Supporting Youth Vaping Cessation With the Crush the Crave Smartphone App: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The use of e-cigarettes, or vaping, has increased exponentially in the past decade, particularly among youth. Emerging evidence indicates growing nicotine dependence among youth, revealing historically higher rates of dependence among current e-cigarette users compared to rates seen in earlier research. Despite the urgent need for youth vaping cessation interventions, there is limited knowledge about the process of vaping cessation, and few evidence-based interventions are available to young people seeking support. A notable literature review on vaping cessation resources for young people recommended technology-based interventions, such as smartphone apps and SMS text messaging services, as a promising area of vaping cessation research and intervention development. OBJECTIVE: The primary aim of our study is to determine the effectiveness of the Crush the Crave app in supporting vaping cessation among youth recruited to the intervention arm via comparison with an assessment-only control group. The primary hypothesis is that participants in the intervention group-those using Crush the Crave-will be more likely to be abstinent at the 6-month follow-up point than participants in the assessment-only control arm. METHODS: A 2-arm, single-blind, parallel randomized controlled trial will be conducted over 12 months. Study invitations will be sent to 600 youth (age: 16-18 years) and young adult (age: 19-29 years) e-cigarette users and randomized between an intervention arm, which will be using Crush the Crave (n=300), and an assessment-only control arm (n=300) in a 1:1 ratio. The primary hypothesis is that participants in the intervention group-those using Crush the Crave-will be more likely to be abstinent at the 6-month follow-up point. RESULTS: Study recruitment began on March 4, 2022. Recruitment is anticipated to be completed in December 2022. CONCLUSIONS: This protocol describes one of the first-ever randomized controlled trial studies to evaluate the effectiveness of an app-based intervention for supporting vaping cessation among youth aged 16 to 18 years and young adults aged 19 to 29 years. The findings from our trial will help increase our understanding of the process of vaping cessation among youth and provide evidence on the effectiveness of an app-based intervention in helping young people quit vaping. The trial results will also have implications in the development of current and future approaches to youth vaping cessation. TRIAL REGISTRATION: OSF Registries osf.io/hmd87; https://doi.org/10.17605/OSF.IO/HMD87. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42956.
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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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.113 | 0.019 |
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".