Development of a WeChat-based Mobile Messaging Smoking Cessation Intervention for Chinese Immigrant Smokers: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Smoking remains a major public health issue among Chinese immigrants. Smoking cessation programs that focus on this population are scarce and have a limited population-level impact due to their low reach. Mobile messaging interventions have the potential to reach large audiences and expand smokers' access to smoking cessation treatment. OBJECTIVE: This study describes the development of a culturally and linguistically appropriate mobile messaging smoking cessation intervention for Chinese immigrant smokers delivered via WeChat, the most frequently used social media platform among Chinese people globally. METHODS: This study had 2 phases. In phase 1, we developed a mobile message library based on social cognitive theory and the US Clinical Practice Guidelines for Treating Tobacco Use and Dependence. We culturally adapted messages from 2 social cognitive theory-based text messaging smoking cessation programs (SmokefreeTXT and Decídetexto). We also developed new messages targeting smokers who were not ready to quit smoking and novel content addressing Chinese immigrant smokers' barriers to quitting and common misconceptions related to willpower and nicotine replacement therapy. In phase 2, we conducted in-depth interviews with 20 Chinese immigrant smokers (including 7 women) in New York City between July and August 2021. The interviews explored the participants' smoking and quitting experiences followed by assessment of the text messages. Participants reviewed 17 text messages (6 educational messages, 3 self-efficacy messages, and 8 skill messages) via WeChat and rated to what extent the messages enhanced their motivation to quit, promoted confidence in quitting, and increased awareness about quitting strategies. The interviews sought feedback on poorly rated messages, explored participant preferences for content, length, and format, discussed their concerns with WeChat cessation intervention, and solicited recommendations for frequency and timing of messages. RESULTS: Overall, participants reported that the messages enhanced their motivation to quit, offered encouragement, and made them more informed about how to quit. Participants particularly liked the messages about the harms of smoking and strategies for quitting. They reported barriers to applying some of the quitting strategies, including coping with stress and staying abstinent at work. Participants expressed strong interest in the WeChat mobile messaging cessation intervention and commented on its potential to expand their access to smoking cessation treatment. CONCLUSIONS: Mobile messages are well accepted by Chinese immigrant smokers. Research is needed to assess the feasibility, acceptability, and efficacy of WeChat mobile messaging smoking cessation interventions for promoting abstinence among Chinese immigrant smokers.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".