Perceptions About Mindfulness and Text Messaging for Smoking Cessation in Vietnam: Results From a Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: With 15.6 million smokers, Vietnam is one of the top 10 largest cigarette-consuming countries in the world. Unfortunately, smoking cessation programs are still scarce in Vietnam. Mindfulness-based and text messaging-based interventions have been increasingly used in smoking cessation studies in developed countries, with promising results. Given the exponential growth of mobile phone usage in Vietnam in recent years, mobile health interventions could be a potential strategy to increase smoking cessation in Vietnam. However, substantial cultural adaptations are needed to optimize the effectiveness of these interventions among Vietnamese smokers. OBJECTIVE: This study aims to involve qualitative research to inform the development of a mindfulness-based text messaging smoking cessation intervention for Vietnamese smokers. METHODS: A total of 10 focus groups were conducted with 71 Vietnamese male smokers aged between 18 and 65 years (5-9 participants per focus group). Overall, 5 focus groups were conducted with smokers who had the intention to quit (ie, preparation stage of change in the transtheoretical model), and 5 focus groups were conducted with smokers who did not have the intention to quit (contemplation or precontemplation stage). The focus groups were audio recorded, transcribed verbatim, and analyzed using NVivo 12 software (QSR International). RESULTS: The major themes included smoking triggers, barriers and facilitators for quitting, the perceptions of text messaging and mindfulness approaches for smoking cessation, and suggestions for the development of a text messaging-based smoking cessation program. Common smoking triggers included stress, difficulties concentrating, and fatigue. Frequently encountering other people who were smoking was a common barrier to quitting. However, participants indicated that concerns about the harmful effects of smoking on themselves and their wives and children, and encouragement from family members could motivate them to quit. The participants preferred diverse message content, including information about the consequences of smoking, encouragement to quit, and tips to cope with cravings. They suggested that text messages be clear and concise and use familiar language. Most smokers perceived that mindfulness training could be useful for smoking cessation. However, some suggested that videos or in-person training may also be needed to supplement teaching mindfulness through text messages. CONCLUSIONS: This study provides important insights to inform the development of a text messaging-based smoking cessation program that incorporates mindfulness for Vietnamese male smokers. The results could also be useful for informing similar programs in other low- and middle-income countries.
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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 | 0,010 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 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 ».