Continuous Monitoring of Turning in Patients with Parkinson's Disease

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DESCRIPTION (provided by applicant): The ability to turn safely while walking is an important motor skill for independent mobility that is affected by aging and neurological diseases. Turning often results in 'freezing' and/or falling in patients with Parkinson's disease (PD), and is commonly associated with hip fractures. However, asking a PD patient to execute a turn in a clinical environment often does not reveal their impairments. Patients at risk for a fall would benefit from a system that identifies and characterizes their daily mobility behavior to predict their risk of falling, benefits or side effects of treatment, and progression of disease. No curren system exists that identifies and characterizes turning performance unobtrusively during spontaneous, daily activity. Our long-term goal is to develop and commercialize a unique system to measure mobility (gait and dynamic balance) using wearable sensors throughout the day. Our OHSU/PSU start-up company, APDM, is developing systems to allow clinicians and clinical researchers to monitor mobility and clinical motor symptoms using their core technology, 'Opal' movement monitors. We already have established the value of automatically characterizing gait and prescribed 180-degree turns in clinical environments during a Get Up and Go task with our ITUG application. We now want to add a new product that takes mobility monitoring into the home and community. This new, instrumented system (ITurn) will allow patients to self-monitor their own daily mobility and allow clinicians to review days or weeks of their patient's daily mobility. The objective of this application is to develop a novel method to identify and quantify turns made by individuals during their daily lives. The specific aims of this Phase I project are: 1) To develop a new algorithm that identifies and characterizes turning events during spontaneous activity with our Opal wearable sensors and 2) To determine the feasibility of having older subjects and subjects with PD to use a docking station to recharge their sensors and upload data onto our server after continuous monitoring in the home. The algorithms developed and hardware and software adaptations made for home use will be used to form the foundation of our Phase II project that will test the benefits of measuring turning deficits throughout the day in patients with Parkinson's disease to predict future falls. The abiliy to monitor turning performance, in addition to straight ahead walking, will provide a major breakthrough for pharmaceutical and exercise clinical trials aimed at improving mobility disability and provide a powerful new tool for patients, caregivers and clinicians who want more accurate information about their ability to safely ambulate in their own environments.