ParkinStep: Automated PD Gait and Balance Assessment for Optimizing DBS
Small Business Information
4415 EUCLID AVE, CLEVELAND, OH, 44103
AbstractDESCRIPTION (provided by applicant): The objective is to design, build, and clinically assess ParkinStep , a compact, portable, wireless movement disorder monitor technology to quantify gait and balance performance in Parkinson's disease (PD). A standardi zed platform for repeatable, automated testing to assess gait and balance in response to deep brain stimulation (DBS) settings should optimize patient outcomes and provide a novel research tool for new DBS protocols targeted to gait and balance. There has currently been tremendous growth and active research into PD pathophysiology and treatment including pharmaceutical interventions and DBS. Efficacy is judged by alleviation of patient symptoms and improved quality of life. After initial DBS surgery, severa l follow-up examinations optimize stimulation parameters to minimize motor symptoms and side effects, increase battery life, and decrease required drug therapy. The current standard in symptom evaluation is the Unified Parkinson's Disease Rating Scale (UPD RS), a qualitative ranking system. During DBS programming sessions, a neurologist typically assesses upper extremity function using a subset of the UPDRS motor exam. While gait and balance are critical components to quality of life measures and irregular l ower extremity function can be disabling, these often receive less attention than upper extremity counterparts. CleveMed has previously developed a technology platform called ParkinSense to quantify upper extremity PD motor symptoms. Clinical trials have been conducted with PD subjects to quantitatively assess severity of tremor and bradykinesia. Outputs were highly correlated with clinicians' qualitative UPDRS scores. Additionally, the objective, quantitative ParkinSense output scores provided increased r esolution on a continuous scale compared to visual observations using the discrete UPDRS. With only minor hardware upgrades required for gait and balance and excellent clinical results to date, this previously existing base enhances likelihood of project s uccess. We hypothesize ParkinStep will successfully capture quantitative symptom variables related to gait and balance, process those variables into a algorithm whose output correlates to qualitative clinician scoring of gait and balance, and demonstrate h igh clinical acceptability by both patients and clinicians. This development will continue CleveMed's path to providing a standardized platform for quantitatively assessing all components of the UPDRS motor section. PUBLIC HEALTH RELEVANCE: Parkinson's dis ease is primarily characterized by the classic motor symptoms of tremor, bradykinesia, and rigidity; however, other lower extremity symptoms such as balance and gait disturbances, especially in advanced patients, can be very debilitating, leading to decr eased mobility and independence, decreased quality of life and an increased falling/hip fracture risk. Clinicians lack quantitative tools to optimize deep brain stimulation programming for alleviating Parkinson's motor symptoms. ParkinStep will be a repea table, automated tool that can quantify lower extremity motor function and assist stimulation programming during outpatient follow up to optimize patient outcomes.
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