Assessing the Quality and Behavior Change Potential of Vaping Cessation Apps: Systematic Search and Assessment
Notice bibliographique
Résumé
Background: An increasing number of people are using vapes (e-cigarettes), and with growing evidence of associated harms, there is a need for acceptable cessation support and interventions. Smartphone apps for health and well-being have increased in popularity and use. Limited published literature assesses the potential of apps to support vaping cessation. Objective: A systematic search of vaping cessation apps currently available in Australia for iOS and Android platforms was conducted. Apps were assessed against established health app assessment tools for quality and behavior change potential. Methods: A systematic search through the Australian Apple iTunes and Google Play stores was conducted using the search terms "vape"; "vaping"; "e-cigarette"; and "cessation," "quit," or "quitting" in May 2023. Only apps that encouraged the cessation of vaping were included. App descriptions were reviewed to determine if they were relevant for inclusion in this study, and relevant apps were downloaded onto the appropriate mobile device for review. The Mobile App Rating Scale (MARS) was used to rate the quality (engagement, functionality, aesthetics, and information) of the apps using an overall score out of 5. The App Behavior Change Scale (ABACUS) was used to assess the behavior change potential of each app using a score out of 21. Results: An initial search of the app stores yielded 220 Android apps and 124 iOS apps. Screening against the inclusion criteria left 20 iOS apps and 10 Android apps for review. Six apps were available on both operating systems, and these were downloaded, reviewed, and reported separately for each operating system. The average MARS score for all apps assessed in this review was 3.1 (SD 0.41) out of 5. The reviewed apps overall performed well for the MARS elements relating to functionality, such as ease of use and navigation, but had the lowest scores for information-related elements, such as credibility. The number of ABACUS behavior change features per app ranged from 0 to 19 out of 21, with a mean of 8.9 (SD 4.51). The apps commonly included information-related features, such as requesting baseline information. The least common behavior change features were those relating to goal-setting, such as asking about the user's willingness for behavior change and providing feedback on current actions in comparison to future goals. Conclusions: The identified vaping cessation apps had moderate levels of quality and some behavior change components. Future vaping cessation apps could benefit from including more features that are known to support behavior change, such as goal-setting, to improve the potential benefit of these apps to support people to stop vaping. As guidelines for vaping cessation continue to be established, future apps need to reference these in their development.
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 | 0,018 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,007 |
| Bibliométrie | 0,023 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».