Clario Partners with Oxford to Advance Parkinson's Disease Research Using Opal Wearable Sensors
Key Insights
Clario (search) has enrolled the first participant in its collaboration with the University of Oxford's NeuroMetrology Lab (search) to enhance Parkinson's disease (search) research using the Opal wearable sensor system (search).
The Opal sensors can predict fall risk in Parkinson's patients with up to 92% accuracy two years in advance and 78% accuracy five years in advance through a 3-minute in-office assessment.
The partnership aims to advance clinical trial endpoints by improving identification of disease progression and movement-based endpoints using digital measures of gait (search) and balance.
Clario (search), a leading provider of endpoint data solutions for clinical trials, has enrolled the first participant in its collaboration with the University of Oxford's NeuroMetrology Lab (search) to advance Parkinson's disease (search) research using the Opal® wearable sensor system. The partnership, led by Professor Chrystalina Antoniades, focuses on enabling more precise measurement of motor symptoms (search) and disease progression in Parkinson's disease patients.
Revolutionary Predictive Capabilities
The collaboration has demonstrated remarkable predictive capabilities for fall risk assessment in Parkinson's patients. Findings from Professor Antoniades' team show that a 3-minute in-office assessment can predict 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 represent a significant advancement in how Parkinson's disease (search) may be managed and provide greater opportunity to evaluate therapeutic efficacy in clinical studies.
Advanced Movement Analysis Technology
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 (search), balance, and mobility with a level of precision that surpasses traditional assessment methods. The NeuroMetrology Lab (search)'s 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 Parkinson's disease (search) patients.
Digital endpoints of gait (search) and balance combined with machine learning enable earlier detection of motor symptom progression, addressing a crucial need for regulatory-approved digital movement endpoints in clinical trials to assess Parkinson's therapies accurately.
Clinical Trial Enhancement
"Our collaboration with Professor Antoniades and her team is an exciting step forward in Parkinson's research," said Ellen Street, Executive Vice President and General Manager, Digital Physiology at Clario (search). "The team's research combined with the unique capabilities of the Opal device can improve monitoring of disease progression and enable a greater ability to understand the effect of a drug candidate in Parkinson's patients. It perfectly embodies Clario's mission to transform lives by unlocking better evidence."
Professor Antoniades emphasized the importance of this collaboration for drug development: "This collaboration will refine predictive models for fall risk and disease progression, improving patient outcomes. There is a crucial need for regulatory-approved digital movement endpoints in clinical trials to assess Parkinson's therapies accurately. Integrating validated, objective measures into drug development will enable precise evaluation and accelerate optimized treatments for patients."
Remote Monitoring Capabilities
This phase of the collaboration will determine the usability and effectiveness of Opal data collected in remote settings via prescribed tasks in the home and real-world passive mobility monitoring, powered by Mobilise-D Digital Mobility Outcomes that Clario (search) now offers. This approach aims to advance clinical trial endpoints by improving identification of disease progression and movement-based endpoints outside traditional clinical settings.
The partnership represents a significant step toward predictive and preventive healthcare in Parkinson's disease (search) management, potentially transforming how therapeutic interventions are evaluated and optimized for patients.