WhatsApp-Based Coaching Program to Support Smoking and Vaping Cessation Among Young People: Pre-Post Study on Acceptance and Preliminary Efficacy
Notice bibliographique
Résumé
Background: The use of tobacco cigarettes and electronic nicotine products is widespread among young people in Switzerland. At the same time, the instant messaging platform WhatsApp (Meta Platforms, Inc) is the most frequently used smartphone app in this population group. The provision of individually tailored, evidence-based coaching messages via WhatsApp seems promising to support smoking cessation in adolescents and young adults. Objective: This study aims to test the feasibility, acceptance, and preliminary efficacy of a newly developed, semiautomated WhatsApp-based intervention program to support smoking and vaping cessation and reduction in adolescents and young adults. Methods: Recruitment took place in Switzerland in 2023 and 2024 via various channels, both online and offline. For a period of 11 weeks, regular users of cigarettes or electronic cigarettes, aged between 16 and 30 years, received individually tailored messages on how to deal with cravings or stressful situations and how to stop or reduce smoking. A separate WhatsApp channel provided the opportunity to ask individual questions to a counselor. A one-group pre-post design was used to obtain preliminary information on the acceptability and potential efficacy of the program. Results: A total of 167 young people (mean age 23.2, SD 4.0 years; n=95, 56.9% women and n=72, 43.1% men) who regularly smoked tobacco cigarettes (n=81, 48.5%), vaped electronic nicotine products (n=17, 10.2%), or used both (n=69, 41.3%) were recruited for participation in the program. Of these, 100 (59.9%) intended to stop smoking or vaping while 67 (40.1%) aimed at reducing their use. The participants actively engaged in an average of 5.5 (SD 3.5) of the 11 program weeks, the average number of interactions with the program was 26.8 (SD 26.1), and the average duration from the start of the program to the last interaction was 45.0 (SD 31.1) days. The follow-up survey at the end of the 11-week coaching program was completed by 108 (64.7%) participants. The generalized estimating equation (GEE) analyses revealed significant reductions in the mean number of days in the last 30 days on which tobacco cigarettes were used from 20.6 (SD 11.8) at baseline to 14.0 (SD 12.0) at post assessment (incidence rate ratio [IRR] 0.68, P<.001) and for electronic nicotine products from 11.1 1 (SD 13.1) days at baseline to 7.7 (SD 11.3) days at follow-up (IRR 0.71, P=.005). Overall, 6/108 (5.6%) participants in the follow-up survey stated that they neither consumed tobacco cigarettes nor electronic nicotine products in the last 30 days. Conclusions: The WhatsApp-based program appears to be a feasible, moderately accepted, and promising intervention for reducing the consumption of tobacco cigarettes and electronic nicotine products among young people. A larger-scale randomized controlled trial would be reasonable in order to make more substantiated statements about the program's efficacy. .
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 | 0,002 | 0,003 |
| 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 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».