Exploration of Digital Interventions for Vaping Cessation: Scoping Review
Notice bibliographique
Résumé
Background: Digital interventions have emerged as a promising approach to support vaping cessation, particularly among youth and young adults. Mobile apps, text messaging programs, telehealth-delivered contingency management, and web-based or social media interventions offer scalable and accessible alternatives to traditional cessation methods. However, there is considerable variation in how these interventions are designed, implemented, and evaluated, with inconsistencies in engagement strategies, theoretical frameworks, and long-term effectiveness. Objective: This scoping review aimed to map the current landscape of digital interventions for vaping cessation and identify key strategies, effectiveness outcomes, and implementation challenges. The following questions were addressed: (1) What digital interventions have been developed or evaluated for vaping cessation? (2) What evidence exists regarding the effectiveness of these digital interventions in promoting vaping cessation and user engagement? (3) What key barriers and facilitators influence the adoption, adherence, and success of digital vaping cessation interventions? (4) What gaps remain in the literature, and what areas should future research prioritize to enhance the design and effectiveness of digital vaping cessation tools? Methods: This scoping review followed the Joanna Briggs Institute methodology and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A systematic search was conducted in CINAHL, MEDLINE, and PsycINFO using search terms related to vaping cessation and digital health interventions. Studies examining mobile apps, text messaging programs, social media or web-based interventions, and telehealth coaching explicitly designed for vaping cessation were included. A narrative synthesis was conducted to identify common themes, barriers, and facilitators. Results: Sixteen studies were identified, including SMS text messaging programs, mobile apps, telehealth-delivered contingency management, and web-based or social media interventions. Many interventions reported moderate to high abstinence rates. Programs incorporating personalized messaging, behavioral tracking, and social and interactive features demonstrated greater retention and cessation success. However, minimal application of evidence-based behavior change frameworks, inconsistent reporting of engagement metrics, reliance on self-reported abstinence, and scalability limitations were noted. Conclusions: Digital interventions show promise for vaping cessation, particularly among youth and young adults, but current evidence highlights both opportunities and limitations. Effective interventions leverage personalization and social support to enhance engagement and quit outcomes. However, challenges such as high dropout rates, accessibility barriers, and limited use of rigorous evaluation methods persist. Future research should prioritize hybrid approaches that combine digital support with human interaction, apply equity-focused design principles, and adopt pragmatic, theory-driven evaluation methods to accelerate translation from pilot success to sustainable public health impact.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».