Which features of postural sway are effective in distinguishing Parkinson's disease from controls? A systematic review. Issue 1 (4th November 2020)

• Record Type: Journal Article
• Authors: Ge, Wenbo; Lueck, Christian J.; Apthorp, Deborah; Suominen, Hanna
• Is Part Of: Brain and behavior. Volume 11:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-04
• Subjects: machine learning -- meta‐analysis -- Parkinson's disease -- postural control -- systematic review; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.1929
• Languages: English
• ISSNs: 2162-3279

Abstract

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. Abstract : Existing studies have analyzed many different features of postural sway in an attempt to characterize Parkinson's disease. Here, we perform a systematic literature review and find the effect size of those features collapsed across several articles. The purpose of this was to create a comprehensive list of features that have been adequately explored, what features require more investigation, and what features are yet to be explored at all.