The Crush the Crave Quit Smoking App and Young Adult Smokers: Qualitative Case Study of Affordances
Bibliographic record
Abstract
BACKGROUND: Mobile phone apps have emerged as a promising way to reach young adult smokers, given their high mobile phone ownership rates and openness to receiving cessation support via digital technologies. Although emerging evidence indicates that quit smoking apps are an effective way to reduce smoking among young adults, lacking is formative evaluative research that captures the perspectives of end-users. OBJECTIVE: The objective of this study was to contribute insights toward understanding how young adults interact with the Crush the Crave (CTC) app, and how this interaction shapes young adults' smoking cessation experiences and practices, with consideration of the influence of gender. METHODS: Semistructured interviews were conducted with 31 young adult CTC end-users. Guided by sociomateriality theory and an affordances approach, data were inductively analyzed to derive thematic findings in relation to the impacts of CTC on quit efforts, and to expose the underlying affordances (mechanisms) that lend to these outcomes. Findings were grouped according to the 4 design components of CTC: credibility, social support, task support, and dialogue support. RESULTS: The credibility component of CTC played an important role in harnessing the trust of young adults because it afforded them promise in relation to its potential effectiveness in assisting them with quitting smoking. The social support component lent to various end-user practices and experiences that rendered this aspect as the weakest component in supporting quit efforts. Although most functions situated in the task and dialogue support components were found to be helpful, there were a few affordances in CTC that resulted in negative experiences, notably weaning from smoking. Gender-related influences were also evident. For example, young men preferred to control and self-manage their quitting and, therefore, did not engage with functions that afforded journaling or reminding to stay on track. Women, in contrast, were more likely to benefit from these affordances. CONCLUSIONS: An affordances approach is productive for gaining an in-depth understanding of how mobile apps interact with end-users to lend to particular outcomes. The study findings have implications for developing and improving apps for helping young adults quit smoking, as well as apps that target other health behaviors. Productive affordances may also serve as a framework for leveraging apps for smoking cessation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".