Comparison of Developers’ and End-Users’ Perspectives About Smoking Cessation Support Through the Crush the Crave App
Bibliographic record
Abstract
BACKGROUND: High smoking rates among end-users, combined with their high rates of app use, render this age group as a particularly captive audience for quit smoking apps. There is emerging evidence that apps are an effective way to support smoking cessation among end-users. How the expectations behind the design of apps align with the needs and preferences of end-users, and if this differs by gender, is poorly understood, limiting the ability to evaluate and scale these interventions. OBJECTIVE: The objective of this qualitative case study was to detail how the overall design approach of Crush the Crave (CTC), a quit smoking app that targets end-users, compares with young adult women's and men's perspectives and experiences, with consideration for the influence of gender. METHODS: Semistructured interviews were conducted with 15 developers involved in the development of CTC and 31 young adult CTC users. Data were analyzed inductively to derive thematic findings of the perceived pros and cons of CTC by both developers and end-users. Findings were grouped under 4 categories (1) technology and platforms utilized for the app, (2) foundation of app content, (3) underlying focus of the app, and (4) look, feel and functionality of the app. RESULTS: Under the category, technology and platforms utilized for the app, it was found that both developers and end-users agreed that apps aligned with the needs and preferences of young adult smokers. Major limitations with the technology identified by end-users were the frequent "glitches" and requirement for internet or data. For the category, foundation of app content,developers agreed that the strength of CTC was in its strong evidence-base. What mattered to end-users, however, was that the content was packaged positively, focusing on the benefits of quitting versus the consequences of smoking. It was found under the category, underlying focus of the app, that the individually-led focus of the app resonated with both developers and end-users, especially young men. Under the final category, look, feel and functionality of the app, it was found that developers were very positive about the app's aesthetics but end-users thought that the aesthetics incited a negative effect. Also, while end-users found it easy to use, they did not find the app intuitive. Finally, end-users thought that, because the app functions were largely based on a user's quit date versus their ongoing efforts, this often lent to unmeaningful data. CONCLUSIONS: The current study findings highlight the importance of understanding multiple perspectives of stakeholders involved in a mobile-based intervention. By gathering the viewpoints of developers and end-users, both problematic and effective approaches that underlie development goals were revealed as a means of informing the development, implementation, and evaluation of future electronic health (eHealth) interventions.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".