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Objective: The aim of this study is to develop a smartphone-basedhigh-frequency remote monitoring platform, assess its feasibility for remotemonitoring of symptoms in Parkinson's disease, and demonstrate the value ofdata collected using the platform by detecting dopaminergic medicationresponse. Methods: We have developed HopkinsPD, a novel smartphone-basedmonitoring platform, which measures symptoms actively (i.e. data are collectedwhen a suite of tests is initiated by the individual at specific times duringthe day), and passively (i.e. data are collected continuously in thebackground). After data collection, we extract features to assess measures offive key behaviors related to PD symptoms -- voice, balance, gait, dexterity,and reaction time. A random forest classifier is used to discriminatemeasurements taken after a dose of medication (treatment) versus before themedication dose (baseline). Results: A worldwide study for remote PD monitoringwas established using HopkinsPD in July, 2014. This study used entirely remote,online recruitment and installation, demonstrating highly cost-effectivescalability. In six months, 226 individuals (121 PD and 105 controls)contributed over 46,000 hours of passive monitoring data and approximately8,000 instances of structured tests of voice, balance, gait, reaction, anddexterity. To the best of our knowledge, this is the first study to havecollected data at such a scale for remote PD monitoring. Moreover, wedemonstrate the initial ability to discriminate treatment from baseline with71.0(+-0.4)% accuracy, which suggests medication response can be monitoredremotely via smartphone-based measures.

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