Virtual exam for Parkinson’s enables frequent & reliable remote measurements of motor function
VERILY WATCH TRIAL
Abstract
Sensor-based remote monitoring could help better track Parkinson’s disease (PD) progression, and measure patients’ response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable, and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. Participants with early-stage PD (N = 388, 64% men, average age 63) wore a smartwatch for a median of 390 days. Participants performed unsupervised motor tasks both in-clinic (once) and remotely (twice weekly for one year). Dropout rate was 5.4%. Median wear-time was 21.1 h/day, and 59% of per-protocol remote assessments were completed. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (⍴ = 0.70), bradykinesia (⍴ = −0.62), and gait (⍴ = −0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC = 0.75–0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen’s d = 0.19–0.54). Of note, in-clinic assessments often did not reflect the patients’ typical status at home. This demonstrates the feasibility of smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements provide a real-life distribution of disease severity, as it fluctuates longitudinally. Sensitivity to medication-induced change and improved reliability imply that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic interventions or disease progression.
Introduction
Parkinson’s disease (PD) affects 6 million people worldwide as of 2018—a number that is projected to grow to 12 million by 20401. Treatments are being developed to slow down or even halt the progression of PD2,3. However, currently used endpoints (e.g., the MDS-UPDRS) exhibit high within-subject variability, and low test-retest reliability, which leads to inefficient clinical trials, and risks potentially missing relevant effects4. Compounding this challenge, clinic-based physical exams provide only a snapshot of PD signs, and may not adequately reflect a patient’s functioning at home4,5. Additionally, many people live far from major medical centers6, so access to clinical trials of new therapeutics becomes restricted to a limited portion of the Parkinson population7,8. These challenges have motivated the search for digital endpoints using wearable sensors, which allow for objective, frequent, and ecologically valid measurements of motor functioning in the patient’s home environment. Sensor-based remote monitoring could also help increase representation for groups whose data have historically not been included in clinical trials9,10. Before such measurements can be used as endpoints in clinical trials to quantify disease progression, a careful evaluation of the clinical validity, reliability, and sensitivity to change is required11,12. A substantial volume of research has demonstrated the feasibility of using sensors placed on various parts of the body to quantify motor signs of PD13. Results suggest that features extracted by digital signal processing can be correlated with clinical outcomes of interest, at least when tests are delivered in a controlled setting and assessments are supervised by a clinician14,15,16,17,18,19,20,21,22. Active assessments measure patients’ maximum capacity, and can be complementary to passive monitoring, which measures the expression of signs in real life. Though some studies have probed the feasibility of using wearable sensors or smartphones for remote, self-guided active assessments, long-term engagement - which is critical to study disease progression - has been an important challenge23,24,25. Studies focusing on passive monitoring of PD motor signs have generally not been able to capture a person’s intent to move, which is particularly relevant for signs of bradykinesia26. Moreover, most of the existing work has focused on comparing sensor-based measures to clinical ratings, with limited work systematically measuring the ability of the remote measures to detect the effects of dopaminergic medication. Finally, test-retest reliability and sensitivity to clinically meaningful change have rarely been reported, and generally not on a large scale. The smartwatch-based Parkinson’s Disease Virtual Motor Exam (PD-VME) can be deployed to remotely measure the severity of tremor, bradykinesia, and gait impairment, via a self-guided active assessment27. Here, we evaluate the feasibility of use and quality of data collected by the system, and report on the reliability, validity, and sensitivity to change of a set of digital measures derived from the PD-VME during a multi-year deployment in the Personalized Parkinson Project (PPP)27.