An APPetite for Quitting: An Exploration of Vaping Cessation App Use Among Youth Using Qualitative Description (Preprint)
Bibliographic record
Abstract
BACKGROUND Youth vaping has become a significant public health concern, with high rates of initiation, nicotine dependence and misperceptions of harm. Although many youth attempt to quit, most do so without formal support, and few report leveraging cessation apps. Mobile health tools have potential as accessible youth-orientated supports, yet little is known about youth experiences with these interventions. OBJECTIVE This study explored Canadian youth’s perceptions and experiences of vaping cessation mobile applications, focusing on preferences, perceived utility and recommendations for improvement. METHODS A Qualitative Descriptive design guided by a constructivist paradigm was employed. Seventeen semi-structured interviews were conducted with youth aged 16-24 years from British Columbia, Ontario and Quebec in fall 2022. Eligible participants had current or past vaping experience, had attempted or wanted to quit, and had used a cessation app. Data were transcribed verbatim and analyzed inductively using Reflexive Thematic Analysis, with reflexivity supported through team-based coding and iterative discussions. RESULTS Three themes described youth’s experiences. Quitting on my Own Terms reflected the importance of autonomy and intrinsic motivation, with varied views as to where app use conflicted with or supported independent cessation. Parallel Experiences- Differing Views on Community, captured ambivalence toward in-app social features: while some valued peer support, others preferred to quit privately or used broader online platforms. Pocket-sized Partners in Cessation highlighted the usefulness of features such as progress tracking, motivational reinforcement and gamification but participants stressed that convenience also was not enough. Youth emphasized the need for apps that feel authentic, emotionally resonate, and capable of balancing independence with opportunities for connection. CONCLUSIONS Youth want cessation apps that function as pocket-sized partners rather than passive trackers. Effective interventions should validate autonomy, account for non-linear cessation journeys and relapses, and integrate supportive, interactive, and youth-friendly features. By addressing equity, sustainability, and integration into broader public health systems, digital cessation tools may better support youth in their quitting journeys.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".