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Lookup NU author(s): Dr Christopher Buckley, Dr Lisa AlcockORCiD, Dr Ríona McArdle, Dr Rana RehmanORCiD, Dr Silvia Del DinORCiD, Professor Alison Yarnall, Professor Lynn RochesterORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Quantifying gait and postural control is fundamental to 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 which are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, 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). We will illustrate, using some recent examples of research across a range of relevant neurological conditions including Parkinson’s disease, ataxia and dementia, evidence to support 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.
Author(s): Buckley C, Alcock L, Mc Ardle R, Rehman RZU, Del Din S, Mazzà C, Yarnall A, Rochester L
Publication type: Review
Publication status: Published
Journal: Brain Sciences
Year: 2019
Volume: 9
Issue: 2
Online publication date: 06/02/2019
Acceptance date: 31/01/2019
ISSN (electronic): 2076-3425
URL: https://doi.org/10.3390/brainsci9020034
DOI: 10.3390/brainsci9020034
