An mHealth App and System Architecture for Respiratory Disease Management: Design Principles, Tool Development, and Pilot Usability Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps are software interfaces that enable users to access and manage wearable technology through smartphones and tablet devices for health improvement purposes. However, many respiratory disease mHealth apps lack transparent development documentation, compromising user confidence in their quality, functionality, and usability. OBJECTIVE: This study aimed to develop and evaluate AIrway, a companion mHealth app designed to interface with an in-house wearable device for monitoring airway symptoms following established mHealth development and reporting standards. METHODS: The development cycle of AIrway comprised 2 study phases. In phase 1, AIrway, a native Android app, was developed following academic and industrial standards (Android material design and Morville's design principles) and privacy regulations (Personal Information Protection and Electronic Documents Act). Core functionalities included location-based environmental monitoring, a clinical diary with action plans, Bluetooth connectivity, and real-time data storage. In phase 2, the usability of AIrway was evaluated by software app developers using standardized assessment tools, namely, the User Version of the Mobile Application Rating Scale survey and the IQVIA questionnaire. RESULTS: AIrway successfully fulfilled 7 of 8 development criteria on usability, privacy, security, appropriateness, transparency, safety, and technical support, with only the technology aspects requiring refinement. Accessibility assessments confirmed that AIrway's content and interface were comprehensible to the general population (grade 9-10 reading level). Technical testing demonstrated reliable Bluetooth data transmission for up to 10 minutes without interruption. User evaluation scores for the User Version of the Mobile Application Rating Scale (3.6/5.0) and IQVIA (8/11) were comparable to those of similar mHealth apps on the market. CONCLUSIONS: By adhering to established mHealth app design principles, AIrway achieved the necessary accessibility standards and wireless communication capabilities for wearable device integration. Future development will focus on expanding cross-platform compatibility and conducting usability evaluation with intended patient populations to validate its clinical effectiveness and support ongoing improvements.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".