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The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack...

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Autores principales: Buckley, Christopher, Alcock, Lisa, McArdle, Ríona, Rehman, Rana Zia Ur, Del Din, Silvia, Mazzà, Claudia, Yarnall, Alison J., Rochester, Lynn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406749/
https://www.ncbi.nlm.nih.gov/pubmed/30736374
http://dx.doi.org/10.3390/brainsci9020034
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author Buckley, Christopher
Alcock, Lisa
McArdle, Ríona
Rehman, Rana Zia Ur
Del Din, Silvia
Mazzà, Claudia
Yarnall, Alison J.
Rochester, Lynn
author_facet Buckley, Christopher
Alcock, Lisa
McArdle, Ríona
Rehman, Rana Zia Ur
Del Din, Silvia
Mazzà, Claudia
Yarnall, Alison J.
Rochester, Lynn
author_sort Buckley, Christopher
collection PubMed
description Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia—we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.
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spelling pubmed-64067492019-03-13 The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control Buckley, Christopher Alcock, Lisa McArdle, Ríona Rehman, Rana Zia Ur Del Din, Silvia Mazzà, Claudia Yarnall, Alison J. Rochester, Lynn Brain Sci Review Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia—we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction. MDPI 2019-02-06 /pmc/articles/PMC6406749/ /pubmed/30736374 http://dx.doi.org/10.3390/brainsci9020034 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Buckley, Christopher
Alcock, Lisa
McArdle, Ríona
Rehman, Rana Zia Ur
Del Din, Silvia
Mazzà, Claudia
Yarnall, Alison J.
Rochester, Lynn
The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control
title The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control
title_full The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control
title_fullStr The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control
title_full_unstemmed The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control
title_short The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control
title_sort role of movement analysis in diagnosing and monitoring neurodegenerative conditions: insights from gait and postural control
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406749/
https://www.ncbi.nlm.nih.gov/pubmed/30736374
http://dx.doi.org/10.3390/brainsci9020034
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