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Original Article

Namuna Sharma, Chandrika PC*

Padmashree Institute of Physiotherapy, Komaghatta, Bangalore, Karnataka, India.

*Corresponding author:

Dr. Chandrika PC, MPT Associate Professor of Padmashree Institute of Physiotherapy, Komaghatta, Bangalore, Karnataka, India. E-mail: Chandrikapc84@gmail.com Affiliated to Rajiv Gandhi University of Health Sciences, Bengaluru, Karnataka.

Received Date: 2021-08-08,
Accepted Date: 2021-09-02,
Published Date: 2021-10-31
Year: 2021, Volume: 1, Issue: 3, Page no. 22-30, DOI: 10.26463/rjpt.1_3_6
Views: 1472, Downloads: 41
Licensing Information:
CC BY NC 4.0 ICON
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0.
Abstract

Background and Objectives: Stroke is the most acquired neurological condition among adult population worldwide. Often stroke survivors have walking limitations and are unable to walk without assistance. Hence, this study was designed to study the effect of surface electromyograph (EMG) biofeedback on gait among stroke subjects.

Methods: A randomized controlled trial was conducted on chronic stroke patients with circumductory gait and single episode of major stroke at National Institute of Unani Medicine, Bengaluru, India. The experimental group (n=15) underwent surface EMG biofeedback for 20 minutes/day, 5 times/week for 4 weeks. The control group (n=15) performed conventional physiotherapy. Both the groups underwent circuit gait training. The primary outcome measures were stride length, step length, stride width, cadence and degree of toe out.The measurement was taken prior to intervention and after the intervention.

Results: Baseline characteristics were similar in both the groups with mean age of 49±6.34 years. Both the groups similarly improved in walking at four weeks. There was no statistically significant improvement between experimental and control group (p > 0.05). However, comparison within group indicated improvement in stride width, step length, degree of toe out and cadence in experimental group.

Conclusion: Although surface EMG biofeedback is used in physiotherapy widely in clinical practice, the result provided little evidence to support the clinical significance of using EMG biofeedback to improve gait variables in chronic phase of stroke.

<p class="MsoNormal" style="text-align: justify; line-height: 150%;"><strong><span lang="EN-GB" style="font-family: 'Segoe UI',sans-serif;">Background and Objectives:</span></strong><span lang="EN-GB" style="font-family: 'Segoe UI',sans-serif;"> Stroke is the most acquired neurological condition among adult population worldwide. Often stroke survivors have walking limitations and are unable to walk without assistance. Hence, this study was designed to study the effect of surface electromyograph (EMG) biofeedback on gait among stroke subjects.</span></p> <p class="MsoNormal" style="text-align: justify; line-height: 150%;"><strong><span lang="EN-GB" style="font-family: 'Segoe UI',sans-serif;">Methods:</span></strong><span lang="EN-GB" style="font-family: 'Segoe UI',sans-serif;"> A randomized controlled trial was conducted on chronic stroke patients with circumductory gait and single episode of major stroke at National Institute of Unani Medicine, Bengaluru, India. The experimental group (n=15) underwent surface EMG biofeedback for 20 minutes/day, 5 times/week for 4 weeks. The control group (n=15) performed conventional physiotherapy. Both the groups underwent circuit gait training. The primary outcome measures were stride length, step length, stride width, cadence and degree of toe out.</span><span style="font-family: 'Segoe UI', sans-serif;">The measurement was taken prior to intervention and after the intervention.</span></p> <p class="MsoNormal" style="text-align: justify; line-height: 150%;"><strong><span lang="EN-GB" style="font-family: 'Segoe UI',sans-serif;">Results:</span></strong><span lang="EN-GB" style="font-family: 'Segoe UI', sans-serif;"> Baseline characteristics were similar in both the groups with mean age of 49&plusmn;6.34 years. Both the groups similarly improved in walking at four weeks. There was no statistically significant improvement between experimental and control group (p &gt; 0.05). However, comparison within group indicated improvement in stride width, step length, degree of toe out and cadence in experimental group.</span></p> <p class="MsoNormal" style="text-align: justify; line-height: 150%;"><strong><span lang="EN-GB" style="font-family: 'Segoe UI',sans-serif;">Conclusion:</span></strong><span lang="EN-GB" style="font-family: 'Segoe UI',sans-serif;"> Although surface EMG biofeedback is used in physiotherapy widely in clinical practice, the result provided little evidence to support the clinical significance of using EMG biofeedback to improve gait variables in chronic phase of stroke.</span></p>
Keywords
Surface EMG, Biofeedback, Stroke, Gait parameters, Circuit gait training
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Introduction

Stroke is the most acquired neurological condition among adult population worldwide.1 It is a leading cause of long-term disability and death affecting 40% of people with moderate impairments and 30% with severe disabilities.2 According to the epidemiological survey, 60% of stroke survivors have walking limitations and 30% are unable to walk without assistance.3

Therefore, assessment of spatiotemporal parameters of gait can provide valuable insights regarding overall health, cognitive performance, quality of life and mortality.4 Knee extensor thrust, flexed knee gait, insufficient knee flexion during swing phase, and medial whip are the common gait abnormalities in knee and ankle joints frequently observed in people with middle cerebral artery (MCA) stroke.5 Hence, gait analysis is important to identify and quantify abnormal gait patterns.

The gait patterns seen in hemiparetic stroke is characterized by abnormalities in step length, decreased stance phase and increased swing phase in paretic limb.6 Total stance and double stance phase are longer in unimpaired extremity compared to impaired extremity resulting in larger swing period in impaired extremity.7,8 Weakness of quadriceps muscle results the knee to move into hyperextension.9 During swing phase, there is insufficient knee flexion caused by hip flexor weakness and rectus femoris spasticity. Furthermore, there is weakness of ankle dorsiflexors and some spasticity on planter flexors leading to decreased foot clearance and toe drag.9

Biofeedback is in use for more than 50 years in rehabilitation to facilitate normal movement pattern after injury that works on principle of motor learning.10 Electromyograph (EMG) biofeedback is a method of retraining muscle by creating new feedback system through the conversion of myoelectric signals in the muscle into visual and auditory signals.11 Surface EMG biofeedback can be used to either increase activity in weak or paretic muscle or to facilitate reduction in tone in spastic muscle.12

Neuromusculoskeletal changes that affect walking and functional pattern of gait is a major drawback that hinders activities of daily living in stroke subjects. The correction of compensations made by the muscles may improve the gait. The study on surface EMG to inhibit overacting muscle so as to functionally re-educate the gait pattern would lead to extensive knowledge on further rehabilitation. The use of surface EMG has been made on ankle and knee; however, the study of hip is still lagging. The resultant compensations of gait in hemiplegic stroke occur by hip hiking, pelvic obliquity and circumduction. Therefore, to use surface EMG in hip abductor for analyzing any significant changes in gait is an area of continued interest.

Methods

Thirty stroke subjects with gait abnormality who met the inclusion criteria were recruited from National Institute of UNANI medicine from the department of Physiotherapy, Bangalore, Karnataka. Subjects aged 40 to 60 years were screened as they could walk 10 metres continuously without any assistance. Patient’s with brunnstrom stage of recovery 4 and above, mini mental state examination of 24 and above and absence of any kind of deformity and contractures were randomly allocated to group A and group B. Subjects who went through previous similar treatment, those who had cognitive and visual deficits and those incompatible with treatment were excluded for homogeneity among the subjects. The written informed consent was taken from successive patients.

Conventional treatment

Circuit gait training

Reaching on sitting

Reaching on standing

Sit to stand

Heel lifts

Step over

Isokinetic strengthening

Experimental treatment

Surface EMG biofeedback

Outcome measures

Stride length: Measured by the distance between successive heel strikes of same foot.

Step length: Measured by the distance between heel strikes of one foot to heel strikes of contra lateral foot.

Degree of toe out: It is the angle formed between the longitudinal bisection of print and line of progression of foot.

Stride width: It was measured between specific landmarks on print and the opposite ipsilateral line of progression.

Cadence: Rate at which person walks expressed as steps per minute.

Intervention

Control group: Circuit training like reaching on sitting and standing, sit to stand, step ups, heel lifts, isokinetic strengthening, walking over obstacles, up and down slopes was given. Each exercise was done for 4 min, 5 days per week, over 1 month with total of 20 sessions and 1 min rest after each exercise. Total time for the specific circuit training session was 1 hour.                                                         

Experimental group: In study subjects, sEMG  Biofeedback was given in addition to circuit training on gait. For electrode placement, gluteus medius muscle was tracked between iliac crest and greater trochanter and was palpated by asking the patient to contract the muscle through hip abduction. Adhesive sEMG electrode was kept 10 mm between innervations zone and tendon insertion point. Wireless Visual biofeedback was provided to subjects during walking. The raw graph formed during walking was seen to know the amplitude. The duration of exercise was 20 sessions, 5 days /week, for 20 min with rest periods in between for 1 month as mentioned above. Total time of training session was 1 hour.

Result

In this study, 15 subjects were included in group A among which 12 (60.0%) were males and 3 (40.0%) were females. Group-B had 10 (66.7%) of males and 5 (33.3%) of females. The baseline characteristics of gender was homogenous in both the groups.

The range and standard deviation of age in years, body mass index (BMI), and apparent limb length of right and left lower limbs in both the groups were compared by their mean, which revealed that the baseline characteristics of age, BMI, limb length were similar in both the groups.

On comparison of the pre-and post-test outcomes between the groups, stride length, stride width, step length, degree of toe out and cadence among stroke subjects showed no statistically significant differences. It was observed that initially before the intervention, the stroke subjects were similar in both the groups. After the intervention, no significant differences were observed in any of the variables in between the groups at p value <0.05.

Discussion

The present study was conducted to evaluate the effect of surface EMG biofeedback on gait in subjects with chronic stroke. The intervention included surface EMG biofeedback along with conventional circuit gait training in one group and only conventional therapy in the other group. The treatment was given for 20 sessions, 5 days/ week. Pre-and post-test assessment of gait parameters were done using clinical foot print method. Statistical analysis was done using paired and unpaired t-test for normally distributed data, Wilcoxon test for skewed data, and chi square test for categorical data, where p value less than 0.05 was considered significant.

An increased prevalence of traditional vascular risk factors among children and young adults which could lead to atherosclerosis in aged supports our findings of greater ischemic subjects. A study by Tapas Kumar on stroke in Indian population found hypertension, hyperglycemia, tobacco use, elevated level of cholesterol, triglycerides, low HDL, sedentary lifestyles, and psychological stress as major contributory factors around the mean age of 41.5 years, which correlates to our findings where majority of the subjects had stroke due to hypertension and cholesterol.13,14

BMI was taken into consideration because many studies have shown increased risk of stroke with greater BMI.

A systematic review and meta-analysis had shown that increased BMI > 24 kg/m2 is associated with greater risk of ischemic stroke. This observed difference was supposed to be accounted by increase in serum cholesterol level,15 which justifies our similar findings.

A lack of smoothness in forward progression, inequality in subsequent stance phases, swing phase and step length were observed in the studies. The variability of all these parameters was likely due to balance deficiencies and difficulty to move body over an unstable limb found during gait.16 Previous study on gait deviations in stroke showed that the variability seen in these patterns is compensation for poorer muscle control. Inclusively, a recent study by Tunc Akbas and his colleagues concluded that the change in gait patterns to stiff knee gait in stroke is not solely a compensatory motion to reduce knee flexion, but is also abnormal co-ordination between gluteus medius and rectus femoris resulting in hip circumduction affecting symmetry of gait.17

In this study, during the intervention, placement of electrode was made at gluteus medius muscle to inhibit its abnormal coordination with rectus femoris. The electrode placement in the spastic/overacting muscle in this study is similar to a study done by D Intiso et al. In their study, they placed EMG biofeedback on spastic gastrocnemius and soleus muscle to inhibit the muscles and prevent foot drop post stroke.18

Stride length: The pre-test score of stride length in the experimental group in the present study was ranging between 22.0 - 47.4. Twelve stroke subjects in both the groups were found to have their stride length approximately double of step length. Most of the subjects were right side dominant and left side was affected in them. Remaining subjects had minimal differences in stride and step length, where more were right side affected. This finding was supported by a similar study which reported that the subjects who exhibited left side lesion walked more asymmetrically in terms of swing phase, and the initial paretic limb propulsion affected the gait parameters.

The statistical analysis showed no significant improvement in the stride length post intervention. It was due to greater variability in ground clearance, ankle planter flexors spasticity and fear of fall due to impaired balance, which was supported by the findings of previous study done by G. Katlin et al. According to previous study, the work of ankle planter flexors is a major contributor to forward motion during gait. In stroke patients, the decreased work of planter flexors (increased spasticity) is the major problem which affects the stride length. Also, lack of balance may be because of lack of push off power.19 Limitation in hemi pelvis rotational range of motion and lack of hip extension seen in pre swing hindered the increasing stride length.

Stride width: The average pre-test score of experimental groups ranged from 6.6-18.3. The post-test statistical analysis showed significant decrease in stride width resulting in improvement after intervention. This observation may indicate that subjects are compensated for their abnormal pattern resulting in improvement with confidence. Biofeedback delivers feedback that is continuous, objective and concurrent with the activity; so it facilitates the patient to analyze and view their pattern of movement. The study by Rosalyn Stanton found improvement in standing and walking after the biofeedback.20 A study conducted by ANM Qurat-ulAin showed that when subjects gained the balance and improved step length, a more stable gait with reduced or near normal base of support can be achieved. They also proved that reduction in the stride width resulted in prominent increase in cadence which supports our clinical findings as well.21

Step length: Initially, the subjects exhibited smaller step length. In experimental group, the pre-test score of step length was 14.1-34.2 and 17.0-35.0 in control group. Hemi paretic subjects showed differences with respect to the starting limb; when starting with the unaffected limb, the step length was shorter. Later, when the subjects were asked to initiate movement with affected limb, the step length was improved. The post-test statistical analysis showed significant improvement within the group. This is supported by the findings of Stefan Hesse et al., on asymmetry of gait initiation between affected and unaffected limbs on swing period, step length, and center of pressure.22 Improvement in step length in experimental group may be due to improvement in lower limb kinematics, increased time of single limb support, increase in gait speed and reduction in knee extensor moment. With the help of EMG-BF, it may be possible for subjects to learn how to use preserved pathways, and this control may result in the recovery of function.

Degree of toe out: The degree of toe out in subjects in pre-test assessment ranged from 7 degree- 15 degree. Normally, there is external rotation of foot in stroke subjects, which causes the impaired limb to move out of line of foot progression. The greater deviation of toe out in stroke subjects can be correlated indirectly to the previous study, which found that stroke patients do not follow the typical strategy of actively controlling medio-lateral foot placement in response to step-by-step variation in center of mass mechanics.23 In this study, initially there was greater activity of gluteus medius muscle in which the center of mass was moving more quickly towards the stance foot resulting in improper foot placement. The post intervention statistical analysis showed improvement in the experimental group where the activity of abnormal coordination between the gluteus medius and rectus femoris were inhibited. The improvement in other gait variable and weight distribution might have decreased the degree of toe out and foot placement along the line of progression.

Cadence: The normal cadence in both the control and experimental groups in the present study ranged between 30- 76. There was an improvement in cadence in both the groups after intervention, but the values were not clinically significant. The Minimal clinically important differences (MCID) value of cadence was 17.1 steps/ min.24

For instance, increase in step length can improve cadence. Similarly, another study reported that this increase in cadence seen may be the result of constituent parts of training and circuit gait training that consisted of stepping and obstacle walking task along with visual biofeedback. With circuit gait training, a reduction in base of support and improvement in cadence can be noted. Similarly in this study, cadence had improved in both the groups who were given circuit gait training as conventional therapy.

Our experimental subjects probably had sufficient hip musculature control because neuromuscular measures about this joint may not have changed as knee and ankle activity improved. In the study titled rehabilitation of walking with EMG biofeedback, it was shown that EMG biofeedback increased the muscle recruitment and improved recovery of locomotion in subjects with hemi paresis. It provided an important supportive finding to our result of improvement in experimental group. A study by Dean et al., also reported that circuit designed for task-oriented training showed better results.

Despite no statistically significant differences between the control and intervention groups, some of the positive effects of biofeedback in experimental group could be explained by the amount of practice carried out compared with the control group. The substitution of treatment, surface EMG biofeedback in the experimental group than the control who practiced same activity might have bought an improvement in some parameters. Trowbridge and Casen in their study demonstrated that the content of feedback is important, with feedback containing specific information regarding ways to improve future practice enhancing learning more than motivational feedback. Biofeedback methods in gait education do result in short term, moderate or significant improvement in gait.

Feedback may facilitate plastic changes within the CNS. Mechanism that works may include elimination of active inhibitory influences, unmasking of existing pathways to serve new functions, transfer of function to new neural structures, development of new movement strategies, use of alternative pathways or sprouting of collateral axons to form new synapses. As visual input of EMG activity is continuously processed by the cerebellum, sensory or motor cortices directly available and responsive motor cells are called into play. The stroke subjects would be capable of improving performances by establishing new sensory engram and eliminating one or other forms of feedback.

Some factors which limited our findings included the environmental distractions that hampered the patient concentration. Since patient had to perform dual task (walking and looking at display) at same time, the inter limb coordination may not have synchronized. Greater duration of stroke onset, difference in the site of lesion, severity might have affected the overall differences. The study done by G.L Gabriela et al., showed that individuals with left side hemisphere lesion were more asymmetric than the right one. Additionally, the affected hemisphere seems to primarily influence inter limb coordination parameters such as gait symmetry which may increase the energy cost of walking, restrict mobility and functional performance. Our study also presented subjects with more of left hemisphere stroke. The above findings could be the reason for insignificant improvement.

Studies have shown that the individuals with stroke can improve gait parameters slowly beyond six months after the onset. This improvement may result from compensatory mechanisms adopted over a year. It was also suggested that parameters such as lesion side, time since stroke onset and lesion etiology can influence the gait pattern in individuals with chronic stroke. It also may support our findings of no improvement between groups because the subjects were chronic in nature varying from six months duration to three years.

Clinical Message

       Circuit gait training exercises improved walking capacity and gait parameters in chronic stroke patients.

       Surface EMG biofeedback improved motor learning in patients. Along with that, it creates feedback mechanism and reduces tone in spastic muscle.

Supporting File
References
  1. World health organization (WHO). Global burden of stroke. The Atlas of heart disease and stroke. Part three 2004.
  2. Duncan PW, Zorowitz R, Bates B, Choi JY, Glasberg JJ, Graham GD, et al. Management of adult stroke rehabilitation care: a clinical practice guideline. Stroke 2005;36(9):e100-143.
  3. Aspen Gaithersbring MD, Post stroke rehabilitation. Guideline panel: Post stroke rehabilitation clinical practice guideline. M 1996 45-0662.
  4. Lauziere S, Betschart M, Aissaoui R, Nadeau S. Understanding spatial and temporal gait asymmetries in individuals post stroke. Int J Phys Med Rehabil 2014;2(3):201.
  5. Kleim JA, Jones TA. Principles of experience dependent neural plasticity: implications for rehabilitation after brain damage. J Speech Lang Hear Res 2008;51(1):S225-39.
  6. Chen CL, Chen HC, Tang SF, Wu CY, Cheng PT, Hong WH. Gait performance with compensatory adaptations in stroke subjects with different degrees of motor recovery. Am J Phys Med Rehabil 2003;82(12):925-35.
  7. Roth EJ, Merbitz C, Mroczek K, Dugan SA, Suh WW. Hemiplegic gait: Relationships between walking speed and temporal parameters. Am J Phys Med Rehabil 1997;76(2):128-33.
  8. Cruz TH, Lewek MD, Dhaher YY. Biomechanical impairments and gait adaptations post stroke: multi-factorial associations. Journal Biomech 2009;42(11):1673-7.
  9. Kerrigan DC, Frates EP, Rogan S, Riley PO. Hip hiking and circumduction: quantitative definitions. Am J Phys Med Rehabil 2000;79(3):247-52.
  10. Inglis J, Donald MW, Monga TN, Sproule M, Young MJ. Electromyographic biofeedback and physical therapy of the hemiplegic upper limb. Arch Phys Med Rehabil 1984;65(12):755-9.
  11. Wasielewski NJ, Parker TM, Kotsko KM. Evaluation of electromyographic biofeedback for the quadriceps femoris: a systematic review. J Athl Train 2011;46:543-554.
  12. . Stanton R, Ada L, Dean CM, Preston E. Biofeedback improves performance in lower limb activities more than usual therapy in people following stroke: a systematic review. J Physiother 2017;63(1):11-6.
  13. Ekker MS, Verhoeven JI, Vaartjes I, Van Nieuwenhuizen KM, Klijn CJ, denLeeuw FE. Stroke incidence in young adults according to age, subtypes, sex and time trends. Neurology 2019;92(21):e2444-54.
  14. Banerjee TK, Das SK. Fifty years of stroke researches in India. Ann Indian Acad Neurol 2016;19(1):1-8.
  15. Gibson CL. Cerebral ischemic stroke: is gender important? J Cereb Blood Flow Metab 2013;33(9):1355-61. 
  16. Jonkers I, Delp S, Patten C. Capacity to increase walking speed is limited by impaired hip and ankle power generation in lower functioning person post stroke. Gait Posture 2009;29(1):129-37.
  17. Akbas T, Sulzer J, Neptune R. Neuromusculoskeletal simulation reveals abnormal rectus femoris-gluteus medius reflex coupling in post stroke gait. Front Neurol 2019;10:301.
  18. Intiso D, Santilli V, Grasso MG, Rossi R, Caruso I. Rehabilitation of walking with electromyographic biofeedback in foot drop after stroke. Stroke 1994;25(6):1189-92.
  19. Wang Y, Mukaino M, Ohtsuka K, Otaka Y, Tanikawa H, Mastuda F, Tsuchiyama K, Yamada J, Saitoh E. Gait characteristics of post stroke hemiparetic subjects with different walking speeds. International Journal of Rehabilitation Research. International Zeitschrift Fur Rehabilitationsforschung. Revue Internationale De Researches De Readaptation. 2020;43(1):69.
  20. Stanton R, Ada L, Dean CM, Preston E. Biofeedback improves activities of lower limb after stroke: a systematic review. J Physiother 2011;57(3):145-55.
  21. . Qurat-ul-Ain AN, Haq U, Ali S. effect of task specific circuit training on gait parameters and mobility in stroke survivors. Pak J Med Sci 2018;34(5):1300- 1303.
  22. Hesse S, Reiter F, Janke M, Dawson M, SarkodieGyan T, Mauritz KH. Asymmetry of gait initiation in hemiparetic stroke subjects. Arch Phys Med Rehabil 1997;78(7):719-24.
  23. Dean JC, Kautz SA. Foot placement control and gait instability among people with stroke. J Rehabil Res Dev 2015;52(5):577-590.
  24. Jonsdottir J, Cattaneo D, Recalcati M, Regola A, Rabuffetti M, Ferrarin M, Casiraghi A. Task oriented Biofeedback to improve gait in individuals with chronic stroke: motor learning approach. Neurorehabil Neural Repair 2010;24(5):478.
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