Changing Rhythms in Parkinson’s Disease: Development of Motor and Non-motor Markers of Progression Based on Continuous Daily Monitoring

Study Rationale: Although new treatments are being developed to slow the progression of Parkinson’s disease (PD), assessing their effectiveness remains a challenge. Standard evaluations, conducted in the clinic, provide periodic “snapshots” of an individual’s condition. Now, with the help of wearable sensors, we can monitor the activity of people with PD at home, as they go about their daily routine. In this study, we aim to develop the methods needed to extract pertinent information from the large amounts of data collected using a state-of-the-art smartwatch. The resulting “digital biomarkers” will allow physicians to track individuals’ disease progression in real time.

Hypothesis: By collecting continuous and objective measurements of the day-to-day health and activity of people with PD, we expect that wearable sensors will be better able to detect relevant changes in both motor symptoms and non-motor symptoms.

Study Design: Two studies — the Personalized Parkinson Project and the Parkinson Progression Marker Initiative — have monitored a large group of people with early-stage PD for at least two years using a state-of-the-art smartwatch, which collects measurement 23 hours per day. Using these unique datasets, we will develop digital biomarkers to measure disease progression over time. We will focus on three types of measurements important for people in the early stages of PD: tremor, arm swing during gait, and autonomic nervous system function (through measures of heart rate variability). We will then develop open-source software for use in future clinical trials.

Impact on Diagnosis/Treatment of Parkinson’s disease: The emergence of new technologies and more sophisticated analyses is changing the way we monitor PD. This project aims to produce tools for assessing how new treatments impact long-term progression in people with PD, which will make clinical trials more efficient and help to identify individuals who respond to therapeutics.

Next Steps for Development: Before these new tools can be used in clinical trials, we will seek independent studies to verify our findings, and we will make our open-source toolbox available to the broader community for continued development and refinement.