An mHealth Intervention With Financial Incentives to Promote Smoking Cessation and Physical Activity Among Black Adults: Protocol for a Feasibility Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Black adults in the United States experience disproportionately high rates of tobacco- and obesity-related diseases, driven in part by disparities in smoking cessation and physical activity. Smartphone-based interventions with financial incentives offer a scalable solution to address these health disparities. OBJECTIVE: This study aims to assess the feasibility and preliminary efficacy of a mobile health intervention that provides financial incentives for smoking cessation and physical activity among Black adults. METHODS: A total of 60 Black adults who smoke (≥5 cigarettes/d) and are insufficiently physically active (engaging in <150 min of weekly moderate-intensity physical activity) will be randomly assigned to either HealthyCells intervention (incentives for smoking abstinence only) or HealthyCells+ intervention (incentives for both smoking abstinence and daily step counts). Participants will use study-provided smartphones, smartwatches, and carbon monoxide monitors for 9 weeks (1 wk prequit date through 8 wk postquit date). Feasibility will be evaluated based on recruitment rates, retention, and engagement. The primary outcomes include carbon monoxide-verified, 7-day smoking abstinence at 8 weeks postquit date and changes in average daily step count. Feasibility benchmarks include a recruitment rate of ≥5 participants per month, a retention rate of ≥75%, and a smoking abstinence rate of ≥20% at 8 weeks postquit date. Expected increases in physical activity include a net gain of 500 to 1500 steps per day compared to baseline. RESULTS: Recruitment is expected to begin in February 2025 and conclude by September 2025, with data analysis completed by October 2025. CONCLUSIONS: This study will evaluate the feasibility of a culturally tailored mobile health intervention combining financial incentives for smoking cessation and physical activity promotion. Findings will inform the design of larger-scale trials to address health disparities through scalable, technology-based approaches. TRIAL REGISTRATION: ClinicalTrials.gov NCT05188287; https://clinicaltrials.gov/ct2/show/NCT05188287. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/69771.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.085 | 0.013 |
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".