User Perceptions of E-Cigarette Cessation Apps: Content Analysis of App Reviews
Bibliographic record
Abstract
BACKGROUND: Vaping rates in Canada are continuing to increase. In 2019, 4.7% of Canadians used an electronic cigarette (e-cigarette) in the past 30 days, which rose to 5.8% in 2022. In the same year, young adults aged 20-24 years demonstrated the highest use among Canadians, at 19.7%. Given this, existing interventions are not resulting in the desired outcomes, and smartphone apps have the potential to address this gap. Although limited, current evidence highlights that apps can be an effective cessation support; however, a gap persists in understanding the user experience of vaping cessation apps. OBJECTIVE: The purpose of this study was to explore the user experience of vaping cessation apps through an analysis of app reviews. More specifically, this study aimed to identify positive and negative experiences of app users, as well as highlight recommendations from app users to improve the quality of these apps. METHODS: Vaping cessation apps were identified through searches on the Canadian and US versions of Apple App Store and Android Google Play Store in August 2022. Searches revealed a total of 11 vaping cessation apps with app reviews, which resulted in a total of 310 reviews for analysis. Review material was analyzed using a deductive content analysis approach and divided into the following primary categories: content, functionality, aesthetic, cost, and other. These were further divided into 3 secondary categories (praise, criticism, and recommendations) and various tertiary categories. RESULTS: The most discussed primary categories were content, functionality and cost. Comments regarding content tended to be positive (n=103, 33.2%), praising features, such as hypnosis audio sessions (n=29, 28.2%) and tracking features. In contrast, comments tended to criticize functionality (n=58, 18.7%), indicating issues with the functioning of an app that either made the whole app unusable (n=29, 50%) or a specific feature unusable (n=28, 48.3%). Reviews regarding cost were mixed, with 27 (8.7%) positive comments, the majority of these encompassing reviewers satisfied with their purchase (n=17, 63%), and 38 (12.3%) negative comments, including individuals both unsatisfied with their purchase (n=15, 39.5%) and unsatisfied with the free version (n=12, 31.6%). CONCLUSIONS: This study is the first of its kind to evaluate the user experience with vaping cessation apps via an analysis of app reviews. App developers may benefit from reading our findings to identify areas to focus on when developing and updating apps. Our study forms a basis for the development of future vaping interventions, as well as future studies. Future research should be conducted on vaping cessation interventions with an emphasis on the user experience because there is limited research available for comparison with the promising results from this study.
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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 | 0.016 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".