Application of modified regression techniques to a quantitative assessment for the motor signs of Parkinson's disease

Effective clinical trials for neuroprotective interventions for Parkinson's disease (PD) require a way to quantify an individual's motor symptoms and analyze the change in these symptoms over time. Clinical scales provide a global picture of function but cannot precisely measure specific aspects of motor control. We have used commercially available sensors to create a protocol called Advanced Sensing for Assessment of Parkinson's disease (ASAP) to obtain a quantitative and reliable measure of motor impairment in early to moderate PD. The ASAP protocol measures grip force as an individual tracks a sinusoidal or pseudorandom target force under three conditions of increasing cognitive load. Thirty individuals with PD have completed the ASAP protocol. The ASAP data for 26 of these individuals were summarized in terms of 36 variables, and modified regression techniques were used to predict an individual's score on the Unified Parkinson Disease Rating Scale based on ASAP data. We observed a mean prediction error of approximately 3.5 UPDRS points, and the predicted score accounted for approximately 76% of the variability of the UPDRS. These results demonstrate that the ASAP protocol can measure differences for individuals who are clinically different. This indicates that the ASAP protocol may be able to measure changes with time in the motor signs of an individual with PD.