Functionality and Quality of Asthma mHealth Apps and Their Consistency With International Guidelines: Protocol for a Systematic Search and Evaluation of Mobile Apps
Bibliographic record
Abstract
BACKGROUND: Asthma is a chronic respiratory disorder that requires long-term pharmacotherapy and patient empowerment to manage the condition and recognize and respond to asthma exacerbations. Mobile health (mHealth) apps represent a potential medium through which patients can improve their ability to self-manage their asthma. Few studies have conducted a systematic evaluation of asthma mobile apps for quality and functionality using a validated tool. None of these reviews have systematically assessed these apps for their content and evaluated them against the available international best practice guidelines. OBJECTIVE: The objective of this study is to conduct a systematic search and evaluation of adult-targeted asthma mHealth apps. As part of this review, the potential of an mHealth app to improve asthma self-management and the overall quality of the app will be evaluated using the Mobile App Rating Scale framework, and the quality of the information within an app will be evaluated using the current Global Initiative for Asthma guidelines as a reference. METHODS: A stepwise methodological approach was taken in creating this review. First, the most recent Global Initiative for Asthma guidelines were independently reviewed by 2 authors to identify key recommendations that could be feasibly incorporated into an mHealth app. A previously developed asthma assessment framework was identified and was modified to suit our research and ensure that all of these identified recommendations were included. In total, 2 popular app stores were reviewed to identify potential mHealth apps. These apps were screened based on predefined inclusion and exclusion criteria. Suitable apps were then evaluated. Technical information was obtained from publicly available information. The next step was to perform an app quality assessment using the validated Mobile App Rating Scale framework to objectively determine the quality of an app. App functionality was assessed using the Intercontinental Medical Statistics Institute for Health Informatics Functionality Scoring System. Finally, the mHealth apps were assessed using our developed checklist. RESULTS: Funding has been received for the project from the Respiratory Department at Northern Health, Victoria. Three reviewers have been recruited to systematically evaluate the apps. The results of this study are expected in 2022. CONCLUSIONS: To our knowledge, this review represents the first study to examine all mHealth apps available in Australia that are targeted to adults with asthma for their functionality, quality, and consistency with international best practice guidelines. Although this review will only be conducted on mHealth apps available in Australia, many apps are available worldwide; thus, this study should be largely generalizable to other English-speaking regions and users. The results of this review will help to fill gaps in the literature and assist clinicians in providing evidence-based advice to patients wishing to use mHealth apps as part of their asthma self-management. TRIAL REGISTRATION: PROSPERO 269894; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=269894. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33103.
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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 | 0.111 | 0.138 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".