Testing the Acceptability and Feasibility of a Gender-Informed Smoking Cessation mHealth App for Women: Mixed Methods Approach
Bibliographic record
Abstract
Background: Cigarette smoking is a leading cause of preventable morbidity and mortality worldwide. Women who smoke face greater health risks than men, including higher rates of cardiovascular disease and more pronounced declines in lung function. Despite this, women experience lower success rates with conventional smoking cessation treatments, due in part to unique sex- and gender-related factors influencing smoking behavior and barriers to quitting. Digital health tools, such as mobile health apps, offer a promising avenue for delivering accessible, tailored smoking cessation support to women. Objective: This study evaluated the acceptability and feasibility of the "My Change Plan-Women" (MCP-W) app, a gender-specific smoking cessation mobile health intervention co-designed with women who smoke, clinicians, and researchers, to address women's unique needs in smoking cessation. Methods: We conducted a single-group, prospective, sequential mixed methods study with 30 women who smoke in Ontario, Canada. Participants used the MCP-W app for 28 days. Acceptability was defined as ≥50% of participants endorsing "agree" or "strongly agree" to the statement "using the app is likely to help me make changes to my smoking habits." Feasibility was defined as ≥50% of participants using the app for 7 or more days during the trial period. Quantitative data on acceptability, smoking behavior, and motivation to quit were collected at baseline and follow-up via REDCap (Research Electronic Data Capture) surveys. App usage metrics were captured through Google Analytics. Semistructured interviews explored participants' experiences using the app and were thematically analyzed using the theoretical framework of acceptability. Results: At follow-up, 37% (11/30; 95% CI 21%-56%) of participants rated the MCP-W app as acceptable, falling below the predefined threshold (≥50%) and indicating that the intervention "needs further work." Feasibility criteria were met, with 60% (18/30) of participants using the app for 7 or more days. Notably, acceptability was higher among those who used the app for more than 14 days (7/11, 64%) compared with those with lower usage (4/19, 21%). Average daily cigarette consumption decreased from 16.4 to 14.6 cigarettes, and the number of participants reporting at least 1 smoke-free day in the previous week increased from 7% (2/27) to 22% (6/27). Qualitative findings revealed that women with higher motivation to quit found the app more helpful, particularly its behavior change tools (eg, cigarette tracking and identifying triggers) and gender-specific content. However, women facing stress, mental health challenges, or low readiness found it harder to engage. Participants suggested enhancements including customizable reminders, more interactive content, and live or artificial intelligence-based emotional support. Conclusions: The MCP-W app is a feasible intervention for delivering gender-specific smoking cessation support. However, its acceptability was limited to a third of users with high levels of motivation. Improvements to interactivity and support features may enhance its relevance and uptake among women with complex barriers to quitting.
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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 | 0.043 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".