Effectiveness of a Quit Vaping Text Message Program in Promoting Abstinence Among Young Adult E-Cigarette Users: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Millions of young adults currently vape electronic cigarettes (e-cigarettes), yet little research on vaping cessation interventions exists. Text messaging is a promising, scalable intervention strategy for delivering vaping cessation treatment. OBJECTIVE: This study evaluates the effectiveness of a text message quit vaping program (This is Quitting) in promoting abstinence from e-cigarettes among young adults; examines changes in self-efficacy, perceived social norms, and social support for quitting as hypothesized mediators of effectiveness; and examines if treatment effectiveness is moderated by gender, race, ethnicity, or sexual minority status. METHODS: Overall, 2600 young adult (aged 18-24 years) e-cigarette users in the United States will be recruited via web advertisements to participate in the study. Participants will be randomized to This is Quitting or an assessment-only control condition. The primary outcome measure is 30-day vaping abstinence at 7 months post enrollment. RESULTS: Study recruitment began on December 18, 2019, and is projected to be completed by spring 2020. The final 7-month follow-up is anticipated to be completed by fall/winter 2020. Because this is the first-ever evaluation of a quit vaping program, we were unable to draw on existing literature to determine the appropriate sample size. Therefore, we examined abstinence rates among an initial pilot sample of 269 participants (This is Quitting: n=148 and control: n=121) who completed the 1-month follow-up to determine the final sample size. The 1-month response rate was 79.2% (213/269), with no difference between arms. Using intention-to-treat analyses that counted nonresponders as still vaping, 30-day abstinence rates were 16.2% (24/148) among those randomized to This is Quitting and 8.3% (10/121) among those randomized to control. A treatment difference of 16% vs 8% is detectable with 80% power at 2-sided alpha=.05 with 260/group (520 total). To detect treatment differences of this magnitude in a 20% subsample (eg, Hispanic or sexual minority young adult e-cigarette users), we will enroll 1300/group (2600 total). CONCLUSIONS: The scientific, clinical, and public health communities are desperate for cessation resources to address vaping among young people. This study is the first-ever comparative effectiveness trial of an intervention to help young people quit vaping. It focuses on evaluating the effectiveness of a theory-grounded, empirically informed text message intervention among young adults. The study is fully powered to examine potentially important subgroup differences among young people who are more vulnerable to e-cigarette use. Although potentially more challenging from a research ethics and pragmatic standpoint, evaluating quit vaping intervention approaches in teens is an important area for future research. Data from this trial will establish a benchmark of effectiveness for other vaping cessation programs and begin to create a body of evidence focused on how best to help young people break free from e-cigarettes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04251273; https://clinicaltrials.gov/ct2/show/NCT04251273. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18327.
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 | 0,012 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».