A model of free-living gait: A factor analysis in Parkinson’s disease

Morris, Rosie; Hickey, Aodhán; Din, Silvia Del; Godfrey, Alan; Lord, Susan; Rochester, Lynn

doi:10.1016/j.gaitpost.2016.11.024

Cited by 67 publications

(66 citation statements)

References 15 publications

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“…Their model is similar to that of healthy subjects except for a few variability parameters (step time, stance time and step width variability) that were allocated in different factors. Other authors [19,20] created gait models specific for patients with PD, but using inertial sensors rather than an electronic walkway. In particular, Horak et al [19] used body-worn sensors to determine functional mobility domains for evaluating gait, postural sway, step initiation, turning, and trunk and arm motion when patients were performing an Instrumented Stand and Walk Test.…”

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confidence: 99%

“…In particular, Horak et al [19] used body-worn sensors to determine functional mobility domains for evaluating gait, postural sway, step initiation, turning, and trunk and arm motion when patients were performing an Instrumented Stand and Walk Test. On the other hand, Morris et al [20] used body-worn sensors for analysing only gait parameter in a controlled and in a free-living environment, finding four factors.…”

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confidence: 99%

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Proposal of a new conceptual gait model for patients with Parkinson’s disease based on factor analysis

et al. 2019

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Background Gait impairment is a risk factor for falls in patients with Parkinson’s disease (PD). Gait can be conveniently assessed by electronic walkways, but there is need to select which spatiotemporal gait variables are useful for assessing gait in PD. Existing models for gait variables developed in healthy subjects and patients with PD show some methodological shortcomings in their validation through exploratory factor analysis (EFA), and were never confirmed by confirmatory factor analysis (CFA). The aims of this study were (1) to create a new model of gait for PD through EFA, (2) to analyze the factorial structure of our new model and compare it with existing models through CFA. Results From the 37 variables initially considered in 250 patients with PD, 10 did not show good-to-excellent reliability and were eliminated, while further 19 were eliminated after correlation matrix and Kaiser–Meyer–Olkin measure. The remaining eight variables underwent EFA and three factors emerged: pace/rhythm, variability, and asymmetry. Structural validity of our new model was then examined with CFA, using the structural equation modeling. After some modifications, suggested by the Modification Indices, we obtained a final model that showed an excellent fit. In contrast, when the structure of previous models of gait was analyzed, no model achieved convergence with our sample of patients. Conclusions Our model for spatiotemporal gait variables of patients with PD is the first to be developed through an accurate EFA and confirmed by CFA. It contains eight gait variables divided into three factors and shows an excellent fit. Reasons for the non-convergence of other models could be their inclusion of highly inter-correlated or low-reliability variables or could be possibly due to the fact that they did not use more recent methods for determining the number of factors to extract.

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Proposal of a new conceptual gait model for patients with Parkinson’s disease based on factor analysis

et al. 2019

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“…Recent work highlighted the most effective algorithm for temporal characteristic estimation from L5 [58]. When utilised with a spatial algorithm [59] they have been validated as a suitable micro gait model for older adults and those with a movement disorder in clinic and during free-living [60]. However, that required tailoring algorithms to define step timing variables [61].…”

Section: Micro Gaitmentioning

confidence: 99%

Wearables for independent living in older adults: Gait and falls

Godfrey

2017

Maturitas

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Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) are one approach to offer a viable means for ubiquitous, sustainable and scalable monitoring in habitual free-living environments. Gait has been presented as a relevant (bio) marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of recent wearable gait assessment literature, focusing on the need for an algorithm fusion approach to measurement cumulating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Opportunities for how this domain needs to progress are also summarised. Highlights• Wearables can meet older adults' needs for independent living.• Gait assessment is a (bio)marker within ageing and different pathologies.• Measuring gait with wearables has been innovative but fraught with inconsistencies.

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“…Factor analysis effectively reduced the dimensionality of our dataset from twenty-five gait parameters to four independent domains of gait. Factors reflecting the spatial and temporal aspects of gait speed are consistently reported in literature [32][33][34][35][36] . Other factors related to gait reported in literature are variability 32,33,35,36 , asymmetry 33,35,36 , postural control 33 , and trunk motion 34 .…”

Section: Discussionmentioning

confidence: 76%

Independent and sensitive gait parameters for objective evaluation in knee and hip osteoarthritis using wearable sensors

Boekesteijn

Smolders²,

Busch³

et al. 2020

Preprint

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Objective:To identify non-redundant gait parameters sensitive to end-stage knee and hip osteoarthritis (OA), with a specific focus on turning, dual task performance, and upper body motion in addition to straight-ahead gait. Design:Gait was compared between individuals with unilateral, end-stage knee (n=25) or hip OA (n=26) scheduled for joint replacement, and healthy controls (n=27). For 2 minutes, subjects walked back-and-forth along a 6 meter trajectory making 180° turns, with and without a secondary cognitive task. Gait parameters were collected using 4 inertial measurement units on the feet and trunk. The dataset was reduced using factor analysis. One gait parameter from each factor was selected based on factor loading and effect size of the comparison between OA groups and healthy controls. Results:Four independent domains of gait were obtained: speed-spatial, speed-temporal, dual task cost, and upper body motion. Turning parameters did not constitute a separate domain.From these domains, stride length (speed-spatial) and cadence (speed-temporal) had the strongest factor loadings and effect sizes for both knee and hip OA, and lumbar sagittal range of motion (upper body motion) for hip OA only. Conclusions:Stride length, cadence, and lumbar sagittal range of motion were non-redundant and sensitive parameters, representing gait adaptations in individuals with knee or hip OA.Turning or dual task parameters had no additional value for evaluating gait in knee and hip OA.These findings hold promise for the objective evaluation of gait in the clinic. Future steps should include testing of responsiveness to interventions aiming to improve mobility.

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A model of free-living gait: A factor analysis in Parkinson’s disease

Cited by 67 publications

References 15 publications

Proposal of a new conceptual gait model for patients with Parkinson’s disease based on factor analysis

Proposal of a new conceptual gait model for patients with Parkinson’s disease based on factor analysis

Wearables for independent living in older adults: Gait and falls

Independent and sensitive gait parameters for objective evaluation in knee and hip osteoarthritis using wearable sensors

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