Desires and Needs for Quitting Both e-Cigarettes and Cigarettes Among Young Adults: Formative Qualitative Study Informing the Development of a Smartphone Intervention for Dual Tobacco Cessation
Bibliographic record
Abstract
BACKGROUND: Dual use of both e-cigarettes and cigarettes is popular among young adults and may lead to greater nicotine dependence and additive adverse health effects than single-product use. However, existing cessation programs target quitting either e-cigarettes or cigarettes, highlighting a need for interventions to help young adults quit both products (ie, dual tobacco cessation). OBJECTIVE: This formative study is part of a larger project to develop a smartphone intervention for dual tobacco cessation among young adults. This study aimed to (1) explore desires for and experiences with quitting both e-cigarettes and cigarettes and (2) identify needs and preferences for dual tobacco cessation intervention programming. METHODS: Semistructured interviews were conducted to elicit the need for and experience with dual tobacco cessation among 14 young adults (18-29 years old) recruited through Instagram (Meta) advertisements in 2023. We conducted a thematic analysis to identify common themes related to quitting experiences and cessation needs. RESULTS: Participants expressed a strong desire for dual tobacco cessation and had attempted to quit both tobacco products, mostly "cold turkey." The priority product for quitting first varied by the individual's perceived harm or level of consumption. Targets for dual tobacco cessation interventions included (1) highlighting the health effects of dual tobacco use compared with single product use, (2) providing cessation support to quit one prioritized product while cutting down the other product with the explicit goal to quit both, (3) emphasizing unique facilitators and barriers to quitting each product (eg, unpleasant smell of cigarettes facilitating smoking cessation and accessibility and flavors of e-cigarettes hindering vaping cessation), and (4) addressing co-use of tobacco with alcohol or cannabis. Participants wanted personalized interventions through smartphone apps that would tailor support to their tobacco use patterns and unique quitting goals and needs. They also suggested presenting intervention content in multimedia (eg, videos, graphic pictures, quizzes, and games) to increase engagement. CONCLUSIONS: This study provides important insights into young adults' experiences, needs, and preferences for dual tobacco product cessation. We highlight important targets for future smartphone apps to deliver personalized and tailored support to meet the heterogeneous needs and preferences of young people who want to quit using both e-cigarettes and cigarettes.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".