Assessment of Fluctuating Parkinson's Disease With Sensor-based Home Monitoring

Parkinson Disease

Parkinson Disease, Home based monitoring, Sensor-based data, Motor fluctuations

The aim of this study is to implement home-based monitoring (HBM) using remote-capture wearable devices and patient reported outcomes (PROs) in a rather homogeneous subgroup of advanced Parkinson's Disease (PD) patients, suffering from significant motor fluctuations (MF) and Levodopa-induced dyskinesia (LID), over a two-week period. The investigators aim to provide a more comprehensive picture of patient symptoms, severity, and fluctuations and compare these data to interview-derived clinical data.

Parkinson's Disease (PD) is a neuro-degenerative disorder affecting millions of people worldwide. PD is associated with both motor and non-motor symptoms that affect patients' functioning and Quality of life. The motor symptoms consist of tremor, rigidity, bradykinesia and gait impairments. Additional important motor symptoms that are associated with chronic Levodopa therapy, are levodopa-induced dyskinesia and motor fluctuations. Currently, the accepted clinical measurement of PD symptom severity is the Movement Disorders Society-unified Parkinson's disease rating scale (MDS-UPDRS), which is based, in part, on subjective and potentially recall-based reports by the patients and on semi-objective observations by the clinician. On average, PD patients see their treating neurologist for in-clinic visits twice a year. These visits are limited in time and may leave some issues unattended regarding all aspects of disease and overall health. This may adversely affect the decision making process and the prescribed treatment plan. In order to understand the accurate clinical status of patients, particularly in the motor fluctuating stage of PD and to monitor results of intervention, the treating neurologist may need a more comprehensive picture of their patients' symptoms and lives during protracted periods and real life in their home environment. The HBM apparatus used in this study will consist of a smartwatch (Apple watch) and a smartphone (Apple iPhone 8). The phone is pre-installed with an application which is part of the Intel® Pharma Analytics Platform. The mobile app was designed by usability experts and was tested with patients to ensure ease of use by an elderly population with PD. The Intel platform is enhanced by a compendium of algorithms to extract clinical insights from the raw sensor data. Passive sensor data is transferred from the study smartwatch. The data includes three measures based on the smartwatch data: activity level, dyskinesia and tremor. The study will be conducted at Movement Disorders Institute at Sheba Medical Center and will include two clinic visits and a 2-week HBM phase. The first clinic visit as well as the 2-week home period will include the following daily assessments (once in off state and once in on state): 1. 3- meter Timed up and go test (3mTUG) 2. Finger tapping on smartphone (10 seconds, both hands) 3. Hand tremor assessment: postural tremor (outstretched, 30 sec), rest tremor (30 sec) 4. Pronation-supination In addition, ePROs are captured with electronic home diaries. Participants will report the severity of PD symptoms they are experiencing on a 5-point scale throughout the day. For the duration of waking hours, participants will receive a notification on their phone to input information about their ON/OFF state every 30 minutes. The primary objective is to correlate severity of fluctuating motor symptoms in PD patients using the Intel® Pharma Analytics Platform's derived passive sensor data (percentage of daily tremor time and percentage of daily dyskinesia time, percentage of daily inactivity) in an exploratory manner with concomitant assessment of motor fluctuations and dyskinesia using the application's based electronic symptom diary and data of tremor, off time and dyskinesia using the MDS-UPDRS scale.

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