Perceptions About Mindfulness and Text Messaging for Smoking Cessation in Vietnam: Results From a Qualitative Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".