AI-Driven Analysis of Wrist-Worn Sensor Data for Monitoring Individual Treatment Response and Optimizing Levodopa Dosing in Parkinson’s Disease

Parkinson's Disease is a progressive neurodegenerative disorder marked by motor fluctuations in later disease stages that complicate treatment with levodopa. Traditional approaches to dosing often fail to capture the complex and dynamic nature of these fluctuations. In this study, we present the PD9™ algorithm, a novel approach to continuous motor state monitoring using data from a wrist-worn inertial measurement unit sensor. The algorithm provides minute-by-minute assessments of motor state severity on a unified scale quantifying bradykinesia, dyskinesia, and ON states. Data collected from 67 patients over 55,482 min were analyzed to assess levodopa response cycles. Across 218 identified levodopa cycles, the algorithm revealed reproducible patterns of symptom development based on the motor state at the time of levodopa administration. In particular, levodopa doses administered during non-ideal motor states (e.g., during dyskinesia) highlighted the limitations of fixed, empirically determined dosing regimens and underscore the need for individualized therapy, based on motor state. These findings demonstrate how AI-enabled continuous monitoring could help realize a more personalized treatment of Parkinson's disease and improve patient outcomes.