Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

A partnership with Clario focuses on advancing the use of wearable sensors to enable more precise measurement of motor symptoms and disease progression in Parkinson's.

© studioroman via canva pro

The first participant has been enrolled in a new study in the NeuroMetrology Lab, led by Professor Chrystalina Antoniades, and supported by Clario, a leading provider of endpoint data solutions for clinical trials

The NeuroMetrology team is using Clario’s Opal® wearable sensor system to enhance Parkinson’s disease (PD) research by enabling more precise measurement of motor symptoms and disease progression. The study aims to determine the usability and effectiveness of this wearable technology to follow people with Parkinson’s (PwP) remotely while they go about their daily routine.

The Opal® wearable sensor collects detailed, objective movement data through single- or multi-sensor setups, enabling the detection of subtle but meaningful changes in gait, balance, and mobility with a level of precision that surpasses traditional assessment methods. The NeuroMetrology Lab’s groundbreaking work, powered by Opal’s cutting-edge movement analysis capabilities, improves monitoring of disease progression and predicts adverse events such as falls, which represent a significant cause of disability and reduced quality of life in PD patients.

Findings from the team demonstrate the game-changing potential of movement analysis in PD research and pave the way for predictive and preventive healthcare. In a recent study, a three-minute in-office assessment predicted fall risk for Parkinson’s patients with 84–92% accuracy up to two years in advance and 78% accuracy up to five years in advance. These predictive capabilities may change how the disease is managed and provide greater opportunity to evaluate therapeutic efficacy in clinical studies.

 

Read the full story on the Nuffield Department of Clinical Neurosciences website.