Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson’s Disease

Research overview

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Prevalence estimates of Parkinson’s disease (PD) – the fastest growing neurodegenerative disease – are 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 gaps between patient symptoms and diagnosis. Early-stage PD (n=82) and age-matched comparison (n=50) participants 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 contexts. These findings provide robust support for further exploring consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

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