTION Mauritius has one of the highest rates of smoking in Africa. Smoking cessation is a priority for preventing tobacco-related morbidity and mortality. The purpose of this study is to identify the predictors of quit intentions among smokers in Mauritius in order to strengthen tobacco control policies and inform the development and delivery of services that may increase the likelihood of successful quitting. METHODS Data were drawn from Wave 1 (2009) of the International Tobacco Control (ITC) Mauritius Survey, a face-to-face cohort survey of a nationally representative sample of 598 adult smokers who were randomly selected from nine geographic districts in Mauritius using a multistage sampling procedure. RESULTS The vast majority of smokers (77.8%) had plans to quit smoking. Longer duration of past quit attempts (6 months or less), perceiving benefits of quitting, worrying about smoking damaging health in the future, and not enjoying smoking were significantly associated with quit intentions. However, socio-demographic characteristics, past quit attempts, overall attitude about smoking, and Heaviness of Smoking Index (HSI) were not associated with quit intentions. CONCLUSIONS The predictors of quit intentions among Mauritian smokers were generally similar to those found among smokers in other high-and middle-income countries. However, in contrast to findings in those other countries, nicotine dependence as measured by the HSI was not a significant predictor of quit intentions among Mauritian smokers. These findings highlight the need to consider the predictors of quit intentions when developing and delivering smoking cessation support services in Mauritius.

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Predictors of successful and unsuccessful quit attempts among smokers motivated to quit

Cited by 110 publications

References 34 publications

Depression among smokers of a web-based intervention to quit smoking: a cross-sectional study

Depression among smokers of a web-based intervention to quit smoking: a cross-sectional study

How Smart are Smartphone Apps for Smoking Cessation? A Content Analysis

Predictors of quit intentions among adult smokers in Mauritius: Findings from the ITC Mauritius Survey

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