Smoking Cessation Apps: A Systematic Review of Format, Outcomes, and Features.
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SUBMITTER: Barroso-Hurtado M
PROVIDER: S-EPMC8583115 | biostudies-literature | 2021 Nov
REPOSITORIES: biostudies-literature
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Smoking Cessation Apps: A Systematic Review of Format, Outcomes, and Features.
Barroso-Hurtado María M Suárez-Castro Daniel D Martínez-Vispo Carmela C Becoña Elisardo E López-Durán Ana A
International journal of environmental research and public health 20211106 21
Smoking cessation interventions are effective, but they are not easily accessible for all treatment-seeking smokers. Mobile health (mHealth) apps have been used in recent years to overcome some of these limitations. Smoking cessation apps can be used in combination with a face-to-face intervention (FFSC-Apps), or alone as general apps (GSC-Apps). The aims of this review were (1) to examine the effects of FFSC-Apps and GSC-Apps on abstinence, tobacco use, and relapse rates; and (2) to describe th ...[more]
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Project description:BackgroundCigarette smoking is a leading cause of preventable death, and identifying novel treatment approaches to promote smoking cessation is critical for improving public health. With the rise of digital health and mobile apps, these tools offer potential opportunities to address smoking cessation, yet the functionality of these apps and whether they offer scientifically based support for smoking cessation are unknown.ObjectiveThe goal of this research was to use the American Psychiatric Association app evaluation model to evaluate the top-returned apps from Android and Apple app store platforms related to smoking cessation and investigate the common app features available for end users.MethodsWe conducted a search of both Android and iOS app stores in July 2021 for apps related to the keywords "smoking," "tobacco," "smoke," and "cigarette" to evaluate apps for smoking cessation. Apps were screened for relevance, and trained raters identified and analyzed features, including accessibility (ie, cost), privacy, clinical foundation, and features of the apps, using a systematic framework of 105 objective questions from the American Psychiatric Association app evaluation model. All app rating data were deposited in mindapps, a publicly accessible database that is continuously updated every 6 months given the dynamic nature of apps available in the marketplace. We characterized apps available in July 2021 and November 2022.ResultsWe initially identified 389 apps, excluded 161 due to irrelevance and nonfunctioning, and rated 228, including 152 available for Android platforms and 120 available for iOS platforms. Some of the top-returned apps (71/228, 31%) in 2021 were no longer functioning in 2022. Our analysis of rated apps revealed limitations in accessibility and features. While most apps (179/228, 78%) were free to download, over half had costs associated with in-app purchases or full use. Less than 65% (149/228) had a privacy policy addressing the data collected in the app. In terms of intervention features, more than 56% (128/228) of apps allowed the user to set and check in on goals, and more than 46% (106/228) of them provided psychoeducation, although few apps provided evidence-based support for smoking cessation, such as peer support or skill training, including mindfulness and deep breathing, and even fewer provided evidence-based interventions, such as acceptance and commitment therapy or cognitive behavioral therapy. Only 12 apps in 2021 and 11 in 2022 had published studies supporting the feasibility or efficacy for smoking cessation.ConclusionsNumerous smoking cessation apps were identified, but analysis revealed limitations, including high rates of irrelevant and nonfunctioning apps, high rates of turnover, and few apps providing evidence-based support for smoking cessation. Thus, it may be challenging for consumers to identify relevant, evidence-based apps to support smoking cessation in the app store, and a comprehensive evaluation system of mental health apps is critically important.
| S-EPMC10375280 | biostudies-literature
Project description:BackgroundMany people use apps for smoking cessation, and the effectiveness of these apps has been proven in several studies. However, no study has classified these apps and only few studies have analyzed the characteristics of these apps that influence their quality.ObjectiveThe purpose of this study was to analyze the content and the quality of smoking cessation apps by type and identify the characteristics that affect their overall quality.MethodsTwo app marketplaces (App Store and Google Play) were searched in January 2018, and the search was completed by May 2020. The search terms used were "stop smoking," "quit smoking," and "smoking cessation." The apps were categorized into 3 types (combined, multifunctional, and informational). The tailored guideline of Clinical Practice Guideline for Treating Tobacco Use and Dependence was utilized for evaluating app content (or functions), and the Mobile App Rating Scale (MARS) was used to evaluate the quality. Chi-square test was performed for the general characteristics, and one-way analysis of variance was performed for MARS analysis. To identify the general features of the apps that could be associated with the MARS and content scores, multiple regression analysis was done. All analyses were performed using SAS software (ver. 9.3).ResultsAmong 1543 apps, 104 apps met the selection criteria of this study. These 104 apps were categorized as combined type (n=44), functional type (n=31), or informational type (n=29). A large amount of content specified in the guideline was included in the apps, most notably in the combined type, followed by the multifunctional and informational type; the MARS scores followed the same order (3.64, 3.26, and 3.0, respectively). Regression analysis showed that the sector in which the developer was situated and the feedback channel with the developer had a significant impact on both the content and MARS scores. In addition, problematic apps such as those made by unknown developers or copied and single-function apps were shown to have a large market share.ConclusionsThis study is the first to evaluate the content and quality of smoking cessation apps by classification. The combined type had higher-quality content and functionality than other app types. The app developer type and feedback channel with the app developer had a significant impact on the overall quality of the apps. In addition, problematic apps and single-function apps were shown to have a large market share. Our results will contribute to the use and development of better smoking cessation apps after considering the problems identified in this study.
| S-EPMC8895289 | biostudies-literature
Project description:This systematic review aims to characterize the utility of machine learning to identify the predictors of smoking cessation outcomes and identify the machine learning methods applied in this area. In the current study, multiple searches occurred through December 9, 2022 in MEDLINE, Science Citation Index, Social Science Citation Index, EMBASE, CINAHL Plus, APA PsycINFO, PubMed, Cochrane Central Register of Controlled Trials, and the IEEE Xplore were performed. Inclusion criteria included various machine learning techniques, studies reporting cigarette smoking cessation outcomes (smoking status and the number of cigarettes), and various experimental designs (e.g., cross-sectional and longitudinal). Predictors of smoking cessation outcomes were assessed, including behavioral markers, biomarkers, and other predictors. Our systematic review identified 12 papers fitting our inclusion criteria. In this review, we identified gaps in knowledge and innovation opportunities for machine learning research in the field of smoking cessation.
| S-EPMC10194042 | biostudies-literature
Project description:BackgroundGiven the low retention and lack of persistent support by traditional tobacco cessation programs, evidence-based smartphone app-supported interventions can be an important tobacco control component. The objective of this systematic review was to identify and evaluate the types of studies that use smartphone apps for interventions in tobacco cessation.MethodsWe conducted a systematic review of PubMed (1946-2019), EMBASE (1974-2019), and PsycINFO (1806-2019) databases with keywords related to smartphone-supported tobacco cessation. Included articles were required to meet 3 baseline screening criteria: 1) be written in English, 2) include an abstract, and 3) be a full, peer-reviewed manuscript. The criteria for the second level of review were: 1) primary outcome of tobacco cessation, 2) intervention study, and 3) smartphone app as primary focus of study.ResultsOf 1973 eligible manuscripts, 18 met inclusion criteria. Most studies (n = 17) recruited adult participants (18 + years); one included teens (16 + years). Tobacco cessation was usually self-reported (n = 11), compared to biochemical verification (n = 3) or both (n = 4). There were 11 randomized controlled trials, 4 of which reported statistically significant results, and 7 single-arm trials that reported a mean abstinence rate of 33.9%.DiscussionThe majority of studies that use tobacco cessation apps as an intervention delivery modality are mostly at the pilot/feasibility stage. The growing field has resulted in studies that varied in methodologies, study design, and inclusion criteria. More consistency in intervention components and larger randomized controlled trials are needed for tobacco cessation smartphone apps.
| S-EPMC7572657 | biostudies-literature
Project description:BACKGROUND: Smoking remains the leading preventable cause of premature deaths. Several pharmacological interventions now exist to aid smokers in cessation. These include Nicotine Replacement Therapy [NRT], bupropion, and varenicline. We aimed to assess their relative efficacy in smoking cessation by conducting a systematic review and meta-analysis. METHODS: We searched 10 electronic medical databases (inception to Sept. 2006) and bibliographies of published reviews. We selected randomized controlled trials [RCTs] evaluating interventions for smoking cessation at 1 year, through chemical confirmation. Our primary endpoint was smoking cessation at 1 year. Secondary endpoints included short-term smoking cessation (approximately 3 months) and adverse events. We conducted random-effects meta-analysis and meta-regression. We compared treatment effects across interventions using head-to-head trials and when these did not exist, we calculated indirect comparisons. RESULTS: We identified 70 trials of NRT versus control at 1 year, Odds Ratio [OR] 1.71, 95% Confidence Interval [CI], 1.55-1.88, P =< 0.0001). This was consistent when examining all placebo-controlled trials (49 RCTs, OR 1.78, 95% CI, 1.60-1.99), NRT gum (OR 1.60, 95% CI, 1.37-1.86) or patch (OR 1.63, 95% CI, 1.41-1.89). NRT also reduced smoking at 3 months (OR 1.98, 95% CI, 1.77-2.21). Bupropion trials were superior to controls at 1 year (12 RCTs, OR1.56, 95% CI, 1.10-2.21, P = 0.01) and at 3 months (OR 2.13, 95% CI, 1.72-2.64). Two RCTs evaluated the superiority of bupropion versus NRT at 1 year (OR 1.14, 95% CI, 0.20-6.42). Varenicline was superior to placebo at 1 year (4 RCTs, OR 2.96, 95% CI, 2.12-4.12, P =< 0.0001) and also at approximately 3 months (OR 3.75, 95% CI, 2.65-5.30). Three RCTs evaluated the effectiveness of varenicline versus bupropion at 1 year (OR 1.58, 95% CI, 1.22-2.05) and at approximately 3 months (OR 1.61, 95% CI, 1.16-2.21). Using indirect comparisons, varenicline was superior to NRT when compared to placebo controls (OR 1.66, 95% CI 1.17-2.36, P = 0.004) or to all controls at 1 year (OR 1.73, 95% CI 1.22-2.45, P = 0.001). This was also the case for 3-month data. Adverse events were not systematically different across studies. CONCLUSION: NRT, bupropion and varenicline all provide therapeutic effects in assisting with smoking cessation. Direct and indirect comparisons identify a hierarchy of effectiveness.
| S-EPMC1764891 | biostudies-other
Project description:Adolescent cigarette smokers have an increased risk of sustained smoking into adulthood. Smartphone applications (apps) for smoking cessation are a promising treatment resource. However, research on apps for adolescent smoking cessation is limited. This study compared smoking cessation mobile apps targeting an adolescent audience with popular cessation apps for a general audience. Adolescent and general-audience apps were identified by searching the Google Play and Apple App Stores (November 2016). Two coders assessed each app for adherence to clinical practice guidelines for tobacco and adolescent-specific content (2016-2017) and developed a summary score that summed all adherence and adolescent content criteria. Eight adolescent apps were identified and compared with the top 38 general apps (as ranked by Apple and Google). Both general and adolescent apps commonly had adherence content related to developing a quit plan (general: 73.68 per cent; adolescent: 87.50 per cent) and enhancing motivation by describing the rewards of not smoking (general: 76.32 per cent; adolescent: 62.50 per cent). Adolescent-specific content such as peer influence on smoking was common in adolescent apps but not in general apps (general: 5.26 per cent; adolescent: 62.50 per cent). Adolescent apps had a higher general adherence content summary score [t (44) = 2.55, p = .01] and a higher adolescent content summary score [t (7.81) = 2.47, p = .04] than the general apps. On average, adolescent cessation apps included more adherence content and adolescent-specific content than general apps. Future research is needed to determine the extent to which adolescents engage with the adherence content available in these apps.
| S-EPMC7295698 | biostudies-literature
Project description:BackgroundSmartphone applications (apps) are increasingly available for smoking cessation.PurposeThis study examined the content of popular apps for smoking cessation for both iPhone and Android operating systems in February 2012.MethodsA total of 252 smoking-cessation apps were identified for the iPhone and 148 for the Android. Across both operating systems, the most popular apps were identified (n=47 for the iPhone and n=51 for the Android) and analyzed for their (1) approach to smoking cessation and (2) adherence to an index based on the U.S. Public Health Service's Clinical Practice Guidelines for Treating Tobacco Use and Dependence. Where available, apps were coded for frequency of downloads. The analysis took place in 2012.ResultsOverall, popular apps have low levels of adherence, with an average score of 12.9 of a possible 42 on the Adherence Index. No apps recommended calling a quitline, and only a handful of apps recommended using approved medications (4.1%). Android apps in the sample were downloaded worldwide between 310,800 and 1,248,000 times per month. For both the iPhone and Android, user ratings were positively associated with scores on the Adherence Index. For the iPhone, display order was also positively associated with scores on the Adherence Index.ConclusionsApps could be improved by better integration with the Clinical Practice Guidelines and other evidence-based practices.
| S-EPMC3836190 | biostudies-literature
Project description:IntroductionAddressing tobacco use is an important issue in general health care. In order to improve smoking cessation advice, spirometry values can be displayed to the smoker to demonstrate possible lung function impairment. The estimate of so-called lung age may show a decrease in lung function associated with smoking. It has been suggested that performing spirometry on patients who smoke but are asymptomatic can be a useful way to show the adverse effects of smoking. The aim of this systematic review was to determine if providing spirometry results in combination with smoking cessation counselling can increase smoking cessation rates compared to what is achieved through counselling alone.MethodsIn this systematic review, we included randomized controlled trials (RCTs) evaluating smoking cessation interventions for adult smokers. The systematic search was performed in PubMed, Medline, Cochrane Library, Cinahl, Embase, Amed and PsycInfo.ResultsThe literature search resulted in 946 studies, which, after reading by two independent reviewers, were reduced to seven trials that matched the inclusion criteria. Two RCTs showed significant improvement in smoking cessation when giving patients feedback on spirometry results in combination with smoking cessation counselling, compared to patients who received only smoking cessation counselling. In both studies, the spirometry results were expressed as lung age. In the other five studies no difference was found. Five further published study protocols for ongoing RCT studies in the field have been found, and therefore this systematic overview will likely need to be updated within a few years.ConclusionsFew studies have been undertaken to examine the efficacy of spirometry in increasing smoking quit rates. Studies conducted to date have shown mixed results, and there is currently limited evidence in the literature that smoking cessation counselling that includes feedback from spirometry and a demonstration of lung age promotes quit rates.
| S-EPMC6662778 | biostudies-literature
Project description:Introduction: Rates of tobacco smoking are high in people with schizophrenia with greater difficulty of quitting smoking compared to the general population, which also relate to the increased cardiovascular and cancer risks in this co-occurring disorder. Therefore, effective smoking cessation pharmacotherapies addressing tobacco co-morbidity are imperative.Areas covered: In this review, the authors performed an extensive systematic electronic literature review examining the efficacy and safety of first-line pharmacotherapies for smoking cessation, including varenicline, sustained-release bupropion, and nicotine replacement therapies (NRT) using continuous abstinence rates over 10-12-week periods in smokers with schizophrenia. Twelve trials reporting smoking cessation outcomes using interventions in schizophrenia were included and risk ratio (RR) was used.Expert opinion: Our findings support the efficacy and safety of first-line pharmacotherapies for the treatment of tobacco use disorder in smokers with schizophrenia. Further research on the long-term effectiveness and safety of these agents in community samples is warranted. Smoking cessation pharmacotherapies may warrant the consideration of the emerging use of electronic nicotine delivery systems while neuromodulation techniques also offer promise.
| S-EPMC7183430 | biostudies-literature
Project description:Tobacco use is the leading cause of preventable disease and death in the USA. However, limited data exists regarding smoking cessation mobile app quality and intervention effectiveness. Innovative and scalable interventions are needed to further alleviate the public health implications of tobacco addiction. The proliferation of the smartphone and the advent of mobile phone health interventions have made treatment more accessible than ever. The purpose of this review was to examine the relation between published scientific literature and available commercial smartphone health apps for smoking cessation to identify the percentage of scientifically supported apps that were commercially available to consumers and to determine how many of the top commercially available apps for smoking cessation were supported by the published scientific literature. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, apps were reviewed in four phases: (1) identified apps from the scientific literature, (2) searched app stores for apps identified in the literature, (3) identified top apps available in leading app stores, and (4) determined which top apps available in stores had scientific support. Seven articles identified six apps with some level of scientific support, three (50%) were available in at least one app store. Conversely, among the top 50 apps suggested by each of the leading app stores, only two (4%) had any scientific support. While half of the scientifically vetted apps remain available to consumers, they are difficult to find among the many apps that are identified through app store searches.
| S-EPMC5526818 | biostudies-literature