Preferences for Mobile-Supported e-Cigarette Cessation Interventions Among Young Adults: Qualitative Descriptive Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Despite the steady rise in electronic cigarette (e-cigarette) uptake among young adults, increasingly more young people want to quit. Given the popularity of smartphones among young adults, mobile-based e-cigarette cessation interventions hold significant promise. Smartphone apps are particularly promising due to their varied and complex capabilities to engage end users. However, evidence around young adults’ preferences and expectations from an e-cigarette cessation smartphone app remains unexplored. OBJECTIVE The purpose of this study was to take an initial step toward understanding young adults’ preferences and perceptions on app-based e-cigarette cessation interventions. METHODS Using a qualitative descriptive approach, we interviewed 12 young adults who used e-cigarettes and wanted to quit. We inductively derived themes using the framework analysis approach and NVivo 12 qualitative data analysis software. RESULTS All participants agreed that a smartphone app for supporting cessation was desirable. In addition, we found 4 key themes related to their preferences for app components: (1) flexible personalization (being able to enter and modify goals); (2) e-cigarette behavior tracking (progress and benefits of quitting); (3) safely managed social support (moderated and anonymous); and (4) positively framed notifications (encouraging and motivational messages). Some gender-based differences indicate that women were more likely to use e-cigarettes to cope with stress, preferred more aesthetic tailoring in the app, and were less likely to quit cold turkey compared with men. CONCLUSIONS The findings provide direction for the development and testing of app-based e-cigarette cessation interventions for young adults.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".