A Text Message Delivered Smoking Cessation Intervention: The Initial Trial of TXT-2-Quit: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mobile technology offers the potential to deliver health-related interventions to individuals who would not otherwise present for in-person treatment. Text messaging (short message service, SMS), being the most ubiquitous form of mobile communication, is a promising method for reaching the most individuals. OBJECTIVE: The goal of the present study was to evaluate the feasibility and preliminary efficacy of a smoking cessation intervention program delivered through text messaging. METHODS: Adult participants (N=60, age range 18-52 years) took part in a single individual smoking cessation counseling session, and were then randomly assigned to receive either daily non-smoking related text messages (control condition) or the TXT-2-Quit (TXT) intervention. TXT consisted of automated smoking cessation messages tailored to individual's stage of smoking cessation, specialized messages provided on-demand based on user requests for additional support, and a peer-to-peer social support network. Generalized estimating equation analysis was used to assess the primary outcome (7-day point-prevalence abstinence) using a 2 (treatment groups)×3 (time points) repeated measures design across three time points: 8 weeks, 3 months, and 6 months. RESULTS: Smoking cessation results showed an overall significant group difference in 7-day point prevalence abstinence across all follow-up time points. Individuals given the TXT intervention, with higher odds of 7-day point prevalence abstinence for the TXT group compared to the Mojo group (OR=4.52, 95% CI=1.24, 16.53). However, individual comparisons at each time point did not show significant between-group differences, likely due to reduced statistical power. Intervention feasibility was greatly improved by switching from traditional face-to-face recruitment methods (4.7% yield) to an online/remote strategy (41.7% yield). CONCLUSIONS: Although this study was designed to develop and provide initial testing of the TXT-2-Quit system, these initial findings provide promising evidence that a text-based intervention can be successfully implemented with a diverse group of adult smokers. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01166464; http://clinicaltrials.gov/ct2/show/NCT01166464 (Archived by WebCite at http://www.webcitation.org/6IOE8XdE0).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".