Christopher Buckley1, Brook Galna2, Lynn Rochester2, Claudia Mazzà3. 1. MRC-Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA), Pam Liversidge Building, University of Sheffield, Sheffield S1 3JD, UK; Department of Mechanical Engineering, University of Sheffield, Sir Frederick Mappin Building, Sheffield S1 3JD, UK; Institute of Neuroscience/Newcastle University Institute for Ageing, Newcastle University, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK. Electronic address: christopher.buckley2@newcastle.ac.uk. 2. Institute of Neuroscience/Newcastle University Institute for Ageing, Newcastle University, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK. 3. MRC-Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA), Pam Liversidge Building, University of Sheffield, Sheffield S1 3JD, UK; Department of Mechanical Engineering, University of Sheffield, Sir Frederick Mappin Building, Sheffield S1 3JD, UK; INSIGNEO Institute for in Silico Medicine, University of Sheffield, Pam Liversidge Building, Sheffield S1 3JD, UK.
Abstract
BACKGROUND: Changes in upper body (UB) motion during gait may be a marker of incipient pathology, intervention response and disease progression in Parkinson's disease (PD), which if independent from the lower body motion, might provide an improved assessment of gait. RESEARCH QUESTION: This study aimed to test this hypothesis and establish whether variables calculated from accelerations measured on the UB are unique from spatiotemporal characteristics and can contribute to an improved classification of PD gait. METHODS: Data was obtained from 70 people with PD (69.2 ± 9.9 y.o. , UPDRS III: 36.9 ± 12.3) and 64 age-matched controls (71.6 ± 6.8 y.o.). Spatiotemporal characteristics were measured using a pressure sensitive mat (GAITRite). Head and pelvis accelerations were synchronously measured with wearable inertial sensors (Opal, APDM). Pearson's product-moment correlations were calculated between 49 selected variables from UB accelerations (representing magnitude, smoothness, regularity, symmetry and attenuation) and 16 traditional spatiotemporal characteristics (representing pace, variability, rhythm, asymmetry and postural control). Univariate and multivariate regression analysis was used to test the variables ability to classify PD gait. RESULTS: The variables were mostly unique from each other (67% of variables recorded an r < 0.3). Univariate and multivariate analysis showed that UB variables were moderately better at classifying PD gait than the spatiotemporal characteristics (Univariate: 0.70 to 0.81, Multivariate: 0.88 to 0.91 AUC). SIGNIFICANCE: This study showed for the first time that, if aiming at objective and optimal sensitive biomarkers for PD, UB variables should be measured in conjunction with spatiotemporal characteristics to obtain a more holistic assessment of PD gait for use in a clinical or free-living environment. Crown
BACKGROUND: Changes in upper body (UB) motion during gait may be a marker of incipient pathology, intervention response and disease progression in Parkinson's disease (PD), which if independent from the lower body motion, might provide an improved assessment of gait. RESEARCH QUESTION: This study aimed to test this hypothesis and establish whether variables calculated from accelerations measured on the UB are unique from spatiotemporal characteristics and can contribute to an improved classification of PD gait. METHODS: Data was obtained from 70 people with PD (69.2 ± 9.9 y.o. , UPDRS III: 36.9 ± 12.3) and 64 age-matched controls (71.6 ± 6.8 y.o.). Spatiotemporal characteristics were measured using a pressure sensitive mat (GAITRite). Head and pelvis accelerations were synchronously measured with wearable inertial sensors (Opal, APDM). Pearson's product-moment correlations were calculated between 49 selected variables from UB accelerations (representing magnitude, smoothness, regularity, symmetry and attenuation) and 16 traditional spatiotemporal characteristics (representing pace, variability, rhythm, asymmetry and postural control). Univariate and multivariate regression analysis was used to test the variables ability to classify PD gait. RESULTS: The variables were mostly unique from each other (67% of variables recorded an r < 0.3). Univariate and multivariate analysis showed that UB variables were moderately better at classifying PD gait than the spatiotemporal characteristics (Univariate: 0.70 to 0.81, Multivariate: 0.88 to 0.91 AUC). SIGNIFICANCE: This study showed for the first time that, if aiming at objective and optimal sensitive biomarkers for PD, UB variables should be measured in conjunction with spatiotemporal characteristics to obtain a more holistic assessment of PD gait for use in a clinical or free-living environment. Crown
Authors: Rana Zia Ur Rehman; Christopher Buckley; Maria Encarna Mico-Amigo; Cameron Kirk; Michael Dunne-Willows; Claudia Mazza; Jian Qing Shi; Lisa Alcock; Lynn Rochester; Silvia Del Din Journal: IEEE Open J Eng Med Biol Date: 2020-02-14
Authors: Christopher Buckley; M Encarna Micó-Amigo; Michael Dunne-Willows; Alan Godfrey; Aodhán Hickey; Sue Lord; Lynn Rochester; Silvia Del Din; Sarah A Moore Journal: Sensors (Basel) Date: 2019-12-19 Impact factor: 3.576
Authors: Thomas Payne; Matilde Sassani; Ellen Buckley; Sarah Moll; Adriana Anton; Matthew Appleby; Seema Maru; Rosie Taylor; Alisdair McNeill; N Hoggard; Claudia Mazza; Iain D Wilkinson; Thomas Jenkins; Thomas Foltynie; O Bandmann Journal: BMJ Open Date: 2020-08-05 Impact factor: 3.006