The objective is to develop and clinically assess Kinesia-D"""""""", a wireless, ergonomic, and portable movement disorder technology designed for continuous monitoring of levodopa-induced dyskinesia (LID) and medication state in Parkinson's disease (PD). Motor symptoms can be ameliorated pharmacologically with the dopamine precursor, levodopa. While significant strides have been made in the management of PD, balancing motor improvement with treatment dyskinesia side effects (brief, rapid, irregular movements) associated with chronic use poses a key therapeutic challenge. Approximately 30 percent of patients exhibit LID within 5 years of treatment and 59-100 percent by 10 years. LID significantly impact quality of life through exhaustion and fatigue, social isolation, and depression. Healthcare costs are substantially greater with increased LID severity. Yearly costs increase over $16,000 when comparing a PD patient with severe LID to one with no side effects. The value of this application is tremendous in that existing clinical tools for capturing he levodopa dose response are completely reliant on in-clinic testing and/or the patient's subjective assessment of and ability to distinguish between PD tremor, dyskinesias, and voluntary movements throughout the day. Kinesia-D will not only objectively and accurately rate dyskinesia and tremor severity on a continuous scale independent of each other and activities of daily living, but also generate reports on levodopa response medication states: OFF (no PD symptomatic improvement), ON (PD symptomatic improvement), and ON with dyskinesia. This Phase II application will develop a system which can continuously rate dyskinesia severity throughout the day independent of activities of daily living while also allowing comfortable wear with minimal burden. This will be seamlessly integrated with existing tremor scoring algorithms for simultaneous assessment of PD symptom improvement and LID occurrence. These scores will be used to determine dyskinesia severity and medication state. Reports will automatically be generated and transmitted to the clinician via a broadband-enabled tablet computer residing in the patient's home to guide titration of levodopa treatment. A reliable method for detecting and monitoring the severity of dyskinesia in an ambulatory setting would be highly valuable both for aiding clinicians in optimizing patient medication regimens and the massive pharmaceutical industry for developing new PD drugs and for post market surveillance. Kinesia-D will first be validated in a home study by showing high correlations to the patient dyskinesia diary and then be used to compare therapy titration patient outcome using the Kinesia-D system versus traditional methods. Clinical outcome measures will be based on improved quality of life and motor scores and increased hours in the ON medication state without dyskinesia. We hypothesize that the commercial Kinesia-D system will continuously quantify dyskinesia severity during activities of daily living, improve patient outcomes by balancing PD motor symptom and dyskinesia side effect response to levodopa, and reduce healthcare and clinical drug trial costs.
Levodopa is the most widely used medication to treat Parkinson's disease;however, levodopa-induced dyskinesia (brief, rapid, irregular movements) is the primary adverse effect associated with chronic use, occurs in approximately 30 percent of patients within 5 years of treatment and 59-100 percent by 10 years, significantly impacts quality of life, and can increase healthcare costs by over $16,000 per year for patients that exhibit levodopa- induced dyskinesia. Existing tools available to clinicians for assessing dyskinesia severity in response to adjustments in PD medication are significantly limited in that 1) the pattern and severity of dyskinesia vary substantially over time and during different activities, 2) home dyskinesia diaries rely entirely on the patient's subjective perception of their medication state and their level of compliance, and 3) patients may underestimate the severity of the dyskinesia and have difficulty distinguishing between dyskinesia, tremor, and normal voluntary movements. Kinesia-D will address these limitations through the development of a wireless, ergonomic, and portable movement disorder technology designed for continuous monitoring of levodopa induced dyskinesia and medication state in Parkinson's disease independent of voluntary movement associated with activities of daily living as well as a cloud-based telemedicine interface for remote therapy titration for the geographically disparate PD population.
| Pulliam, Christopher L; Heldman, Dustin A; Brokaw, Elizabeth B et al. (2018) Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors. IEEE Trans Biomed Eng 65:159-164 |
| Hssayeni, Murtadha D; Adams, Jamie L; Ghoraani, Behnaz (2018) Deep Learning for Medication Assessment of Individuals with Parkinson's Disease Using Wearable Sensors. Conf Proc IEEE Eng Med Biol Soc 2018:1-4 |
