A Mobile Smoking Cessation Intervention for Mexico (Vive sin Tabaco... ¡Decídete!): Single-Arm Pilot Study
Bibliographic record
Abstract
BACKGROUND: Of the 14.3 million Mexicans who smoke, only a minority take advantage of evidence-based approaches to smoking cessation. Mobile health interventions have the potential to increase the reach of effective cessation interventions in Mexico. OBJECTIVE: This study aimed to assess the feasibility and acceptability of an innovative, personalized, and interactive smoking cessation mobile intervention developed for Mexican smokers. METHODS: We recruited 40 Mexican smokers to participate in Vive sin Tabaco... ¡Decídete!, a smoking cessation program that uses a tablet-based decision support software to drive a 12-week text messaging smoking cessation program and pharmacotherapy support. Outcome measures included participant text messaging interactivity with the program, participant satisfaction, and 12-week verified abstinence using urinary cotinine testing or exhaled carbon monoxide. RESULTS: Average age of the participants was 36 years (SD 10.7), and they were primarily male (65%, 26/40) with at least an undergraduate degree (62%, 25/40). Most participants (95%, 38/40) smoked daily and were interested in quitting in the next 7 days. As an indicator of participant interactivity, participants sent an average of 21 text messages during the 12-week intervention (SD 17.62). Of the 843 messages that participants sent to the program, only 96 messages (11.3%, 96/843) used keywords. At 12 weeks, 40% (16/40) of participants were biochemically verified (87%, 35/40, follow-up rate). The majority of participants (85%, 30/35) reported being very satisfied or extremely satisfied with the program. CONCLUSIONS: The Vive sin Tabaco... ¡Decídete! smoking cessation mobile intervention was accepted by participants, generated high satisfaction and high text messaging interactivity, and resulted in a noteworthy cessation rate at the end of treatment. This intervention is a promising strategy for smoking cessation in Mexico. Additional testing as a formal randomized clinical trial appears warranted.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".