Sigcha Luis, Polvorinos-Fernández Carlos, Costa Nélson, Costa Susana, Arezes Pedro, Gago Miguel, Lee Chaiwoo, López Juan Manuel, de Arcas Guillermo, Pavón Ignacio
Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain.
ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal.
Front Neurol. 2023 Dec 7;14:1326640. doi: 10.3389/fneur.2023.1326640. eCollection 2023.
Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms.
This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation. To implement this framework, an m-health tool named Monipar was developed that uses off-the-shelf smart devices.
An experimental protocol was conducted with the participation of 21 early-stage PD patients and 7 healthy controls who used Monipar installed in off-the-shelf smartwatches and smartphones. Movement data collected using the built-in acceleration sensors were used to extract relevant digital indicators (features). These indicators were then compared with clinical evaluations performed using the MDS-UPDRS scale.
The results showed moderate to strong (significant) correlations between the clinical evaluations (MDS-UPDRS scale) and features extracted from the movement data used to assess resting tremor (i.e., the standard deviation of the time series: = 0.772, < 0.001) and data from the pronation and supination movements (i.e., power in the band of 1-4 Hz: = -0.662, < 0.001).
These results suggest that the proposed framework could be used as a complementary tool for the evaluation of motor symptoms in early-stage PD patients, providing a feasible and cost-effective solution for remote and ambulatory monitoring of specific motor symptoms such as resting tremor or bradykinesia.
帕金森病（PD）是一种常见的神经退行性疾病，其特征通常为运动障碍。可穿戴设备和智能设备等移动健康（m-health）技术的发展，为实施能够支持早期诊断和症状客观量化等任务的临床工具提供了契机。
本研究基于标准化运动（如临床评估期间所进行的运动）的表现，评估一个用于监测帕金森病患者运动症状的框架。为实施该框架，开发了一款名为Monipar的移动健康工具，该工具使用现成的智能设备。
进行了一项实验方案，21名早期帕金森病患者和7名健康对照参与其中，他们使用安装在现成智能手表和智能手机中的Monipar。使用内置加速度传感器收集的运动数据用于提取相关数字指标（特征）。然后将这些指标与使用MDS-UPDRS量表进行的临床评估进行比较。
结果显示，临床评估（MDS-UPDRS量表）与用于评估静止性震颤的运动数据中提取的特征（即时间序列的标准差：=0.772，<0.001）以及旋前和旋后运动数据（即1-4Hz频段的功率：=-0.662，<0.001）之间存在中度至强（显著）相关性。
这些结果表明，所提出的框架可作为评估早期帕金森病患者运动症状的辅助工具，为远程和动态监测特定运动症状（如静止性震颤或运动迟缓）提供了一种可行且具有成本效益的解决方案。
