ArticleJMIR mHealth and uHealth2016
Design Considerations for Smoking Cessation Apps: Feedback From Nicotine Dependence Treatment Providers and Smokers.
Article in JMIR mHealth and uHealth, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- Glitches to growth: Lessons learned from developing and testing an augmented reality smartphone app for substance use treatment.Experimental and clinical psychopharmacology · 2026Trial
- Feasibility, Acceptability, and Potential Impact of a Novel mHealth App for Smokers Ambivalent About Quitting: Randomized Pilot Study.JMIR mHealth and uHealth · 2023Trial
- Effectiveness of a Dyadic Buddy App for Smoking Cessation: Randomized Controlled Trial.Journal of medical Internet research · 2021Trial
- The QuitIT Coping Skills Game for Promoting Tobacco Cessation Among Smokers Diagnosed With Cancer: Pilot Randomized Controlled Trial.JMIR mHealth and uHealth · 2019Trial
- Features of mHealth Apps for Tobacco Cessation Important to Black Adults: Discrete Choice Experiment.JMIR formative research · 2026Article
- User Experience in mHealth Research: Bibliometric Analysis of Trends and Developments (2007-2023).JMIR mHealth and uHealth · 2025Review
- Preferences for Mobile App Features to Support People Living With Chronic Heart Diseases: Discrete Choice Study.JMIR mHealth and uHealth · 2025Article
- Proximal Effects of a Just-in-Time Adaptive Intervention for Smoking Cessation With Wearable Sensors: Microrandomized Trial.JMIR mHealth and uHealth · 2025Article
- A feature-based qualitative assessment of smoking cessation mobile applications.PLOS digital health · 2024Article
- Perceptions of the Use of Mobile Technologies for Smoking Cessation: Focus Group Study With Individuals of Low Socioeconomic Status Who Smoke.JMIR formative research · 2024Article
- A Novel mHealth App for Smokers Living With HIV Who Are Ambivalent About Quitting Smoking: Formative Research and Randomized Feasibility Study.JMIR formative research · 2024Article
- Informing the development of interventions for e-cigarette use and prevention of transition to cigarette smoking in young adults: A qualitative study.Preventive medicine reports · 2023Article
- Black Smokers' Preferences for Features of a Smoking Cessation App: Qualitative Study.JMIR formative research · 2023Article
- Testing the Outcomes of a Smoking Cessation Smartphone App for Nondaily Smokers: Protocol for a Proof-of-concept Randomized Controlled Trial.JMIR research protocols · 2023Article
- Real-time prediction of smoking activity using machine learning based multi-class classification model.Multimedia tools and applications · 2022Article
- A Smoking Cessation App for Nondaily Smokers (Version 2 of the Smiling Instead of Smoking App): Acceptability and Feasibility Study.JMIR formative research · 2021Article
- Perceived Barriers to and Recommendations for Cessation among Poly-tobacco-using Young Adults.Health behavior and policy review · 2021Article
- Shared Decision-Making Model for Adolescent Smoking Cessation: Pilot Cohort Study.International journal of environmental research and public health · 2021Article
- Approaches for Implementing App-Based Digital Treatments for Drug Use Disorders Into Primary Care: A Qualitative, User-Centered Design Study of Patient Perspectives.Journal of medical Internet research · 2021Article
- Are Machine Learning Methods the Future for Smoking Cessation Apps?Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundHundreds of smoking cessation apps are commercially available, but most are not theory-based or designed to take advantage of mobile technology in ways that could make them more engaging and possibly more effective. Considering input from both clinical experts (who understand best practice nicotine dependence treatment requirements) to inform appropriate content and from smokers (the end users) to express their preferences is important in designing these programs in the future.
objectiveTo assess and compare the opinions of nicotine dependence treatment providers and smokers regarding the design of future smoking cessation apps.
methodsWe surveyed providers (n=264) and smokers who own smartphones (n=40) to assess their opinions on the importance of 21 app design features. Features represented 5 domains: cost, reputation, privacy and security, content and user experience, and communication. Domains were chosen to reflect best practice treatment, leverage mobile technology to support smoking cessation, and elicit important user preferences. Data were collected between June and July 2015.
resultsMost providers agreed that mHealth apps hold promise for helping people quit smoking (203/264, 76.9%) and would recommend them to their clients/patients (201/264, 76.1%), especially if the app were empirically validated (236/264, 89.4%). Few providers believe effective cessation apps currently exist (112/264, 42.4%). Few smokers (5/40, 13%) had ever downloaded a smoking cessation app; of the ones who had not, most said they would consider doing so (29/35, 83%). Both respondent groups indicated the following features were very to extremely important to include in cessation apps: free or low cost, keeps information private, matches individual needs and interests, adapts as one's needs and interests change, helps to manage nicotine withdrawal symptoms and medication side effects, and allows users to track their progress. Providers and smokers also indicated gaming and social media connectivity were less important than other features. Despite these similarities, the groups had significantly different opinions about the relative importance of various features. In particular, providers rated privacy as the most important feature, whereas smokers rated low cost and the ability to adaptively tailor content as the most important features.
conclusionsSmoking cessation apps hold great promise as intervention tools but only if they engage users and appropriately treat nicotine dependence. Intervention development should take into consideration the perspectives of both treatment experts and smokers. This paper highlights important perspectives from each of these groups to be considered when designing future app-based smoking cessation programs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.