Refereed review article in scientific journal (A2)

Which features of postural sway are effective in distinguishing Parkinson's disease from controls? A systematic review




List of AuthorsWenbo Ge, Christian J. Lueck, Deborah Apthorp, Hanna Suominen

PublisherWiley

Publication year2021

JournalBrain and Behavior

Journal name in sourceBRAIN AND BEHAVIOR

Journal acronymBRAIN BEHAV

Article numberARTN e01929

Volume number11

Issue number1

Number of pages9

ISSN2162-3279

DOIhttp://dx.doi.org/10.1002/brb3.1929

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/51195406


Abstract
Background Postural sway may be useful as an objective measure of Parkinson's disease (PD). Existing studies have analyzed many different features of sway using different experimental paradigms. We aimed to determine what features have been used to measure sway and then to assess which feature(s) best differentiate PD patients from controls. We also aimed to determine whether any refinements might improve discriminative power and so assist in standardizing experimental conditions and analysis of data.Methods In this systematic review of the literature, effect size (ES) was calculated for every feature reported by each article and then collapsed across articles where appropriate. The influence of clinical medication status, visual state, and sampling rate on ES was also assessed.Results Four hundred and forty-three papers were retrieved. 25 contained enough information for further analysis. The most commonly used features were not the most effective (e.g., PathLength, used 14 times, had ES of 0.47, while TotalEnergy, used only once, had ES of 1.78). Increased sampling rate was associated with increased ES (PathLength ES increased to 1.12 at 100 Hz from 0.40 at 10 Hz). Measurement during "OFF" clinical status was associated with increased ES (PathLength ES was 0.83 OFF compared to 0.21 ON).Conclusions This review identified promising features for analysis of postural sway in PD, recommending a sampling rate of 100 Hz and studying patients when OFF to maximize ES. ES complements statistical significance as it is clinically relevant and is easily compared across experiments. We suggest that machine learning is a promising tool for the future analysis of postural sway in PD.

Downloadable publication

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Last updated on 2022-08-09 at 09:26