Mobile Phone Text Messages to Support People to Stop Smoking by Switching to Vaping: Codevelopment, Coproduction, and Initial Testing Study
Bibliographic record
Abstract
Background SMS text messages are affordable, scalable, and effective smoking cessation interventions. However, there is little research on SMS text message interventions specifically designed to support people who smoke to quit by switching to vaping. Objective Over 3 phases, with vapers and smokers, we codeveloped and coproduced a mobile phone SMS text message program. The coproduction paradigm allowed us to collaborate with researchers and the community to develop a more relevant, acceptable, and equitable SMS text message program. Methods In phase 1, we engaged people who vape via Twitter and received 167 responses to our request to write SMS text messages for people who wish to quit smoking by switching to vaping. We screened, adjusted, refined, and themed the messages, resulting in a set of 95 that were mapped against the Capability, Opportunity, and Motivation–Behavior constructs. In phase 2, we evaluated the 95 messages from phase 1 via a web survey where participants (66/202, 32.7% woman) rated up to 20 messages on 7-point Likert scales on 9 constructs: being understandable, clear, believable, helpful, interesting, inoffensive, positive, and enthusiastic and how happy they would be to receive the messages. In phase 3, we implemented the final set of SMS text messages as part of a larger randomized optimization trial, in which 603 participants (mean age 38.33, SD 12.88 years; n=369, 61.2% woman) received SMS text message support and then rated their usefulness and frequency and provided free-text comments at the 12-week follow-up. Results For phase 2, means and SDs were calculated for each message across the 9 constructs. Those with means below the neutral anchor of 4 or with unfavorable comments were discussed with vapers and further refined or removed. This resulted in a final set of 78 that were mapped against early, mid-, or late stages of quitting to create an order for the messages. For phase 3, a total of 38.5% (232/603) of the participants provided ratings at the 12-week follow-up. In total, 69.8% (162/232) reported that the SMS text messages had been useful, and a significant association between quit rates and usefulness ratings was found (χ21=9.6; P=.002). A content analysis of free-text comments revealed that the 2 most common positive themes were helpful (13/47, 28%) and encouraging (6/47, 13%) and the 2 most common negative themes were too frequent (9/47, 19%) and annoying (4/47, 9%). Conclusions In this paper, we describe the initial coproduction and codevelopment of a set of SMS text messages to help smokers stop smoking by transitioning to vaping. We encourage researchers to use, further develop, and evaluate the set of SMS text messages and adapt it to target populations and relevant contexts.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".