Apps for People With Rheumatoid Arthritis to Monitor Their Disease Activity: A Review of Apps for Best Practice and Quality
Notice bibliographique
Résumé
BACKGROUND: Rheumatoid arthritis (RA) is a chronic inflammatory arthritis requiring long-term treatment with regular monitoring by a rheumatologist to achieve good health outcomes. Since people with RA may wish to monitor their own disease activity with a smartphone app, it is important to understand the functions and quality of apps for this purpose. OBJECTIVE: The aim of our study was to assess the features and quality of apps to assist people to monitor their RA disease activity by (1) summarizing the available apps, particularly the instruments used for measurement of RA disease activity; (2) comparing the app features with American College of Rheumatology and European League against Rheumatism (ACR and EULAR) guidelines for monitoring of RA disease activity; and (3) rating app quality with the Mobile App Rating Scale (MARS). METHODS: Systematic searches of the New Zealand iTunes and Google Play app stores were used to identify all apps for monitoring of RA disease activity that could be used by people with RA. The apps were described by both key metadata and app functionality. App adherence with recommendations for monitoring of RA disease activity in clinical practice was evaluated by identifying whether apps included calculation of a validated composite disease activity measure and recorded results for future retrieval. App quality was assessed by 2 independent reviewers using the MARS. RESULTS: The search identified 721 apps in the Google Play store and 216 in the iTunes store, of which 19 unique apps met criteria for inclusion (8 from both app stores, 8 iTunes, and 3 Google Play). In total, 14 apps included at least one validated instrument measuring RA disease activity; 7 of 11 apps that allowed users to enter a joint count used the standard 28 swollen and tender joint count; 8 apps included at least one ACR and EULAR-recommended RA composite disease activity (CDA) measure; and 10 apps included data storage and retrieval. Only 1 app, Arthritis Power, included both an RA CDA measure and tracked data, but this app did not include the standard 28 tender and swollen joint count. The median overall MARS score for apps was 3.41/5. Of the 6 apps that scored ≥4/5 on the overall MARS rating, only 1 included a CDA score endorsed by ACR and EULAR; however, this app did not have a data tracking function. CONCLUSIONS: This review found a lack of high-quality apps for longitudinal assessment of RA disease activity. Current apps fall into two categories: simple calculators primarily for rheumatologists and data tracking tools for people with RA. The latter do not uniformly collect data using validated instruments or composite disease activity measures. There is a need for appropriate, high-quality apps for use by rheumatologists and patients together in co-management of RA.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».