Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: A systematic review

Hubble, Ryan P., Naughton, Geraldine A, Silburn, Peter A. and Cole, Michael H. (2015) Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: A systematic review. PLoS One, 10 4: e0123705-e0123705. doi:10.1371/journal.pone.0123705


Author Hubble, Ryan P.
Naughton, Geraldine A
Silburn, Peter A.
Cole, Michael H.
Title Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: A systematic review
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2015-04-20
Year available 2015
Sub-type Article (original research)
DOI 10.1371/journal.pone.0123705
Open Access Status DOI
Volume 10
Issue 4
Start page e0123705
End page e0123705
Total pages 22
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Collection year 2016
Language eng
Formatted abstract
Background
Postural instability and gait disability threaten the independence and well-being of people with Parkinson’s disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson’s disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson’s disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population.

Methods
Papers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i) did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii) did not include a control group or control condition; iii) were an abstract and/or included in the proceedings of a conference; or iv) were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed to meet the inclusion criteria. Included articles were assessed for methodological quality and relevant data from the papers were extracted and synthesized.

Results
Quality assessment of these included articles indicated that 31% were of low methodological quality, while 58% were of moderate methodological quality and 11% were of high methodological quality. All studies adopted a cross-sectional design and used a variety of sensor types and outcome measures to assess standing balance or walking stability in people with Parkinson’s disease. Despite the typically low to moderate methodological quality, 81% of the studies reported differences in sensor-based measures of standing balance or walking stability between different groups of Parkinson’s disease patients and/or healthy controls.

Conclusion
These data support the use of wearable sensors for detecting differences in standing balance and walking stability between people with PD and controls. Further high-quality research is needed to better understand the utility of wearable sensors for the early identification of Parkinson’s disease symptoms and for assessing falls risk in this population.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2016 Collection
 
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
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