A Novel mHealth App for Smokers Living With HIV Who Are Ambivalent About Quitting Smoking: Formative Research and Randomized Feasibility Study
Bibliographic record
Abstract
BACKGROUND: More people who smoke and are living with HIV now die from tobacco-related diseases than HIV itself. Most people are ambivalent about quitting smoking and want to quit someday but not yet. Scalable, effective interventions are needed to motivate and support smoking cessation among people ambivalent about quitting smoking (PAQS) who are living with HIV. OBJECTIVE: This study aims to develop an app-based intervention for PAQS who are living with HIV and assess its feasibility, acceptability, and potential impact. Results of this study will inform plans for future research and development. METHODS: In phase 1, PAQS living with HIV (n=8) participated in user-centered design interviews to inform the final intervention app design and recruitment plan for a subsequent randomized pilot study. In phase 2, PAQS living with HIV were randomized to either a standard care control app or a similar experimental app with additional content tailored for PAQS and those with HIV. Participants were followed for 3 months. Feasibility focused on recruitment, retention, and participants' willingness to install the app. The study was not powered for statistical significance. Indices of acceptability (satisfaction and use) and impact (smoking behavior change and treatment uptake) were assessed via automated data and self-report among those who installed and used the app (n=19). RESULTS: Recruitment for both study phases was a challenge, particularly via web-based and social media platforms. Enrollment success was greater among people living with HIV recruited from a health care provider and research registry. Once enrolled, retention for the phase 2 randomized study was good; 74% (14/19) of the participants completed the 3-month follow-up. Phase 1 findings suggested that PAQS living with HIV were receptive to using an app-based intervention to help them decide whether, when, and how to stop smoking, despite not being ready to quit smoking. Phase 2 findings further supported this conclusion based on feedback from people who agreed to use an app, but group differences were observed. Indices of acceptability favored the experimental arm, including a descriptively higher mean number of sessions and utilization badges. Similarly, indices of potential impact were descriptively higher in the experimental arm (proportion reducing smoking, making a quit attempt, or calling free tobacco quitline). No participants in either arm quit smoking at the 3-month follow-up. CONCLUSIONS: On the basis of this formative work, PAQS living with HIV may be receptive to using a mobile health-based app intervention to help them decide whether, when, or how to stop using tobacco. Indices of acceptability and impact indicate that additional research and development are warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT05339659; https://clinicaltrials.gov/study/NCT05339659.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".