Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson’s Disease
Shane Johnson, Michalis Kantartjis, Joan Severson, Ray Dorsey, Jamie Adams, Tairmae Kangarloo, Melissa Kostrzebski, Allen Best, Michael Merickel, Dan Amato, Brian Severson, Sean Jezewski, Steve Polyak, A.P. Keil, Joshua D. Cosman, David E. Anderson
Abstract
Prevalence estimates of Parkinson’s disease (PD)—the fastest-growing neurodegenerative disease—are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.