Temporal, Kinematic, and Kinetic Variables Related to Gait Speed in Subjects With Hemiplegia: A Regression Approach

Olney, Sandra J.; Griffin, Malcolm; McBride, I.

doi:10.1093/ptj/74.9.872

Cited by 338 publications

(218 citation statements)

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“…The high correlation coefficients found in the sagittal plane concur with the results by Olney et al 6 and further support the importance of muscle function for both the paretic and non-paretic limbs in this plane, particularly of the hip flexors during pull-off and the ankle plantarflexors during stance and push-off. The maximum knee absorption power (K3-S), which represents the work of the quadriceps to eccentrically control the flexing knee during propulsion, was highly correlated with gait speed on both sides indicating that the slower walkers did not require this eccentric control as the weak ankle push-off/hip pull-off did not result in the forces that normally collapse the knee.…”

Section: Discussionsupporting

confidence: 89%

“…[4][5][6][7] Relative to values of neurologically healthy persons walking at their selfselected speeds, the sagittal joint magnitude has been generally reported as decreased for both lower limbs with a greater reduction on the paretic side. 5,[8][9] Furthermore, contrary to the gait of healthy individuals, which is fairly consistent in pattern across subjects, marked variation in gait patterns has been noted in persons with stroke.…”

Section: Introductionmentioning

confidence: 99%

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Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed

2004

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The purpose of this study was to identify 3D kinematic and kinetic gait profiles in individuals with chronic stroke and to determine whether the magnitude or pattern (shape and direction of curve) of these profiles relate to gait performance (as measured by self-selected gait speed). More than one type of kinematic and kinetic pattern was identified in all three planes in 20 individuals with stroke (age = 61.2 ± 8.4 years). Persons in the "fast" speed group did not necessarily exhibit the gait patterns closest to the ones reported for healthy adults. For example, in the frontal plane, a variation from the typical pattern (i.e., a hip abductor pattern in swing) was more common among the "fast" group. Correlations revealed that in addition to the sagittal profiles, the magnitudes of the frontal and transverse profiles are also related to speed, particularly the frontal hip powers. The results support the importance of hip abductors, in addition to the sagittal plane muscle groups, for both the paretic and non-paretic limbs. Furthermore, profiles which resemble gait patterns of neurologically healthy adults do not necessarily result in the faster gait speeds for individuals with chronic stroke.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed

2004

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“…A force plate placed along the walkway during gait, and in the first step during stair ascent, was used to record ground reaction forces. In this study, only the peaks in the power profiles of the gait cycle and the stair ascent cycle, which are known or suspected to be determinants of gait speed, were considered for the analyses (95)(96)(97)(98)(99)(100). At the ankle, the power peak, corresponding to the plantarflexor moment occurring at push off, was retained.…”

Section: Personal Variablesmentioning

confidence: 99%

Preoperative predictors of locomotor ability two months after total knee arthroplasty for severe osteoarthritis

Parent

Moffet

2003

Arthritis & Rheumatism

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Objective. To identify preoperative predictors of locomotor ability 2 months after total knee arthroplasty (TKA). Methods. Sixty-five participants scheduled for a first TKA were recruited. The dependent variable was the distance covered during the 6-minute gait test 2 months after TKA. The independent variables, measured before surgery, were grouped into 3 categories: 1) personal; 2) organic system, and 3) capability variables. Multiple regression analyses were conducted to determine the best predictors of the dependent variable. Modified cross-validation of the model combining predictors from the three categories was obtained with 10 random samples derived from the original cohort by resampling with replacement. Results. Predictors were identified in the 3 categories: 1) gender, number of comorbidities, body mass index, 2) knee pain, flexion and strength, lower limb mechanical power, and 3) preoperative 6-minute gait distance. When these variables were modeled together only the preoperative 6-minute gait distance and knee pain and flexion remained significant (adjusted R 2 ‫؍‬ 0.66). Conclusion. Using variables easily measured before surgery, it is possible to predict with good accuracy locomotor ability 2 months after TKA.

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“…Olney et al [26], for example, argue that using gait velocity figures alone can neither assist in understanding the nature of gait deficits nor support direct treatment. They suggest that while gait velocity is reflective of gait performance it does not have "explicative capacity", and is often insufficient in discriminating among post-stroke ambulators.…”

Section: Patient/participant Ambulatory Classificationmentioning

confidence: 99%

Questioning Classic Patient Classification Techniques in Gait Rehabilitation: Insights from Wearable Haptic Technology

Georgiou

Holland

Linden

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Classifying stroke survivors based on their walking abilities is an important part of the gait rehabilitation process. It can act as powerful indicator of function and prognosis in both the early days after a stroke and long after a survivor receives rehabilitation. This classification often relies solely on walking speed; a quick and easy measure, with only a stopwatch needed. However, walking speed may not be the most accurate way of judging individual's walking ability. Advances in technology mean we are now in a position where ubiquitous and wearable technologies can be used to elicit much richer measures to characterise gait. In this paper we present a case study from one of our studies, where within a homogenous group of stroke survivors (based on walking speed classification) important differences in individual results and the way they responded to rhythmic haptic cueing were identified during the piloting of a novel gait rehabilitation technique.

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Temporal, Kinematic, and Kinetic Variables Related to Gait Speed in Subjects With Hemiplegia: A Regression Approach

Cited by 338 publications

References 1 publication

Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed

Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed

Preoperative predictors of locomotor ability two months after total knee arthroplasty for severe osteoarthritis

Questioning Classic Patient Classification Techniques in Gait Rehabilitation: Insights from Wearable Haptic Technology

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