Targeted walking training of patients in the early recovery period of cerebral stroke (preliminary research)

Cover Page

Cite item

Full Text

Abstract

Background: Currently, training of the gait function for patients with cerebral stroke using the biofeedback technology is an independent, effective, and promising method. The most common training and exposure parameters are the gait speed, cycle length, and cadence. However, the application of basic and more complex types of selective training using wearable sensor technology is rare due to the technological complexity of their use for biofeedback.

Aims: To study the possibility of using the biofeedback training technology with a targeted effect on one of the basic parameters characterizing the symmetry of walking, the duration of the support period, in patients in the early recovery period of cerebral stroke.

Methods: We examined 12 patients who underwent a course of biofeedback training to harmonize the period of support during the early recovery period of hemispheric cerebral stroke in the middle cerebral artery basin. The biomechanics of voluntary walking was investigated before and after the training. The spatio-temporal parameters of walking, kinematics of movements in the hip, knee, and ankle joints, and the maximum EMG amplitudes of the main muscle groups responsible for walking were recorded. The classical clinical scales were also used. The biofeedback training on a treadmill consisted of 10 sessions; the duration of the support period was the training parameter.

Results. As a result of the treatment, a significant improvement was noted according to the «Up&Go» clinical scale and Hauser’s walking index. The differences in the trained support phase after the treatment are not significant and demonstrate positive changes. The kinematics of movements in the joints also demonstrates relatively small, but significant changes for the knee joint. For the hip joint, no dynamics in the parameters’ values is observed; the joint function does not change significantly, and the amplitude asymmetry remains unchanged. For the knee joint, the greatest dynamics is observed for the main swing amplitude and its phase.

Conclusion: The study has shown that the purposeful biofeedback training of the gait function using the support period to reduce the functional asymmetry in this parameter, and also has a positive effect on other gait parameters.

About the authors

Dmitry V. Skvortsov

Federal Center for Cerebrovascular Pathology and Stroke; The Russian National Research Medical University named after N.I. Pirogov; Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies FMBA of Russia

Author for correspondence.
Email: skvortsov.biom@gmail.com
ORCID iD: 0000-0002-2794-4912
SPIN-code: 6274-4448

M.D., Ph.D., Dr. Sci. (Med.), Professor

Russian Federation, Moscow; Moscow; 28, Orekhoviy bulvar, Moscow, 115682

Sergey N. Kaurkin

Federal Center for Cerebrovascular Pathology and Stroke; The Russian National Research Medical University named after N.I. Pirogov; Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies FMBA of Russia

Email: kaurkins@bk.ru
ORCID iD: 0000-0001-5232-7740
SPIN-code: 4986-3575

M.D., Ph.D.

Russian Federation, Moscow; Moscow; 28, Orekhoviy bulvar, Moscow, 115682

Galina E. Ivanova

Federal Center for Cerebrovascular Pathology and Stroke; The Russian National Research Medical University named after N.I. Pirogov

Email: reabilivanova@mail.ru
SPIN-code: 4049-4581

M.D., Ph.D., Dr. Sci. (Med.)

Russian Federation, Moscow; Moscow

Boris B. Polyaev

Federal Center for Cerebrovascular Pathology and Stroke; The Russian National Research Medical University named after N.I. Pirogov

Email: b.polyaev@gmail.com
SPIN-code: 6714-0595

M.D., Ph.D.

Russian Federation, Moscow; Moscow

Mariya A. Bulatova

Federal Center for Cerebrovascular Pathology and Stroke; The Russian National Research Medical University named after N.I. Pirogov

Email: inface@mail.ru
ORCID iD: 0000-0002-7510-7107
SPIN-code: 5864-7146

M.D., Ph.D.

Russian Federation, Moscow; Moscow

References

  1. Chamorro-Moriana G, José Moreno A, Sevillano H. Technology-based feedback and its efficacy in improving gait parameters in patients with abnormal gait: a systematic review. Sensors (Basel). 2018;18(1):142. doi: 10.3390/s18010142
  2. Gordt K, Gerhardy T, Najafi B, Schwenk M. Effects of wearable sensor-based balance and gait training on balance, gait, and functional performance in healthy and patient populations: a systematic review and meta-analysis of randomized controlled trials. Gerontology. 2018;64:74–89. doi: 10.1159/000481454
  3. Spencer J, Wolf SL, Kesar TM. Biofeedback for post-stroke gait retraining: a review of current evidence and future research directions in the context of emerging technologies. Front Neurol. 2021;12:637199. doi: 10.3389/fneur.2021.637199
  4. Ma CZ, Zheng YP, Lee WC. Changes in gait and plantar foot loading upon using vibrotactile wearable biofeedback system in patients with stroke. Top Stroke Rehabil. 2018;25(1):20–27.
  5. Boudarham J, Roche N, Pradon D, et al. Variations in kinematics during clinical gait analysis in stroke patients. PLoS One. 2013;8(6):e66421. doi: 10.1371/journal.pone.0066421
  6. Chantraine F, Filipetti P, Schreiber C, et al. Proposition of a classification of adult patients with hemiparesis in chronic phase. PLoS One. 2016;11(6):e0156726. doi: 10.1371/journal.pone.0156726
  7. Wang Y, Mukaino M, Ohtsuka K, et al. Gait characteristics of post-stroke hemiparetic patients with different walking speeds. Int J Rehabil Res. 2020;43(1):69–75. doi: 10.1097/MRR.0000000000000391
  8. Schenck C, Kesar TM. Effects of unilateral real-time biofeedback on propulsive forces during gait. J Neuroeng Rehabil. 2017; 14:52. doi: 10.1186/s12984-017-0252-z
  9. Genthe K, Schenck C, Eicholtz S, et al. Effects of real-time gait biofeedback on paretic propulsion and gait biomechanics in individuals post-stroke. Top Stroke Rehabil. 2018;25(3):186–193. doi: 10.1080/10749357.2018.1436384
  10. Begg R, Galea MP, James L, et al. Real-time foot clearance biofeedback to assist gait rehabilitation following stroke: a randomized controlled trial protocol. Trials. 2019;20:317. doi: 10.1186/s13063-019-3404-6
  11. Bowman T, Gervasoni E, Arienti C, et al. Wearable devices for biofeedback rehabilitation: a systematic review and meta-analysis to design application rules and estimate the effectiveness on balance and gait outcomes in neurological diseases. Sensors (Basel). 2021;21(10):3444. doi: 10.3390/s21103444
  12. Druzbicki M, Przysada G, Guzik A, et al. The efficacy of gait training using a body weight support treadmill and visual biofeedback in patients with subacute stroke: a randomized controlled trial. Biomed Res Int. 2018;2018:3812602. doi: 10.1155/2018/3812602
  13. Drużbicki M, Guzik A, Przysada G, et al. Changes in gait symmetry after training on a treadmill with biofeedback in chronic stroke patients: a 6-month follow-up from a randomized controlled trial. Med Sci Monit. 2016;22:4859–4868. doi: 10.12659/MSM.898420
  14. Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Physical therapy. 2000;80(9):896–903. doi: 10.1093/ptj/80.9.896
  15. Hauser SL, Dawson DM, Lehrich JR, et al. Intensive immunosuppression in progressive multiple sclerosis. A randomized, threearm study of high-dose intravenous cyclophosphamide, plasma exchange, and ACTH. N Engl J Med. 1983;308(4): 173–180.
  16. Супонева Н.А., Юсупова Д.Г., Зимин А.А., и др. Валидация шкалы баланса Берга в России // Неврология, нейропсихиатрия, психосоматика. 2021. Т. 13, № 3. Р. 12–18. [Suponeva NA, Yusupova DG, Zimin AA, et al. Validation of the Berg balance scale in Russia. Neurology, neuropsychiatry, psychosomatics. 2021; 13(3):12–18. (In Russ).] doi: 10.14412/2074-2711-2021-3-12-18
  17. Bohannon RW. Objective measures. Phys Ther. 1989;69(7): 590–593. doi: 10.1093/ptj/69.7.590
  18. Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol. 2000;10:361–374.
  19. Нейрософт. Официальный сайт. Реабилитация ходьбы (тренажер ходьбы с биологической обратной связью Стэдис). Обзор. [Neurosoft. Official website. Walking rehabilitation (walking simulator with biofeedback Stadis). Review. (In Russ).] Режим доступа: https://neurosoft.com/ru/catalog/gait-assessment/steadys_rehabilitation. Дата обращения: 15.10.2021.
  20. Скворцов Д.В. Диагностика двигательной патологии инструментальными методами: анализ походки, стабилометрия. Москва, 2007. 640 с. [Skvortsov DV. Diagnostics of motor pathology by instrumental methods: gait analysis, stabilometry. Moscow; 2007. 640 p. (In Russ).]
  21. Jonsdottir J, Cattaneo D, Recalcati M, et al. Task-oriented biofeedback to improve gait in individuals with chronic stroke: motor learning approach. Neurorehabilitation Neural Repair. 2010;24: 478–485. doi: 10.1177/1545968309355986

Supplementary files

Supplementary Files
Action
1. JATS XML
2. Figure 1

Download (1MB)
3. Figure 2

Download (102KB)
4. Fig. 1. The acquisition of the gait parameters.

Download (1MB)
5. Fig. 2. The training process.

Download (1MB)

Copyright (c) 2021 Skvortsov D.V., Kaurkin S.N., Ivanova G.E., Polyaev B.B., Bulatova M.A.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies