Functionality and Quality of Asthma mHealth Apps and Their Consistency With International Guidelines: Protocol for a Systematic Search and Evaluation of Mobile Apps
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,111 | 0,138 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,005 |
| Méta-épidémiologie (sens large) | 0,018 | 0,018 |
| Bibliométrie | 0,017 | 0,015 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,008 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,044 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».