Black Smokers’ Preferences for Features of a Smoking Cessation App: Qualitative Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».