Objective: To implement home-based monitoring (HBM) using wearable devices, a smartphone dedicated app and patient reported outcomes in order to provide a more comprehensive picture of fluctuating Parkinson’s disease (PD) symptoms throughout the day. Initial results of feasibility are presented.

Background: Traditionally, PD motor symptoms and associated dysfunction are assessed by a clinician within bi-yearly clinic visits, collecting subjective data via interview or by use of disease-specific scales such as the Movement Disorders Society-unified PD rating scale. In addition, for l-dopa-treated PD patients who suffer from motor fluctuations and dyskinesia, daily OFF-ON diaries filled by patients on selected days are used. The lack of continuity and poor objectivity of the above collected measures during the period between clinic visits may lead to imprecision and biases in clinical evaluations and therapeutic interventions.

Method: Sensor-based data of l-dopa-treated fluctuating PD patients were collected using an Apple Watch (AW) for two weeks (12 hours per day and additional 2 nights) and a smartphone-dedicated app (Intel® Pharma Analytics Platform) through which data were stored and patients received reminders on their medication schedule. In particular, the AW continuously collected passive data (tremor, dyskinesia, level of activity) and active data, i.e., electronic patient reported outcomes (ePROs) during daily assessments both in OFF and ON times (3m TUG, finger tapping, hand tremor, hand rotation).

Results: Initial results show that participants wore the AW on average 82.87% of the time during the whole ~2-weeks experiment, without significant drops over time. They also complied very well with the daily medication schedule, albeit slight delay across the whole experiment (report: 7-19 minutes, intake: 14-45 minutes, average daily delay). Finally, all participants completed the active data ePROs, with 3 participants completing all acquisitions on time, while the others required an extension of the experiment of 1 to 6 days to conclude them.

Conclusion: These initial data are encouraging in suggesting that HBM technologies are a promising tool to aid and enrich PD evaluations and therapies. Future research might focus on comparing automatically monitored outcomes, e.g., PD motor symptoms between HBM and standard clinical measures.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/assessment-of-fluctuating-parkinsons-disease-with-sensor-based-home-monitoring-feasibility-results/