A Self-Regulation Theory–Based Asthma Management Mobile App for Adolescents: A Usability Assessment
Bibliographic record
Abstract
BACKGROUND: Self-regulation theory suggests people learn to influence their own behavior through self-monitoring, goal-setting, feedback, self-reward, and self-instruction, all of which smartphones are now capable of facilitating. Several mobile apps exist to manage asthma; however, little evidence exists about whether these apps employ user-centered design processes that adhere to government usability guidelines for mobile apps. OBJECTIVE: Building upon a previous study that documented adolescent preferences for an asthma self-management app, we employed a user-centered approach to assess the usability of a high-fidelity wireframe for an asthma self-management app intended for use by adolescents with persistent asthma. METHODS: Individual interviews were conducted with adolescents (ages 11-18 years) with persistent asthma who owned a smartphone (N=8). Adolescents were asked to evaluate a PDF app wireframe consisting of 76 screen shots displaying app features, including log in and home screen, profile setup, settings and info, self-management features, and graphical displays for charting asthma control and medication. Preferences, comments, and suggestions for each set of screen shots were assessed using the audio-recorded interviews. Two coders reached consensus on adolescent evaluations of the following aspects of app features: (1) usability, (2) behavioral intentions to use, (3) confusing aspects, and (4) suggestions for improvement. RESULTS: The app wireframe was generally well received, and several suggestions for improvement were recorded. Suggestions included increased customization of charts and notifications, reminders, and alerts. Participants preferred longitudinal data about asthma control and medication use to be displayed using line graphs. All participants reported that they would find an asthma management app like the one depicted in the wireframe useful for managing their asthma. CONCLUSIONS: Early stage usability tests guided by government usability guidelines (usability.gov) revealed areas for improvement for an asthma self-management app for adolescents. Addressing these areas will be critical to developing an engaging and effective asthma self-management app that is capable of improving adolescent asthma outcomes.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".