Suppr超能文献

用于帕金森病随访的开源数据管理系统。

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

作者信息

Folador João Paulo, Vieira Marcus Fraga, Pereira Adriano Alves, Andrade Adriano de Oliveira

机构信息

Centre for Innovation and Technology Assessment in Health, Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil.

Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Goiânia, Goiás, Brazil.

出版信息

PeerJ Comput Sci. 2021 Feb 17;7:e396. doi: 10.7717/peerj-cs.396. eCollection 2021.

Abstract

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 ( < 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.

摘要

背景

帕金森病(PD)是一种中枢神经系统的神经退行性疾病,会导致运动和非运动功能障碍。该疾病影响全球60岁以上人群的1%,且仍然无法治愈。对帕金森病的了解和监测对于为患者提供更好的生活条件至关重要。因此,该疾病的诊断检查和监测可以从特定患者那里生成大量数据。本研究提出开发一个集成系统并对其可用性进行评估,该系统可用于临床和研究环境,以管理从帕金森病患者收集的生物医学数据。

方法

按照模型-视图-控制器(MVC)标准设计了一个名为Sistema Integrado de Dados Biomédicos(SIDABI)(综合生物医学数据系统)的系统。创建了一种模块化架构,其中所有其他模块都连接到一个中央安全模块。三十六名审查员通过系统可用性量表(SUS)评估了系统的可用性。审查员之间的一致性通过肯德尔系数进行测量,显著性水平为1%。

结果

使用模块化和响应式方法实现了基于网络的免费开源系统,以便在多个平台上适配系统功能。SUS平均得分为82.99±13.97分。通过肯德尔系数测量,总体一致性为70.2%(<0.001)。

结论

根据SUS得分,所开发的系统具有良好的可用性。这里提出的系统可以帮助研究人员组织和共享信息,避免数据丢失和碎片化。此外,它可以帮助对帕金森病患者进行随访,培训参与该疾病治疗的专业人员,以及开展旨在发现数据中隐藏相关性的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3914/7959639/68945110ce60/peerj-cs-07-396-g001.jpg

相似文献

1
Open-source data management system for Parkinson's disease follow-up.
PeerJ Comput Sci. 2021 Feb 17;7:e396. doi: 10.7717/peerj-cs.396. eCollection 2021.
7
Enabling breakthroughs in Parkinson's disease with wearable technologies and big data analytics.
Mhealth. 2016 May 12;2:20. doi: 10.21037/mhealth.2016.04.02. eCollection 2016.
9
A mobile-assisted voice condition analysis system for Parkinson's disease: assessment of usability conditions.
Biomed Eng Online. 2021 Nov 21;20(1):114. doi: 10.1186/s12938-021-00951-y.

引用本文的文献

2
: the architecture and organization of a serious game to evaluate motor signs in Parkinson's disease.
PeerJ Comput Sci. 2023 Mar 15;9:e1267. doi: 10.7717/peerj-cs.1267. eCollection 2023.

本文引用的文献

1
Big data in digital healthcare: lessons learnt and recommendations for general practice.
Heredity (Edinb). 2020 Apr;124(4):525-534. doi: 10.1038/s41437-020-0303-2. Epub 2020 Mar 5.
2
Design and development of a gait training system for Parkinson's disease.
PLoS One. 2018 Nov 12;13(11):e0207136. doi: 10.1371/journal.pone.0207136. eCollection 2018.
4
Developing a Tool for Remote Digital Assessment of Parkinson's Disease.
Mov Disord Clin Pract. 2015 Oct 20;3(1):59-64. doi: 10.1002/mdc3.12239. eCollection 2016 Jan-Feb.
5
Automated Deep Brain Stimulation Programming for Tremor.
IEEE Trans Neural Syst Rehabil Eng. 2018 Aug;26(8):1618-1625. doi: 10.1109/TNSRE.2018.2852222. Epub 2018 Jul 2.
6
Average annual cost of Parkinson's disease in São Paulo, Brazil, with a focus on disease-related motor symptoms.
Clin Interv Aging. 2017 Dec 14;12:2095-2108. doi: 10.2147/CIA.S151919. eCollection 2017.
7
Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:655-658. doi: 10.1109/EMBC.2016.7590787.
10
A reliability assessment software using Kinect to complement the clinical evaluation of Parkinson's disease.
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6860-3. doi: 10.1109/EMBC.2015.7319969.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验