New Artificial-Intelligence-based Tools for Monitoring Parkinson's Disease using Wearable Devices

New results from the PPMI Data Modeling Core reveal the power of digital health technologies to remotely detect motor symptoms of Parkinson's Disease Brain research and advocacy non-profit Cohen Veterans Bioscience (CVB) announces the publication of results from its digital health research program analyzing data from the Parkinson's Progression Markers Initiative (PPMI) to detect the presence or absence of Parkinson's disease (PD). PD is one of the most common and fastest growing neurological disorders that results in a progressive decline in both motor and non-motor (e.g., cognition and mood) symptoms. Since there are currently no objective biomarkers in PD, diagnosis is complicated and typically involves clinically administered subjective questionnaires to assess severity of symptoms, potentially leading to symptoms being undetected or misclassified. Sensor technology has shown promise in aiding in detection and classification of diseases like PD but have very limited validation in real-world settings. As part of the Parkinson's Progression Markers Initiative (PPMI) study cohort, investigators collected data passively and continuously using the Verily Study Watch in a subject's natural environment.