Design Considerations for Smoking Cessation Apps: Feedback From Nicotine Dependence Treatment Providers and Smokers
Bibliographic record
Abstract
BACKGROUND: Hundreds 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. OBJECTIVE: To assess and compare the opinions of nicotine dependence treatment providers and smokers regarding the design of future smoking cessation apps. METHODS: We 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. RESULTS: Most 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. CONCLUSIONS: Smoking 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".