Objective: To characterize the home lives of individuals with Parkinson’s disease (PD) using a passive radio wave sensor with a focus on motor, non-motor, and social function.

Background: Traditional assessments of PD have relied on self-reporting of symptoms and episodic, frequently subjective evaluations performed in clinic. Passive monitoring at home can capture real-world function objectively and continuously over prolonged periods.

Method: We conducted an 8-week observational study using a passive sensor called Emerald installed in the homes of participants. Emerald emits and detects radio waves that reflect off individuals and enable assessment of physical and physiological function. Participants also completed typical clinic-based assessments of motor function, cognition, mood, quality of life, and medical comorbidities at baseline. We assessed the ability of the device to characterize disease features in the home including motor (gait speed), non-motor (sleep), and social (time at home, time active) function. We compared Emerald-measured gait speed at home with participant performance on an in-clinic motor assessment (Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III) using a Pearson correlation.

Results: We enrolled 16 individuals with PD (mean age 66.9 years, 33.3% women) and 3 control participants (mean age 57.6 years, 57.1% women). Across all participants, we collected 19,854 hours of monitoring data, including 5,442 hours of sleep. Among those with PD, mean (SD) in-home gait speed was 0.62 (0.11) m/s, time within range of the device was 10.2 (3.3) hours per day, time active at home was 1.1 (0.5) hours per day, and time in bed was 6.5 (2.0) hours per day. Correlation between device-measured in-home gait speed and clinic-performed MDS-UPDRS III score was excellent (r=-0.79, p<0.001).

Conclusion: The Emerald passive sensor collected an unprecedented amount of data, allowing novel insights into the home lives of individuals with PD. Ongoing data analysis will expand the characterization of home features of PD (tremor, dyskinesias, schedule and symptom variability), assess similar metrics among control participants, compare PD vs control metrics, and assess correlations between other device-derived measures vs clinic-performed assessments. This technology has the potential to dramatically expand our understanding of the effect of PD on patients at home.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/a-day-in-the-life-of-parkinsons-using-passive-monitoring-to-characterize-the-disease-at-home/