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      Quantifying Motor Impairment in Movement Disorders

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          Abstract

          Until recently the assessment of many movement disorders has relied on clinical rating scales that despite careful design are inherently subjective and non-linear. This makes accurate and truly observer-independent quantification difficult and limits the use of sensitive parametric statistical methods. At last, devices capable of measuring neurological problems quantitatively are becoming readily available. Examples include the use of oculometers to measure eye movements and accelerometers to measure tremor. Many applications are being developed for use on smartphones. The benefits include not just more accurate disease quantification, but also consistency of data for longitudinal studies, accurate stratification of patients for entry into trials, and the possibility of automated data capture for remote follow-up. In this mini review, we will look at movement disorders with a particular focus on Parkinson's disease, describe some of the limitations of existing clinical evaluation tools, and illustrate the ways in which objective metrics have already been successful.

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          Most cited references67

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          Rivastigmine for gait stability in patients with Parkinson's disease (ReSPonD): a randomised, double-blind, placebo-controlled, phase 2 trial.

          Falls are a frequent and serious complication of Parkinson's disease and are related partly to an underlying cholinergic deficit that contributes to gait and cognitive dysfunction in these patients. Gait dysfunction can lead to an increased variability of gait from one step to another, raising the likelihood of falls. In the ReSPonD trial we aimed to assess whether ameliorating this cholinergic deficit with the acetylcholinesterase inhibitor rivastigmine would reduce gait variability.
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            A METHOD OF MEASURING EYE MOVEMENT USING A SCLERAL SEARCH COIL IN A MAGNETIC FIELD.

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              A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease

              Background There is growing interest in having objective assessment of health-related outcomes using technology-based devices that provide unbiased measurements which can be used in clinical practice and scientific research. Many studies have investigated the clinical manifestations of Parkinson’s disease using such devices. However, clinimetric properties and clinical validation vary among the different devices. Methods Given such heterogeneity, we sought to perform a systematic review in order to (i) list, (ii) compare and (iii) classify technological-based devices used to measure motor function in individuals with Parkinson's disease into three groups, namely wearable, non-wearable and hybrid devices. A systematic literature search of the PubMed database resulted in the inclusion of 168 studies. These studies were grouped based on the type of device used. For each device we reviewed availability, use, reliability, validity, and sensitivity to change. The devices were then classified as (i) ‘recommended’, (ii) ‘suggested’ or (iii) ‘listed’ based on the following criteria: (1) used in the assessment of Parkinson’s disease (yes/no), (2) used in published studies by people other than the developers (yes/no), and (3) successful clinimetric testing (yes/no). Results Seventy-three devices were identified, 22 were wearable, 38 were non-wearable, and 13 were hybrid devices. In accordance with our classification method, 9 devices were ‘recommended’, 34 devices were ‘suggested’, and 30 devices were classified as ‘listed’. Within the wearable devices group, the Mobility Lab sensors from Ambulatory Parkinson’s Disease Monitoring (APDM), Physilog®, StepWatch 3, TriTrac RT3 Triaxial accelerometer, McRoberts DynaPort, and Axivity (AX3) were classified as ‘recommended’. Within the non-wearable devices group, the Nintendo Wii Balance Board and GAITRite® gait analysis system were classified as ‘recommended’. Within the hybrid devices group only the Kinesia® system was classified as ‘recommended’. Electronic supplementary material The online version of this article (doi:10.1186/s12984-016-0136-7) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                11 April 2018
                2018
                : 12
                : 202
                Affiliations
                [1] 1NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford , Oxford, United Kingdom
                [2] 2Nuffield Department of Surgical Sciences, University of Oxford , Oxford, United Kingdom
                [3] 3Department of Neurology, West China Hospital of Medicine, Sichuan University , Chengdu, China
                [4] 4Exeter College, University of Oxford , Oxford, United Kingdom
                Author notes

                Edited by: Aysegul Gunduz, University of Florida, United States

                Reviewed by: Jee Hyun Choi, Korea Institute of Science and Technology (KIST), South Korea; Kazutaka Takahashi, University of Chicago, United States; J. Luis Lujan, Mayo Clinic College of Medicine & Science, Mayo Clinic, United States

                *Correspondence: James J. FitzGerald james.fitzgerald@ 123456nds.ox.ac.uk

                This article was submitted to Neuroprosthetics, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2018.00202
                5904266
                29695949
                5dc0d3aa-5616-4cc6-b546-040e7ec9c0c6
                Copyright © 2018 FitzGerald, Lu, Jareonsettasin and Antoniades.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 October 2017
                : 14 March 2018
                Page count
                Figures: 1, Tables: 0, Equations: 0, References: 79, Pages: 7, Words: 6284
                Categories
                Neuroscience
                Mini Review

                Neurosciences
                quantification,neurosciences,eye trackers,accelerometers,technologies,rating scales,movement disorders

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