Initial Evaluation of Acceptability, Engagement, and Effectiveness of the MO App to Provide Tailored and Comprehensive Support for Smoking Cessation: Development and Usability Study
Notice bibliographique
Résumé
BACKGROUND: Despite the growing availability of smoking cessation apps, low engagement and cessation rates have remained a significant challenge. To address this issue, we used a user-centered design to iteratively develop a mobile app (MO) to provide comprehensive, tailored, and evidence-based content to support smokers in their quitting journey. OBJECTIVE: This study examined the acceptability, use, and preliminary efficacy of the MO app for smoking cessation. Specifically, we sought to understand smokers' preferred features, engagement, and satisfaction with MO; identify concerns in using the app and ways to improve the app; and evaluate its smoking cessation outcomes. METHODS: Through 3 cohorts, we recruited 10, 12, and 85 adult smokers who attempted to quit smoking to pilot-test the MO app between December 2019 and July 2022. Participants were instructed to complete a baseline survey, interact with the app for 6 weeks, and fill in a postsurvey at week 6. Participants in cohort 3 completed an additional postsurvey at week 12. Participants' app use was tracked and analyzed. The primary outcome measures were participants' 7-day point prevalence abstinence at 6 and 12 weeks. RESULTS: Participants reported high levels of satisfaction with the MO app across all 3 cohorts, rating it between 4.40 and 4.76 on a scale of 5 for acceptability. Users engaged with app activities for an average of 89 to 159 times over 35 days. The most liked features of the app included "quit plan," "tracking," "reminders and notifications," "MOtalks," and "motivational quotes." The 7-day point prevalence abstinence rate of the modified intention to treat population in cohort 3 was 58% at 6 weeks and 52% at 12 weeks. Those who interacted more frequently with app features and engaged with more diverse activities were more likely to maintain abstinence at weeks 6 and 12. For each additional time logged into the app, the odds of staying abstinent at week 12 increased by 5% (odds ratio [OR] 1.05, 95% CI 1.01-1.08). Participants who earned >5000 points during app use also had higher odds of quitting at both 6 weeks (OR 3.12, 95% CI 1.25-7.75) and 12 weeks (OR 4.65, 95% CI 1.83-11.76), compared with those who earned <5000 points. CONCLUSIONS: Our study demonstrated that MO is a feasible mobile phone app with high acceptability and usability and can effectively deliver smoking cessation support to individuals who want to quit. Implications for developing and evaluating mobile phone apps for smoking cessation are discussed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,016 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».