High Frequency Remote Monitoring of Parkinson's Disease via Smartphone: Platform Overview and Medication Response Detection

TL;DR

This study introduces HopkinsPD, a scalable smartphone platform for high-frequency remote monitoring of Parkinson's disease symptoms, demonstrating its ability to detect medication response with promising accuracy from large-scale data collection.

Contribution

We developed and validated a novel smartphone-based platform for remote PD symptom monitoring, capable of large-scale data collection and medication response detection.

Findings

Collected over 46,000 hours of passive data and 8,000 test instances.

Achieved 71% accuracy in distinguishing medication response from baseline.

Demonstrated feasibility of remote, scalable PD monitoring using smartphones.

Abstract

Objective: The aim of this study is to develop a smartphone-based high-frequency remote monitoring platform, assess its feasibility for remote monitoring of symptoms in Parkinson's disease, and demonstrate the value of data collected using the platform by detecting dopaminergic medication response. Methods: We have developed HopkinsPD, a novel smartphone-based monitoring platform, which measures symptoms actively (i.e. data are collected when a suite of tests is initiated by the individual at specific times during the day), and passively (i.e. data are collected continuously in the background). After data collection, we extract features to assess measures of five key behaviors related to PD symptoms -- voice, balance, gait, dexterity, and reaction time. A random forest classifier is used to discriminate measurements taken after a dose of medication (treatment) versus before the medication dose (baseline). Results: A worldwide study for remote PD monitoring was established using HopkinsPD in July, 2014. This study used entirely remote, online recruitment and installation, demonstrating highly cost-effective scalability. In six months, 226 individuals (121 PD and 105 controls) contributed over 46,000 hours of passive monitoring data and approximately 8,000 instances of structured tests of voice, balance, gait, reaction, and dexterity. To the best of our knowledge, this is the first study to have collected data at such a scale for remote PD monitoring. Moreover, we demonstrate the initial ability to discriminate treatment from baseline with 71.0(+-0.4)% accuracy, which suggests medication response can be monitored remotely via smartphone-based measures.