Identifying an effective mobile health application for the self-management of allergic rhinitis and asthma in Australia
Bibliographic record
Abstract
Objective: People with allergic rhinitis (AR) often self-manage in the community pharmacy setting without consulting health care professionals and trivialize their comorbidities such as asthma. A mobile health application (mHealth app) with a self-monitoring and medication adherence system can assist with the appropriate self-management of AR and asthma. This study aimed to identify an app effective for the self-management of AR and/or asthma.Methods: MHealth apps retrieved from the Australian Apple App Store and Android Google Play Store were included in this study if they were developed for self-management of AR and/or asthma; in English language; free of charge for the full version; and accessible to users of the mHealth app. The mHealth app quality was evaluated on three domains using a two-stage process. In Stage 1, the apps were ranked along Domain 1 (Accessibility in both app stores). In Stage 2, the apps with Stage 1, maximum score were ranked along Domain 2 (alignment with theoretical principles of the self-management of AR and/or asthma) and Domain 3 (usability of the mHealth app using Mobile App Rating Scale instrument).Results: Of the 418 apps retrieved, 31 were evaluated in Stage 1 and 16 in Stage 2. The MASK-air achieved the highest mean rank and covered all self-management principles except the doctor’s appointment reminder and scored a total MARS mean score of 0.91/1.Conclusions: MASK-air is ranked most highly across the assessment domains for the self-management of both AR and coexisting asthma. This mHealth app covers the majority of the self-management principles and is highly engaging.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".