Text Messaging to Enhance Mindfulness-Based Smoking Cessation Treatment: Program Development Through Qualitative Research
Bibliographic record
Abstract
BACKGROUND: Mindfulness-based programs show promise for promoting smoking cessation in diverse populations. Mobile health strategies could increase treatment engagement and in-the-moment support, thus enhancing the effects of mindfulness-based smoking cessation interventions. However, most mobile health programs have been developed without sufficient input from the target population. OBJECTIVE: By eliciting input from the target population, predominantly low socioeconomic status (SES) African American adult smokers, throughout the development of an SMS (short message service) text messaging program that teaches mindfulness for smoking cessation, we aimed for the resulting program to be optimally effective and consistent with participants' needs and preferences. METHODS: Two qualitative studies (N=25) were conducted with predominantly low SES, African American adult smokers. In Study 1 (initial qualitative input; n=15), participants engaged in focus groups to provide suggestions for program development. In Study 2 (abbreviated trial; n=10), participants received a 1-week version of the SMS text messaging program and provided feedback through in-depth interviews. RESULTS: In Study 1, participants suggested that the SMS text messaging program should be personalized and interactive (ie, involve two-way messaging); provide strategies for coping with cravings and recovering from smoking lapses; involve relatively short, to-the-point messages; and include pictures. In Study 2, participants were highly engaged with the texts, indicated that the program was useful, and provided additional suggestions for improvement. CONCLUSIONS: Eliciting feedback from the target population throughout the intervention development process allowed for iterative revisions to increase feasibility, acceptability, and effectiveness. Overall, SMS text messaging appears to be a feasible, appealing way to provide in-the-moment personalized support and encourage mindfulness among low-income African American smokers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".