App-Based Smoking Urge Reduction Intervention for Young Adults: Protocol Combining a Microrandomized Trial and Conventional Between-Subject Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Tobacco smoking is the leading preventable cause of morbidity and mortality in the United States, and young adults have high smoking rates. Although most young adult smokers are interested in quitting, they underutilize professional cessation support. Smartphones have wide reach and integration into young adults' lives, and these devices offer great opportunities to deliver cessation interventions by delivering messages suggesting coping strategies "in the moment" when smokers need cessation support. OBJECTIVE: The overall goal of this trial is to evaluate the efficacy of cognitive behavioral therapy (CBT) and mindfulness or acceptance and commitment therapy (ACT) messages for young adults targeted at specific high-risk situations for smoking. METHODS: We will conduct a microrandomized trial (MRT; within-subject randomization) to test the efficacy of CBT and mindfulness or ACT compared with control messages for reducing smoking urge up to 15 minutes after message delivery, nested in a conventional between-subject randomized controlled trial (RCT). A conventional between-subject control group of participants who will complete ecological momentary assessment (EMA) only without intervention messages will allow us to test if messages reduce cigarettes per day at the end of treatment, 3-month follow-up, and 6-month follow-up. Among MRT intervention group participants, we will explore how message efficacy may be moderated by substance co-use (cannabis, alcohol, other drugs) and exposure to specific settings (home, work, bars). RESULTS: As of June 2025, we had enrolled 58 participants of the target sample of 160, with 52% (30/58) assigned to the MRT group and 48% (28/58) assigned to the EMA-only control. CONCLUSIONS: Smoking onset is now more common among young adults than adolescents, and early cessation substantially reduces morbidity and mortality from smoking, making age-appropriate, tailored, and scalable interventions for this high-priority population even more important. Results of this trial will provide evidence on the efficacy of tailored intervention messages to help young adult smokers cope with smoking urges as an integral part of smartphone interventions. Findings will inform the field about key principles, strategies, and efficacy of situational tailoring of app-based tobacco use urge reduction messages. TRIAL REGISTRATION: ClinicalTrials.gov NCT05836103; https://clinicaltrials.gov/study/NCT05836103. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/74388.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".