Youth and Young Adults’ Perspectives on Augmented Reality–Driven Vaping Cessation Interventions: Interpretive Description Study
Bibliographic record
Abstract
Background: Vaping among youth and young adults has become a significant public health issue, with increasing prevalence and associated health risks. Despite awareness of these risks, many youth and young adults struggle to quit due to complex social pressures, stress, and a lack of tailored interventions. Digital tools, including augmented reality (AR), offer an opportunity to address these challenges by creating engaging and personalized support systems. Objective: The aim of this study was to determine what can be learned from youth and young adult vapers who are motivated to quit vaping to inform the design of mobile app-based AR intervention strategies. Methods: This qualitative study applied an interpretive description (ID) approach to explore youth and young adults' perspectives on vaping cessation and their preferences for digital intervention features. Semistructured interviews were conducted with participants (N=12) who shared their experiences with vaping, quitting attempts, and ideas for app-based AR support. Reflexive thematic analysis and ID were used to code the data and identify patterns, resulting in the generation of themes that reflected the individualized and contextual nature of vaping cessation. Results: The findings collectively yielded four major themes: (1) social and cultural context play a role in youth and young adults' experiences of cessation, (2) quitting vaping is an individual endeavor that does not always mean success, (3) digital support as a bridge between individual and social needs, and (4) AR as a catalyst for personalized support. These themes address the motivations, challenges, and opportunities identified by participants in their cessation journeys, as well as their perspectives on integrating AR technology as a supportive tool. Our findings reveal that vaping cessation is a deeply personal process influenced by internal motivations (eg, health improvement and personal milestones) and external factors (eg, social context). Participants identified AR as a promising app-based tool for cessation support, with interest in potential AR-integrated features such as gamified rewards, health visualizations, and anonymous support. Youth and young adults emphasized the need for sensitive design to avoid negative or punitive content. Conclusions: This study provides actionable insights for designing youth and young adult-centered digital health tools that leverage app-based AR to support vaping cessation. By addressing the unique sociocultural and behavioral needs of youth and young adults, app-based AR interventions can bridge gaps in traditional cessation strategies. These findings contribute to the development of innovative public health approaches aimed at reducing vaping prevalence in vulnerable populations.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".