Effectiveness of Mobile Apps in Improving Medication Adherence Among Chronic Kidney Disease Patients: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Chronic kidney disease (CKD) is a serious condition affecting millions of individuals worldwide. Adherence to medication regimens among patients with CKD is often suboptimal, leading to poor health outcomes. In recent years, mobile apps have gained popularity as a promising tool to improve medication adherence and self-management in various chronic diseases. OBJECTIVE: This study aimed to evaluate the effectiveness of mobile apps to improve medication adherence among patients with CKD (including end-stage and renal replacement therapy). METHODS: A systematic search was conducted using Scopus, Cochrane, PubMed, and EBSCOhost to include eligible articles that studied mobile apps to improve medication adherence among patients with CKD. The quality of the selected studies was evaluated using the Newcastle‒Ottawa Scale and the Cochrane risk-of-bias tool. RESULTS: Out of 231 relevant articles, only 9 studies were selected for this systematic review. Based on Newcastle‒Ottawa Scale, 7 were deemed to be of high quality, while others were of fair quality. The Cochrane risk-of-bias tool indicated a low to moderate risk of bias across the included studies. Most of the included studies had a randomized controlled design. Of the 9 selected studies, 3 papers represented medication adherence by a coefficient of 10 variability of tacrolimus, 3 papers used adherence measurement scales to calculate the score for assessing medication adherence, 2 papers represented medication adherence by self-reporting, 2 papers represented medication adherence using electronic monitoring, and 1 represented medication adherence by pill count. The mobile apps were identified as Transplant Hero (Transplant Hero LLC), Perx (Perx Health), Smartphone Medication Adherence Saves Kidneys (developed by John McGillicuddy), Adhere4U (developed by Ahram Han), My Dialysis (developed by Benyamin Saadatifar), Kidney Love (developed by National Kidney foundation), and iCKD (developed by Dr Vivek Kumar). Of these apps, 3 focused on evaluating Transplant Hero, while the remaining investigated each of the other mentioned apps individually. The apps use various strategies to promote medication adherence, including reminders, gamification, patient education, and medication monitoring. A majority, 5 out of 9 mobile apps, had a statistically significant (P<.05) effect on medication adherence. There was strong evidence for a positive effect of interventions focusing on games and reminders combined with electronic medication tray monitoring and patient education. CONCLUSIONS: Mobile apps effectively improved medication adherence in patients with CKD, but low evidence and short intervention duration warrant caution. Future research should identify ideal features, provider costs, and user-friendly, secure apps.
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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 | 0,019 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».