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BackgroundWe have previously shown that wearable technology and machine learning techniques can accurately discriminate between progressive supranuclear palsy (PSP), Parkinson's disease, and healthy controls. To date these techniques have not been applied in longitudinal studies of disease progression in PSP.ObjectivesWe aimed to establish whether data collected by a body-worn inertial measurement unit (IMU) network could predict clinical rating scale scores in PSP and whether it could be used to track disease progression.MethodsWe studied gait and postural stability in 17 participants with PSP over five visits at 3-month intervals. Participants performed a 2-minute walk and an assessment of postural stability by standing for 30 seconds with their eyes closed, while wearing an array of six IMUs.ResultsThirty-two gait and posture features were identified, which progressed significantly with time. A simple linear regression model incorporating the three features with the clearest progression pattern was able to detect statistically significant progression 3 months in advance of the clinical scores. A more complex linear regression and a random forest approach did not improve on this.ConclusionsThe reduced variability of the models, in comparison to clinical rating scales, allows a significant change in disease status from baseline to be observed at an earlier stage. The current study sheds light on the individual features that are important in tracking disease progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Original publication

DOI

10.1002/mds.29194

Type

Journal article

Journal

Movement disorders : official journal of the Movement Disorder Society

Publication Date

11/2022

Volume

37

Pages

2263 - 2271

Addresses

NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.

Keywords

Humans, Parkinson Disease, Supranuclear Palsy, Progressive, Gait Disorders, Neurologic, Disease Progression, Movement