Mobile Health App for Adolescent Asthma Self-Management: Development and Usability Study of the Pulmonary Education and Knowledge Mobile Asthma Action Plan
Bibliographic record
Abstract
Background: Adolescents with asthma are vulnerable to poor asthma outcomes due to inadequate self-management skills and nonadherence to medications. Mobile health (mHealth) apps have shown promise in improving asthma control, medication adherence, and self-efficacy. However, existing mHealth asthma apps lack personalization and real-time feedback and are not tailored for at-risk adolescents. Objective: This study aimed to design, develop, and test a smartphone-based mHealth Asthma Action Plan for adolescents, called Pulmonary Education and Knowledge Mobile Asthma Action Plan (PEAK-mAAP), in preparation for a large-scale randomized controlled trial. Methods: We employed user-centered design principles to develop our app, leveraging our previous work and following guidelines from the National Heart, Lung, and Blood Institute. The app consists of a patient-facing mobile app and a provider-facing portal. A convenience sample of 13 adolescents (aged 12-20 years) was recruited from the Arkansas Children's Research Institute database or direct health care provider referrals. Participants underwent a task-based usability assessment followed by the System Usability Scale assessment to measure user satisfaction, interface effectiveness, and overall system usability. Results: PEAK-mAAP integrates 7 core modules supporting personalized asthma self-management, symptom monitoring, medication tracking, and real-time feedback. The mean System Usability Scale score was 83/100 (SD 5.54), indicating high user satisfaction and system usability. Notably, older adolescents (>17 years) reported higher usability scores (87.5) than younger users (77.5), suggesting potential age-related differences in app navigation and engagement. Conclusions: The results demonstrate that PEAK-mAAP is a feasible and user-friendly mHealth intervention for adolescent asthma self-management. While the high usability score reflects a positive user experience, some participants encountered initial usability challenges, highlighting the need for minor refinements and user training materials. The integration of personalized self-management tools and real-time feedback distinguishes PEAK-mAAP from existing asthma apps, addressing key barriers to adherence and engagement. Moving forward, an ongoing randomized controlled trial will assess its clinical effectiveness, long-term engagement, and impact on asthma outcomes, providing further insights into its potential as a scalable solution for adolescent asthma care.
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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 | 0.027 |
| 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 | 0.000 | 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".