Adolescent Preferences and Design Recommendations for an Asthma Self-Management App: Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Approximately 10% of adolescents in the United States have asthma. Adolescents widely use apps on mobile phones and tablet technology for social networking and gaming purposes. Given the increase in recreational app use among adolescents, leveraging apps to support adolescent asthma disease management seems warranted. However, little empirical research has influenced asthma app development; adolescent users are seldom involved in the app design process. OBJECTIVE: The aim of this mixed-methods study was to assess adolescent preferences and design recommendations for an asthma self-management app. METHODS: A total of 20 adolescents with persistent asthma (aged 12-16 years) provided feedback on two asthma self-management apps during in-person semistructured interviews following their regularly scheduled asthma clinic visit and via telephone 1 week later. Interviews were audiorecorded, transcribed verbatim, analyzed using SPSS v24, and coded thematically using MAXQDA 11. RESULTS: Regarding esthetics, app layout and perceived visual simplicity were important to facilitate initial app use. Adolescents were more likely to continually engage with apps that were deemed useful and met their informational needs. Adolescents also desired app features that fit within their existing paradigm or schema and included familiar components (eg, medication alerts that appear and sound like FaceTime notifications and games modeled after Quiz Up and Minecraft), as well as the ability to customize app components. They also suggested that apps include other features, such as an air quality tracker and voice command. CONCLUSIONS: Adolescents desire specific app characteristics including customization and tailoring to meet their asthma informational needs. Involving adolescents in early stages of app development is likely to result in an asthma app that meets their self-management needs and design preferences and ultimately the adoption and maintenance of positive asthma self-management behaviors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".