Health Internet Technology for Chronic Conditions: Review of Diabetes Management Apps
Notice bibliographique
Résumé
BACKGROUND: Mobile health (mHealth) smartphone apps have shown promise in the self-management of chronic disease. In today's oversaturated health app market, selection criteria that consumers are employing to choose mHealth apps for disease self-management are of paramount importance. App quality is critical in monitoring disease controls but is often linked to consumer popularity rather than clinical recommendations of effectiveness in disease management. Management of key disease variances can be performed through these apps to increase patient engagement in disease self-management. This paper provides a comprehensive review of features found in mHealth apps frequently used in the self- management of diabetes. OBJECTIVE: The purpose of this study was to review features of frequently used and high consumer-rated mHealth apps used in the self-management of diabetes. This study aimed to highlight key features of consumer-favored mHealth apps used in the self-management of diabetes. METHODS: , insulin, physical activity, and prescription medication. A subsequent Google search was conducted using the search term "best Apple diabetes apps." The top 3 search results-"Healthline," "Everyday Health," and "Diabetes Apps-American Diabetes Association"-were explored. RESULTS: In total, 12 mHealth apps were reviewed due to their appearing across 4 evaluated sources. Only 1 health app-Glucose Buddy Diabetes Tracker-appeared as the most frequently used within the Apple iOS store and across the other 3 sources. The OneTouch Reveal app ranked first on the list in the iOS store with 39,000 consumer reviews and a rating of 4.7 out of 5.0 stars but only appeared in 1 of the other 3 sources. Blood glucose tracking was present across all apps, but other disease management features varied in type with at least 3 of the 5 key features being present across the 12 reviewed apps. Subscription cost and integration needs were present in the apps which could impact consumers' decision to select apps. Although mobile app preference was assessed and defined by the number of consumer reviews and star ratings, there were no scientific standards used in the selection and ranking of the health apps within this study. CONCLUSIONS: mHealth apps have shown promise in chronic disease management, but a surge in development of these nonregulated health solutions points to a need for regulation, standardization, and quality control. A governing body of health IT professionals, clinicians, policymakers, payors, and patients could be beneficial in defining health app standards for effective chronic disease management. Variabilities in features, cost, and other aspects of management could be reduced by regulatory uniformity, which would increase patient engagement and improve disease outcomes.
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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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,011 | 0,010 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».