Automated Mobile Delivery of Financial Incentives for Smoking Cessation Among Socioeconomically Disadvantaged Adults: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Socioeconomic disadvantage is associated with a reduced likelihood of smoking cessation. Smartphone ownership is increasing rapidly, including among low-income adults, and smartphone interventions for smoking cessation may increase access to smoking cessation treatment among socioeconomically disadvantaged adults. OBJECTIVE: This study aimed to evaluate the feasibility of an automated smartphone-based approach to delivering financial incentives for smoking cessation. METHODS: Socioeconomically disadvantaged adults initiating tobacco cessation treatment were followed from 1 week before a scheduled quit attempt through 26 weeks after the quit date. Participants received telephone counseling and nicotine replacement therapy. Smoking cessation was verified 5 times per week via smartphone prompts to self-report smoking status and submit a breath sample via a portable carbon monoxide (CO) monitor that was connected with participants' smartphones. Identity was verified during smoking status assessments using smartphone-based facial recognition software. When smoking abstinence and identity were verified, an automated credit card payment was triggered. Participants were incentivized for abstinence on the quit date and up to five days per week during the first 4 weeks after the scheduled quit date, with additional incentives offered during postquit weeks 8 and 12. In total, participants had the opportunity to earn up to US $250 in abstinence-contingent incentives over the first 12 weeks of the quit attempt. RESULTS: Participants (N=16) were predominantly female (12/16, 75%) and non-Hispanic white (11/16, 69%), black (4/16, 25%), or Hispanic of any race (1/16, 6%). Most participants (9/16, 56%) reported an annual household income of
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".