The effects of virtual reality training on gait, balance, and upper extremity function in patients with stroke: A meta-analysis

Ⅰ. Introduction

A stroke occurs when a blood vessel in the brain that supplies blood to the brain ruptures, bleeds, or becomes blocked. And also, they show various impairments and impairments in motor, sensory, mental, cognitive and language functions (O’Sullivan, 2014). Stroke-induced upper extremity injury is one of the most common disorders, resulting in dysfunction and muscle weakness in the contralateral upper extremity of the damaged brain hemisphere (Lloyd-Jones et al, 2010). Balance problems are very common and affect patients' ability to perform activities of daily living and restore mobility and increase the risk of falls (Loewen et al, 1990; Lamb et al, 2001; Kim et al, 2020). Balance is the ability to maintain a position on a surface of limited stability or support (Shumay-Cook et al, 2014; Lee et al, 2020). Posture control is defined as the movement of maintaining or restoring a state of balance in a certain posture or activity (Pollock et al, 2000). Hemiplegic gait disorder with muscle weakness, stiffness, and abnormal central nervous pattern muscle activity significantly reduces the efficiency of gait exercise (Fish et al, 1999). Gait uses a repetitive sequence of limb movements to move the body forward while maintaining postural stability. It is divided into stance and swing phases, and is further subdivided into initial contact, load response, mid stance, terminal stance, swing pre swing, initial swing, middle swing, and terminal swing. Physical therapy intervention plays an important role in improving balance disorders, pathological gait, and upper extremity injuries. Interventions for stroke patients include inhibition-guided exercise therapy (E Silva et al, 2017), robot-assisted treadmill training (T George Hornby et al, 2005), visual gait training (Sidaway et al, 2006), EMG (Langhorne et al, 2009), weight-assisted treadmill training (Jung et al, 2014), physical fitness training (Laver et al, 2015), virtual reality (Schuster-Amft et al, 2018), repetitive occupational therapy (Behrman et al, 2005), underwater gait training (Eyvaz et al, 2018). Recently, virtual reality has been used to apply real situations to the rehabilitation of stroke patients. Virtual reality is a computer-based technology that allows users to interact and receive immediate responses in a variety of sensory stimulus environments. By providing the patient with an environment similar to the actual situation, visual and spatial stimulation can be given, and effective motor learning can be induced through direct interaction with the environment through repetition (Zhang et al, 2001). Virtual reality technology uses motor learning and neuroplasticity as principles to optimize recovery after brain injury. It is reported that the application of virtual reality improves gait control ability, including re-education of gait and balance, and rehabilitation of upper and lower extremities of stroke patients. Virtual reality is the latest intervention in stroke rehabilitation and performs a higher level of functional tasks than conventional interventions (Cho and Lee, 2013). In addition, it has the advantage of inducing interest and motivation by performing various tasks in various environments (Flynn et al, 2007; Rand et al, 2004). However, despite the many advantages such as the ability to easily change and control the visual, auditory, and tactile inputs and the enthusiasm and motivation provided by the system, it is expensive and cannot be used in many places (Weiss and Katz, 2004).

Many studies have reported various effects on upper extremity damage, balance disorder, and gait disorder in stroke patients (Brunner et al, 2017; Laver et al, 2017). Most of these studies are quantitative studies such as randomized controlled trials. As the application of virtual reality programs to stroke patients increased in clinical practice, studies on the effects of virtual reality program training on stroke patients began to be published in Korea from the late 2000s (Shin, 2008; Seo, 2009; Choi, 2012; Jo, 2012). Effects of virtual reality training on upper extremity function and daily life of stroke patients (Kwon and Yang, 2013; Song and Park, 2016; Yoo et al, 2014; Lee et al, 2013), effect on balance and gait (Kim et al, 2011; Lee and Shin, 2013; Lee et al, 2012; Cheon et al, 2015) were conducted. Various virtual reality programs were used according to the study, and various variables were used as dependent variables.

However, it is difficult to synthesize and summarize the effects of the interventions because the intervention methods performed in these intervention studies are diverse and show inconsistent results. In order to comprehensively judge the effects of various studies, it is necessary to analyze the results of each study statistically and examine them comprehensively and systematically.

Meta-analysis is a statistical method that summarizes and analyzes research papers accumulated over many years as a very effective method of integrating evidence. In particular, it is a quantitative analysis method that systematically and objectively evaluates and synthesizes numerous studies that present contradictory results when they continue to accumulate (Song, 2006). In other words, meta-analysis is a research method that can draw comprehensive and macroscopic research conclusions by statistically integrating or comparing individual research results beyond the limitations of literature research (Korean Society of Adult Education, 2004). In Korea, one meta-analysis study was conducted on the effect of virtual reality intervention on balance in stroke patients (Noh, 2017). However, this study has limitations in not including foreign studies. Most of the studies in abroad are about systematic review some meta-analyses were being conducted.

By synthesizing and analyzing the previously accumulated therapeutic effects of virtual reality program training for stroke patients, it suggests the importance of virtual reality program training as an alternative treatment strategy and provides evidence-based data. Therefore, this study aims to synthesize and analyze the accumulated domestic and foreign interventional studies on virtual reality program training for stroke patients to see the effect on the therapeutic performance of gait, balance, and upper extremity function, which are closely related to daily life. These results will improve the excellence of physical therapy by presenting evidence-based data on the treatment of virtual reality program training, and suggest the direction of future virtual reality program training intervention research. Therefore, this study tried to confirm the intervention effect of the virtual reality program on gait, balance, and upper extremity function in stroke patients through systematic analysis.

Ⅱ. Methods

1. Data search and data selection

As of May 1, 2018, research literature related to virtual reality of stroke patients was collected. Overseas data were searched for ‘stroke’, virtual reality, training’ through Pubmed, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, and EMBASE. The search was limited to English data from 1991 to October 1, 2018. A total of 2813 articles were searched.

Approval of request for exemption from deliberation by the institutional life review committee for data retrieval and collection; It took place from January 2018 to May 2018 after receiving (IRB No. SUIRB20). In addition, the data search was supplemented through the references of the searched papers. The search was limited to papers in English and papers published from 1991 to January 2018 that provided data from the DB. The data collection procedure of the study to be analyzed was based on a systematic literature review process. Literature search was conducted by a researcher and a research assistant with the advice of a meta-analysis expert. First, in MEDLINE, 1) mesh words, which are control words of simulation education intervention, were checked, and then all four mesh words were included. After including the related central keywords identified in the abstract, EMBASE checked the control words in EMTREE and performed the same method as above. In the Cochrane Library CENTRAL and CINAHL, the search was performed through the mesh identified in MEDLINE. Related articles were searched through a core electronic database. Duplicate data were removed from the literature managementDB (reference management database, [Refworks]). The original text of the selected thesis was reviewed after first checking the related thesis through the title and abstract of the thesis. Two researchers independently searched and selected the data, and inconsistent literature was matched through discussion. The authors and publication year, country, study design, age of subjects, number of subjects, characteristics of intervention type, and research results were extracted from the finally selected study and recorded in the electronic coding book. If the data did not match, the original text was reviewed and a final decision was made.

A total of 2813 articles were searched through thesis titles and abstracts in EMBASE, MEDLINE, Cochrane Library CENTRAL and CINAHL.Duplicate data were excluded through the reference management database ([Refworks]), leaving 499 articles.With the exception of 352 studies that did not meet the selection criteria, 47 studies were selected first. The original texts of the 47 first-selected studies were reviewed, and among them, the final 21 studies were selected, excluding studies that did not meet the selection criteria (Figure 1).

Figure 1.

Flow diagram of study screening

Ⅲ. Results

1. General characteristics of study subjects

Table 1 shows the general characteristics of 21 virtual reality intervention studies analyzed in this study. There were 3 titles before 2010 (14.3%), 6 films from 2011 to 2013 (28.6%), and 12 films from 2014 to 2016 (57.1%). The source of the study was in the order of 10 Korea (47.6%) 2 USA (9.5%) 2 Brazil (9.5)% Canada 2 (9.5%) Other countries (UK, Israel, Spain) 5 (23.8%) appear. As for the research design, all 21 papers were presented as RCT experimental papers. The mean age of the participants in the total study was 60 years, 8 (38.1%) 50-60 years old, and 10 61-70 years old (47.6%). The total number of participants in the study was 642. The types of interventions were virtual reality training (VRT) in 16 patients (76.2%) and VRT + general physical therapy (EST, NDT, occupational) in 5 patients (23.8%). As for the intervention period, the average intervention time for each virtual reality training (VRT) was 38.4 minutes/time, and the weekly average was 4.12 times/week to 4.5 weeks. In the intervention group, in which virtual reality training (VRT) and general physical therapy were added, 42.0 minutes/time for virtual reality training (VRT), and 4.2 times/week to 4.4 weeks for general physical therapy 19.0 minutes/time, and 4.0 times/week in 4.5 weeks. Finally, the outcome variables were physical activity, balance, muscle strength and endurance, and quality of life. Finally, the outcome variables were physical activity, balance, strength and endurance, and quality of life. Among them, the measurement variables of gait affecting daily living activities were the Timed Up and Go test (TUG) stride length, gait function, and gait rate. The Berg balance scale was selected as the balance variable, and the box and block test, the modified Ashworth scale, and the joint range of motion were selected as the measurement variables of upper extremity function. The effects of virtual reality training on gait, balance, and upper extremity function of stroke patients were reviewed.

Table 1.

Descriptive summary of included studies(N=21)

2. Effect of virtual reality intervention on gait

Measured variables on gait as an intervention effect of virtual reality training: Timed Up and Go test (TUG), stride length, and gait function A total of 6 studies were presented to measure the effect size of all four variables in walking rate on walking, and the number of subjects was 166, 84 in the experimental group and 82 in the control group. The effect size for gait was -2.19 (95% CI: -3.99, 0.38), and the effect size between the experimental group and the control group was statistically significant (p=.02). And the heterogeneity test did not show a significant result (p=.94), indicating homogeneity (Figure 2).

Figure 2.

Forest plot of effect on gait in virtual reality rehabilitation intervention

First, four studies were presented to measure the effect size of the standing walking test, and the number of subjects was 120, 61 in the experimental group and 59 in the control group. The effect size for TUG was -1.74 (95% CI: -3.67, 0.19), and the effect size between the experimental group and the control group was not statistically significant (p=.08) (Figure 4).

Figure 4.

Forest plot of effect on gait in virtual reality rehabilitation (TUG)

A total of 3 studies were presented to measure the stride effect size, and the number of study subjects was 60, 30 in the experimental group and 30 in the control group. The effect size between the experimental group and the control group was -0.45 (95% CI: -0.97, 0.06), and the effect size between the experimental group and the control group was not statistically significant (p=.08) (Figure 6).

Figure 6.

Forest plot of effect on virtual reality rehabilitation gait (Step length)

A total of 3 studies were presented to measure the effect size of the gait function, and the number of study subjects was 60, 30 in the experimental group and 30 in the control group. The effect size on the gait function was -0.30 (95% CI: -0.81, 0.21), and the effect size between the experimental group and the control group was not statistically significant (p=.25) (Figure 8).

Figure 8.

Forest plot of effect on virtual reality rehabilitation intervention on gait (Stride)

A total of 3 studies were presented to measure the effect size of the walking rate, and the number of subjects was 60, 30 in the experimental group and 30 in the control group. The effect size on the gait function was -0.22 (95% CI: -0.42, 0.02), and the effect size between the experimental group and the control group showed a statistically significant result (p=.03). However, the size of the effect was -0.22, indicating that the effect was small (Figure 10).

Figure 10.

Forest plot of effect on gait in virtual reality rehabilitation intervention (Cadence)

2. Effect on Balance of Virtual Reality Intervention

As an intervention effect of virtual reality training, the effect size of the berg balance scale was tested as a measurement variable on balance. A total of 5 studies were presented to measure the BBS effect size, and the number of study subjects was 100, 50 in the experimental group and 50 in the control group. The effect size for BBS was -2.05 (95% CI: -3.55, -0.61), and the effect size between the experimental group and the control group was statistically significant (p=.005). The size of the effect was -2.05, indicating that the effect was very large (Figure 12).

Figure 12.

Forest plot of effect on the balance of virtual reality rehabilitation interventions (BBS)

As an intervention effect of virtual reality training, the effect size of each of the three variables was tested with the box and block test, the modified Ashworth scale, and the joint range of motion as the measurement variables on the upper extremity function. First, four studies were presented to measure the effect size of the box and block test, and the number of study subjects was 222, 113 in the experimental group and 109 in the control group. The effect size for the box and block test was 0.46 (95% CI: -1.03, 0.12), and the effect size between the experimental group and the control group was not statistically significant (p=.12) (Figure 14).

Figure 14.

Forest plot of effect on upper extremity function of virtual reality rehabilitation intervention (BBT)

A total of 3 studies were presented to measure the effect size of the modified Ashworth scale, and the number of subjects was 83, 42 in the experimental group and 41 in the control group. The effect size for the modified Ashworth scale was 0.04 (95% CI: -0.26, 0.33), and the effect size between the experimental group and the control group was not statistically significant (p=.80) (Figure 16).

Figure 16.

Forest plot of effect on upper extremity function of virtual reality rehabilitation intervention (MAS)

A total of three studies were presented to measure the effect size of the joint ROM, and the number of subjects was 60, 30 in the experimental group and 30 in the control group. The effect size for the joint ROM was 0.05 (95% CI: -0.56, 0.46), which was not statistically significant (p=.84). The effect of virtual reality training on upper extremity function was found to have no effect size in the measurement variable box and block test, the modified Ashworth scale, and the joint ROM (Figure 18).

Figure 18.

Forest plot of effect on upper extremity function of virtual reality rehabilitation intervention (ROM)

Ⅳ. Discussion

Impairment of gait, balance and upper extremity function due to stroke impairs activities of daily living and social participation, reduces quality of life, and increases the risk of falls (WHO, 2001; Mayo et al, 2002). Physiotherapy interventions play an important role in improving pathological gait and balance disorders and upper extremity injuries (Langhorne et al, 2009). Near virtual reality induces the rehabilitation of stroke patients to be applied in an environment similar to the real situation. The application of virtual reality is being applied in various ways, including re-education of gait and balance, and rehabilitation of upper and lower extremities. According to the study results of Cho and Lee (2013), it was reported that virtual reality training improves balance and gait control ability of stroke patients. This study focuses on confirming the effects of virtual reality training in stroke patients with gait disturbance, balance impairment, and upper extremity impairment through a systematic literature review. In this study, after applying virtual reality interventions to a total of 21 stroke patients, the effect was verified. Based on the results of this study, we would like to discuss the future research direction of virtual reality therapy intervention. As a result of the 21 studies (642 subjects in total) analyzed in this study, the effect size of the virtual reality intervention in stroke patients was 'small effect' according to the classification suggested by Cohen (1988), ES=0.20-0.50. , an effect size of ES=0.50-0.80 is interpreted as meaning a 'moderate effect', and an effect size of ES=0.80 or more is interpreted as meaning a 'large effect'. In this study, the effect size for stride length was -0.45, which was the highest among the effect sizes for walking in virtual reality. The effect size of the walking rate was -0.22, which showed a statistically significant result, but the effect size was small. The effect size on the balance of virtual reality training was -2.05, which was found to be very large. The effect size on upper extremity function was very weak with a box and block test of -0.45, a modified Ashworth scale of 0.04, and a range of motion of -0.05.

Ⅴ. Conclusion

The purpose of this study was to investigate the effects of virtual reality training on gait, balance, and upper extremity function in stroke patients by comparing the group with and without VR training intervention on gait and balance and upper extremity function and activity of stroke patients. As a result of the study, it was found that virtual reality had a significant effect on the gait and balance functions of stroke, but did not affect the upper extremity function. Therefore, it is shown that virtual reality training can be an effective method to improve the balance and gait status of stroke patients. Although virtual reality training has many advantages that the subject can concentrate more efficiently during exercise through the virtual environment, the effect of virtual reality has not been sufficiently investigated, so it is thought that more studies should be conducted in future studies. When the use of virtual reality and interactive video games is used as an adjunct to general treatment (to prolong the total treatment time), or when compared with the same amount as conventional treatment, it is considered that virtual reality has the effect of improving balance and gait function. However, there was insufficient evidence to draw conclusions about the effects of virtual reality and video games on walking speed and overall motor function.

References

• Behrman AL, Lawless- Dixon AR, Davis SB, et al. Locomotor training progression and outcomes after incomplete spinal cord injury. Phys Ther 2005;85(12):1356-71. [https://doi.org/10.1093/ptj/85.12.1356]
• Bethoux F, Rogers HL. Nolan KJ, et al. The effects of peroneal nerve functional electrical stimulation versus ankle-foot orthosis in patients with chronic stroke: a randomized controlled trial. Neurorehabil Neural Repair 2014; 28(7):688-97. [https://doi.org/10.1177/1545968314521007]
• Broeren J, Rydmark M, Sunnerhagen KS. Virtual reality and haptics as a training device for movement rehabilitation after stroke: a single-case study. Arch Phys Med Rehabil 2004;85(8):1247-50. [https://doi.org/10.1016/j.apmr.2003.09.020]
• Brunner I, Skouen JS, Hofstad H, et al. Virtual reality training for upper extremity in subacute stroke (VIRTUES): a multicenter RCT. Neurology 2017;89(24):2413-21. [https://doi.org/10.1212/WNL.0000000000004744]
• Caillet F, Mertens P, Rabaséda S, et al. Intérêt de l'analyse tridimensionnelle de la marche, dans la prise en charge du pied spastique [Three dimensional gait analysis and controlling spastic foot on stroke patients]. Ann Readapt Med Phys. 2003;46(3):119-31. [https://doi.org/10.1016/S0168-6054(03)00015-1]
• Cheon SG, Go TS, Lee HJ. The effects of treadmill training using mirror and virtual reality on walking of stroke patients. J Korean Acad Ther 2015;7(1):68-73.
• Cho KH, Lee WH. Virtual walking training program using a real-world video recording for patients with chronic stroke. Am J Phys Med Rehabil 2013;92:371–84. [https://doi.org/10.1097/PHM.0b013e31828cd5d3]
• Choi D. Effects of Virtual Reality Training on the Balancing, Pedestrian and Visual Perception of Stroke Patients Using the Wii Fit Game System [dissertation]. Sahmyook Univ.; 2012.
• Cohen J. The effect size. Statistical power analysis for the behavioral sciences. 1988. p.77-83.
• Coupar FM. Exploring upper limb interventions after stroke [Doctoral dissertation]. University of Glasgow; 2012.
• David TY, Chae J, Walker ME, et al. Percutaneous intramuscular neuromuscular electric stimulation for the treatment of shoulder subluxation and pain in patients with chronic hemiplegia: a pilot study. Arch Phys Med Rehabil 2001;82(1):20-5. [https://doi.org/10.1053/apmr.2001.18666]
• E Silva EMGdS, Ribeiro TS, da Silva TCC, et al. Effects of constraint-induced movement therapy for lower limbs on measurements of functional mobility and postural balance in subjects with stroke: a randomized controlled trial. Top Stroke Rehabil 2017;24(8):555-61. [https://doi.org/10.1080/10749357.2017.1366011]
• Eyvaz N, Dundar U, Yesil H. Effects of water-based and land-based exercises on walking and balance functions of patients with hemiplegia. NeuroRehabilitation 2018;43(2):237-46. [https://doi.org/10.3233/NRE-182422]
• Fish D, Kosta CS. Walking impediments and gait inefficiencies in the CVA patient. J Prosthet Orthot 1999;11(2):33-7. [https://doi.org/10.1097/00008526-199901120-00005]
• Flynn S, Palma P, Bender A. Feasibility of using the sony playstation 2 gaming platform for an individual poststorke: a case report. J Neurol Phys Ther 2007;31(4):180-9. [https://doi.org/10.1097/NPT.0b013e31815d00d5]
• Hsu AL, Tang PF, Jan MH. Analysis of impairments influencing gait velocity and asymmetry of hemiplegic patients after mild to moderate stroke. Arch Phys Med Rehabil 2003;84(8):1185-93. [https://doi.org/10.1016/S0003-9993(03)00030-3]
• Jaffe DL, Brown DA, Pierson-Carey CD, et al. Stepping over obstacles to improve walking in individuals with poststroke hemiplegia. J Rehabil Res Dev 2004;41(3A):283–92. [https://doi.org/10.1682/JRRD.2004.03.0283]
• Jo JH. Effect of virtual reality training using treadmill in children with cerebral palsy [dissertation]. Sahmyook Univ.; 2012.
• Jung T, Ozaki Y, Lai B, et al. Comparison of energy expenditure between aquatic and overground treadmill walking in people post-stroke. Physiother Res Int 2014;19:55-64. [https://doi.org/10.1002/pri.1564]
• Jung J, Yu J, Kang H. Effects of virtual reality treadmill training on balance and balance self-efficacy in stroke patients with a history of falling. J Phys Ther Sci 2012;24(11):1133–6. [https://doi.org/10.1589/jpts.24.1133]
• Kim J, Oh M, Lee J, et al. The effects of training using virtual reality games on stroke patients´ functional recovery. Korean J of Occup Ther 2011;19(3):101-14.
• Kim JH. Effects of Virtual Reality Program on Balance, Gait and Brain Activation Patterns in Stroke Patients [dissertation]. Daegu Univ.; 2005.
• Kim JH, Lee JS, Lee SH. The Effects of Virtual Reality Exercise Program with Wii-Fit TM on Dynamic Balance and Walking Ability in Patients with Stroke. J Korean Med Rehabil 2011;21(2):227-38.
• Kim SJ, Oh GB, Lee HJ. The effect of obstacle gait training on balance, gait and ADL of the stroke patient-pilot study. J Korean Phys Ther Sci 2020;27(2):1-12. [https://doi.org/10.26862/jkpts.2020.09.27.2.1]
• Korean Society of Adult Education. Educational evaluation terminology dictionary. Hakjisa; 2004.
• Kwon J, Park M, Yoon I, et al. Effects of virtual reality on upper extremity function and activities of daily living performance in acute stroke: a double-blind randomized clinical trial. Neurorehabilitation 2012;31(4):379–85. [https://doi.org/10.3233/NRE-2012-00807]
• Kwon JS, Yang NY. Feasibility of Virtual Reality for Enhancement of Upper Extremity Function Post Stroke. Neurorehabilitation Ther Sci 2013;1(2):35-40.
• Lamb SE, Ferrucci L, Volapto S, et al. Risk factors for falling in home-dwelling older women with stroke. Stroke 2001;34:494-501. [https://doi.org/10.1161/01.STR.0000053444.00582.B7]
• Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet Neurol 2009;8:741-54. [https://doi.org/10.1016/S1474-4422(09)70150-4]
• Laver KE, George S, Thomas S, et al. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev 2015;2:1-107. [https://doi.org/10.1002/14651858.CD008349.pub3]
• Laver KE, Lange B, George S, et al. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev 2017;11. [https://doi.org/10.1002/14651858.CD008349.pub4]
• Lee B, Lee S, Kim S. Correlation of motor function, balance, and cognition in patients with stroke. J Korean Phys Ther Sci 2020;27(1):56-65. [https://doi.org/10.26862/jkpts.2020.06.27.1.56]
• Lee BH, Chung EJ, Lee Sh. The Effects of Virtual Reality-Based with EMG Triggered-Functional Electric Stimulation on Muscle Tone and Gait Capability in Stroke. J Rehabil Res Dev 2012;16(3):361-78.
• Lee DY, Shin WS. The Effects of Virtual Reality-based Exercise on Energy Expenditure during Gait in Chronic Stroke Patients. Journal of the Korea Academia-Industrial cooperation Society 2013;11(10):3826-32. [https://doi.org/10.5762/KAIS.2010.11.10.3826]
• Lee JA, Hwang SJ, Song CS. Effects of Home-based Virtual Reality on Upper Extremity Motor Function for Stroke - An Experimenter Blind Case Study. Journal of the Korea Academia-Industrial cooperation Society 2013;11(10):3826-32.
• Lloréns R, Gil-Gómez JA, Alcañiz 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. [https://doi.org/10.1177/0269215514543333]
• Lloyd-Jones D, Adams R. J, Brown T, et al. Executive summary: Heart disease and stroke statistics–2010 update: A report from the American Heart Association. Circulation 2010;121(7):948–54. [https://doi.org/10.1161/CIRCULATIONAHA.109.192666]
• Loewen SC, Anderson BA. Predictors of stroke outcome using objective measurement scales. Stroke 1990;21:78-81. [https://doi.org/10.1161/01.STR.21.1.78]
• 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. [https://doi.org/10.2340/16501977-0405]
• Mauritz KH. Gait training in hemiplegia. Eur J Neurol 2002;9:23-9. [https://doi.org/10.1046/j.1468-1331.2002.0090s1023.x]
• Mayo NE, Wood-Dauphinee S, Cote R, et al. Activity, participation, and quality of life 6 months poststroke. Arch Phys Med Rehabil 2002;83:1035-42. [https://doi.org/10.1053/apmr.2002.33984]
• Merians AS, Jack D, Boian R, et al. Virtual reality–augmented rehabilitation for patients following stroke. Phys ther 2002;82(9):898-915. [https://doi.org/10.1093/ptj/82.9.898]
• Mirelman A, Bonato P, Deutsch JE. Effects of training with a robot-virtual reality system compared with a robot alone on the gait of individuals after stroke. Stroke 2008;40:169–74. [https://doi.org/10.1161/STROKEAHA.108.516328]
• Noh JS. The effect of virtual reality based rehabilitation program on balance of patient with stroke : a meta-analysis of studies in Korea. J Korean Phys Ther Sci 2017;24(1):59-68. [https://doi.org/10.1080/10749357.2016.1192361]
• O’Sullivan SB, Schmitz TJ, Fulk GD. Physical rehabilitation. 6th ed. Philadelphia: F.A. Davis; 2014.
• Pollock AS, Durqard BR, Rowe PJ. What is balance? Chinrehabil 2000;14:402-6. [https://doi.org/10.1191/0269215500cr342oa]
• Rand D, Kizony R, Weiss PL. Virtual reality rehabilitation for all: Vivid GX versus Sony PlayStation II EyeToy. In 5th Intl. Conf. On Disability, Virtual Environments and Assoc. Technologies 2004;87-94.
• Rizzo AA, Buckwalter JG. The status of virtual reality for the cognitive rehabilitation of persons with neurological disorders and acquired brain injury. Stud Health Technol Inform 1997a;39:22-33. [https://doi.org/10.3233/978-1-60750-883-0-22]
• Sakuma K, Ohata K, Izumi K, et al. Relation between abnormal synergy and gait in patients after stroke. J NeuroEng Rehabil 2014;11(1):1-7. [https://doi.org/10.1186/1743-0003-11-141]
• Scalha TB, Miyasaki E, Lima NM, et al. Correlations between motor and sensory functions in upper limb chronic hemiparetics after stroke. Arq Neuropsiquiatr 2011;69(4):624-9. [https://doi.org/10.1590/S0004-282X2011000500010]
• Schuster-Amft C, Eng K, Suica Z, et al. Effect of a four-week virtual reality-based training versus conventional therapy on upper limb motor function after stroke: A multicenter parallel group randomized trial. PloS one 2018;13(10):e0204455. [https://doi.org/10.1371/journal.pone.0204455]
• Seo SM. Video Game-Based Exercise for upper-extremity function, strentgth, visual perception of stroke patients. [dissertation]. Sahmyook Univ.; 2009.
• Shin WS. Effects of Virtual Reality-based Exercise on Static Balance and Gait Abilities in Chronic Stroke. [dissertation]. Sahmyook Univ.; 2008.
• Shumay-cook A, woollacott MN. Motor control practice, 4th ed. Philadelphia: Lippincott Williams & Wilkins; 2014.
• Sidaway B, Anderson J, Danielson G, et al. Effects of long-term gait training using visual cues in an individual with Parkinson disease. Phys Ther 2006;86(2):186-94. [https://doi.org/10.1093/ptj/86.2.186]
• Ski æ ret-Maroni N, Vonstad EK, Ihlen EA, et al. Exergaming in Older Adults: Movement Characteristics While Playing Stepping Games. Front Psychol 2016;7. [https://doi.org/10.3389/fpsyg.2016.00964]
• Song HH. Meta-analysis. Seoul: Chungmungak; 2006.
• Song GB, Park EC. Comparison of the Effects of Task-oriented training and Virtual reality training on upper extremity function, balance ability, and depression in stroke patients. J korean soc phys med 2016;11(1):115-25. [https://doi.org/10.13066/kspm.2016.11.1.115]
• T George Hornby, David HZ, Donielle C. Robotic-assisted, body weight supported treadmill training in individuals following motor incomplete spinal cord injury. Phys Ther 2005;85(1):52-66. [https://doi.org/10.1093/ptj/85.1.52]
• von Schroeder HP, Coutts RD, Lyden PD, et al. Gait parameters following stroke: a practical assessment. J Rehabil Res Dev 1995;32(1):25-31.
• Wagenaar RC, Beek WJ. Hemiplegic gait: a kinematic analysis using walking speed as a basis. J Biomech 1992;25(9):1007-15. [https://doi.org/10.1016/0021-9290(92)90036-Z]
• Weiss PL, Katz N. The potential of virtual reality for rehabilitation. J Rehabil Res Dev 2004;41(5):7-10.
• WHO. International Classification of Functioning Disability and Health. Geneve: ICF-introduction; 2001.
• Yang YR, Tsai MP, Chuang TY, et al. Virtual reality-based training improves community ambulation in individuals with stroke: a randomized controlled trial. Gait Posture 2008;28(2):201-6. [https://doi.org/10.1016/j.gaitpost.2007.11.007]
• Yavuzer G, Senel A, Atay MB, Stam HJ. ''Playstation eyetoy games'' improve upper extremity-related motor functioning in subacute stroke: a randomized controlled clinical trial. Eur J Phys Rehabil Med 2008;44(3):237-44. [https://doi.org/10.1016/j.apmr.2007.08.162]
• Yoo DH, Hong DG, Choi SY. Effect of Virtual Reality-Based Task Training on Upper Extremity Function and Activities of Daily Living in Stroke Patients: Single Subject Research. J Rehabil Res 2014;18(2):289-308.
• Zhang L, Abreu BC, Masel B, et al. Virtual reality in the assessment of selected cognitive function after brain injury. Am J Phys Med Rehabil 2001;80(8):597-604. [https://doi.org/10.1097/00002060-200108000-00010]