Publication Date

12-2018

Abstract

Introduction: Given the high rate of falls during walking in people with idiopathic Parkinson's disease (PD), identifying at risk individuals and developing targeted interventions to reduce falls incidence is paramount. Numerous studies have investigated gait-related risk factors for falls in PD, however findings are inconsistent across studies, and thus a synthesis of the current evidence is needed to guide clinical practice and the development of interventions to reduce falls risk. The objective of this study was to systematically review the literature regarding the association between walking biomechanics and falls in people with PD, and where possible, perform meta-analyses. Methods: The study was performed in accordance with the PRISMA guidelines. Databases were searched until January 2018 to identify articles that reported on the association between walking biomechanics and prospective or retrospective falls in people with PD. Results: Twenty-six articles were included (15 prospective studies, 11 retrospective studies). Articles reported on spatiotemporal and kinematic characteristics, and muscle activation patterns. Meta-analyses revealed slower walking speed, lower cadence, shorter strides and more mediolateral head and pelvis motion in those at higher risk of future falls. Findings from prospective and retrospective articles were largely consistent. Conclusion: Our findings identify spatiotemporal and kinematic characteristics of gait that are risk factors for falls in PD. Modification of these characteristics may have the potential to mediate falls risk, and future research to investigate this possibility is merited. The influence of body and ground reaction forces, and muscle activation patterns on falls risk in PD is currently under-researched.

School/Institute

School of Exercise Science

Document Type

Open Access Journal Article

Access Rights

Open Access

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Tuesday, December 31, 2019

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