Testing the Efficacy of a Scalable Telephone-Delivered Guided Imagery Tobacco Cessation Treatment: Protocol for a Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Tobacco use continues to be a leading preventable cause of death and disease in the United States, accounting for >480,000 deaths each year. Although treatments for tobacco use are effective for many, there is substantial variability in outcomes, and these approaches are not effective for all individuals seeking to quit smoking cigarettes. New, effective therapeutic approaches are needed to meet the preferences of people who want to stop smoking. Guided imagery (GI) is a mind-body technique that involves the guided visualization of specific mental images, which is enhanced with other sensory modalities and emotions. Preliminary evidence provides initial support for the use of GI as a treatment for cigarette smoking. Meta-analyses have shown that standard treatment for cigarette smoking delivered over the telephone via quitlines is effective. A telephone-based intervention that uses GI might provide another effective treatment option and increase the reach and effectiveness of quitlines. OBJECTIVE: This study aims to test the efficacy of Be Smoke Free, a telephone-delivered GI treatment for smoking cessation. METHODS: This multisite randomized clinical trial (RCT) will compare a novel telephone-delivered GI tobacco cessation treatment with a standard evidence-based behavioral treatment. The study will be conducted over 5 years. In phase 1, we refined protocols and procedures for the New York State and West Virginia sites for use in the RCT. During phase 2, we will conduct an RCT with 1200 participants: 600 (50%) recruited via quitlines and 600 (50%) recruited via population-based methods. Participants will be randomly assigned to either the GI condition or the behavioral condition; both treatments will be delivered by trained study coaches located at the University of Arizona. Assessments will be conducted at baseline and 3 and 6 months after enrollment by University of Arizona research staff. The primary outcome will be self-reported 30-day point prevalence abstinence 6 months after enrollment. Secondary outcomes include biochemically verified 7-day point prevalence abstinence 6 months after enrollment. RESULTS: Recruitment in West Virginia and New York began in October 2022. As of March 31, 2023, a total of 242 participants had been enrolled. Follow-up assessments began in November 2022. As of March 31, 2023, of the 118 eligible participants, 97 (82.2%) had completed the 3-month assessment, and 93% (26/28) of eligible participants had completed the 6-month assessment. Biochemical verification and qualitative interviews began in April 2023. Recruitment will continue through 2025 and follow-up assessments through 2026. Primary results are expected to be published in 2027. CONCLUSIONS: The Be Smoke Free study is a first-of-its-kind RCT that incorporates GI into telephone-based tobacco cessation treatment. If successful, Be Smoke Free will have substantial benefits for the long-term health of people who use tobacco across the United States. TRIAL REGISTRATION: ClinicalTrials.gov NCT05277831; https://clinicaltrials.gov/ct2/show/NCT05277831. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48898.
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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 | 0.050 | 0.054 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.084 | 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".