30 October 2023 | Monday | News
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Through a comprehensive study leveraging Clario's wearable sensors and machine learning, researchers were able to track the motor symptom progression in individuals with Parkinson's Disease (PD) better than the conventionally used clinical rating scales. Importantly, they found that the wearable technology detected disease progression in a significantly shorter timeframe than the traditional method.
Clario's scientific and technical expertise empowered the reliable collection of precise digital endpoints of movement enabling earlier detection of disease progression. This ultimately should accelerate the development of new medicines for PD, and potentially allow for earlier intervention for patients afflicted with this debilitating condition.
The University of Oxford study, led by Chrystalina Antoniades, Associate Professor of Neuroscience, Clinical Neurology, is a part of the Oxford Quantification in Parkinsonism (OxQUIP) project, in which Clario's Precision Motion Opal wearable sensors have been deployed to capture motor impairment in both PD and Progressive Supranuclear Palsy (PSP).
"We are honored to support Professor Antoniades with her pivotal research and are very excited about the positive implications of these results on the Parkinsonism field," said Kristen Sowalsky, PhD, DC, VP of Medical & Scientific Affairs. "This breakthrough demonstrates the essential benefit of using wearable technology and machine learning algorithms to track Parkinson's Disease progression more accurately and assess the efficacy of early therapeutic intervention. The potential impact of these findings for neuroscience clinical trials is outstanding."
"Time is crucial in clinical trials," said Professor Antoniades, Associate Professor of Neuroscience, Clinical Neurology at the University of Oxford. "If we can tell whether a treatment is working earlier, we can speed up its translation. It is also important to be able to tell rapidly which things are not working, so we can divert resources to more promising targets."