Monitoring Risk Factors and Improving Adherence to Therapy in Patients With Chronic Kidney Disease (Smit-CKD Project): Pilot Observational Study.
CKD
SMIT-CKD
adherence
cardiac
cardiology
cardiovascular
chronic kidney disease
eHealth
health app
integrated system
kidney
mHealth
mobile app
mobile health
monitoring
renal
risk factor
therapy adherence
Journal
JMIR bioinformatics and biotechnology
ISSN: 2563-3570
Titre abrégé: JMIR Bioinform Biotechnol
Pays: Canada
ID NLM: 101769661
Informations de publication
Date de publication:
15 Nov 2022
15 Nov 2022
Historique:
received:
05
07
2022
accepted:
05
11
2022
revised:
26
10
2022
medline:
15
11
2022
pubmed:
15
11
2022
entrez:
27
6
2024
Statut:
epublish
Résumé
Chronic kidney disease is a major public health issue, with about 13% of the general adult population and 30% of the elderly affected. Patients in the last stage of this disease have an almost uniquely high risk of death and cardiovascular events, with reduced adherence to therapy representing an additional risk factor for cardiovascular morbidity and mortality. Considering the increased penetration of mobile phones, a mobile app could educate patients to autonomously monitor cardiorenal risk factors. With this background in mind, we developed an integrated system of a server and app with the aim of improving self-monitoring of cardiovascular and renal risk factors and adherence to therapy. The software infrastructure for both the Smit-CKD server and Smit-CKD app was developed using standard web-oriented development methodologies preferring open source tools when available. To make the Smit-CKD app suitable for Android and iOS, platforms that allow the development of a multiplatform app starting from a single source code were used. The integrated system was field tested with the help of 22 participants. User satisfaction and adherence to therapy were measured by questionnaires specifically designed for this study; regular use of the app was measured using the daily reports available on the platform. The Smit-CKD app allows the monitoring of cardiorenal risk factors, such as blood pressure, weight, and blood glucose. Collected data are transmitted in real time to the referring general practitioner. In addition, special reminders improve adherence to the medication regimen. Via the Smit-CKD server, general practitioners can monitor the clinical status of their patients and their adherence to therapy. During the test phase, 73% (16/22) of subjects entered all the required data regularly and sent feedback on drug intake. After 6 months of use, the percentage of regular intake of medications rose from 64% (14/22) to 82% (18/22). Analysis of the evaluation questionnaires showed that both the app and server components were well accepted by the users. Our study demonstrated that a simple mobile app, created to self-monitor modifiable cardiorenal risk factors and adherence to therapy, is well tolerated by patients affected by chronic kidney disease. Further studies are required to clarify if the use of this integrated system will have long-term effects on therapy adherence and if self-monitoring of risk factors will improve clinical outcomes in this population.
Sections du résumé
BACKGROUND
BACKGROUND
Chronic kidney disease is a major public health issue, with about 13% of the general adult population and 30% of the elderly affected. Patients in the last stage of this disease have an almost uniquely high risk of death and cardiovascular events, with reduced adherence to therapy representing an additional risk factor for cardiovascular morbidity and mortality. Considering the increased penetration of mobile phones, a mobile app could educate patients to autonomously monitor cardiorenal risk factors.
OBJECTIVE
OBJECTIVE
With this background in mind, we developed an integrated system of a server and app with the aim of improving self-monitoring of cardiovascular and renal risk factors and adherence to therapy.
METHODS
METHODS
The software infrastructure for both the Smit-CKD server and Smit-CKD app was developed using standard web-oriented development methodologies preferring open source tools when available. To make the Smit-CKD app suitable for Android and iOS, platforms that allow the development of a multiplatform app starting from a single source code were used. The integrated system was field tested with the help of 22 participants. User satisfaction and adherence to therapy were measured by questionnaires specifically designed for this study; regular use of the app was measured using the daily reports available on the platform.
RESULTS
RESULTS
The Smit-CKD app allows the monitoring of cardiorenal risk factors, such as blood pressure, weight, and blood glucose. Collected data are transmitted in real time to the referring general practitioner. In addition, special reminders improve adherence to the medication regimen. Via the Smit-CKD server, general practitioners can monitor the clinical status of their patients and their adherence to therapy. During the test phase, 73% (16/22) of subjects entered all the required data regularly and sent feedback on drug intake. After 6 months of use, the percentage of regular intake of medications rose from 64% (14/22) to 82% (18/22). Analysis of the evaluation questionnaires showed that both the app and server components were well accepted by the users.
CONCLUSIONS
CONCLUSIONS
Our study demonstrated that a simple mobile app, created to self-monitor modifiable cardiorenal risk factors and adherence to therapy, is well tolerated by patients affected by chronic kidney disease. Further studies are required to clarify if the use of this integrated system will have long-term effects on therapy adherence and if self-monitoring of risk factors will improve clinical outcomes in this population.
Identifiants
pubmed: 38935948
pii: v3i1e36766
doi: 10.2196/36766
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e36766Informations de copyright
©Antonio Vilasi, Vincenzo Antonio Panuccio, Salvatore Morante, Antonino Villa, Maria Carmela Versace, Sabrina Mezzatesta, Sergio Mercuri, Rosalinda Inguanta, Giuseppe Aiello, Demetrio Cutrupi, Rossella Puglisi, Salvatore Capria, Maurizio Li Vigni, Giovanni Tripepi, Claudia Torino. Originally published in JMIR Bioinformatics and Biotechnology (https://bioinform.jmir.org), 15.11.2022.