Wrist accelerometry and machine learning sensitively capture disease progression in prodromal Parkinson’s disease
Abstract 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 compos...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
|
| Series: | npj Parkinson's Disease |
| Online Access: | https://doi.org/10.1038/s41531-025-01034-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract 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. |
|---|---|
| ISSN: | 2373-8057 |