Identification and Evaluation of Methodologies to Assess the Quality of Mobile Health Apps in High-, Low-, and Middle-Income Countries: Rapid Review
Notice bibliographique
Résumé
BACKGROUND: In recent years, there has been rapid growth in the availability and use of mobile health (mHealth) apps around the world. A consensus regarding an accepted standard to assess the quality of such apps has yet to be reached. A factor that exacerbates the challenge of mHealth app quality assessment is variations in the interpretation of quality and its subdimensions. Consequently, it has become increasingly difficult for health care professionals worldwide to distinguish apps of high quality from those of lower quality. This exposes both patients and health care professionals to unnecessary risks. Despite progress, limited understanding of the contributions of researchers in low- and middle-income countries (LMICs) exists on this topic. Furthermore, the applicability of quality assessment methodologies in LMIC settings remains relatively unexplored. OBJECTIVE: This rapid review aims to identify current methodologies in the literature to assess the quality of mHealth apps, understand what aspects of quality these methodologies address, determine what input has been made by authors from LMICs, and examine the applicability of such methodologies in LMICs. METHODS: This review was registered with PROSPERO (International Prospective Register of Systematic Reviews). A search of PubMed, EMBASE, Web of Science, and Scopus was performed for papers related to mHealth app quality assessment methodologies, which were published in English between 2005 and 2020. By taking a rapid review approach, a thematic and descriptive analysis of the papers was performed. RESULTS: Electronic database searches identified 841 papers. After the screening process, 52 papers remained for inclusion. Of the 52 papers, 5 (10%) proposed novel methodologies that could be used to evaluate mHealth apps of diverse medical areas of interest, 8 (15%) proposed methodologies that could be used to assess apps concerned with a specific medical focus, and 39 (75%) used methodologies developed by other published authors to evaluate the quality of various groups of mHealth apps. The authors in 6% (3/52) of papers were solely affiliated to institutes in LMICs. A further 15% (8/52) of papers had at least one coauthor affiliated to an institute in an LMIC. CONCLUSIONS: Quality assessment of mHealth apps is complex in nature and at times subjective. Despite growing research on this topic, to date, an all-encompassing appropriate means for evaluating the quality of mHealth apps does not exist. There has been engagement with authors affiliated to institutes across LMICs; however, limited consideration of current generic methodologies for application in LMIC settings has been identified. TRIAL REGISTRATION: PROSPERO CRD42020205149; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=205149.
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 | 0,115 | 0,340 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,011 |
| Bibliométrie | 0,042 | 0,030 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,010 | 0,012 |
| Science ouverte | 0,004 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».