Quantitative mobility measures complement the MDS-UPDRS for characterization of Parkinson's disease heterogeneity
| Autor: | Oluwafunmiso Fagbongbe, C. Grant Mangleburg, Lisa M. Shulman, Sindhu Rao, Amanda Stillwell, Joshua M. Shulman, Hiba Saade, Brittany Ripperger, Arjun Tarakad, Emily Hill, Rainer von Coelln, Aron S. Buchman, Isabel Alfradique-Dunham, Joseph Jankovic, Christine Hunter, Robert J. Dawe |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Male medicine.medical_specialty Parkinson's disease Movement disorders Mds updrs Postural instability Diagnostic Techniques Neurological Severity of Illness Index Article 03 medical and health sciences Wearable Electronic Devices 0302 clinical medicine Physical medicine and rehabilitation Rating scale Tremor medicine Humans Motor Manifestations Postural Balance Gait Disorders Neurologic Aged business.industry Montreal Cognitive Assessment Cognition Regression analysis Parkinson Disease Middle Aged medicine.disease Gait 030104 developmental biology Neurology Quartile Female Neurology (clinical) Geriatrics and Gerontology medicine.symptom business 030217 neurology & neurosurgery |
| Zdroj: | Parkinsonism Relat Disord |
| ISSN: | 1873-5126 |
| Popis: | IntroductionEmerging 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).MethodsWe evaluated 176 subjects with PD (mean age 65, 65% male, 66% H&Y stage 2) at the time of routine clinic visits using the motor MDS-UPDRS and a structured 10-minute motor protocol, which included a 32-ft walk, Timed Up and Go (TUG), and standing posture with eyes closed, while wearing a body-fixed sensor (DynaPort MT, McRoberts BV). 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.ResultsQuantitative mobility measures from gait, TUG transitions, turning, and posture were significantly associated with motor MDS-UPDRS (7 of 12 measures, p< 0.05) and subtype (6 of 12 measures, p< 0.05). Compared with motor MDS-UPDRS, several quantitative mobility measures accounted for ∼1.5-fold increased variance in either cognition or physical functioning disability. Among minimally-impaired subjects within the bottom quartile of motor MDS-UPDRS, including subjects with normal gait exam, the measures captured substantial residual motor heterogeneity.ConclusionClinic-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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect