Changing rhythms in Parkinson’s disease
Development of motor and non-motor digital progression biomarkers based on continuous, real-life monitoring
Main applicant: Luc Evers
Affiliation(s): Radboud university medical center, Nijmegen (NL)
Abstract: Although new treatments are being developed to slow the progression of Parkinson’s disease, assessing their effectiveness remains a challenge. Evaluations conducted in the clinic can only provide a periodic “snapshot” of an individual’s condition. Unobtrusive wearable sensors – such as the Study Watch used in the Personalized Parkinson Project – now allow us to capture objectively and continuously how patients function at home, as they go about their daily routines. The goal of this project is to develop the algorithms needed to extract reliable insights from the raw sensor data from the Study Watch. By combining the unique longitudinal data from the Personalized Parkinson Project with data from smaller reference datasets, we are developing AI models to study tremor, gait and heart-rate variability in daily life, and assess their long-term progression. The resulting “digital biomarkers” could pave the way for more efficient clinical trials and more personalized treatment for people with Parkinson disease.