Gait variability measures may represent different constructs

Moe‐Nilssen, Rolf; Aaslund, Mona Kristin; Hodt-Billington, Caroline; Helbostad, Jorunn L.

doi:10.1016/j.gaitpost.2010.03.019

Cited by 85 publications

(76 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, while variability may be related to stability, this relationship is hardly ever straightforward, since we do not know the control strategies of the nervous system, or the prevailing constraints. Nonetheless, variability measures have unrivalled popularity in the gait community [8,35,46,78,80,86,87,89,[107][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122][123][124][125]. This may in part be explained by the simplicity of the calculations involved, in combination with easily understandable metrics.…”

Section: Variability Measures 2141 General Descriptionmentioning

confidence: 99%

Assessing the stability of human locomotion: a review of current measures

Bruijn

Meijer

Beek³

et al. 2013

J. R. Soc. Interface.

529

365

View full text Add to dashboard Cite

Falling poses a major threat to the steadily growing population of the elderly in modern-day society. A major challenge in the prevention of falls is the identification of individuals who are at risk of falling owing to an unstable gait. At present, several methods are available for estimating gait stability, each with its own advantages and disadvantages. In this paper, we review the currently available measures: the maximum Lyapunov exponent (l S and l L ), the maximum Floquet multiplier, variability measures, long-range correlations, extrapolated centre of mass, stabilizing and destabilizing forces, foot placement estimator, gait sensitivity norm and maximum allowableperturbation. We explain what these measures represent and how they are calculated, and we assess their validity, divided up into construct validity, predictive validity in simple models, convergent validity in experimental studies, and predictive validity in observational studies. We conclude that (i) the validity of variability measures and l S is best supported across all levels, (ii) the maximum Floquet multiplier and l L have good construct validity, but negative predictive validity in models, negative convergent validity and (for l L ) negative predictive validity in observational studies, (iii) long-range correlations lack construct validity and predictive validity in models and have negative convergent validity, and (iv) measures derived from perturbation experiments have good construct validity, but data are lacking on convergent validity in experimental studies and predictive validity in observational studies. In closing, directions for future research on dynamic gait stability are discussed.

show abstract

Section: Variability Measures 2141 General Descriptionmentioning

confidence: 99%

Assessing the stability of human locomotion: a review of current measures

Bruijn

Meijer

Beek³

et al. 2013

J. R. Soc. Interface.

529

365

View full text Add to dashboard Cite

show abstract

“…In the actual literature [11,39], the terms used to translate the variability (or the less-regularity) of the gait are more often the ''variability of the stride length'' or the ''variability of the stride time'' or the ''variability of the step width'' as expressed in terms of the coefficients of variation for each term [CV or CoV calculated as (SD/mean) 9 100]. According to Moe-Nilssen [40], step time variability seems to be correlated with vertical (cranio-caudal) interstep trunk variability.…”

Section: Discussionmentioning

confidence: 99%

Gait speed or gait variability, which one to use as a marker of risk to develop Alzheimer disease? A pilot study

Gillain¹,

Dramé

Lekeu³

et al. 2015

Aging Clin Exp Res

View full text Add to dashboard Cite

show abstract

“…To investigate whether the changes in gait speed or gait variability precede MCI, we conducted a 4-year prospective cohort study of elderly with NC who had no evidence of cognitive impairment. Gait variability was quantified by step time variability since temporal variability measures such as step time variability and stride time variability are the most widely reported factors that predict cognitive decline, and the use of steps instead of strides to calculate gait variability has been suggested in previous studies [17,18].…”

Section: Introductionmentioning

confidence: 99%

Gait Variability Can Predict the Risk of Cognitive Decline in Cognitively Normal Older People

Byun

Han

Kim

et al. 2018

Dement Geriatr Cogn Disord

View full text Add to dashboard Cite

Background: The aim of this study was to investigate the association of gait speed and gait variability, an index of how much gait parameters, such as step time, fluctuate step-to-step, with risk of cognitive decline in cognitively normal elderly individuals. While high gait variability is emerging as an early indicator of dementing illnesses, there is little research on whether high gait variability predicts cognitive decline in cognitively normal elderly who have no evidence of cognitive impairment. Methods: In this 4-year prospective cohort study on 91 community-dwelling cognitively normal elderly individuals without cerebral ischemic burden or Parkinsonism, we evaluated gait speed and step time variability using a tri-axial accelerometer placed on the center of body mass, and diagnosed mild cognitive impairment (MCI) according to the International Working Group on MCI. We performed Kaplan-Meier analysis with consecutive log-rank testing for MCI-free survival by cohort-specific tertiles of gait speed; hazard ratios (HR) of incident MCI were estimated using Cox proportional hazards regression analysis adjusted for age, sex, education level, Cumulative Illness Rating Scale score, GDS score, and presence of the apolipoprotein E ε4 allele. Results: Out of the 91 participants in the baseline assessment, 87 completed one or more 2-year follow-up assessments, and the median duration of follow-up was 47.1 months. Kaplan-Meier curves of incident MCI show evident differences in risk by gait variability group (χ² = 9.64, p = 0.002, log-rank test). Mean MCI-free survival in the high variability group was 12% shorter than in the mid-to-low tertile group (47.4 ± 1.74 [SD] vs. 54.04 ± 0.52 months), while it was comparable between gait speed groups (51.59 ± 0.70 vs. 50.64 ± 1.77 months; χ² = 1.16, p = 0.281). In multivariate analysis, subjects with high gait variability showed about 12-fold higher risk of MCI (HR = 11.97, 95% CI = 1.29–111.37) than those with mid-to-low variability. However, those with slow gait speed showed comparable MCI risk to those with mid-to-high speed (HR = 5.04, 95% CI = 0.53–48.18). Conclusions: Gait variability may be a better predictor of cognitive decline than gait speed in cognitively normal elderly individuals without cerebral ischemic burden or Parkinsonism.

show abstract

Gait variability measures may represent different constructs

Cited by 85 publications

References 26 publications

Assessing the stability of human locomotion: a review of current measures

Assessing the stability of human locomotion: a review of current measures

Gait speed or gait variability, which one to use as a marker of risk to develop Alzheimer disease? A pilot study

Gait Variability Can Predict the Risk of Cognitive Decline in Cognitively Normal Older People

Contact Info

Product

Resources

About