DOI QR코드

DOI QR Code

Effects of Robot-Assisted, Gait-Training-Combined Virtual Reality Training on the Balance and Gait Ability of Chronic Stroke Patients

가상현실훈련과 로봇보행훈련이 만성 뇌졸중 환자의 균형과 보행능력에 미치는 영향

  • Dong-Hoon Kim (Department of Physical Therapy, Gimcheon University) ;
  • Kyung-Hun Kim (Department of Physical Therapy, Daejeon Health University)
  • 김동훈 (김천대학교 물리치료학과) ;
  • 김경훈 (대전보건대학교 물리치료학과)
  • Received : 2024.01.14
  • Accepted : 2024.02.06
  • Published : 2024.05.31

Abstract

PURPOSE: This study evaluated the effects of robot-assisted gait training combined with virtual reality training on balance and gait ability in stroke patients. METHODS: Thirty-one stroke patients were allocated randomly into one of two groups: robot-assisted gait training combined virtual reality training group (RGVR group; n = 16) and control group (n = 15). The RGVR group received 30 minutes of robot-assisted gait training combined with virtual reality training. Robot-assisted gait training was conducted in parallel using a virtual reality device. In the Control group, neurodevelopmental therapy was performed according to the function of chronic stroke patients. Both groups underwent training for 30 minutes, three times per week for eight weeks. The balance assessment system (BioRescue, Marseille, France), BBS, and TUG were used to evaluate the balance ability. The OptoGait (Microgate Srl, Bolzano, Italy) and 10 mWT were measured to evaluate the gait ability. The measurements were performed before and after the eight-week intervention period. RESULTS: Both groups showed significant improvement in their balance and gait ability during the intervention. RGVR showed significant differences in balance and gait ability compared to the control group groups (p < .05). These results showed that RGVR was more effective on balance and gait ability in patients with chronic stroke. CONCLUSION: RGVR can improve balance and gait ability, highlighting the benefits of RGVR. This study provides intervention data for recovering the balance and gait ability of chronic stroke patients.

Keywords

Acknowledgement

This work was supported by a National Research Foundation Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2022R1G1A1012388).

References

  1. American Stroke Association. Understand stroke, What is stroke. 2017; https://www.stroke.org/en/about-stroke.
  2. Sin SY, Jang WH, Kim DY et al. The Long-term survival and recurrence rate of stroke patients in Korea: The multicenter prospective cohort study. Public Health Weekly Report. 2022;15(43):2719-33.
  3. Bang DH, Cho HS. Effect of body awareness training on balance and walking ability in chronic stroke patients: A randomized controlled trial. J Phys Ther Sci. 2016;28(1):198-201.
  4. Hidler J, Nichols D, Pelliccio M, et al. Advances in the understanding and treatment of stroke impairment using robotic devices. Topics in Stroke Rehabilitation. 2005;12(2):22-35.
  5. Jung JH, Lee NG, You JH, et al. Validity and feasibility of intelligent walkbot system. Electronics Letters, 2009;45(20):1016-7.
  6. Schwartz I, Sajin A, Fisher I, et al. The effectiveness of locomotor therapy using robotic-assisted gait training in subacute stroke patients: A randomized controlled trial. PM & R. 2009;1(6):516-23.
  7. Hornby TG, Campbell DD, Kahn JH, et al. Enhanced gait-related improvements after therapist-versus robotic-assisted locomotor training in subjects with chronic stroke a randomized controlled study. Stroke. 2008;39(6):1786-92.
  8. Llorens R, Gil-Gomez JA, Alcaniz M, et al. Improvement in balance using a virtual reality-based stepping exercise: A randomized controlled trial involving individuals with chronic stroke. Clin Rehabil. 2015;29(3):261-8.
  9. Flynn S, Palma P, Bender A. Feasibility of using the Sony PlayStation2 gaming platform for an individual poststroke: a case report. J Neurol Phys Ther. 2007;31(4):180-9.
  10. Husemann B, Muller F, Krewer C, et al. Effects of locomotion training with assistance of a robot-driven gait orthosis in hemiparetic patients after stroke: A randomized controlled pilot study. Stroke. 2007;38(2):349-54.
  11. Kim SY, Yang L, Park IJ, et al. Effects of innovative WALKBOT robotic-assisted locomotor training on balance and gait recovery in hemiparetic stroke: a prospective, randomized, experimenter blinded case control study with a four-week follow-up. IEEE Trans Neural Syst Rehabil Eng. 2015;23(4):636-42.
  12. Skjaeret-Maroni N, V onstad EK, Ihlen EA, et al. Exergaming in older adults: movement characteristics while playing stepping games. Front Psychol. 2016;24:964.
  13. Berg K, Wood-Dauphinee S, Williams JI, et al. Measuring balance in the elderly: Preliminary development of an instrument. Physiother Can. 1989;41(6):304-11.
  14. Latash ML, Ferreira SS, Wieczorek SA, et al. Movement sway: Changes in postural sway during voluntary shifts of the center of pressure. Exp Brain Res. 2003;150(3):314-24.
  15. Verheyden G, Vereeck L, Truijen S, et al. Trunk performance after stroke and the relationship with balance, gait and functional ability. Clin Rehabil. 2006;20:451-8.
  16. Steffen TM, Hacker TA, Mollinger L. Age-and gender-related test performance in community-dwelling elderly people: six-minute walk test, berg balance scale, timed up & go test, and gait speeds. Phys Ther. 2002;82(2):128-37.
  17. Chan WL, Pin TW. Reliability, validity and minimal detectable change of 2-minute walk test, 6-minute walk test and 10-meter walk test in frail older adults with dementia. Exp Gerontol. 2019;115:9-18.
  18. Lee HY, Park JH, Kim TW. Comparisons between locomat and walkbot robotic gait training regarding balance and lower extremity function among non- ambulatory chronic acquired brain injury survivors. Medicine. 2021;100(18):e25125.
  19. Dean CM, Richards CL, Malouin F. Task-related circuit training improves performance of locomotor tasks in chronic stroke: a randomized, controlled pilot trial. Arch Phys Med Rehabil. 2000;81(4):409-17.
  20. Hong SK, Lee GC. Effects of an immersive virtual reality environment on muscle strength, proprioception, balance, and gait of a middle-aged woman who had total knee replacement: A case report. Am J Case Rep. 2019;20:1636-42.
  21. Christovaao TCL, Neto HP, Grecco LAC, et al. Effect of different insoles on postural balance: a systematic review. J Phys Ther Sci. 2013;25(10):1353-6.
  22. Srivastava S, Kao PC, Reisman DS, et al. Robotic assist-as-needed as an alternative to therapist-assisted gait rehabilitation. Int J Phys Med Rehabil. 2016;4(5):370.
  23. Llorens R, Noe E, Colomer C, et al. Effectiveness, usability, and cost-benefit of a virtual reality-based telerehabilitation program for balance recovery after stroke: A randomized controlled trial. Arch Phys Med Rehabil. 2015;96(3): 418-25.
  24. Kiper P, Turolla A, Piron L, et al. Virtual reality for stroke rehabilitation: assessment, training and the effect of virtual therapy. Med Rehabil. 2010;14:23-32.
  25. Morone G, Bragoni M, Iosa M, et al. Who may benefit from robotic-assisted gait training? A randomized clinical trial in patients with subacute stroke. Neurorehabil Neural Repair. 2011;25(7):636-44.
  26. Crosbie JH, Lennon S, Basford JR, et al. Virtual reality in stroke rehabilitation: still more virtual than real. Disabil Rehabil. 2017;29(14):1139-46.
  27. Levanon Y. The advantages and disadvantages of using high technology in hand rehabilitation. J Hand Ther. 2013;26(2):179-83.
  28. Lucca LF. Virtual reality and motor rehabilitation of the upper limb after stroke: a generation of progress?. J Rehabil Med. 2009;41(12):1003-6.
  29. Saposnik G, Levin M, Stroke Outcome Research Canada (SORCan) Working Group. Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians. Stroke. 2011;42(5):1380-6.
  30. Kim JJ, Gu S, Lee JJ, et al. The effects of virtural reality-based continuous slow exercise on factors for falls in the elderly. J Korean Phys Ther. 2012;24(2):90-7.
  31. Walker ML, Ringleb SI, Maihafer GC, et al. V irtual reality-enhanced partial body weight-supported treadmill training poststroke: feasibility and effectiveness in 6 subjects. Arch Phys Med Rehabil. 2010;91(1):115-22.
  32. Bonnyaud C, Zory R, Boudarham J, et al. Effect of a robotic restraint gait training versus robotic conventional gait training on gait parameters in stroke patients. Exp Brain Res. 2014;232(1):31-42.
  33. Hidler J, Wisman W, Neckel N. Kinematic trajectories while walking within the Lokomatrobotic gait-orthosis. Clin Biomech (Bristol, Avon). 2008;23(10):1251-9.