Search

Background and Aims: Models of tobacco smoking behaviour propose that anticipated pleasure or satisfaction, the need to alleviate a nicotine-induced drive state and a stimulus-driven impulse potentially play an important role. This study aimed to provide a preliminary assessment of how far urges to smoke are reported by smokers and whether the strength of such urges prior to a quit attempt predicts short-term success at quitting., Methods: In a prospective study, 566 smokers attending a treatment programme to help smokers quit completed a written questionnaire covering frequency of different types of urge to smoke (automatic impulse - 'automatic urges', anticipated pleasure - 'pleasure urges', and fulfilling a need - 'need urges'). They were asked to rate this for whichever of these urges was dominant for them. The questionnaire also assessed daily cigarette consumption, time to first cigarette of the day, age and gender. Carbon monoxide verified smoking status was recorded at 1 and 4weeks after the target quit date., Findings: A total of 47.9% (271) of smokers reported that automatic urges were dominant, 21.7% (123) reported pleasure urges to be dominant, and 30.4% (172) reported need urges to be dominant. The strength of automatic urges predicted abstinence at both 1week (OR=0.74, p=0.005, 95%CI=0.60-0.92) and 4weeks (OR=0.73, p=0.008, 95%CI=0.59-0.92). Associations between other urge types and abstinence were not statistically significant., Conclusions: A substantial proportion of smokers attending a clinic for help with quitting report that their dominant urges to smoke occur without any anticipation of pleasure or relief and the strength of the automatic urges predicts failure to sustain abstinence following a quit attempt., (Copyright © 2017. Published by Elsevier Ltd.)

iOS and Android smartphone users may differ in ways that affect their use and likelihood of success when using a smoking cessation application (app). If so, it may be necessary to take the device type (iOS and Android) into account when designing smoking cessation apps and in studies evaluating app effectiveness. How do socio-demographic and smoking characteristics, potentially relevant to engagement and cessation outcomes, of the SF28 app users differ between those using the iOS version and those using the Android version? Data were collected between October 2013 and April 2015. The variables measured were age, gender, social grade, time since the most recent quit attempt, choice of medication use (nicotine replacement therapy or varenicline), weekly expenditure on cigarettes, cigarettes smoked per day, reason for using the app and quit date set. The alpha was set to p < 0.006 to adjust for multiple comparisons. A total of 1368 users were included in the analysis. iOS and Android device users were similar in terms of age, social grade, weekly expenditure on cigarettes and cigarettes smoked per day. Compared with Android users, iOS users were more likely to have downloaded the app for a serious quit attempt (74.3 versus 69.6%, p = 0.001), made a quit attempt within the last 12 months (59.6 versus 45.9%, p < 0.001) and set their quit date on the day of registration (61 versus 46.2%, p < 0.001). They were less likely to have used stop-smoking medication to support their quit attempt (31.5 versus 48.6%, p < 0.001). Differences between smokers using the iOS version of smoking cessation apps and those using the Android version may influence quit success.

Background: Public health organisations such as the National Health Service in the United Kingdom and the National Institutes of Health in the United States provide access to online libraries of publicly endorsed smartphone applications (apps); however, there is little evidence that users rely on this guidance. Rather, one of the most common methods of finding new apps is to search an online store. As hundreds of smoking cessation and alcohol-related apps are currently available on the market, smokers and drinkers must actively choose which app to download prior to engaging with it. The influences on this choice are yet to be identified. This study aimed to investigate 1) design features that shape users' choice of smoking cessation or alcohol reduction apps, and 2) design features judged to be important for engagement., Methods: Adult smokers (n = 10) and drinkers (n = 10) interested in using an app to quit/cut down were asked to search an online store to identify and explore a smoking cessation or alcohol reduction app of their choice whilst thinking aloud. Semi-structured interview techniques were used to allow participants to elaborate on their statements. An interpretivist theoretical framework informed the analysis. Verbal reports were audio recorded, transcribed verbatim and analysed using inductive thematic analysis., Results: Participants chose apps based on their immediate look and feel, quality as judged by others' ratings and brand recognition ('social proof'), and titles judged to be realistic and relevant. Monitoring and feedback, goal setting, rewards and prompts were identified as important for engagement, fostering motivation and autonomy. Tailoring of content, a non-judgmental communication style, privacy and accuracy were viewed as important for engagement, fostering a sense of personal relevance and trust. Sharing progress on social media and the use of craving management techniques in social settings were judged not to be engaging because of concerns about others' negative reactions., Conclusions: Choice of a smoking cessation or alcohol reduction app may be influenced by its immediate look and feel, 'social proof' and titles that appear realistic. Design features that enhance motivation, autonomy, personal relevance and credibility may be important for engagement.

Background and Aims: Smartphone applications (apps) offer a potentially cost-effective and a wide-reach aid to smoking cessation. In 2012, a content analysis of smoking cessation apps suggested that most apps did not adopt behaviour change techniques (BCTs), which according to previous research had suggested would promote higher success rates in quitting smoking. This study examined whether or not, this situation had changed by 2014 for free smoking cessation apps available in the Apple App Store. It also compared the use of engagement and ease-of-use features between the two time points., Methods: 137 free apps available in the Apple App Sore in 2014 were coded using an established framework for the presence or absence of evidence-based BCTs, and engagement and ease-of-use features. The results from the 2014 data were compared with a similar exercise conducted on 83 free apps available in 2012., Results: BCTs supporting identity change, rewarding abstinence and advising on changing routines were less prevalent in 2014 as compared with 2012 (14.6% vs. 42.2%, 18.2% vs. 48.2%, and 17.5% vs. 24.1%, respectively). Advice on coping with cravings and advice on the use of stop-smoking medication were more prevalent in 2014 as compared with 2012 (27.7% vs. 20.5% and 14.6% vs 3.6%, respectively). The use of recognised engagement features was less common in 2014 than in 2012 (45.3% vs. 69.6%) while ease-of-use features remained very high (94.5% vs. 82.6%)., Conclusion: There was little evidence of improvement in the use of evidence-based BCTs in free smoking cessation iPhone-based apps between 2012 and 2014., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background and aim: It is useful, for theoretical and practical reasons, to be able to specify functions for continuous abstinence over time in smoking cessation attempts. This study aimed to find the best‐fitting models of mean proportion abstinent with different smoking cessation pharmacotherapies up to 52 weeks from the quit date. Methods: We searched the Cochrane Database of Systematic Reviews to identify randomized controlled trials (RCTs) of pharmacological treatments to aid smoking cessation. For comparability, we selected trials that provided 12 weeks of treatment. Continuous abstinence rates for each treatment at each follow‐up point in trials were extracted along with methodological details of the trial. Data points for each pharmacotherapy at each follow‐up point were aggregated where the total across contributing studies included at least 1000 participants per data point. Continuous abstinence curves were modelled using a range of different functions from the quit date to 52‐week follow‐up. Models were compared for fit using R2 and Bayesian information criterion (BIC). Results: Studies meeting our selection criteria covered three pharmacotherapies [varenicline, nicotine replacement therapy (NRT) and bupropion] and placebo. Power functions provided the best fit (R2 > 0.99, BIC < 17.0) to continuous abstinence curves from the target quit date in all cases except for varenicline, where a logarithmic function described the curve best (R2 = 0.99, BIC = 21.2). At 52 weeks, abstinence rates were 22.5% (23.0% modelled) for varenicline, 16.7% (16.0% modelled) for bupropion, 13.0% (12.4% modelled) for NRT and 8.3% (8.9% modelled) for placebo. For varenicline, bupropion, NRT and placebo, respectively, 55.9, 65.0, 62.3 and 56.5% of participants who were abstinent at the end of treatment were still abstinent at 52 weeks. Conclusions: Mean continuous abstinence rates up to 52 weeks from initiation of smoking cessation attempts in clinical trials can be modelled using simple power functions for placebo, nicotine replacement therapy and bupropion and a logarithmic function for varenicline. This allows accurate prediction of abstinence rates from any time point to any other time point up to 52 weeks. [ABSTRACT FROM AUTHOR]