Pelvic excursion during walking post-stroke: A novel classification system

Little, Virginia L.; McGuirk, Theresa E.; Perry, Lindsay; Patten, Carolynn

doi:10.1016/j.gaitpost.2018.03.052

Cited by 12 publications

(15 citation statements)

References 28 publications

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“…Prior studies typically compared a small number of gait speeds, limiting our ability to identify sub-maximal speeds that optimize gait biomechanics post-stroke [1,3,9]. Multiple studies have reported that walking faster than the individual's self-selected (SS) gait speed improves post-stroke gait deficits, such as paretic push-off and ankle power, without increasing reliance on compensatory mechanisms or increasing inter-limb asymmetry [1,3,4,9,10]. For example, Lamontagne and Fung (2004) reported improvements in kinematics and muscle activity as well as reductions in inter-limb asymmetry for some temporal variables when walking at participants' fastest safe speed compared to the self-selected (SS) speed [1].…”

Section: Introductionmentioning

confidence: 99%

Fastest may not maximize gait quality: differential and individual-specific immediate effects of gait speed on biomechanical variables post-stroke

Rosenberg

Christianson

Liu

et al. 2022

Preprint

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Background: A common perspective in post-stroke gait training is that walking at the fastest safe speed maximizes the quality of gait biomechanics, with limited detrimental effects on compensatory biomechanics and inter-limb asymmetry. This fastest is best perspective is highly relevant to treadmill training paradigms, as mass high-intensity stepping practice with high-quality biomechanics can improve walking function and reinforce desirable gait patterns. However, it is unclear if walking at the fastest safe speed maximizes the quality of (i.e., optimizes) post-stroke gait biomechanics across variables, individuals, and walking function levels, or if there exists a significant cost (i.e., benefit lost) of walking at the fastest speed when fastest is not optimal. Methods: Here, we determined if walking at the fastest speed optimized 16 biomechanical magnitude and inter-limb asymmetry variables, in 14 low- (n=7) and high-functioning (n=7) stroke survivors. Participants walked at six speeds ranging from their self-selected to fastest safe speed. To characterize the relative benefit of optimizing, rather than maximizing, gait speed for each variable, we compared the biomechanical cost (i.e., immediate speed-induced change versus the self-selected speed) of walking at the fastest versus the optimal speed. Finally, we used linear regression to characterize how each variable's quality changed with absolute speed. Results: Across speeds, 50% of magnitude and 17% of asymmetry variables were optimized at the fastest speed, but which variables were optimized differed between participants. Compared to walking at the optimal speed for each variable, the fastest speed elicited large biomechanical costs for some inter-limb asymmetry variables (difference in Cohen's d=0.1-0.9). Both low- and high-function subgroups exhibited significant positive correlations between walking speed and paretic-leg trailing limb angle, peak ankle moment, and peak hip and ankle power magnitudes (all p<0.001), though the magnitude of changes in some variables differed between groups. Changes in inter-limb asymmetry were highly variable, even within-groups. Conclusions: These results refine the perspective that fastest is best, showing that the training speeds that maximize gait quality may not be the fastest for all individuals and biomechanical variables. Individual-specific stroke gait quality metrics encompassing multiple biomechanical variables are needed to guide gait speed optimization for precision rehabilitation.

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Section: Introductionmentioning

confidence: 99%

Fastest may not maximize gait quality: differential and individual-specific immediate effects of gait speed on biomechanical variables post-stroke

Rosenberg

Christianson

Liu

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Mulroy et al (2003) demonstrated that the main factors classifying gait pattern were walking speed, peak knee extension in the MS, and peak dorsiflexion during swing [ 25 ]. Little suggested a classification based on pelvic excursion deviation in gait in patients after stroke [ 26 ]. Recently, Wang et al (2021) revealed a classification based on abnormal gait kinematics (i.e., drop foot, circumduction, hip hiking, and back knee) in patients after stroke based on deep neural networks [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Ankle Joint Stiffness during Walking to Determine the Use of Ankle Foot Orthosis after Stroke

et al. 2021

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Categorization based on quasi-joint stiffness (QJS) may help clinicians select appropriate ankle foot orthoses (AFOs). The objectives of the present study were to classify the gait pattern based on ankle joint stiffness, also called QJS, of the gait in patients after stroke and to clarify differences in the type of AFO among 72 patients after stroke. Hierarchical cluster analysis was used to classify gait patterns based on QJS at least one month before the study, which revealed three distinct subgroups (SGs 1, 2, and 3). The proportion of use of AFOs, articulated AFOs, and non-articulated AFOs were significantly different among SGs 1–3. In SG1, with a higher QJS in the early and middle stance, the proportion of the patients using articulated AFOs was higher, whereas in SG3, with a lower QJS in both stances, the proportion of patients using non-articulated AFOs was higher. In SG2, with a lower QJS in the early stance and higher QJS in the middle stance, the proportion of patients using AFOs was lower. These findings indicate that classification of gait patterns based on QJS in patients after stroke may be helpful in selecting AFO. However, large sample sizes are required to confirm these results.

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“…More and more experts believe that a precise assessment of the neuromuscular function should include both gait analysis and SEMG, as the combined method shows higher accuracy to assess the physiological and functional status of peripheral nervous systems rather than simply anatomical and structural evaluation. Di Nardo et al devised a method to investigated patients with lumbar spinal stenosis, hemiplegic cerebral palsy, and stroke.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical Evaluation of Preoperative Rehabilitation in Patients of Anterior Cruciate Ligament Injury

Qie

et al. 2020

Orthopaedic Surgery

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Objectives: To investigate the biomechanical characteristics of patients with anterior cruciate ligament (ACL) injury by gait analysis, surface electromyography (SEMG), and proprioception test, and provide rehabilitation suggestions according to the results.Methods: In this retrospective cohort study, 90 adults with unilateral ACL injury, ranging in age from 19 to 45 years (66 men and 24 women, average age: 30.03 AE 7.91) were recruited for this study form May 2018 to July 2019. They were divided into three groups according to the time after the injury: group A (3-week to 1.5-month), group B (1.5-month to 1 year), and group C (more than 1 year). The SEMG signals were collected from the bilateral rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL) and the root mean square (RMS) were used to assess muscular activity. SEMG were used to analyze muscles function, gait analysis was used to evaluate the walking stability, balance and location assessment were used to analyze the proprioception.Results: Through the comparison between bilateral limbs, all muscles strength shown decreased (RF: 239.94 AE 129.70 vs 364.81 AE 148.98, P = 0.001; VM: 298.88 AE 175.41 vs 515.79 AE 272.49, P = 0.001; VL:389.54 AE 157.97 vs 594.28 AE 220.31, P < 0.001) and the division of proprioception became larger (tandem position: 7.79 AE 1.57 vs 6.33 AE 1.49, P = 0.001; stance with one foot: 8.13 AE 0.84 vs 7.1 AE 0.57, P = 0.003; variance of 30 : 6.96 AE 3.15 vs 4.45 AE 1.67, P = 0.03; variance of 60 : 4.64 AE 3.38 vs 2.75 AE 1.98, P = 0.044) in the injured side when compared to the non-injured and 26 gait parameters were shown difference in group A. In group B, the muscle strength of VL shown decreased (VL: 381.23 AE 142.07 vs 603.9 AE 192.72, P < 0.001) and the division of location of 30 became larger (7.62 AE 4.98 vs 4.33 AE 3.24, P = 0.028) in the injured side when compared to the non-injured side and there were eight gait parameters that showed differences. In group C, the muscle strength and proprioception showed no differences and only 16 gait parameters showed differences between the bilateral limbs. Conclusion:The results proved the deterioration of proprioception in 30 of injured side will not recover and non-injury side and will become worse after 1 year from the injury; among the VL, VM, and RF, the recovery rate of VL is the slowest and bilateral straight leg raising (SLR) (30 ) is the best way to train it; the gait stability will be worse after 1 year from the injury. Therefore, we suggest that the training for proprioception in 30 and VL are important for the rehabilitation, and the ACL reconstruction should be performed within 1 year. AE 62.75 205.55 AE 86.79 0.158 216.15 AE 61.52 196.07 AE 44.06 0.152 VL 382.7 AE 136.09 439.62 AE 119.74 0.091 287.18 AE 106.76 374.9 AE 103.22 0.002* 363.7 AE 98.95 408.1 AE 83.12 0.065 243.77 AE 91.62 281.47 AE 84.63 0.103 258.88 AE 79.23 231.9 AE 91.51 0.227 248.63 AE 86.51 217.35 AE 79.63 0.151 Group C RF 269.16 AE 74.43 310.44 AE 93.12 0.063 213.53 AE 92.05 267.27 AE 139.1 0...

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Pelvic excursion during walking post-stroke: A novel classification system

Cited by 12 publications

References 28 publications

Fastest may not maximize gait quality: differential and individual-specific immediate effects of gait speed on biomechanical variables post-stroke

Fastest may not maximize gait quality: differential and individual-specific immediate effects of gait speed on biomechanical variables post-stroke

Classification of Ankle Joint Stiffness during Walking to Determine the Use of Ankle Foot Orthosis after Stroke

Biomechanical Evaluation of Preoperative Rehabilitation in Patients of Anterior Cruciate Ligament Injury

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