Design, Development, and Usability Evaluation of a Mobile Application for Monitoring Voice and Upper Airway Health
Bibliographic record
Abstract
IntroductionAsthma and chronic obstructive pulmonary disease (COPD) are highly prevalent chronic respiratory diseases worldwide and in Canada. Despite the availability of telemonitoring methods, adequate symptom control in asthma and COPD remains a challenge due to the unpredictable nature of exacerbations and nighttime awakenings, which can lead to increased hospitalizations, healthcare costs and mortality. Mobile health (mHealth) applications (apps) provide a viable option to remotely monitor patients’ health conditions and control symptom progression. The objectives of this thesis are to design, develop, and evaluate a mHealth app, namely AIrway, to support patients with asthma and COPD in symptom monitoring and self-management. MethodsThe AIrway app was designed and developed for Android smartphone devices in accordance with Morville’s mobile app design principles and clinical guidelines, such as the Global Initiative for Asthma (GINA) and the Global Initiative for Chronic Obstructive Lung Disease (GOLD), to meet the needs of symptom monitoring. The app also complies with the Personal Information Protection and Electronic Documents Act (PIPEDA) and Quebec privacy law to ensure the protection of user information. Essential features include journaling asthma/COPD diaries, utilizing the Google Firestore cloud service for data storage, parsing local weather information, as well as sending medication reminders and action plans.We recruited 5 app developers (i.e., technical raters) who were Computer Science Ph.D. students or post-doctoral fellows who took at least one mobile app development course or had at least one year of mobile app programming experience to evaluate the AIrway app. These technical raters completed the User Version of the Mobile Application Rating Scale (uMARS) survey, provided open-ended feedback, and responded to the IQVIA items after testing the app. The uMARS survey was designed to assess the app's engagement, functionality, aesthetics, and information quality while the IQVIA items were intended to assess the app's self-management functionality.We collected, processed, and reported the uMARS and IQVIA data in accordance with the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) guidelines to enhance the reliability of the findings. We also performed a literacy analysis using an open-source readability calculator to evaluate the app’s contents.Descriptive statistics of uMARS and IQVIA were computed and compared to the scores of similar self-management apps in the literature. A qualitative analysis of the open-ended feedback provided by the technical raters was also conducted to identify areas for product improvement.Results and Discussion The results indicate that the AIrway app was easily understandable to individuals with nine years of formal education. The app received a high uMARS overall mean score (3.6 out of 5.0) and an IQVIA overall median score (8 out of 11). These scores align with similar mHealth apps in the literature (MARS: 3.0-4.2 and IQVIA: 6-10). The open-ended feedback suggested incorporating more graphical icons for better user interface display and improving the input field for the password reset function.Overall, the app met the mobile app design principles and development guidelines, achieving appropriate colour contrast, readability, and layout presentations. Furthermore, asthma and COPD diaries, medication profiles, and action plans were developed based on clinically validated guidelines. These steps are essential to meet industrial standards and safeguard the app’s credibility, accessibility, and usability. Future steps include expanding the app’s compatibility to cross-platform frameworks to enhance the app’s accessibility and equity
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".