Smartphone-Based Smoking Cessation Intervention (OKquit) for Oklahoma Tobacco Helpline Users: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Tobacco quitlines provide effective resources (eg, nicotine replacement therapy, smoking cessation counseling, and text and web-based support) for those who want to quit smoking in the United States. However, quitlines reach approximately only 1%-3% of people who smoke each year. Novel, smartphone-based, and low-burden interventions that offer 24/7 access to smoking cessation resources that are tailored to current readiness to quit may increase appeal, reach, and effectiveness of smoking cessation interventions. OBJECTIVE: This study will examine the efficacy of OKquit, a low-burden smartphone-based app for smoking cessation. METHODS: Approximately 500 people who smoke cigarettes and access the Oklahoma Tobacco Helpline (OTH) will be randomized to receive standard OTH care (SC) or SC plus the novel OKquit smartphone app for smoking cessation (OKquit). All participants will use a smartphone app to complete study surveys (ie, baseline, 27 weekly surveys, brief daily check-ins, and 27-week follow-up). Upon completion of daily check-ins and weekly surveys, participants will receive either trivia type messages (SC) or messages that are tailored to current readiness to quit smoking and currently experienced lapse triggers (OKquit). In addition, those assigned to receive the OKquit app will have access to on-demand smoking cessation content (eg, quit tips, smoking cessation medication tips). It is hypothesized that participants assigned to OKquit will be more likely to achieve biochemically verified 7-day point prevalence abstinence than those assigned to SC at 27 weeks post enrollment. In addition, participants who use more OTH resources (eg, more cessation coaching sessions completed) or more OKquit resources (eg, access more quit tips) will have greater biochemically verified smoking cessation rates. RESULTS: Data collection began in September 2022 and final follow-ups are expected to be completed by May 2025. CONCLUSIONS: Data from this randomized controlled trial will determine whether the OKquit smartphone app combined with OTH care will increase smoking cessation rates over standard OTH care alone. If successful, OKquit could provide tailored intervention content at a fraction of the cost of traditional interventions. Furthermore, this type of low-burden intervention may offer a way to reach underserved populations of adults who smoke and want to quit. TRIAL REGISTRATION: ClinicalTrials.gov NCT05539209; https://clinicaltrials.gov/study/NCT05539209. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56827.
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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.117 | 0.015 |
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".