Abstract
Most wearable sensor studies in Parkinson’s disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson’s disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson’s walked significantly less (median [inter-quartile range]: 4980 [2835–7163] steps/day) than controls (7367 [5106–8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4–5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1–4) of individuals with Parkinson’s, which was significantly higher than the 0.5 [0.3–2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson’s in real-world settings.
Highlights
Parkinson’s disease (PD) is the world’s fast-growing neurological disorder[1] and results in motor[2], cognitive[3], psychiatric[4], and nonmotor symptoms
Twenty individuals with PD and 22 controls were enrolled in the study
Three individuals with PD were excluded from the analysis due to sensor problems
Summary
Parkinson’s disease (PD) is the world’s fast-growing neurological disorder[1] and results in motor[2], cognitive[3], psychiatric[4], and nonmotor symptoms. Wearable sensors can provide continuous, objective, and longitudinal data in both clinical and real-world settings. Several studies have used wearable sensors to perform activity, gait, and motor assessments in PD16–19. To effectively summarize the fine-grained variations over time revealed by the wearable sensor data, we present a useful clock-based visualization that allows physicians, researchers, and patients to readily understand and interpret the results. In this observational study, our goal was to examine the activity profile and subsequently analyze the gait and tremor characteristics of participants in the clinic and real-world using wearable sensors
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