A Systematic Review of Non-Pharmacological Interventions to Improve Gait Asymmetries in Neurological Populations

Meder, Krista; LoJacono, Chanel T.; Rhea, Christopher K.

doi:10.3390/sym14020281

Cited by 9 publications

(7 citation statements)

References 59 publications

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“…Interventions to improve gait symmetry in individuals with mild pain may improve step regularity and could be investigated in future studies. [32] The idea of altered neuromotor control in the presence of pain is further supported by our nding of lower sample entropy (i.e., lower gait complexity) being related to mild pain.. Importantly, the association of sample entropy with knee pain was present after adjusting for age given that with aging, gait becomes less complex [33] .…”

Section: Discussionmentioning

confidence: 83%

Gait Alterations Associated With Mild Unilateral Knee Pain During Walking: a Machine Learning Approach Using Wearable Sensors

Felson

Jafarzadeh

Kolachalama

et al. 2022

Preprint

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Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis(OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-meter walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique (“super learning”) to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain.Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR=0.80 (0.64-1.00) and aOR=0.79(0.66-0.95), respectively].

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Section: Discussionmentioning

confidence: 83%

Gait Alterations Associated With Mild Unilateral Knee Pain During Walking: a Machine Learning Approach Using Wearable Sensors

Felson

Jafarzadeh

Kolachalama

et al. 2022

Preprint

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“…This hypothesis aligns with theories [33][34][35] suggesting a link between neurobiological mechanisms underlying chronic pain and control of movement. Interventions to improve gait symmetry in individuals with mild pain may improve step regularity and could be investigated in future studies 36 .…”

Section: Discussionmentioning

confidence: 99%

Relation of gait measures with mild unilateral knee pain during walking using machine learning

Felson

Jafarzadeh

Kolachalama

et al. 2022

Sci Rep

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Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis (OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-m walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique (“super learning”) to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain. Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR 0.80 (0.64–1.00) and aOR 0.79 (0.66–0.95), respectively].

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“…Indeed, the magnitude of walking quality improvements observed in this study are comparable with other rehabilitation paradigms including wearable robotics, functional electrical stimulation, and treadmill training. Improvements following these rehabilitation approaches range from 0% to 50% reduction in temporal asymmetries 47,[54][55][56][57][58] and 0% to 32% reduction in the energetic cost of walking. [59][60][61][62][63] Our study is especially notable given that we report changes resulting from a single session of training, whereas most other intervention studies report improvements after several days or weeks of training.…”

Section: Walking Quality Improvements In Contextmentioning

confidence: 99%

Autonomous Control of Music to Retrain Walking After Stroke

Collimore

Cataldo

Aiello

et al. 2023

Neurorehabil Neural Repair

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Background Post-stroke care guidelines highlight continued rehabilitation as essential; however, many stroke survivors cannot participate in outpatient rehabilitation. Technological advances in wearable sensing, treatment algorithms, and care delivery interfaces have created new opportunities for high-efficacy rehabilitation interventions to be delivered autonomously in any setting (ie, clinic, community, or home). Methods We developed an autonomous rehabilitation system that combines the closed-loop control of music with real-time gait analysis to fully automate patient-tailored walking rehabilitation. Specifically, the mechanism-of-action of auditory-motor entrainment is applied to induce targeted changes in the post-stroke gait pattern by way of targeted changes in music. Using speed-controlled biomechanical and physiological assessments, we evaluate in 10 individuals with chronic post-stroke hemiparesis the effects of a fully-automated gait training session on gait asymmetry and the energetic cost of walking. Results Post-treatment reductions in step time (Δ: −12 ± 26%, P = .027), stance time (Δ: −22 ± 10%, P = .004), and swing time (Δ: −15 ± 10%, P = .006) asymmetries were observed together with a 9 ± 5% reduction ( P = .027) in the energetic cost of walking. Changes in the energetic cost of walking were highly dependent on the degree of baseline energetic impairment ( r =− .90, P < .001). Among the 7 individuals with a baseline energetic cost of walking larger than the normative value of healthy older adults, a 13 ± 4% reduction was observed after training. Conclusions The closed-loop control of music can fully automate walking rehabilitation that markedly improves walking after stroke. Autonomous rehabilitation delivery systems that can safely provide high-efficacy rehabilitation in any setting have the potential to alleviate access-related care gaps and improve long-term outcomes after stroke.

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A Systematic Review of Non-Pharmacological Interventions to Improve Gait Asymmetries in Neurological Populations

Cited by 9 publications

References 59 publications

Gait Alterations Associated With Mild Unilateral Knee Pain During Walking: a Machine Learning Approach Using Wearable Sensors

Gait Alterations Associated With Mild Unilateral Knee Pain During Walking: a Machine Learning Approach Using Wearable Sensors

Relation of gait measures with mild unilateral knee pain during walking using machine learning

Autonomous Control of Music to Retrain Walking After Stroke

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