Longitudinal assessment of falls in patients with Parkinson’s disease using inertial sensors and the Timed Up and Go test

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Title: Longitudinal assessment of falls in patients with Parkinson’s disease using inertial sensors and the Timed Up and Go test
Authors: Greene, Barry R.Caulfield, BrianLamichhane, Dronacharyaet al.
Permanent link: http://hdl.handle.net/10197/10770
Date: 12-Jan-2018
Online since: 2019-06-10T09:00:23Z
Abstract: Objective: To examine the predictive validity of a TUG test for falls risk, quantified using body-worn sensors (QTUG) in people with Parkinsons Disease (PD). We also sought to examine the inter-session reliability of QTUG sensor measures and their association with the Unified Parkinsons Disease Rating Scale (UPDRS) motor score. Approach: A six-month longitudinal study of 15 patients with Parkinsons disease. Participants were asked to complete a weekly diary recording any falls activity for six months following baseline assessment. Participants were assessed monthly, using a Timed Up and Go test, quantified using body-worn sensors, placed on each leg below the knee. Main results: The results suggest that the QTUG falls risk estimate recorded at baseline is 73.33% (44.90, 92.21)accurate in predicting falls within 90 days, while the Timed Up and Go time at baseline was 46.67% (21.27, 73.41)accurate. The Timed Up and Go time and QTUG falls risk estimate were strongly correlated with UPDRS motor score. Fifty-two of 59 inertial sensor parameters exhibited excellent inter-session reliability, five exhibited moderate reliability, while two parameters exhibited poor reliability. Significance: The results suggest that QTUG is a reliable tool for the assessment of gait and mobility in Parkinsons disease and, furthermore, that it may have utility in predicting falls in patients with Parkinsons disease.
Type of material: Journal Article
Publisher: Sage
Journal: Journal of Rehabilitation and Assistive Technologies Engineering
Volume: 5
Start page: 1
End page: 8
Copyright (published version): 2018 the Authors
Keywords: FallsParkinson’s diseaseSensorsReliability
DOI: 10.1177/2055668317750811
Language: en
Status of Item: Peer reviewed
Appears in Collections:Insight Research Collection

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