Gait Characteristics Related to Fall Risk in Patients with Cerebral Small Vessel Disease

Overview

Journal Front Neurol

Specialty Neurology

Date 2023 Jun 22

PMID 37346167

Authors

Yajing Wang

Yanna Li

Shoufeng Liu

Peipei Liu

Zhizhong Zhu

Jialing Wu

Affiliations

Soon will be listed here.

Abstract

Background: Falls and gait disturbance are significant clinical manifestations of cerebral small vessel disease (CSVD). However, few relevant studies are reported at present. We aimed to investigate gait characteristics and fall risk in patients with CSVD.

Methods: A total of 119 patients with CSVD admitted to the Department of Neurology at Tianjin Huanhu Hospital between 17 August 2018 and 7 November 2018 were enrolled in this study. All patients underwent cerebral magnetic resonance imaging scanning and a 2-min walking test using an OPAL wearable sensor and Mobility Lab software. Relevant data were collected using the gait analyzer test system to further analyze the time-space and kinematic parameters of gait. All patients were followed up, and univariate and multivariate logistic regression analyses were conducted to analyze the gait characteristics and relevant risk factors in patients with CSVD at an increased risk of falling.

Results: All patients were grouped according to the presence or absence of falling and fear of falling and were divided into a high-fall risk group ( = 35) and a low-fall risk group ( = 72). Logistic multivariate regression analysis showed that the toe-off angle [odds ratio (OR) = 0.742, 95% confidence interval (CI) 0.584-0.942, < 0.05], toe-off angle coefficient of variation (CV) (OR = 0.717, 95% CI: 0.535-0.962, < 0.05), stride length CV (OR = 1.256, 95% CI: 1.017-1.552, < 0.05), and terminal double support CV (OR = 1.735, 95% CI: 1.271-2.369, < 0.05) were statistically significant ( < 0.05) and were independent risk factors for high-fall risk in patients with CSVD.

Conclusion: CSVD patients with seemingly normal gait and ambulation independently still have a high risk of falling, and gait spatiotemporal-kinematic parameters, gait symmetry, and gait variability are important indicators to assess the high-fall risk. The decrease in toe-off angle, in particular, and an increase in related parameters of CV, can increase the fall risk of CSVD patients.

Citing Articles

Wearable sensors and machine learning fusion-based fall risk prediction in covert cerebral small vessel disease.

Zhou Y, Zhang D, Ji Y, Bu S, Hu X, Zhao C Front Neurosci. 2025; 19:1493988.

PMID: 40046433 PMC: 11879974. DOI: 10.3389/fnins.2025.1493988.

References

Ichihashi N, Ikezoe T, Sato S, Ibuki S . Gait asymmetry assessment for older adults by measuring circular gait speed. Geriatr Gerontol Int. 2019; 19(8):736-739. DOI: 10.1111/ggi.13691. View

Cabral S, Fernandes R, Selbie W, Moniz-Pereira V, Veloso A . Inter-session agreement and reliability of the Global Gait Asymmetry index in healthy adults. Gait Posture. 2016; 51:20-24. DOI: 10.1016/j.gaitpost.2016.09.014. View

Tinetti M, Powell L . Fear of falling and low self-efficacy: a case of dependence in elderly persons. J Gerontol. 1993; 48 Spec No:35-8. DOI: 10.1093/geronj/48.special_issue.35. View

Serrao M, Chini G, Caramanico G, Bartolo M, Castiglia S, Ranavolo A . Prediction of Responsiveness of Gait Variables to Rehabilitation Training in Parkinson's Disease. Front Neurol. 2019; 10:826. PMC: 6688512. DOI: 10.3389/fneur.2019.00826. View

Anderson F, Goldberg S, Pandy M, Delp S . Contributions of muscle forces and toe-off kinematics to peak knee flexion during the swing phase of normal gait: an induced position analysis. J Biomech. 2004; 37(5):731-7. DOI: 10.1016/j.jbiomech.2003.09.018. View

Bower K, Thilarajah S, Pua Y, Williams G, Tan D, Mentiplay B . Dynamic balance and instrumented gait variables are independent predictors of falls following stroke. J Neuroeng Rehabil. 2019; 16(1):3. PMC: 6322221. DOI: 10.1186/s12984-018-0478-4. View

Kim Y, Kwon H, Lee J, Cho H, Kim H, Park H . Gray and white matter changes linking cerebral small vessel disease to gait disturbances. Neurology. 2016; 86(13):1199-207. DOI: 10.1212/WNL.0000000000002516. View

Sakuma K, Tateuchi H, Nishishita S, Okita Y, Kitatani R, Koyama Y . Immediate effects of stance and swing phase training on gait in patients with stroke. Int J Rehabil Res. 2021; 44(2):152-158. DOI: 10.1097/MRR.0000000000000464. View

Prateek G, Mazzoni P, Earhart G, Nehorai A . Gait Cycle Validation and Segmentation Using Inertial Sensors. IEEE Trans Biomed Eng. 2019; 67(8):2132-2144. PMC: 7305840. DOI: 10.1109/TBME.2019.2955423. View

10.

van der Holst H, van Uden I, Tuladhar A, de Laat K, van Norden A, Norris D . Factors Associated With 8-Year Mortality in Older Patients With Cerebral Small Vessel Disease: The Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC) Study. JAMA Neurol. 2016; 73(4):402-9. DOI: 10.1001/jamaneurol.2015.4560. View

11.

Son M, Han S, Lyoo C, Lim J, Jeon J, Hong K . The effect of levodopa on bilateral coordination and gait asymmetry in Parkinson's disease using inertial sensor. NPJ Parkinsons Dis. 2021; 7(1):42. PMC: 8121791. DOI: 10.1038/s41531-021-00186-7. View

12.

Auvinet E, Multon F, Manning V, Meunier J, Cobb J . Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using Microsoft Kinect data. Gait Posture. 2016; 51:162-168. DOI: 10.1016/j.gaitpost.2016.08.022. View

13.

Li P, Wang Y, Jiang Y, Zhang K, Yang Q, Yuan Z . Cerebral small vessel disease is associated with gait disturbance among community-dwelling elderly individuals: the Taizhou imaging study. Aging (Albany NY). 2020; 12(3):2814-2824. PMC: 7041732. DOI: 10.18632/aging.102779. View

14.

Lindemann U . Spatiotemporal gait analysis of older persons in clinical practice and research : Which parameters are relevant?. Z Gerontol Geriatr. 2019; 53(2):171-178. DOI: 10.1007/s00391-019-01520-8. View

15.

Jabbar K, Tan D, Seah W, Lau L, Pang B, Ng D . Enhanced gait variability index and cognitive performance in Asian adults: Results from the Yishun Study. Gait Posture. 2022; 97:216-221. DOI: 10.1016/j.gaitpost.2022.07.156. View

16.

Schlachetzki J, Barth J, Marxreiter F, Gossler J, Kohl Z, Reinfelder S . Wearable sensors objectively measure gait parameters in Parkinson's disease. PLoS One. 2017; 12(10):e0183989. PMC: 5636070. DOI: 10.1371/journal.pone.0183989. View

17.

Michnik R, Nowakowska K, Jurkojc J, Jochymczyk-Wozniak K, Kopyta I . Motor functions assessment method based on energy changes in gait cycle. Acta Bioeng Biomech. 2018; 19(4):63-75. View

18.

Friedman S, Munoz B, West S, Rubin G, Fried L . Falls and fear of falling: which comes first? A longitudinal prediction model suggests strategies for primary and secondary prevention. J Am Geriatr Soc. 2002; 50(8):1329-35. DOI: 10.1046/j.1532-5415.2002.50352.x. View

19.

Galna B, Lord S, Rochester L . Is gait variability reliable in older adults and Parkinson's disease? Towards an optimal testing protocol. Gait Posture. 2012; 37(4):580-5. DOI: 10.1016/j.gaitpost.2012.09.025. View

20.

Liu T, Ng G, Ng S . Effectiveness of a combination of cognitive behavioral therapy and task-oriented balance training in reducing the fear of falling in patients with chronic stroke: study protocol for a randomized controlled trial. Trials. 2018; 19(1):168. PMC: 5842580. DOI: 10.1186/s13063-018-2549-z. View