Objective: Smartphone-based assessments and sensors have the potential to enable remote, passive monitoring of gait and mobility in early-stage Parkinson’s disease (PD) patients. Such sensor data can provide novel insights into how PD impacts patients’ daily lives. Here we present data collected using such an approach in a clinical trial setting.

Background: Continuous, passive monitoring of PD symptoms using smartphone sensors can complement and extend physician-administered rating scales by providing previously inaccessible insights about patient behavior. Showing correlations of sensor data with existing clinical scales in a trial setting is a prerequisite for leveraging such data in clinical practice and drug development.

Methods: PD patients (n=44) of a Phase I Multiple Ascending Dose clinical trial of PRX002/RG7935 performed smartphone-based assessments for 24 weeks. Assessments were performed in a home-based setting. We also performed a control study with 35 age and gender-matched healthy control participants (HC) who performed the assessments for 6 weeks. For “passive monitoring”, subjects carried the smartphone in their pocket or waist pack throughout the day as part of their daily routine. Sensor data were recorded continuously, and included movement and location data. Data were categorized into different types of human activities employing deep learning. Subsequently clinically meaningful features were extracted. These were then compared to the study’s clinical data, including the Movement Disorder Society – Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) rating scale.

Results: Patient acceptance of the technology was strong: over 25,000 hours of passive monitoring data were collected. Based on these data, we present first evidence that mobility patterns measured by passive monitoring correlate with disease severity as measured by clinical gold standards.

Conclusions: It is feasible to measure gait and mobility in early-stage PD patients using smartphone-based passive monitoring. Sensor data collected during passive monitoring provide previously inaccessible, ecologically valid insights into patient’s daily behavior and functioning. This holds great promise for use in clinical practice and supporting drug development.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/successful-passive-monitoring-of-early-stage-parkinsons-disease-patient-mobility-in-phase-i-rg7935prx002-clinical-trial-with-smartphone-sensors/