Black Smokers’ Preferences for Features of a Smoking Cessation App: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) interventions for smoking cessation have grown extensively over the last few years. Although these interventions improve cessation rates, studies of these interventions consistently lack sufficient Black smokers; hence knowledge of features that make mHealth interventions attractive to Black smokers is limited. Identifying features of mHealth interventions for smoking cessation preferred by Black smokers is critical to developing an intervention that they are likely to use. This may in turn address smoking cessation challenges and barriers to care, which may reduce smoking-related disparities that currently exist. OBJECTIVE: This study aims to identify features of mHealth interventions that appeal to Black smokers using an evidence-based app developed by the National Cancer Institute, QuitGuide, as a reference. METHODS: We recruited Black adult smokers from national web-based research panels with a focus on the Southeastern United States. Participants were asked to download and use QuitGuide for at least a week before participation in remote individual interviews. Participants gave their opinions about features of the QuitGuide app and other mHealth apps they may have used in the past and suggestions for future apps. RESULTS: Of the 18 participants, 78% (n=14) were women, with age ranging from 32 to 65 years. Themes within five major areas relevant for developing a future mHealth smoking cessation app emerged from the individual interviews: (1) content needs including health and financial benefits of quitting, testimonials from individuals who were successful in quitting, and strategies for quitting; (2) format needs such as images, ability to interact with and respond to elements within the app, and links to other helpful resources; (3) functionality including tracking of smoking behavior and symptoms, provision of tailored feedback and reminders to users, and an app that allows for personalization of functions; (4) social network, such as connecting with friends and family through the app, connecting with other users on social media, and connecting with a smoking cessation coach or therapist; and (5) the need for inclusivity for Black individuals, which may be accomplished through the inclusion of smoking-related information and health statistics specific for Black individuals, the inclusion of testimonials from Black celebrities who successfully quit, and the inclusion of cultural relevance in messages contained in the app. CONCLUSIONS: Certain features of mHealth interventions for smoking cessation were highly preferred by Black smokers based on their use of a preexisting mHealth app, QuitGuide. Some of these preferences are similar to those already identified by the general population, whereas preferences for increasing the inclusivity of the app are more specific to Black smokers. These findings can serve as the groundwork for a large-scale experiment to evaluate preferences with a larger sample size and can be applied in developing mHealth apps that Black smokers may be more likely to use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".