Concurrent E-cigarette Use While Enrolled in a Smoking Cessation Program: Associations Between Frequency of Use, Motives for Use, and Smoking Cessation
Notice bibliographique
Résumé
INTRODUCTION: Trial evidence suggests that e-cigarettes may aid in quitting smoking, while observational studies have found conflicting results. However, many observational studies have not adjusted for important differences between e-cigarette users and non-users. AIMS AND METHODS: We aimed to determine the association between e-cigarette use frequency and motivation to use e-cigarettes to quit smoking, and smoking cessation using data from Canada's largest smoking cessation program. Participants who completed a baseline assessment and 6-month follow-up questionnaire were divided post hoc into four groups based on their self-reported e-cigarette use during the 30 days before baseline: (1) non-users; (2) users of e-cigarettes not containing nicotine; (3) occasional users; and (4) frequent users. Occasional and frequent users were further divided into two groups based on whether they reported using e-cigarettes to quit smoking. Abstinence at 6-month follow-up (7-day point prevalence abstinence) was compared among groups. RESULTS: Adjusted quit probabilities were significantly higher (both p < .001) for frequent baseline e-cigarette users (31.6%; 95% CI = 29.3%, 33.8%) than for non-users (25.8%; 25.3% and 26.3%) or occasional users (24.2%; 22.5% and 26.0%). Unadjusted proportions favored non-users over occasional users (p < .001), but this was not significant after adjustment (p = .06). People using e-cigarettes to quit smoking were not likelier than other users to be successful, but were likelier to report frequent e-cigarette use during follow-up. CONCLUSIONS: Frequent baseline e-cigarette use predicted successful smoking cessation, compared to occasional and non-users. Use of e-cigarettes to quit did not predict smoking cessation but was associated with continued use during follow-up, perhaps due in part to planned transitions to e-cigarettes. IMPLICATIONS: Prior observational studies investigating e-cigarette use for smoking cessation have found that occasional users have poorer outcomes than either frequent or non-users. Consistent with these studies, occasional users in our data also had poorer outcomes. However, after adjustment for variables associated with cessation success, we found that cessation probabilities did not differ between occasional and non-users. These findings are consistent with trial data showing the benefit of e-cigarette use among people trying to quit smoking. Results of this study suggest that differences between trials and previous observational studies may be because of unaddressed confounding in the latter.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».