BackgroundLittle is known about the effectiveness of mobile apps in aiding smoking cessation or their validity for automated collection of data on smoking cessation outcomes.ObjectiveWe conducted a preliminary evaluation of SF28 (SF28 is the name of the app, short for SmokeFree28)—an app aimed at helping smokers to be smoke-free for 28 days.MethodsData on sociodemographic characteristics, smoking history, number of logins, and abstinence at each login were uploaded to a server from SF28 between August 2012 and August 2013. Users were included if they were aged 16 years or over, smoked cigarettes at the time of registration, had set a quit date, and used the app at least once on or after their quit date. Their characteristics were compared with data from a representative sample of smokers trying to stop smoking in England. The percentage of users recording 28 days of abstinence was compared with a value of 15% estimated for unaided quitting. Correlations were assessed between recorded abstinence for 28 days and well-established abstinence predictors.ResultsA total of 1170 users met the inclusion criteria. Compared with smokers trying to quit in England, they had higher consumption, and were younger, more likely to be female, and had a non-manual rather than manual occupation. In total, 18.9% (95% CI 16.7-21.1) were recorded as being abstinent from smoking for 28 days or longer. The mean number of logins was 8.5 (SD 9.0). The proportion recording abstinence for 28 days or longer was higher in users who were older, in a non-manual occupation, and in those using a smoking cessation medication.ConclusionsThe recorded 28-day abstinence rates from the mobile app, SF28, suggest that it may help some smokers to stop smoking. Further evaluation by means of a randomized trial appears to be warranted.

BackgroundPublic 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.MethodsAdult 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.ResultsParticipants 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.ConclusionsChoice 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0422-8) contains supplementary material, which is available to authorized users.

The aim of this study was to assess whether or not behaviour change techniques (BCTs) as well as engagement and ease-of-use features used in smartphone applications (apps) to aid smoking cessation can be identified reliably. Apps were coded for presence of potentially effective BCTs, and engagement and ease-of-use features. Inter-rater reliability for this coding was assessed. Inter-rater agreement for identifying presence of potentially effective BCTs ranged from 66.8 to 95.1 % with ‘prevalence and bias adjusted kappas’ (PABAK) ranging from 0.35 to 0.90 (p < 0.001). The intra-class correlation coefficients between the two coders for scores denoting the proportions of (a) a set of engagement features and (b) a set of ease-of-use features, which were included, were 0.77 and 0.75, respectively (p < 0.001). Prevalence estimates for BCTs ranged from <10 % for medication advice to >50 % for rewarding abstinence. The average proportions of specified engagement and ease-of-use features included in the apps were 69 and 83 %, respectively. The study found that it is possible to identify potentially effective BCTs, and engagement and ease-of-use features in smoking cessation apps with fair to high inter-rater reliability.Electronic supplementary materialThe online version of this article (doi:10.1007/s13142-015-0352-x) contains supplementary material, which is available to authorized users.

Background and aimsSmartphone 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.Methods137 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.ResultsBCTs 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%).ConclusionThere was little evidence of improvement in the use of evidence-based BCTs in free smoking cessation iPhone-based apps between 2012 and 2014.

Background: Smartphone applications (apps) are popular aids for smoking cessation. Smoke Free is an app that delivers behaviour change techniques used in effective face-to-face behavioural support programmes. The aim of this study was to assess whether the full version of Smoke Free is more effective than the reduced version. Methods:  This was a two-arm exploratory randomised controlled trial. Smokers who downloaded Smoke Free were randomly offered the full or reduced version; 28,112 smokers aged 18+ years who set a quit date were included. The full version provided updates on benefits of abstinence, progress (days smoke free), virtual ‘badges’ and daily ‘missions’ with push notifications aimed at preventing and managing cravings. The reduced version did not include the missions. At baseline the app recorded users’: device type (iPhone or Android), age, sex, daily cigarette consumption, time to first cigarette of the day, and educational level. The primary outcome was self-reported complete abstinence from the quit date in a 3-month follow-up questionnaire delivered via the app. Analyses conducted included logistic regressions of outcome on to app version (full versus reduced) with adjustment for baseline variables using both intention-to-treat/missing-equals smoking (MES) and follow-up-only (FUO) analyses. Results: The 3-month follow-up rate was 8.5% (n=1,213) for the intervention and 6.5% (n=901) for the control. A total of 234 participants reported not smoking in the intervention versus 124 in the control, representing 1.6% versus 0.9% in the MES analysis and 19.3% versus 13.8% in the FUO analysis. Adjusted odds ratios were 1.90, 95%CI=1.53-2.37 (p<0.001) and 1.50, 95%CI=1.18-1.91 (p<0.001) in the MES and FUO analyses respectively. Conclusions: Despite very low follow-up rates using in-app follow up, both intention-to-treat/missing equals smoking and follow-up only analyses showed the full version of the Smoke Free app to result in higher self-reported 3-month continuous smoking abstinence rates than the reduced version.

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 R 2 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 ( R 2 > 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 ( R 2 = 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.

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: Smartphone applications (apps) are popular aids for smoking cessation. Smoke Free is an app that delivers behaviour change techniques used in effective face-to-face behavioural support programmes. The aim of this study was to assess whether the full version of Smoke Free is more effective than the reduced version. Methods:  This was a two-arm randomised controlled trial. Smokers who downloaded Smoke Free were randomly offered the full or reduced version; 28,112 smokers aged 18+ years who set a quit date were included. The full version provided updates on benefits of abstinence, progress (days smoke free), virtual ‘badges’ and daily ‘missions’ with push notifications aimed at preventing and managing cravings. The reduced version did not include the missions. At baseline the app recorded users’: device type (iPhone or Android), age, sex, daily cigarette consumption, time to first cigarette of the day, and educational level. The primary outcome was self-reported complete abstinence from the quit date in a 3-month follow-up questionnaire delivered via the app. Analyses conducted included logistic regressions of outcome on to app version (full versus reduced) with adjustment for baseline variables using both intention-to-treat/missing-equals smoking (MES) and follow-up-only (FUO) analyses. Results: The 3-month follow-up rate was 8.5% (n=1,213) for the intervention and 6.5% (n=901) for the control. A total of 234 participants reported not smoking in the intervention versus 124 in the control, representing 1.6% versus 0.9% in the MES analysis and 19.3% versus 13.8% in the FUO analysis. Adjusted odds ratios were 1.90, 95%CI=1.53-2.37 (p<0.001) and 1.50, 95%CI=1.18-1.91 (p<0.001) in the MES and FUO analyses respectively. Conclusions: Despite very low follow-up rates using in-app follow up, both intention-to-treat/missing equals smoking and follow-up only analyses showed the full version of the Smoke Free app to result in higher self-reported 3-month continuous smoking abstinence rates than the reduced version.