Identifying an effective mobile health application for the self-management of allergic rhinitis and asthma in Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it