Patient Compliance with Parkinson's Disease Home Monitoring System (P02.244) (2012)

Objective: To determine if patients with Parkinson9s disease (PD) could successfully perform unsupervised motor assessments at home using a touch-screen tablet PC and finger-worn motion sensor unit. Compliance algorithms were developed to validate motor task performance. Background Despite the prevalence and distribution of PD, there is limited access to movement disorder specialty centers for a significant portion of this population, especially in rural communities. Additionally, clinical examination alone cannot capture complex fluctuation patterns that may occur throughout the day. Design/Methods: Six patients with PD completed automated motor assessments six times per day, two hours apart, one day per week for four months at home while wearing a six-degree-of-freedom motion sensor on the index finger. Video instructions on the tablet PC guided patients through each automated motor assessment, which included tasks to evaluate tremor at rest, posture, and action as well as bradykinesia, hypokinesia, and dysrhythmia through repetitive finger-tapping, hand-grasping, and pronation-supination. Patient compliance during task performance was evaluated by subsequent review of videos recorded via a camera in the tablet PC. Tasks were rejected if the sensor unit was worn incorrectly, the wrong task was performed, or the task was not performed for the entire instructed period (15s per task). Additionally, we developed and tested automated compliance algorithms that use kinematic data collected by the motion sensor unit to classify task performances as correct or incorrect. Results: All six patients completed 2,500 total tasks over four months. Based on video review, 93.6% of the tasks were performed as instructed. The automated compliance algorithms correctly accepted or rejected 96.4% of all task performances. Conclusions: Patients accurately and consistently performed motor tasks to objectively monitor PD motor symptoms at home at regular intervals throughout the day. Repeatable, automated home monitoring should increase access to therapy, improve outcomes, and decrease healthcare costs. Supported by: NIH 5R43MD004049-02. Disclosure: Ms. Filipkowski has received personal compensation for activities with Great Lakes NeuroTechnologies Inc., as an employee. Dr. Heldman has received personal compensation for activities with Great Lakes NeuroTechnologies Inc. as an employee. Dr. Espay has received personal compensation for activities with Solvay S.A., Abbott Labratories, Inc., Chelsea Therapeutics, TEVA Neuroscience, Eli Lilly & Company, Novartis, the American Academy of Neurology and the Movement Disoriders Society. Dr. Espay has received personal compensation in an editorial capacity for serving on the editorial board of The European Neurological Journal and Frontiers in Movement Disorders. Dr. Espay has received research support from K23 Research Scholars mentored career development awards, Medtronic, CleveMed, Davis Phinney Foundation and Michael J Fox Foundation. Dr. Mishra has nothing to disclose. Mr. Mera has received personal compensation for activities with Great Lakes NeuroTechnologies Inc. as an employee. Dr. Giuffrida has received personal compensation for activities with Great Lakes NeuroTechnologies Inc. as an employee. Dr. Giuffrida holds stock and/or stock options in Great Lakes NeuroTechnologies Inc., which sponsored research in which Dr. Giuffrida was involved as an investigator.