Gait Changes in Older Adults: Predictors of Falls or Indicators of Fear?

Maki, Brian E.

doi:10.1111/j.1532-5415.1997.tb00946.x

Cited by 1,432 publications

(1,262 citation statements)

References 42 publications

(104 reference statements)

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“…Disparities in testing protocols and disease duration between the samples (mean 13 years), compared to the current study (mean 6 years) may account for these observed differences (Sustakoski, Perera, VanSwearingen, Studenski, & Brach, 2015). Although similar investigations of the relationships between the Walk‐12G and performance‐based gait measures are lacking, fear of falling has been associated with slower gait speeds in healthy older adults (Maki, 1997) and people with PD (Bryant, Rintala, Hou, & Protas, 2014; Rochester et al., 2008). Additionally, Curtze et al.…”

Section: Discussionmentioning

confidence: 99%

Patient‐reported and performance‐based measures of walking in mild–moderate Parkinson's disease

et al. 2018

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BackgroundKnowledge of the relationships between patient‐reported and performance‐based walking measures in Parkinson's disease (PD) should inform clinical decision‐making. The Walk‐12G reliably captures perceived walking difficulties but has not been compared to performance‐based walking in laboratory or free‐living settings or across different groups.ObjectivesTo investigate the relationship between patient‐reported walking difficulties (Walk‐12G) and performance‐based walking in laboratory and free‐living conditions and to determine whether the Walk‐12G can distinguish between the subgroups, (i) people with/without PD and (ii) mild/moderate disease stages.MethodsForty‐seven people without and 49 people with PD (Hoehn and Yahr stage II and III) were assessed in relation to patient‐reported walking difficulties (Walk‐12G scale); spatiotemporal gait characteristics (Pace; Rhythm; Asymmetry; Variability; and Postural control) using a laboratory‐based electronic walkway; and walking behavior (mean steps/day and minutes of brisk walking/day) using accelerometers in free‐living conditions.ResultsThe Walk‐12G correlated moderately with the spatiotemporal domain step velocity (r = −0.46) and walking behavior, measured as mean steps/day (r = −0.46). Weaker correlations were observed for step length and minutes spent in brisk walking (r = −0.36 and r = −0.35, respectively). Poor correlations were observed for all other spatiotemporal domains. The Walk‐12G could distinguish between people with and without PD (Effect size, r = 0.82) and between those at mild/moderate disease stages (r = 0.34).ConclusionsPerceived walking difficulties showed weak to moderate associations with performance‐based measures of walking in mild–moderate PD. As the strongest associations were observed for step velocity and walking behavior, targeting these specific gait aspects could improve perceived walking difficulties in daily life.

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Section: Discussionmentioning

confidence: 99%

Patient‐reported and performance‐based measures of walking in mild–moderate Parkinson's disease

et al. 2018

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“…Subsequently, the generated time series of stride time (and/or swing time or other parameters) can then become the input to algorithms that quantify the dynamics. For example, stride time variability, the magnitude of the stride-to-stride fluctuations in the gait cycle duration, is calculated by determining the standard deviation (SD) or the coefficient of variation (CV) of each subject's stride time time series (Maki, 1997;Hausdorff, Edelberg et al, 1997;Hausdorff, Rios et al, 2001). Similar methods can be used to study the variability of other measures of gait timing (e.g., swing time).…”

Section: Notes On Nomenclature and Data Collection Methodsmentioning

confidence: 99%

“…Although many gait parameters might appear to be useful for distinguishing between fallers and non-fallers, studies suggest that this is not actually the case; rather, features often associated with fallers more closely reflect fear of falling and a cautious gait (Feltner, MacRae, & McNittGray, 1994;Hausdorff, Edelberg et al, 1997;Hausdorff, Rios et al, 2001;Maki, 1997). Building on the suggestions of Gabell and Nayak (1984), we hypothesized that quantitative measures of gait unsteadiness, as reflected in the temporal parameters of gait, might be increased in elderly fallers.…”

Section: Quantifying Fall Risk and Gait Instabilitymentioning

confidence: 99%

“…As discussed above, gait variability apparently predicts falls in older adults with 'idiopathic falls' as well as in patients with Alzheimer's disease (Azizah, Lajoie, & Teasdale, 2003;Hausdorff, Edelberg et al, 1997;Hausdorff, Rios et al, 2001;Maki, 1997;Nakamura, Meguro, & Sasaki, 1996;Visser, 1983;). In PD, there is a need for better predictors of falls (Bloem, Grimbergen, Cramer, Willemsen, & Zwinderman, 2001;Bloem, Van Vugt, & Beckley, 2001).…”

Section: Gait Variability and Fall Risk In Parkinson's Disease (Pd)mentioning

confidence: 99%

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Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking

Hausdorff

2007

Human Movement Science

729

594

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Until recently, quantitative studies of walking have typically focused on properties of a typical or average stride, ignoring the stride-to-stride fluctuations and considering these fluctuations to be noise. Work over the past two decades has demonstrated, however, that the alleged noise actually conveys important information. The magnitude of the stride-to-stride fluctuations and their changes over time during a walk -gait dynamics -may be useful in understanding the physiology of gait, in quantifying age-related and pathologic alterations in the locomotor control system, and in augmenting objective measurement of mobility and functional status Indeed, alterations in gait dynamics may help to determine disease severity, medication utility, and fall risk, and to objectively document improvements in response to therapeutic interventions, above and beyond what can be gleaned from measures based on the average, typical stride. This review discusses support for the idea that gait dynamics has meaning and may be useful in providing insight into the neural control of locomtion and for enhancing functional assessment of aging, chronic disease, and their impact on mobility.

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“…In knee osteoarthritis, reduced variability of knee movements has been reported (Fallah-Yakhdani et al, 2010;Lewek et al, 2006). Reduced variability may be harmful to the joint (Lewek et al, 2006), but increased variability suggests a lack of control, and coincides with a higher risk of falling (e.g., Hausdorff, 2007;Leitner et al, 2007;Maki, 1997). Earlier, we hypothesised that subjects with knee osteoarthritis co-contract in order to reduce variability (Fallah-Yakhdani et al, 2010), which may enhance the control over knee motion (e.g., Benedetti et al, 2003;Kean et al, 2009;Schmitt and Rudolph, 2008;Van Dieën et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Determinants of co-contraction during walking before and after arthroplasty for knee osteoarthritis

Fallah-Yakhdani¹,

Bafghi²,

Meijer³

et al. 2012

Clinical Biomechanics

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Background: Knee osteoarthritis patients co-contract in knee-related muscle pairs during walking. The determinants of this co-contraction remain insufficiently clear. Methods: A heterogeneous group of 14 patients was measured before and one year after knee arthroplasty, and compared to 12 healthy peers and 15 young subjects, measured once. Participants walked on a treadmill at several imposed speeds. Bilateral activity of six muscles was registered electromyographically, and cocontraction time was calculated as percentage of stride cycle time. Local dynamic stability and variability of sagittal plane knee movements were determined. The surgeon's assessment of alignment was used. Preoperatively, multivariate regressions on co-contraction time were used to identify determinants of cocontraction. Post-operatively it was assessed if predictor variables had changed in the same direction as co-contraction time. Findings: Patients co-contracted longer than controls, but post-operatively, differences with the healthy peers were no longer significant. Varus alignment predicted co-contraction time. No patient had post-operative varus alignment. The patients' unaffected legs were more unstable, and instability predicted co-contraction time in both legs. Post-operatively, stability normalised. Longer unaffected side co-contraction time was associated with reduced affected side kinematic variability. Post-operatively, kinematic variability had further decreased. Interpretations: Varus alignment and instability are determinants of co-contraction. The benefits of cocontraction in varus alignment require further study. Co-contraction probably increases local dynamic stability, which does not necessarily decrease the risk of falling. Unaffected side co-contraction contributed to decreasing affected side variability, but other mechanisms than co-contraction may also have played a role in decreasing variability.

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Gait Changes in Older Adults: Predictors of Falls or Indicators of Fear?

Cited by 1,432 publications

References 42 publications

Patient‐reported and performance‐based measures of walking in mild–moderate Parkinson's disease

Patient‐reported and performance‐based measures of walking in mild–moderate Parkinson's disease

Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking

Determinants of co-contraction during walking before and after arthroplasty for knee osteoarthritis

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