Remote Assessment, in Real-World Setting, of Tremor Severity in Parkinson's Disease Patients Using Smartphone Inertial Sensors

Current clinimetrics assessment of Parkinson's disease (PD) is insensitive, episodic, subjective, and provider-centered. Ubiquitous technologies such as smartphones promise to fundamentally change PD assessments. To enable frequent remote assessment of PD tremor severity, here we present a 39-month smartphone research study in a real-world setting without supervision. More than 15,000 consented participants used the smartphone application, mPower, to perform self-administered active tasks. In the scope of this abstract, we developed an objective smartphone measure of PD tremor severity called mPower Tremor Scores (mPTS) using machine learning. Efficacy, and reliability of mPTS was further tested and validated in a separate cohort in the real world and in-clinic setting. This study demonstrates the utility of using structured activities with built-in smartphone sensors to measure PD tremor severity remotely and objectively in a real-world setting using more than 1100 participants.