Open-source data management system for Parkinson’s disease follow-up

• Main Authors: João Paulo Folador, Marcus Fraga Vieira, Adriano Alves Pereira, Adriano de Oliveira Andrade
• Format: Article
• Language: English
• Published: PeerJ Inc. 2021-02-01
• Series: PeerJ Computer Science
• Subjects: Data management; System usability scale; Parkinson’s disease
• Online Access: https://peerj.com/articles/cs-396.pdf

Background Parkinson’s disease (PD) is a neurodegenerative condition of the central nervous system that causes motor and non-motor dysfunctions. The disease affects 1% of the world population over 60 years and remains cureless. Knowledge and monitoring of PD are essential to provide better living conditions for patients. Thus, diagnostic exams and monitoring of the disease can generate a large amount of data from a given patient. This study proposes the development and usability evaluation of an integrated system, which can be used in clinical and research settings to manage biomedical data collected from PD patients. Methods A system, so-called Sistema Integrado de Dados Biomédicos (SIDABI) (Integrated Biomedical Data System), was designed following the model-view-controller (MVC) standard. A modularized architecture was created in which all the other modules are connected to a central security module. Thirty-six examiners evaluated the system usability through the System Usability Scale (SUS). The agreement between examiners was measured by Kendall’s coefficient with a significance level of 1%. Results The free and open-source web-based system was implemented using modularized and responsive methods to adapt the system features on multiple platforms. The mean SUS score was 82.99 ± 13.97 points. The overall agreement was 70.2%, as measured by Kendall’s coefficient (p < 0.001). Conclusion According to the SUS scores, the developed system has good usability. The system proposed here can help researchers to organize and share information, avoiding data loss and fragmentation. Furthermore, it can help in the follow-up of PD patients, in the training of professionals involved in the treatment of the disorder, and in studies that aim to find hidden correlations in data.