Abstract
Introduction: Emerging technologies show promise for enhanced characterization of Parkinson's Disease (PD) motor manifestations. We evaluated quantitative mobility measures from a wearable device compared to the conventional motor assessment, the Movement Disorders Society-Unified PD Rating Scale part III (motor MDS-UPDRS). Methods: We evaluated 176 PD subjects (mean age 65, 65% male, 66% H&Y stage 2) during routine clinic visits using the motor MDS-UPDRS and a 10-min motor protocol with a body-fixed sensor (DynaPort MT, McRoberts BV), including the 32-ft walk, Timed Up and Go (TUG), and standing posture with eyes closed. Regression models examined 12 quantitative mobility measures for associations with (i) motor MDS-UPDRS, (ii) motor subtype (tremor dominant vs. postural instability/gait difficulty), (iii) Montreal Cognitive Assessment (MoCA), and (iv) physical functioning disability (PROMIS-29). All analyses included age, gender, and disease duration as covariates. Models iii-iv were secondarily adjusted for motor MDS-UPDRS. Results: Quantitative mobility measures from gait, TUG transitions, turning, and posture were significantly associated with motor MDS-UPDRS (7 of 12 measures, p < 0.05) and motor subtype (6 of 12 measures, p < 0.05). Compared with motor MDS-UPDRS, several quantitative mobility measures accounted for a 1.5- or 1.9-fold increased variance in either cognition or physical functioning disability, respectively. Among minimally-impaired subjects in the bottom quartile of motor MDS-UPDRS, including subjects with normal gait exam, the measures captured substantial residual motor heterogeneity. Conclusion: Clinic-based quantitative mobility assessments using a wearable sensor captured features of motor performance beyond those obtained with the motor MDS-UPDRS and may offer enhanced characterization of disease heterogeneity.
| Original language | English |
|---|---|
| Pages (from-to) | 105-111 |
| Number of pages | 7 |
| Journal | Parkinsonism and Related Disorders |
| Volume | 84 |
| DOIs | |
| State | Published - Mar 2021 |
Keywords
- Device
- Parkinson's disease
- Wearable sensors
- Wearables
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