腕部加速度计和机器学习灵敏地捕捉前驱帕金森病的疾病进展。

IF 6.7 1区医学 Q1 NEUROSCIENCES

NPJ Parkinson's Disease Pub Date : 2025-06-17 DOI:10.1038/s41531-025-01034-8

Anoopum S Gupta,Siddharth Patel

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引用次数: 0

摘要

敏感性运动测量需要支持帕金森病（PD）的试验。在家中连续收集269名PD患者（106名前驱PD患者）的腕关节传感器数据。PD和前驱PD的亚运动更小、更慢、变化更少。机器学习复合测量比MDS-UPDRS第三部分运动评分更敏感地捕获前驱PD的疾病进展。基于可穿戴传感器的测量方法可能在即将到来的临床试验中有用。

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Wrist accelerometry and machine learning sensitively capture disease progression in prodromal Parkinson's disease.

Sensitive motor measures are needed to support trials in Parkinson's disease (PD). Wrist sensor data was collected continuously at home from 269 individuals with PD (106 with prodromal PD). Submovements were smaller, slower, and less variable in PD and prodromal PD. A machine-learned composite measure captured disease progression in prodromal PD more sensitively than the MDS-UPDRS Part III motor score. Wearable sensor-based measures may be useful in upcoming clinical trials.

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来源期刊

NPJ Parkinson's Disease Medicine-Neurology (clinical)

CiteScore

9.80

自引率

5.70%

发文量

156

审稿时长

11 weeks

期刊介绍： npj Parkinson's Disease is a comprehensive open access journal that covers a wide range of research areas related to Parkinson's disease. It publishes original studies in basic science, translational research, and clinical investigations. The journal is dedicated to advancing our understanding of Parkinson's disease by exploring various aspects such as anatomy, etiology, genetics, cellular and molecular physiology, neurophysiology, epidemiology, and therapeutic development. By providing free and immediate access to the scientific and Parkinson's disease community, npj Parkinson's Disease promotes collaboration and knowledge sharing among researchers and healthcare professionals.