Assessment scales for motor symptoms in Parkinson’s disease (PD) lack the sensitivity and resolution to monitor symptoms over time. Wearable sensors in people with PD have shown potential to assess motor symptoms. The DIGI.PARK study explores the use of consumer- and research-grade wearables such as Fitbit Sense (FS), Oura ring (OR) and Empatica E4 (EM) to track behavioral patterns and symptoms of PD over time.

The DIGI.PARK pilot study (12.2021 to 12.2022) included N = 30 participants living in Bergen, Norway (N=15 persons with PD and N=15 controls). Outcome measures: self-reported diary of symptoms and behavior combined with data streams from three wearable devices (FS, OR, EM). Data was collected over 2 weeks: continuously by devices, and diary data every second day consisting of activities, sleep, medication timing (PD) and symptom occurrence (PD). The device data were segmented into 24-hour epochs. Heart rate (HR), heart rate variability (HRV), acceleration, blood volume pulse (BVP), inter-beat interval (IBI), electrodermal activity, metabolic equivalent of task (MET) and hypnogram were visualized as time series. The resulting graphs were annotated with the reported diary data and a manual checking procedure was applied to determine the correlation between sensor outputs and the logged instances of activity, sleep and symptoms.

Self-reported behavior was discernable in the measurements of HR, EDA, BVP, HRV, acceleration, MET and hypnogram. We found considerable differences in device outputs regarding data type, data size, resolution, and periods of active measurements. Tremor symptoms were observable in the raw data provided by EM when worn on the affected hand. Behavioral patterns such as sleep, waking and physical activities were illustrated using aggregated data.

Sensor congruence with diary data support their usefulness for long term monitoring of behavioral patterns and symptoms in PD. For PD research, output from consumer- and research-grade devices have both shown usefulness. The choice of device should be tailored to the purpose and be mindful of the specific strengths and weaknesses of different device types. Aggregated data allow for monitoring behavioral patterns over time, whereas raw data provided the resolution to discern symptoms.