Open dataset of simultaneous bilateral wrist accelerometry and patient symptom diaries from 66 Parkinson's patients (≈394 total days) enabling development and validation of wearable-based motor symptom monitoring.

Provides a real-world, labelled dataset to develop objective, continuous digital biomarkers and remote monitoring endpoints that can improve assessment of motor fluctuations and support clinical trial measurements, though it does not address underlying disease mechanisms or novel therapeutics.

Treatment adjustments in Parkinson's Disease (PD) are often based on clinical evaluations at single time points which are insufficient to adequately assess real-life motor fluctuations. Patient-written symptom diaries on the other hand are highly subjective and require well-educated and adherent patients to provide reliable results. Wearable accelerometry might provide a reliable, objective, and continuous diagnostic method to assess PD motor symptoms & fluctuations. However, large datasets of simultaneous sensor data and symptom diaries are needed for such method development and validation. We here provide a well-described, open-science dataset of simultaneous, bilateral, wrist-worn accelerometry and symptom diary data from 66 participants (41 male, 25 female) with PD. On average, participants provided data for 6.0 consecutive days resulting in a total of 393.8 days for the dataset as a whole. Symptom diaries include data on kinetic state, tremor, freezing of gait, falls, and PD-related medication intake. Further demographic information is also provided. This dataset will support the development and validation of accelerometry-based approaches to assessing motor symptoms and fluctuations in PD.