A Financial Incentives Program to Promote Smoking Cessation Among Recently Hospitalized Individuals: Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Hospitalization is an opportunity to engage underserved individuals in tobacco treatment who may not otherwise have access to it. Tobacco treatment interventions that begin during hospitalization and continue for at least 1 postdischarge month are effective in promoting smoking cessation. However, there is low usage of postdischarge tobacco treatment services. Financial incentives for smoking cessation are an intervention in which participants receive incentives, such as cash payments or vouchers for goods, to encourage individuals to stop smoking or to reward individuals for maintaining abstinence. OBJECTIVE: We sought to determine the feasibility and acceptability of a novel postdischarge financial incentive intervention that uses a smartphone application paired to measurements of exhaled carbon monoxide (CO) concentration levels to promote smoking cessation in individuals who smoke cigarettes. METHODS: We collaborated with Vincere Health, Inc. to tailor their mobile application that uses facial recognition features, a portable breath test CO monitor, and smartphone technology to deliver financial incentives to a participant's digital wallet after the completion of each CO test. The program includes 3 racks. Track 1: Noncontingent incentives for conducting CO tests. Track 2: Combination of noncontingent and contingent incentives for CO levels <10 parts per million (ppm). Track 3: Contingent incentives only for CO levels <10 ppm. After obtaining informed consent, we pilot-tested the program from September to November 2020 with a convenience sample of 33 hospitalized individuals at Boston Medical Center, a large safety-net hospital in New England. Participants received text reminders to conduct CO tests twice daily for 30 days postdischarge. We collected data on engagement, CO levels, and incentives earned. We measured feasibility and acceptability quantitatively and qualitatively at 2 and 4 weeks. RESULTS: Seventy-six percent (25/33) completed the program and 61% (20/33) conducted at least 1 breath test each week. Seven patients had consecutive CO levels <10 ppm during the last 7 days of the program. Engagement with the financial incentive intervention as well as in-treatment abstinence was highest in Track 3 that delivered financial incentives contingent on CO levels <10 ppm. Participants reported high program satisfaction and that the intervention helped motivate smoking cessation. Participants suggested increasing program duration to at least 3 months and adding supplemental text messaging to increase motivation to stop smoking. CONCLUSIONS: Financial incentives paired to measurements of exhaled CO concentration levels is a novel smartphone-based tobacco cessation approach that is feasible and acceptable. Future studies should examine the efficacy of the intervention after it is refined to add a counseling or text-messaging component.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".