The objective is to design, build, and clinically assess Kinesia-D', a compact, portable, wireless movement disorder system with continuous monitoring capabilities to detect and quantify the severity of levodopa-induced choreatic dyskinesias, or irregular rapid involuntary movements, in Parkinson's disease (PD). Disease incidence affects over 1,000,000 people in the United States and continues to increase. While significant strides have been made in the management of PD motor symptoms, treatment side effects such as dyskinesias pose a key therapeutic challenge. Approximately 30 percent of patients diagnosed with PD exhibit levodopa-induced dyskinesias within 5 years of treatment and 59-100 percent by 10 years. Assessment of dyskinesia impairment is typically performed during office visits using subjective measures. These rating scales are limited by relying on patient recall and may not accurately reflect the duration, severity, and disability fluctuations of dyskinesia experienced in the home setting throughout the day. Therefore, current dyskinesia evaluation methods may limit the clinician's ability to effectively adjust medication dose for both optimal reduction of PD motor symptoms AND levodopa-induced dyskinesias. The Kinesia-D application will leverage CleveMed motion sensor technology to create a stand-alone portable system to capture and quantify therapy-induced choreatic dyskinesias through a clinical study. It is hypothesized that this project will successfully 1) capture quantitative variables highly correlated to choreatic dyskinesias, 2) detect the presence or absence of choreatic dyskinesias regardless of background voluntary movements, and 3) demonstrate feasibility for automated detection of levodopa-induced dyskinesias. .
Upon initial onset of Parkinson's disease (PD), Levodopa is the most-widely used and effective treatment of PD motor symptoms; however, medication dose side effects can cause debilitating choreatic dyskinesia, or irregular brief rapid movements. A reliable method for detecting and monitoring the severity of dyskinesias outside of the clinical setting would be highly valuable both for aiding clinicians in optimizing medication regimens for patients and for monitoring dyskinesias in clinical trials for advanced PD. Kinesia-D will address the growing concern of Medicare usage cost in the aging adult population and over 1.5 million people in the U.S. living with PD by providing a cost effective dyskinesia assessment method that is not constrained to clinical office visits, will potentially facilitate participation of under- represented rural patient populations in clinical trials, and may show early detection of drug adverse events such as dyskinesias in clinical trials to potentially impact trial designs for novel therapeutic PD treatments.
| 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 |
| Pulliam, Christopher L; Burack, Michelle A; Heldman, Dustin A et al. (2014) Motion sensor dyskinesia assessment during activities of daily living. J Parkinsons Dis 4:609-15 |
| Mera, Thomas O; Burack, Michelle A; Giuffrida, Joseph P (2013) Objective motion sensor assessment highly correlated with scores of global levodopa-induced dyskinesia in Parkinson's disease. J Parkinsons Dis 3:399-407 |
| Mera, Thomas O; Burack, Michelle A; Giuffrida, Joseph P (2012) Quantitative assessment of levodopa-induced dyskinesia using automated motion sensing technology. Conf Proc IEEE Eng Med Biol Soc 2012:154-7 |
