Remote Monitoring Using Wearable Effective in Tracking Parkinson’s Disease

A study published in npj Digital Medicine found that a remote patient monitoring (RPM) method, called the Parkinson’s Disease Virtual Motor Exam (PD-VME), that involves the use of a wearable smartwatch, effectively tracks Parkinson’s Disease (PD) patients and their motor functions.

The study consisted of 520 patients who had early-stage Parkinson’s Disease. During the study period, participants wore a smartwatch for up to 23 hours a day and for a median of 390 days, which collected raw data from different sensors, including inertial measurement unit and skin conductance sensors.

Participants performed unsupervised motor tasks in-clinic once a week and remotely twice a week for one year. They wore the smartwatch for more than 21 hours a day.

After researchers determined that the completion rate of per-protocol remote assessments was 59 percent, they assessed the relationship between in-clinic MDS-UPDRS Part III ratings and ratings for rest tremor, bradykinesia, and gait.

MDS-UPDRS is a scale that evaluates factors associated with PD, such as non-motor and motor experiences. 

Regarding rest tremors, the measurement that had the most prominent correlation to in-clinic MDS-UPDRS ratings was lateral tremor acceleration measurement.

Researchers also observed a relatively high in-clinic MDS-UPDRS rating correlation with the arm twist amplitude measure.

Further, regarding gait impairment, researchers selected arm swing acceleration as the measurement to assess. Arm swing acceleration displayed a moderate-to-strong MDS-UPDRS rating.

Researchers determined that those with PD received effective treatment from PD-VME only if used regularly. They also observed that the PD-VME system provided data that matched observations reported by clinicians.

But "further research is needed to more firmly establish the ability of these and other measures to serve as progression biomarkers," they concluded.

 Several efforts have been made to apply technology to PD management in recent years.

For example, a November 2020 study from Purdue University described how artificial intelligence (AI) could review the speech of PD patients. The study team used a grant to create a telehealth and AI platform that provided speech treatment through the SpeechVive device, a wearable that monitors the speaking patterns of patients with the disease.

Another AI tool created in March 2021 by University of Florida researchers aims to monitor PD and other conditions. The tool was tested using MRI images from 315 patients. Researchers used a noninvasive biomarker to distinguish between PD, Parkinsonian variant, and progressive supranuclear palsy, as well as the motor and non-motor features that the variant may share.