Skip to main content
Download PDF
Download ePub

Efficacy of virtual reality-based intervention on balance and mobility disorders post-stroke: a scoping review

Journal of NeuroEngineering and Rehabilitation volume 12, Article number: 46 (2015) Cite this article

Abstract

Rehabilitation interventions involving virtual reality (VR) technology have been developed for the promotion of functional independence post stroke. A scoping review was performed to examine the efficacy of VR-based interventions on balance and mobility disorders post stroke. Twenty-four articles in the English language examining VR game-based interventions and outcomes directed at balance and mobility disorders were included. Various VR systems (customized and commercially available) were used as rehabilitation tools. Outcome measures included laboratory and clinical measures of balance and gait. Outcome measures of dynamic balance showed significant improvements following VR-based interventions as compared to other interventions. Further, it was observed that VR-based intervention may have favorable effects in improving walking speed and the ability to deal with environmental challenges, which may also facilitate independent community ambulation. VR-based therapy thus has the potential to be a useful tool for balance and gait training for stroke rehabilitation. Utilization of motor learning principles related to task-related training may have been an important factor leading to positive results. Other principles such as repetition, feedback etc. were used in studies but were not explored explicitly and may need to be investigated to further improve the strength of results. Lastly, robust study designs with appropriate attention towards the intensity and dose-response aspects of VR training, clear study objectives and suitable outcomes would further aid in determining evidence-based efficacy for VR game-based interventions in the future.

Introduction

Although the length of in-hospital stay following an episode of stroke has consistently decreased [1-3], many individuals return home with residual deficits. Balance and gait deficits are commonly observed in this population, leading to reduced ambulatory activity [4], limitations in activities of daily living and community participation [5,6], physical inactivity and subsequent deterioration in quality of life [7,8]. Therefore, rehabilitation efforts geared towards improving balance and mobility are important to facilitate functional independence and optimize community ambulation and participation. One of the promising intervention tools that is sought to facilitate this goal is virtual reality (VR) technology.

VR consists of a range of technologies that can be used to artificially generate sensory information in the form of a virtual environment (VE) that is interactive and perceived as similar to the real world [9,10]. Since VEs are interactive and game-like, they encourage active exploration, enhance engagement and provide motivation and enjoyment, thus allowing longer exercise sessions and improved treatment adherence [11-13]. VEs can be designed to resemble real-life scenarios including those encountered in the community [9,14]. It is not feasible to physically replicate realistic, community scenarios in the clinic or to safely train patients in the community. VR thus affords therapists with the unique opportunity to expose and train patients in these scenarios in a risk-free, graded manner, while providing intensive training and multi-sensory feedback [15,16]. These and other factors make VR-based intervention a useful adjunct or alternative to conventional therapy in re-training balance and gait dysfunctions post stroke. A review of the literature to explore the effect of VR-based interventions in re-training balance and gait and promoting independent community ambulation in this population is therefore important.

Several systematic reviews [17,18], meta-analyses [19,20] and a Cochrane review [21] have been undertaken to review the utility of VR technologies in retraining post-stroke individuals. Most of these reviews (with one exception [20]) had broader scopes of investigation and included upper limb retraining and/or cognitive rehabilitation. Further, these reviews considered only stronger study designs such as randomized controlled trials (RCT) for inclusion and thereby overlooked studies with different designs. We were, however, interested in examining the evidence on VR interventions on a targeted area (balance and gait post-stroke), with a broader and more flexible inclusion criteria as allowed in scoping reviews [22]. This allowed us to explore the added evidence to identify aspects of VR-based intervention that may prove useful in the treatment of balance and gait dysfunctions post-stroke. Further, we were interested in exploring with this scoping review, the utility of VR-based interventions in enhancing abilities required for community ambulation.

Community ambulation entails independent mobility outside the home [6] and involves dealing with environmental challenges such as low light, uneven terrain, external physical load, traffic, obstacles, time constraints etc. [23]. Various studies define diverse criteria for successful community ambulation [24]. For this review, we used one of the following criteria to identify results predictive of independent community ambulation:

1) post training gait speed ≥ 0.8 m/s, 2) functional ambulation category (FAC) of 5 (independent community ambulator) [25], 3) gait outcomes recorded in the community and, 4) outcomes related to negotiation of the environmental challenges (such as slope walking, obstacle negotiation etc.) [23].

The objectives of this scoping review were, therefore, to appraise the current state of information about the effects of VR intervention on balance and gait in post-stroke individuals and to explore the utility of VR-based interventions in facilitating independent community ambulation. The scoping review was conducted using the framework of Arksey and O’Malley [22], described in greater detail by Levac et al. [26].

Review

Search strategy

OVID MEDLINE, OVID EMBASE, PUBMED and PSYCINFO databases were searched using the terms “rehabilitation”, “virtual reality”, “stroke”, “balance”, “gait” etc. between the periods January 1950 to December 2013. The search strategies used in Ovid Medline and Pubmed databases are illustrated in Table 1. Similar strategies were employed for the other databases. Furthermore, cross references obtained from the included articles were also considered.

Table 1 Search strategies
Full size table

Selection of articles for review

1696 retrieved articles were first screened for relevance based on their titles and abstracts. Studies that were not published in the English language, available as abstracts only, that did not include post-stroke individuals or included a mixed etiology sample without separate description of outcomes related to the stroke sample, were excluded. Studies that - 1) used VR as a training tool for balance and mobility, 2) reported at least one outcome related to gait or balance and 3) published in the English language were included.

The included articles were scrutinised to extract information about VR systems, the training paradigms, outcomes and results. Twenty-four studies that met the inclusion criteria were retained for this scoping review. The subsequent sub-sections provide a synopsis of the study design, virtual environments (VE), outcome measures and the findings.

Study designs

Of the included studies, eleven were small RCTs (Level II evidence according to CEBM levels of evidence [27]) [28-38], four were controlled trials with concurrent control group (Level III evidence) [39-42], two studies had no [43] or a historical control group (Level IV evidence) [44] and six studies presented non-empirical evidence consisting of case reports, case series or proof-of-principle studies (Level V evidence) [10,45-50]. The proportion of RCTs has shown a consistent rise over the years, suggesting an increase in methodological rigor of studies in the field.

Participant characteristics

Some variability with respect to subject characteristics was observed among the included studies. Fifteen studies [28-35,38-40,42,45,47,49,50] recruited subjects with chronic stroke, seven [10,36,41,43,44,48] with sub-acute to chronic stroke, one [37] with acute stroke and one study did not provide information stroke chronicity [46]. Most studies included individuals between the ages of 50 and 80 years. Further, four studies [34-37] recruited in-patients receiving rehabilitation, twelve [10,28,30,38-40,42,45-48,50] recruited community-dwelling participants while seven studies [29,31,32,41,43,44,49] did not provide these details. A typical sample in most studies was thus middle-aged and old, community-dwelling chronic stroke survivors.

VR systems

Both custom-made and commercially available VR systems were used in studies (Table 2). Custom-made systems were usually laboratory specific and often combined other devices with the VR interface such as robotic devices [31,33,45] treadmills [10,30,34,40,43] and others. Among commercially available systems, two studies used the Interactive Rehabilitation and Exercise system, developed to comply with specific rehabilitation requirements and customization according to the patient’s needs [29,32]. Seven studies used over the counter gaming consoles such as the Nintendo Wii™, Sony Playstation™ or the Kinect™ systems [35,37-39,41,42,47]. These are designed for the general population but are being increasingly used for rehabilitation.

Table 2 VR systems and training protocols
Full size table

VR-based interventions

  1. a)

    VR tasks: The tasks used in the studies generally reflected the training objectives. For instance, studies aimed at improving balance utilized balance training tasks [32,35,37,38,41,46,47] while those aimed at improving gait utilized treadmill walking [10,28,30,36,40,43,49,50] or components of gait training such as ankle range of motion (ROM), strength [31,33,45] or appropriate activation/deactivation of ankle muscles [48].

  2. b)

    Training dosage: Most studies used training sessions lasting 40-60 min (Table 2). Some studies employed shorter (20 min) training sessions [30,34]. The training frequency varied from 2-5 times per week and total training duration lasted between 2-8 weeks. Consequently, the total VR intervention showed a wide variation ranging between 2 to 22 hours. Typical training doses comprised of sessions of 40-60 min duration, 3-5 times per week for 3-6 weeks.

  3. c)

    Feedback: Apart from the obvious intrinsic visual feedback perceived from the VE, additional intrinsic auditory, somatosensory or proprioceptive information were manipulated in some studies. For instance, Fung et al. [10] used a six-degree of freedom motion platform to simulate slopes in the VE to impart proprioceptive information congruent to walking on inclined surfaces, while Deutsch et al. [45] used haptic inputs to simulate turbulence or sensation of collision. This multisensory feedback could have acted as an important facilitator of intrinsic learning of the tasks, while enhancing engagement with the VE. Some studies also provided additional extrinsic feedback through knowledge of performance (KP) or knowledge of results (KR). Nine studies provided both KR and KP [10,29,31,32,38,43-46], three studies provided only KR [28,41,47], while others [30,34-37,40,42,48] did not provide or report on provision of KR/KP feedback. KP was provided either by the system [10,29,32,43,45,46] through graphs depicting an outcome or movement quality, or from verbal feedback (about movement quality, area of improvement etc.) by the therapists present during training [31,38,44]. KR was usually provided by the system as visual (e.g. success scores, placards) or auditory (e.g. cheering and other sounds) feedback.

Other motor learning principles such as motivation, variable practice, and attention through enhanced engagement were not addressed explicitly in most studies. [Table 2].

Outcome measures

The outcome measures utilized in the studies reflected body function and activities domain of the International Classification of Function (ICF [51]) and comprised of both laboratory measures and clinical tests. Balance assessment included center of pressure (CoP) measurements (CoP sway, sway velocity etc.) during static (quiet standing) [32,34,35,46], and dynamic postural tasks [34,36] as well as clinical tests such as the Berg Balance Score (BBS) [32,35-38,43,44,47]. Gait related outcomes were commonly measured during overground walking (reflecting transfer of VR training to overground gait) and included gait speed [28,30-32,34,36,38,39,42-44,48-50], spatiotemporal gait parameters (stride length, step length, cadence etc.) [28,31,32,34,36,49,50] and kinematic as well as kinetic gait parameters [33,48]. Clinical tests such as Timed-Up and Go (TUG) test [35,36,38,42,44,46,47], the 6-minute walk test [28,31,38,39,42,47] and Functional Ambulation Category (FAC) [29,31] were also reported.

Two studies measured ambulation in the community environment. Yang et al. [30] used the community walk test (time taken to walk 400 m in a community environment) while Mirelman et al. [31] reported on community walking activity (number of steps/day, average daily distance walked, speed, cadence etc.) using the Patient Activity Monitor. In addition, few studies reported outcomes reflecting challenges encountered during community ambulation. For e.g. Jaffe et al. [28] reported the obstacle test (the longest obstacle successfully crossed among a range of obstacle heights and lengths) and Fung et al. [10] reported on meeting time constraints, slope walking and obstacle avoidance. The details of all the outcomes reported can be found in Table 3.

Table 3 Summary of studies
Full size table

Effectiveness of VR-based interventions

Varying results were obtained in CoP measures comparing VR-based interventions with non-VR-based interventions. While no significant differences among VR-based and other interventions were found for CoP measures during static balance tasks, significant differences between interventions were noted in these measures during dynamic balance tasks. For example, Yang et al. [34] found a significant improvement in bilateral symmetry and CoP excursion under the paretic leg during the sit-to-stand task, while Kim et al. [32] reported a significant improvement in sway angles during a dynamic weight shift task only following a VR-based intervention. Similarly, for clinical measures of balance, five studies reported significant improvements in BBS scores [32,35,36,43,47] and two studies [37,47] reported an improvement on the Functional Reach Test following VR intervention as compared to other interventions.

Ten of the fourteen studies that reported gait speed found significant increases after VR-based intervention in comparison with other interventions. In addition, significant improvements in other spatiotemporal gait parameters such as cadence [32], step length [32,36,50], step time [32], stride length [36] and gait symmetry [50] as well as improvements in ankle, knee and hip ROM, and greater ankle moment and power were found following VR-based intervention [33,48] over other interventions. (Please see Table 3 for a detailed account of the results).

Further, we examined study outcomes using our criteria for independent community ambulation. Participants from four studies were able to achieve an average gait speed of ≥ 0.8 m/s following VR intervention [30,31,36,38]. Also, among participants who received VR-based intervention, 3/5 participants from You et al. [29] and 4/9 participants from Mirelman et al. [31] achieved an FAC of 5 placing them in the unlimited community ambulation category. In addition, when gait measures included community walking, Yang et al. [30] and Mirelman et al. [31] found significant increases in community ambulation time and community ambulation distance and speed respectively following VR intervention. Furthermore, Jaffe et al. [28] and Fung et al. [10] also found improvements in the ability to negotiate perturbations encountered in the community such as slopes and obstacles. An increase in gait speed of ≥ 0.8 m/s, improvement on FAC, improvement in community ambulation measures, as well as increased competency in dealing with environmental perturbations, could lead to independent community ambulation in some participants. These improvements were not seen following other interventions, suggesting an added advantage of VR-based interventions over other interventions in facilitating independent community ambulation.

One study [29] explored the effect of VR intervention on cortical re-organization post-stroke, wherein a significant shift from bilateral (pre-training) to ipsilesional (post-training) activation of the primary sensory-motor cortex during walking-like movements was found, suggesting that VR-based training can facilitate neuroplastic changes in the cortex.

Discussion

This scoping review was undertaken to appraise the impact of VR intervention on balance and gait in people post-stroke. Since our review included studies published up to December 2013, we were able to include additional papers as compared to the recent reviews [18,19]. Findings from this review indicate that VR-based interventions have an added advantage over conventional interventions in the improvement of balance while performing functional tasks, as well as in the improvement of gait speed and the quality of gait. Preliminary results also suggest that VR-based interventions may prove advantageous in promoting independent community ambulation. Some of the factors (e.g., repetitive variable practice, enhanced engagement, motivation, added feedback etc.) associated with the VR systems and the training paradigms used could be responsible for this added benefit.

VR systems

An accurate estimation of the VR system that yielded maximum gains could not be obtained from this review. Both customized and commercially available systems seemed to have equally beneficial effects over non-VR-based interventions. A recent review by Lohse et al. [19] also reported a similar view.

VR-based interventions

Intensive, task specific, variable practice in enriched environments with extrinsic (additional) feedback facilitates motor learning [52]. All of the studies included in this review used some or all of these components during VR training. However, some variability in the utilization of learning principles was found.

  1. a)

    VR tasks: The congruence of the study objectives, VR tasks and outcomes was an important factor that influenced results. For instance, outcomes related to static balance were not responsive to VR-based interventions that used dynamic balance training. Similarly, studies that used standing VR tasks failed to achieve improvements on gait-related outcomes [38,42,44]. However, tasks that trained walking-like activities or specific components essential for gait (ROM, strength etc.) did transfer to improved walking [29,31,45]. Task-specificity thus seems to be an important variable but not the only consideration in utilizing VR-based interventions.

  2. b)

    Training dosage: Considerable variability was observed in total training durations that ranged from 2 to 22 hours. Although, a higher number of repetitions and longer training times are known to have beneficial effects [52], study outcomes did not seem to depend exclusively upon the training durations. In fact, a combination of task-related training and dosage may have influenced outcomes. This was also observed by Fluet et al. [18] in their recent review.

  3. c)

    Feedback: VR-based interventions are inherently designed to provide rich visual feedback through the VEs. Further addition of auditory, haptic or proprioceptive inputs not only enhances engagement with the VE but also provides enriched environments for practice that may facilitate learning and underlying neuroplasticity [52,53]. In addition, most studies included in this review provided extrinsic feedback in the form of KP or KR. This may be especially important for learning in the post-stroke population as their intrinsic motor learning abilities may be compromised by the stroke [54]. However, the effect of this feedback on learning abilities in post-stroke individuals was not explicitly explored or reported in any study. Therefore, although it could be assumed that extrinsic feedback may have facilitated the performance, it was difficult to gauge the extent to which it may have impacted results following VR training.

In addition to the above factors, increased motivation and engagement due to game-like nature of these interventions and variable practice provided by the interactive simulations may have also contributed to the added benefits of VR observed in this review. However, even these aspects were not adequately addressed in most studies. This review, therefore, cannot comprehensively address benefits of VR-based interventions pertaining to facilitation of motor learning and dosing parameters.

VR-based interventions to promote community ambulation

As mentioned in the earlier sections, VR can be used to create scenarios simulating real-life situations including those that simulate community environments and its challenges. This provides therapists with a unique opportunity to train patients in community scenarios but in a risk-free, graded fashion. All of the studies in this review that used treadmill walking with VR utilized VEs simulating walking in the community such as in a park or a road intersection. However, only two studies used measures that assessed transfer of training to actual community ambulation, whereas most others utilized clinical measures like gait speed and FAC that could at best be considered relevant for community ambulation. Nevertheless, positive results indicating improved abilities to navigate in the community were observed suggesting that VR-based interventions could prove to be a useful tool to train independent community ambulation. Future studies should identify this unique advantage and explore utility of VR-based training in this area though use of robust study designs and appropriate outcomes.

Conclusion

Evidence from this scoping review suggests that VR-based interventions have the potential to become an effective tool in the treatment of balance and gait deficits post stroke. However, robust study designs that identify specific objectives and choose congruent and appropriate training tasks and outcome measures need to be employed in the future to ascertain appropriate intervention and dosing parameters and achieve optimal training of balance and gait in the post-stroke population.

Abbreviations

VR:

Virtual reality

VE:

Virtual environment

RCT:

Randomized controlled trial

TUG:

Timed Up and Go

BBS:

Berg balance score

CoP:

Center of pressure

FAC:

Functional ambulation category

ROM:

Range of motion

References

  1. Fang J, Alderman MH, Tu JV. Trend of stroke hospitalization, United States, 1988-1997. Stroke. 2001;32:2221–6.

    Article  CAS  PubMed  Google Scholar 

  2. WRITING GROUP, Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, et al. Heart disease and stroke statistics-2010 Update: A report from the American heart Association. Circulation. 2010;121:e46–215.

    Article  Google Scholar 

  3. Mayo NE, Nadeau LM, Daskalopoulous MP, Coté RM. The evolution of stroke in Quebec: A 15-year perspective. Neurology. 2007;68:1122–7.

    Article  PubMed  Google Scholar 

  4. Michael KM, Allen JK, Macko RF. Reduced ambulatory activity after stroke: The role of balance, gait, and cardiovascular fitness. Arch Phys Med Rehab. 2005;86:1552–6.

    Article  Google Scholar 

  5. Mayo NE, Wood-Dauphinee S, Coté R, Durcan L, Carlton J. Activity, participation, and quality of life 6 months poststroke. Arch Phys Med Rehabil. 2002;83:1035–42.

    Article  PubMed  Google Scholar 

  6. Lord SE, McPherson K, McNaughton HK, Rochester L, Weatherall M. Community ambulation after stroke: how important and obtainable is it and what measures appear predictive? Arch Phys Med Rehabil. 2004;85:234–9.

    Article  PubMed  Google Scholar 

  7. Rand D, Eng JJ, Tang PF, Jeng JS, Hung C. How active are people with stroke? Use of accelerometers to assess physical activity. Stroke. 2009;40:163–8.

    Article  PubMed  Google Scholar 

  8. 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;81:409–17.

    Article  CAS  PubMed  Google Scholar 

  9. Rand D, Kizony R, Weiss PT. The Sony PlayStation II EyeToy: low-cost virtual reality for use in rehabilitation. J Neurol Phys Ther. 2008;32:155–63.

    Article  PubMed  Google Scholar 

  10. Fung J, Richards CL, Malouin F, McFadyen BJ, Lamontagne A. A treadmill and motion coupled virtual reality system for gait training post-stroke. Cyber Psychol Behav. 2006;9:157–62.

    Article  Google Scholar 

  11. Rizzo AA, Schultheis M, Kerns KA, Mateer C. Analysis of assets for virtual reality applications in neuropsychology. Neuropsychol Rehabil. 2004;14:207–40.

    Article  Google Scholar 

  12. Holden MK. Virtual environments for motor rehabilitation: Review. Cyberpsychol Behav. 2005;8:187–211.

    Article  PubMed  Google Scholar 

  13. Brummels KA, Blasius T, Cortright T, Oumedian D, Solberg B. Comparison of efficacy between traditional and video game based balance programs. Clin Kinesiol. 2008;62:26–31.

    Google Scholar 

  14. Schultheis MT, Rizzo AA. The application of virtual reality technology in rehabilitation. Rehabil Psychol. 2001;46:296–311.

    Article  Google Scholar 

  15. Burdea GC. Virtual rehabilitation-benefits and challenges. Methods Inf Med. 2003;42:519–23.

    CAS  PubMed  Google Scholar 

  16. Rose FD, Attree EA, Johnson DA. Virtual reality: an assistive technology in neurological rehabilitation. Curr Opin Neurol. 1996;9:461–7.

    Article  CAS  PubMed  Google Scholar 

  17. Crosbie JH, Lennon S, Basford JR, McDonough SM. Virtual reality in stroke rehabilitation: still more virtual than real. Disabil Rehabil. 2007;29:1139–46.

    Article  CAS  PubMed  Google Scholar 

  18. Fluet GG, Deutsch JE. Virtual reality for sensorimotor rehabilitation post-stroke: the promise and current state of the field. Curr Phy Med Rehabil Reports. 2013;1:9–20.

    Article  Google Scholar 

  19. Lohse KR, Hilderman CGE, Cheung KL, Van der Loos HFM. Virtual reality therapy for adults post-stroke: a systematic review and meta-analysis exploring virtual environments and commercial games in therapy. PLoS One. 2014;9, e93318.

    Article  PubMed Central  PubMed  Google Scholar 

  20. Moreira MC, de Amorim Lima AM, Ferraz KM, Benedetti Rodrigues MA. Use of virtual reality in gait recovery among post stroke patients - a systematic literature review. Disabil Rehabil Assist Technol. 2013;8:357–62.

    Article  PubMed  Google Scholar 

  21. Laver KE, George S, Thomas S, Deutsch JE, Crotty M. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev. 2015; Art. No. CD008349 (http://dx.doi.org/10.1002/14651858.CD008349.pub3)

  22. Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8:19–32.

    Article  Google Scholar 

  23. Patla AE, Shumway-Cook A. Dimensions of mobility: defining the complexity and difficulty associated with community mobility. J Aging Phys Activ. 1999;7:7–19.

    Google Scholar 

  24. Lord SE, Rochester L. Measurement of community ambulation after stroke. Stroke. 2005;36:1457–61.

    Article  PubMed  Google Scholar 

  25. Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke. 1995;26:982–9.

    Article  CAS  PubMed  Google Scholar 

  26. Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5:69.

    Article  PubMed Central  PubMed  Google Scholar 

  27. American Academy of Cerebral Palsy and Development medicine treatment outcomes committee. AACPDM methodology to develop systematic reviews of treatment interventions (Revision 1.2). 2008. [http://www.aacpdm.org/UserFiles/file/systematic-review-methodology.pdf]

  28. Jaffe DL, Brown DA, Pierson-Carey CD, Buckley EL, Lew HL. Stepping over obstacles to improve walking in individuals with poststroke hemiplegia. J Rehabil Res Dev. 2004;41:283–92.

    Article  PubMed  Google Scholar 

  29. You SH, Jang SH, Kim YH, Hallett M, Ahn SH, Kwon YH, et al. Virtual reality-induced cortical reorganization and associated locomotor recovery in chronic stroke: an experimenter-blind randomized study. Stroke. 2005;36:1166–71.

    Article  PubMed  Google Scholar 

  30. Yang YR, Tsai MP, Chuang TY, Sung WH, Wang RY. Virtual reality-based training improves community ambulation in individuals with stroke: a randomized controlled trial. Gait Pos. 2008;28:201–6.

    Article  Google Scholar 

  31. 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. 2009;40:169–74.

    Article  PubMed  Google Scholar 

  32. Kim JHPP, Jang SHM, Kim CSPP, Jung JHMM, You JHP. Use of virtual reality to enhance balance and ambulation in chronic stroke: a double-blind, randomized controlled study. Am J Phys Med Rehabil. 2009;88:693–701.

    Article  PubMed  Google Scholar 

  33. Mirelman A, Patritti BL, Bonato P, Deutsch JE. Effects of virtual reality training on gait biomechanics of individuals post-stroke. Gait Pos. 2010;31:433–7.

    Article  Google Scholar 

  34. Yang SP, Hwang WHM, Tsai YCP, Liu FKM, Hsieh LFM, Chen JSP. Improving balance skills in patients who had stroke through virtual reality treadmill training. Am J Phys Med Rehabil. 2011;90:969–78.

    Article  PubMed  Google Scholar 

  35. Cho KH, Lee KJ, Song CH. Virtual-reality balance training with a video-game system improves dynamic balance in chronic stroke patients. Tohoku J Exp Med. 2012;228:69–74.

    Article  PubMed  Google Scholar 

  36. Cho KH, Lee WH. Virtual walking training program using a real-world video recording for patients with chronic stroke: a pilot study. Am J Phy Med Rehabil. 2013;92:371–84.

    Article  Google Scholar 

  37. Rajaratnam BS, KaiEn JG, JiaLin L, SweeSin K, FenRu SS, Enting L, et al. Does the inclusion of virtual reality games within conventional rehabilitation enhance balance retraining after a recent episode of stroke? Rehabil Res Pract. 2013. doi:10.1155/2013/64956. 6 pages.

    Google Scholar 

  38. Fritz SL, Peters DM, Merlo AM, Donley J. Active video-gaming effects on balance and mobility in individuals with chronic stroke: a randomized controlled trial. Top Stroke Rehabil. 2013;20:218–25.

    Article  PubMed  Google Scholar 

  39. Shin WS, Lee DY, Lee SW. The effects of rehabilitation exercise using a home video game (PS2) on gait ability of chronic stroke patients. J Korea Acad Industr Coop Soc. 2010;11:368–74.

    Article  Google Scholar 

  40. 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;24:1133–6.

    Article  Google Scholar 

  41. Kim EK, Kang JH, Park JS, Jung BH. Clinical feasibility of interactive commercial Nintendo gaming for chronic stroke rehabilitation. J Phys Ther Sci. 2012;24:901–3.

    Article  Google Scholar 

  42. Singh DKA, Nordin NAM NAA, Lim BK, Soh LC. Effects of substituting a portion of standard physiotherapy time with virtual reality games among community-dwelling stroke survivors. BMC Neurol. 2013;13:199.

    Article  PubMed Central  PubMed  Google Scholar 

  43. Walker ML, Ringleb SI, Maihafer GC, Walker R, Crouch JR, Van LB, et al. Virtual reality-enhanced partial body weight-supported treadmill training poststroke: feasibility and effectiveness in 6 subjects. Arch Phys Med Rehabil. 2010;91:115–22.

    Article  PubMed  Google Scholar 

  44. Cikajlo I, Rudolf M, Goljar N, Burger H, Matjačić Z. Telerehabilitation using virtual reality task can improve balance in patients with stroke. Disabil Rehabil. 2011;34:13–8.

    Article  PubMed  Google Scholar 

  45. Deutsch JE, Merians AS, Adamovich S, Poizner H, Burdea GC. Development and application of virtual reality technology to improve hand use and gait of individuals post-stroke. Restor Neurol Neurosci. 2004;22:371–86.

    PubMed  Google Scholar 

  46. Betker AL, Szturm T, Moussavi ZK, Nett C. Video game-based exercises for balance rehabilitation: a single-subject design. Arch Phys Med Rehabil. 2006;87:1141–9.

    Article  PubMed  Google Scholar 

  47. Flynn S, Palma P, Bender A. Feasibility of using the Sony PlayStation 2 gaming platform for an individual poststroke: a case report. J Neurol Phys Ther. 2007;31:180–9.

    Article  PubMed  Google Scholar 

  48. Dunning K, Levine P, Schmitt L, Israel S, Fulk G. An ankle to computer virtual reality system for improving gait and function in a person 9 months post stroke. Top Stroke Rehabil. 2008;15:602–10.

    Article  PubMed  Google Scholar 

  49. Feasel J, Whitton MC, Kassler R, Brooks FP, Lewek MD. The integrated virtual environment rehabilitation treadmill system. IEEE Trans Neural Syst Rehabil Eng. 2011;19:290–7.

    Article  PubMed  Google Scholar 

  50. Lewek MD, Feasel J, Wentz E, Brooks FP, Whitton MC. Use of visual and proprioceptive feedback to improve gait speed and spatiotemporal symmetry following chronic stroke: A case series. Phys Ther. 2012;92:748–56.

    Article  PubMed Central  PubMed  Google Scholar 

  51. World Health Organisation: International Classification of Functioning, Disability and Health. http://apps.who.int/classifications/icfbrowser. (2001). Accessed February 6, 2015.

  52. Kitago T, Krakauer JW. Motor learning principles for neurorehabilitation. In: Barnes MP, Good DC, editors. Handbook of clinical neurology, vol 110. Amsterdam: Elsevier B.V; 2013. p.93-103.

  53. Yu K, Wu Y, Zhang Q, Xie H, Liu G, Guo Z, et al. Enriched environment induces angiogenesis and improves neural function outcomes in a rat stroke model. J Neurol Sci. 2014;347:275–80.

    Article  PubMed  Google Scholar 

  54. Van Vliet PM, Wulf G. Extrinsic feedback for motor learning after stroke: What is the evidence? Disabil Rehabil. 2006;28:831–40.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

We would like to thank Valérie Plante, Ève Potvin, Laurie Thiboutot for their assistance in providing supplementary references [39] and [50]. AD received a stipend from the strategic innovative project “Rehabilitation Mall as a Living Lab” funded by the Fonds de Recherche du Québec en Santé (FRQS). AL and JF are members of the Sensorimotor Rehabilitation Research Team funded by the Canadian Institutes of Health Research (CIHR) http://errsm.ca/.

Author information

Authors and Affiliations

  1. School of Physical and Occupational Therapy, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

    Anuja Darekar, Anouk Lamontagne & Joyce Fung

  2. Feil and Oberfeld Research Center, Jewish Rehabilitation Hospital, Research site of the Montreal Centre for Interdisciplinary Research in Rehabilitation (CRIR), Laval, Quebec, Canada

    Anuja Darekar, Anouk Lamontagne & Joyce Fung

  3. Centre for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) at the Quebec Rehabilitation Institute and Department of Rehabilitation, Faculty of Medicine, Laval University, Quebec, Canada

    Bradford J McFadyen

Authors
  1. Anuja Darekar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bradford J McFadyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anouk Lamontagne
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joyce Fung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anuja Darekar.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

AD conceptualized, designed, carried out the review and drafted the manuscript. AL and BJM helped in the critical review and drafting of the manuscript. JF helped in the conception, design, critical review and drafting of the manuscript. All authors read and approved the final manuscript.

authors' information

AD is a physiotherapist who received the Bachelors in Physiotherapy from the Maharashtra University of Health Sciences, Maharashtra, India and Masters in Physiotherapy (PT in Neurosciences) from Mumbai University, India. She is currently a PhD Candidate at the School of Physical and Occupational Therapy at McGill University in Montreal, Canada. She is a student member of the International Society of Posture and Gait Research and the International Society of Virtual Rehabilitation. Her research interests include the study of role of sensorimotor interactions in the control of locomotion in individuals with neurological deficits using biomechanical analyses and mathematical modeling and application of technology particularly virtual reality in treating gait dysfunctions caused by neurological deficits. BJM is a professor within the Department of Rehabilitation at Laval University in Quebec City, Canada and a researcher at the Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) within the Quebec Rehabilitation Institute. He is also the current president of the International Society for Posture and Gait Research. His research program is motivated by the need to understand the complex, multi-factor, systemic nature of gait and mobility in both healthy and brain injured populations. Research techniques include 3D biomechanical analyses, electrophysiology, modeling and virtual reality. AL is a researcher at Jewish Rehabilitation Hospital Site of the Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, where she leads a laboratory on Mobility and Virtual Reality. She is also Associate Professor at the School of Physical and Occupational Therapy of McGill University. She graduated from the Physiotherapy Program at Laval University (Quebec, Canada) and earned a M.Sc. and Ph.D. in Neurobiology from the same institution. Her research focuses on the visuomotor control of locomotion in older adults and persons with stroke using an approach that combines biomechanical, behavioural and perceptual experiments. JF received the professional diploma in Physiotherapy from the Hong Kong Polytechnic University in 1982. She received the Ph.D degree in Rehabilitation Science from McGill University, Montreal, Qc, Canada in 1992. She is an Associate Professor and William Dawson Scholar at the School of Physical and Occupational Therapy, McGill University. She is also director at the Feil and Oberfeld Research Centre of the Jewish Rehabilitation Hospital (site of the Montreal Centre for Interdisciplinary Research in Rehabilitation). Her research interests range from the study of basic sensorimotor integration mechanisms in the control of posture and balance, to the clinical application of new tools and technologies for the assessment and intervention of balance and mobility disorders.

Rights and permissions

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Darekar, A., McFadyen, B.J., Lamontagne, A. et al. Efficacy of virtual reality-based intervention on balance and mobility disorders post-stroke: a scoping review. J NeuroEngineering Rehabil 12, 46 (2015). https://doi.org/10.1186/s12984-015-0035-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12984-015-0035-3

Keywords

Journal of NeuroEngineering and Rehabilitation

ISSN: 1743-0003

Contact us