Emily J Hill1, C Grant Mangleburg2, Isabel Alfradique-Dunham3, Brittany Ripperger4, Amanda Stillwell4, Hiba Saade4, Sindhu Rao4, Oluwafunmiso Fagbongbe4, Rainer von Coelln5, Arjun Tarakad4, Christine Hunter4, Robert J Dawe6, Joseph Jankovic4, Lisa M Shulman5, Aron S Buchman7, Joshua M Shulman8. 1. Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, 77030, USA; Current Address: Gardner Family Center for Movement Disorders, University of Cincinnati, Cincinnati, OH, 45219, USA. 2. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA; Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, 77030, USA. 3. Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, 77030, USA; Current Address: Department of Neurology, Washington University, St Louis, MO, 63130, USA. 4. Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, 77030, USA. 5. Department of Neurology, University of Maryland Medical Center, Baltimore, MD, 21201, USA. 6. Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA; Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, 60612, USA. 7. Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, 60612, USA. 8. Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, 77030, USA; Center for Alzheimer's and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX, 77030, USA. Electronic address: Joshua.Shulman@bcm.edu.
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.
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.
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