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Background:  Parkinson's disease (PD) is a neurodegenerative disease that can lead to impaired motor function and execution of activities of daily living (ADL). Since clinicians typically can only observe patients' symptoms during visits, prescribed medication schedules may not reflect the full range of symptoms experienced throughout the day. Therefore, objective tools are needed to provide comprehensive symptom data to optimize treatment. One such tool is the Parkinson's KinetiGraph ®  (PKG), a wearable sensor that measures motor symptoms of Parkinson's disease.   Objective:  To build a mathematical model to determine if PKG data measuring Parkinson's patients' motor symptoms can predict patients' ADL impairment.   Methods:  Thirty-four patients with PD wore the PKG device for 6 days while performing their ADL. Patients' PKG scores for bradykinesia and dyskinesia, as well as their responses to a questionnaire asking if their ADL-level had been impacted by various motor symptoms, were used to build a multiple regression model predicting the patients' Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part II scores.   Results:  Calculation of bradykinesia score response to medication showed that using a dosage response time of 30 min yielded a greater bradykinesia response than when the response time was set to 40, 50, 60, 70, 80, or 90 min. The overall multiple regression model predicting MDS-UPDRS part II score was significant ( R  2  = 0.546,  p  < 0.001).   Conclusion:  The PKG's ability to provide motor symptom data that correlates with clinical measures of ADL impairment suggests that it has strong potential as a tool for the assessment and management of Parkinson's disease motor symptoms.

Comparing Objective and Subjective Measures of Parkinson's Disease Using the Parkinson's KinetiGraph