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Virtual Reality, a Neuroergonomic
and Neurorehabilitation Tool for Promoting
Neuroplasticity in Stroke Survivors:
A Systematic Review with Meta-analysis
Echezona Nelson Dominic Ekechukwu1,2,3(B), Ikenna Collins Nzeakuba1,
Olumide Olasunkanmi Dada4, Kingsley Obumneme Nwankwo5,
Paul Olowoyo6, Victor Adimabua Utti7, and Mayowa Ojo Owolabi8,9,10
1Department of Medical Rehabilitation, FHST, College of Medicine, University of Nigeria,
Nsukka, Nigeria
nelson.ekechukwu@unn.edu.ng
2Environmental and Occupational Health Unit, Institute of Public Health, College of Medicine,
University of Nigeria, Nsukka, Nigeria
3LANCET Physiotherapy Wellness and Research Centre, Enugu, Nigeria
4Department of Physiotherapy, Faculty of Clinical Sciences, College of Medicine,
University of Ibadan, Ibadan, Nigeria
5Stroke Control Innovations Initiative of Nigeria, Abuja, Nigeria
6Department of Medicine, Federal Teaching Hospital, Ido Ekiti, Nigeria
7University of Essex, Colchester, UK
8Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan,
Ibadan, Nigeria
9University College Hospital, Ibadan, Ibadan, Nigeria
10 Blossom Specialist Medical Centre, Ibadan, Nigeria
Abstract. Virtual Reality (VR) is an emerging neuroergonomics tool for stroke
rehabilitation. It can be employed to promote post-stroke recovery during reha-
bilitation as a result of its neuroplasticity enhancing effects. This study system-
atically reviewed and meta-synthesised evidence on the effectiveness of virtual
reality on selected markers of neuroplasticity among stroke survivors (SSv). The
databases searched were PEDro, CINHAL, the Cochrane Library, and PUBMed
using combinations of Medical subject heading (MeSH) terms and keywords in the
titles, abstracts and text for the population, intervention and major outcome (PICO
format). The studies included were randomized clinical trials that compared the
effects VR among adult SSv. The PEDro scale was used for quality appraisal of
the included studies. Forest plot (RevMan version 5.3) was used for the metasyn-
thesis of the results, level of significance was set at α=0.05. A total of 6 studies
were included in the meta-analysis (involving 441 stroke survivors). The pooled
effects on the improvement in motor function (SMD =−1.05; CI =−1.53, −0.56,
Z=4.22, p <0.0001, I2 =93%) and balance performance (SMD =−3.06; CI =
−3.80, −2.32, Z =8.11, p <0.0001, I2 =94%) was significantly in the favour of
VR. There is evidence that virtual reality is an effective neuroergonomics modality
for encouraging neuroplasticity through its effects on the motor function, balance
and muscle strength of stroke survivors.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
N. L. Black et al. (Eds.): IEA 2021, LNNS 223, pp. 495–508, 2022.
https://doi.org/10.1007/978-3-030-74614-8_64
496 E. N. D. Ekechukwu et al.
Keywords: Virtual reality ·Neuroergonomics ·Neurorehabilitation ·
Neuroplasticity
1 Introduction
Stroke is the major cause of disability worldwide, with a high social-economic impact
[1,2]. One out of every four stroke cases is fatal and between 25 to 50% of the survivors
requires a rehabilitative treatment [3,4]. The World Health Organization reported that
15 million people globally experience a stroke annually [5]. Of these, 5 million die
and another 5 million are left permanently disabled, placing a burden on family and
community. Stroke affects about 62 million people worldwide [6], and is the second
leading cause of death and the third leading contributor to burden of disease globally
[6–8].
Stroke rehabilitation is complex, long lasting and expensive and its functional out-
come is influenced not only by the brain lesion site and extension, but also by med-
ical, demographic and neuropsychologic factors. Neurorehabilitation after a stroke is
valued highly by patients, and studies have shown a strong evidence for its effective-
ness [2,9–11]. There are various models of neurorehabilitation techniques available for
the management of stroke patients. The two conventional models commonly described
are rehabilitation through facilitation like Bobath technique and the motor re-learning
model [8]. There are other specific neurorehabilitation techniques for which systematic
reviews are available, they include constraint induced movement therapy (in which the
unaffected arm is immobilised for few hours each day in order to encourage learned use
of the affected arm), body-weight supported treadmill training and other aerobic exercise
training [7]. Stroke recovery and management requires neurorehabilitation techniques
that enhances neuroplasticity. Current trend and studies have shown a transitioning from
these conventional therapies to neuro-engineering models. Such emerging approaches
to stroke rehabilitation include virtual reality, motor imagery and robotics [8].
Virtual reality is a new technology that simulates a three-dimensional virtual world
on a computer and enables the generation of visual, audio, and haptic feedback for the
full immersion of users [12]. Users of virtual reality can interact with and observe objects
in three-dimensional visual space without limitation. Virtual reality is a neuroergonomic
tool [13], capable of enhancing neuroplasticity/learning [14], thus supporting its use in
neurorehabilitation. At present, virtual reality training has been widely used in rehabil-
itation of balance dysfunction [15]. When patients perform virtual reality training, the
prefrontal, parietal cortical areas and other motor cortical networks are activated [16].
Growing evidence from clinical studies reveals that virtual reality training improves
the neurological function of patients with spinal cord injury [17], cerebral palsy [18],
and other neurological impairments [19–21]. These findings suggest that virtual reality
training can activate the cerebral cortex and improve the spatial orientation capacity of
patients, thus facilitating the cortical control on balance and improved motor functioning
in stroke patients.
Literature appears unsettled with regards to the effects of virtual reality on the health
outcomes of stroke survivors. While the study by Wang et al. reported significant bene-
ficial effects of virtual reality in improving motor function of stroke survivors [22], the
Virtual Reality, a Neuroergonomic and Neurorehabilitation Tool 497
study by Brunner et al. reported a non-significant effect [23]. When literature becomes
shrouded with conflicting reports from primary studies, systematic reviews can be used
to provide superior evidence [24]. This study therefore systematically reviewed the evi-
dences from randomised clinical trials on the effects of virtual reality in the rehabilitation
of post-stroke patients.
2 Methods
2.1 Design
A systematic review with meta-analysis of randomized controlled trials on the effects
of virtual reality on functional outcome of stroke survivors.
2.2 Inclusion Criteria
Types of Studies: Original research manuscripts in peer-reviewed journals published
in English Language were included. Only randomized control trials that evaluated the
effects of virtual reality on functional outcomes of stroke survivors were included.
Types of Participants: The participants in the primary studies were adults of any
gender with a clinically diagnosed incidence of stroke.
Types of Intervention: Only studies whose primary aim was to determine the effects
of virtual reality as an intervention for stroke rehabilitation were included.
Types of Outcome Measures: Studies involving any of post stroke functional out-
come measures such as barthel index, functional independence measure, Fugl-Meyer
assessment scale etc.
2.3 Information Sources
An extensive search strategy to recognize studies that can be used for the review was
grouped into the search of bibliographic database and grey literature and eligibility
criteria system of study inclusion. This procedure was created in accordance with the
rules of the Cochrane Handbook of systematic reviews of intervention [25]. And advice
for Healthcare review by the centre for reviews and dissemination [26].
Search Strategy: An extensive study strategy created to search bibliographic databases
and grey literature that involved several combinations of search terms from Medical
subject Heading (MeSH) terms and keywords in the titles, abstracts and text for the
population, intervention and major outcome measures first in a pilot search to establish
sensitivity and specificity of the search strategy. A host of commands which included the
use of Boolean logic and search truncations was employed for the searches. There were
modification of the strategy to suit the syntax and subject heading of the databases. The
databases for the search were PEDro, CINHAL, the Cochrane Library, and PUBMed.
Trial register and directory of open-access repository websites including https://www.
clinicaltrial.gov,https://www.opendor.org and the web of science conference proceed-
ings were also searched. Additionally, hand search was done from the reference list of
identified studies and suggested articles.
498 E. N. D. Ekechukwu et al.
Study Record and Data Management: Search results were exported to Ref works to
check for duplication of studies. Bibliographic records were exported from Ref works
into Microsoft Excel (Microsoft 2010) to facilitate articles inclusion and exclusion. On
the basis of inclusion criteria, eligibility review questions and structures for the studies,
considerations to the two levels of eligibility assessment were produced, piloted and
refined when appropriate.
Selection Process: The eligibility criteria were liberally applied at the beginning to
ensure that relevant studies were included and that no study was excluded without thor-
ough evaluation. At the outset, studies were only excluded if they clearly met one or more
of the exclusion criteria. Screening was conducted online simultaneously on the title and
abstract by two reviewers to identify potentially relevant studies. Each reviewer cross-
checked the initial screening results of the other. The two reviewers then read through
the full text of selected studies for further screening (using the prior eligibility criteria).
Differences of opinions occurring at any stage regarding inclusion or exclusion were
resolved by discussion and reflection, in consultation with a third reviewer if warranted.
When decision could not be made based on available information, study authors were
contacted (to the maximum of three email attempts) to clarify issues of selection of any
study. Studies were excluded and the reasons for exclusion were recorded when authors
fail to respond to requests for clarifications on unclear issues regarding their reports.
Details of the flow of studies throughout the process of assessment of eligibility and
study selection were presented, along with the reasons for exclusion in a flow chart
(PRISMA diagram).
2.4 Data Collection Processes
Quality Appraisal for Included Studies: The quality of the selected studies were
assessed using the Physiotherapy Evidence Database (PEDro) quality appraisal tool.
The PEDro is an eleven-item scale in which the first item relates to external validity and
the other ten items assess the internal validity of a clinical trial. One point is given for
each satisfied criterion (except for the first item) yielding a maximum score of 10. The
higher the score, the better the quality of the study and the following grades were used:
9–10 (excellent); 6–8 (good); 4–5 (fair); <4 (poor). A point for a particular criterion
was awarded only if the article explicitly reported that the criterion was met. A score of
one was given for each yes answer and zero for no, unclear and not applicable (N/A)
answers. The overall score was reported as a tally of all yes answers out of 10 based
on the applicable answers for each study. Scores of individual items from the critical
appraisal tool were added to present the total score.
Data Synthesis and Assessment of Heterogeneity: The Research question on the
overall effects of virtual reality on the functional outcomes of stroke survivors were
asked and answers attempted and appropriate statistical method was used. Given that
the variables were on the ratio scale (continuous variable), weighted mean difference
was used when outcomes were consistent or standard mean differences when there was
the existence of variation in outcomes with a confidence interval of 95%. Meta-analysis
Virtual Reality, a Neuroergonomic and Neurorehabilitation Tool 499
was done whenever two or more studies existed that assessed similar outcomes using
similar intervention. This was done to determine the pooled effect sizes across studies
using a random effect model and relying on the level of heterogeneity of the outcomes.
Assessment of heterogeneity was done via the Cochrane Chi-square test (10% signifi-
cant level) and Higgins I2for which values of 25%, 50% and 75% were interpreted as
low, medium and high heterogeneity respectively as stipulated by the guidance on the
Cochrane Handbook for Systematic Reviews of interventions [25].
2.5 Data Analysis
Investigation and presentation of outcomes were made using the main outcome. Studies
that were homogenous in study design, intervention and control were pooled together for
meta-analysis using a random effect model [25]. Appropriate statistical techniques were
used for each type of continuous (weighted mean differences if outcomes are consistent
or standard mean difference if different outcomes were used, with 95% CI). Interpretation
of studies that are heterogeneous was done by narrative synthesis following the guideline
of the Centre for Reviews and Dissemination to investigate the relationship and findings
within and between the included studies [26]. Data analysis (Meta-analysis) was done
using RevMan 5.3 software.
3 Results
3.1 Flow of Studies through the Review
The initial searches identified a number of potential relevant papers. The flow of papers
through the process of assessment of eligibility is represented with reasons for exclusion
of papers at each stage of the process as in Fig. 1.
3.2 Characteristics of Included Trials
A total of 5,496 articles were generated from the aforementioned search strategy (Fig. 1)
while 5,490 articles were eliminated after reading the abstracts and titles. Only six studies
that contributed data for 441 stroke survivors were finally included in this review (see
Table 1). All and none of the studies had random and concealed allocations respectively
as shown in Tables 2and 3. Considering both the PEDro ratings and sample size used, one
study provided level-1 evidence whereas the others were considered as level 2 studies
as shown in Table 3.
500 E. N. D. Ekechukwu et al.
3.3 Methodological Quality Appraisal
The methodological quality of the included trials ranged from fair to good, with a average
PEDro score of 7.9. Two trials had methodologically good quality with scores ≥6. The
individual PEDro items satisfied by almost all the trials were random allocation to groups
and point estimates and variability data as shown in Table 2.
Studies/publications excluded
Search strategy
Potentially relevant publications
obtained by combined searches
from PubMed, PEDro, CINAHL
COCHRANE library (n =5,496)
Titles and abstracts screened
(n = 4,569)
Full texts Screened (n=20)
Full texts evaluated (n=6)
•Not an RCT (n=2,285)
•Ineligible intervention (n = 1,111)
•Ineligible participants (n= 1,153)
Published protocols, pilot and
feasibility studies and non English
language articles (n= 927)
Identification
Screenin
g
Eli
g
ibilit
y
Included
•Mixed Participants: Stroke and
non- Stroke (n = 8)
•Duplicates (n = 6)
Fig. 1. PRISMA flow chart of studies through the review
3.4 Interventions
The major intervention used was exercise based virtual training. The most common
exercise frequency and duration of time used was 3–5 days per week and 40–60 min per
day, respectively. The most commonly prescribed treatment duration of the programme
was ≥4weeks as shown in Tables 1and 4.
Virtual Reality, a Neuroergonomic and Neurorehabilitation Tool 501
Table 1. Summary of study characteristics
Study ID n Intervention Intervention Parameters Location Outcome
measures
Freq Time Duration
Wang et al. [22]26 VR 1×545 min 4wk China WMFT
Brunner et al. [23]50 VR 1×560 min 4wk Norway ARAT
Kim et al. [27]24 VR 1×340 min 4wk korea Balancia
Software
Bang et al. [28]40 VR 1×340 min 8wk korea Pedoscan
Park et al. [29]30 VR 1×530 min 8wk korea BioRescue
Yang et al. [30]14 VR 1×340 min 3wk Taiwan Footscan
Keys: n: number of participants, Freq: Frequency of treatment (session*days/week), VR: Virtual
Reality, ARAT: Action reach arm test, WMFT: Wolf Motor Function Test
3.5 Outcome Measures
The Pedoscan, Biorescue, Footscan and Balancia Software were used to assess balance.
Action reach arm test (ARAT) and Wolf motor function test (WMFT) were used in
assessing motor function.
3.6 The Effect of Virtual Reality on Motor Function
The meta-analysis incorporated three trials that assessed motor function resulting in a
total of 93 participants. There was a significant pooled effect (Z =4.22, p <0.0001) on
motor function in favour of virtual reality group (SMD =−1.05; CI =−1.53, −0.56).
The included studies were weakly homogenous (X2=27.63, I2=93%) and a moderate
risk of bias (42.8%). All the studies were however in favour of the experimental group
as shown in Fig. 2.
3.7 The Effect of Virtual Reality on Balance Performance
The meta-analysis incorporated three trials that assessed balance performance resulting
in a total of 87 participants. There was a significant pooled effect (Z =8.11, p <0.0001)
on balance performance in favour of the virtual reality group (SMD =−3.06; CI =−
3.80, −2.32). The included studies were strongly homogenous (X2=35.57, I2=94%)
and had a moderate risk of bias (57.1%). All the studies were in favour of virtual reality
as shown in Fig. 3.
502 E. N. D. Ekechukwu et al.
Table 2. Pedro quality appraisal of studies that investigated effect of aerobic exercise on diabetic health profile.
Study Random
allocation
Concealed
allocation
Group
similar at
baseline
Participant
blinding
Therapist
blinding
Assessor
blinding
<15%
drop-outs
Intention to
treat analysis
Between-group
result reported
Point
estimate &
variability
reported
Total
Wang et al.
[22]
1 0 1 0 0 0 1 1 1 0 5
Brunner
et al. [23]
1 0 1 0 0 0 1 1 1 1 6
Kim et al.
[27]
1 0 1 0 0 0 0 1 1 0 4
Bang et al.
[28]
1 0 0 0 0 0 1 1 1 0 4
Park et al.
[29]
1 0 1 1 1 1 1 1 1 1 9
Yang et al.
[30]
1 0 0 0 0 1 0 0 1 1 4
Key: 1 =yes; 2 =No
Virtual Reality, a Neuroergonomic and Neurorehabilitation Tool 503
Table 3. Summary of quality and level of evidence of the studies
Methodological quality Number of studies %
Pedro rating criteria
Random allocation to groups
Concealed allocation
Groups similar at baseline
Subject blinding
Therapist blinding
Assessor blinding
Less than 15% dropout
Intention to treat analysis
Btw groups statistics reported
Point estimates & variability data
6
0
4
1
1
2
4
5
6
2
100
0
66.7
16.7
16.7
33.3
66.7
83.2
100
33.3
Pedro total score
Excellent (9–10)
Good (6–8)
Fair (4–5)
Poor (0–3)
1
1
4
0
16.7
16.7
66.7
0
Level of evidence
Level 1
Level 2
1
5
16.7
83.3
Table 4. Summary of treatment protocols
Variables Categories N % Studies
Treatment time per session (mins) <20 0 0 None
21–30 233.3 27, 29
31–40 116.7 28
41–50 116.7 22
51–60 233.3 23, 30
Number of treatment session per week 1–2 116.7 23
3–5 6100 22, 23, 27–30
>5 0 0 None
Duration of treatment program (weeks) 1–3 116.7 30
4–8 583.3 22, 23, 27–29
504 E. N. D. Ekechukwu et al.
Study or Subgroup
Brunner et al, 2014
Kutner et al, 2010
Wang et al, 2017
Total (95% CI)
Heterogeneity: Chi≤ = 27.63, df = 2 (P < 0.00001); I≤ = 93%
Test for overall effect: Z = 4.22 (P < 0.0001)
Mean
-28.8
-28
-0.46
SD
16.1
1.5
0.11
Total
25
10
13
48
Mean
-23.2
-17.9
-0.16
SD
19
4.5
0.04
Total
25
7
13
45
Weight
75.8%
10.0%
14.2%
100.0%
IV, Fixed, 95% CI
-0.31 [-0.87, 0.25]
-3.12 [-4.66, -1.58]
-3.51 [-4.80, -2.22]
-1.05 [-1.53, -0.56]
Experimental Control Std. Mean Difference
Risk of bias legend
(A) Random sequence generation (selection bias)
(B) Allocation concealment (selection bias)
(C) Blinding of participants and personnel (performance bias)
(D) Blinding of outcome assessment (detection bias)
(E) Incomplete outcome data (attrition bias)
(F) Selective reporting (reporting bias)
(G) Other bias
++ + +
+ + + + +
+++
Risk of Bias
ABCDE FG
Std. Mean Difference
IV, Fixed, 95% CI
-4 -2 0 2 4
Favours [experimental] Favours [control]
–––
––
––– –
Fig. 2. Forest Plot for the Meta-analysis on the effects of Virtual Reality on Motor Function of
stroke survivors
Study or Subgroup
Bang et al, 2016
Kim et al, 2015
Park et al, 2016
Total (95% CI)
Heterogeneity: Chi≤ = 35.57, df = 2 (P < 0.00001); I≤ = 94%
Test for overall effect: Z = 8.11 (P < 0.00001)
Mean
-5.8
-0.13
-78.7
SD
1.35
0.07
0.1
Total
20
10
15
45
Mean
-3.3
0.08
-56.4
SD
0.1
0.05
1.8
Total
20
7
15
42
Weight
74.8%
22.7%
2.5%
100.0%
IV, Fixed, 95% CI
-2.56 [-3.42, -1.70]
-3.18 [-4.73, -1.62]
-17.02 [-21.70, -12.34]
-3.06 [-3.80, -2.32]
Experimental Control Std. Mean Difference
Risk of bias legend
(A) Random sequence generation (selection bias)
(B) Allocation concealment (selection bias)
(C) Blinding of participants and personnel (performance bias)
(D) Blinding of outcome assessment (detection bias)
(E) Incomplete outcome data (attrition bias)
(F) Selective reporting (reporting bias)
(G) Other bias
+ +
+ +
+ + + + +
Risk of Bias
ABCDE FG
Std. Mean Difference
IV, Fixed, 95% CI
-20 -10 010 20
Favours [experimental] Favours [control]
––– ––
––– ––
––
Fig. 3. Forest Plot for the Meta-analysis on the effects of Virtual Reality on Balance Performance
of stroke survivors
Virtual Reality, a Neuroergonomic and Neurorehabilitation Tool 505
4 Discussion
Virtual reality is an approach to user-computer interface that involves real-time simula-
tion of an environment, scenario or activity that allows for user interaction via multiple
sensory channels [31]. It creates sensory illusions that produce a more or less believable
simulation of reality with the aim of fostering brain and behavioural responses in the vir-
tual world that are analogous to those that occur in the real world [32]. VR simulations
can be highly engaging, which provides crucial motivation for rehabilitative applica-
tions that require consistent, repetitive practice. Following damage to the brain as seen
in stroke survivors, their ability to interact with the physical environment is diminished,
thus compounding their disability. Virtual reality may potentially help reduce the bur-
den of such physical limitations by providing an alternative, favourable environment in
which to practice motor skills. It can be used to deliver meaningful and relevant stim-
ulation to an individual’s nervous system and thereby capitalise on the plasticity of the
brain to promote motor learning and rehabilitation [33].
In this review, the use of virtual reality was found to be effective in promoting motor
functional recovery among stroke survivors. It may be argued that motor plan is rep-
resented by the two premovement components [Negative Slope (NS) and Bereitschaft
Potential (BP)] of the Motor Related Cortical Potential (MRCP) from an electroen-
cephalogram (EEG) [34]. While the NS-wave (activity in the premotor area) which
starts about 500 ms before the movement is believed to represent the urge to act, the BP
(seen 1–3 s before the movement) is thought to reflect the early motor preparation (motor
programme) in the supplementary motor (SM) area, as well as the superior and inferior
parietal lobe [34–37]. Similar cerebral motor plans in the motor and pre-motor areas
have been reported for real and virtual tasks actions [34]. It is therefore possible that
virtual reality rehabilitation mimics the neural mechanisms of actual neurorehabilitation
viz-a-viz the neuroplastic effects.
Virtual reality faccilitates the motor functional recovery of the paretic upper limb
through neural reorganization. This can be clinically revealed by a functional magnetic
resonance imaging (fMRI) scan that is capable of measuring the blood oxygen level
dependent (BOLD) signal. Changes in both the location and level of the BOLD signal
can reveal evidence of neuroplasticity [38]. In an RCT on the effects of Leap-Motion
based virtual reality of motor functional recovery and cortical reorganization of subacute
stroke survivors, Wang and his colleagues using an fMRI reported a shift in the activated
motor area from the ipsilateral to contralateral motor area that was more obvious in the
experimental groups [22]. This led to a significantly improved motor function compared
with the control group that received conventional therapy. This change may be attributed
to increased practice-induced neuroplasticity as a result of repetitive practice associated
with virtual reality training and/or imitation-dependent neuroplasticity initiated in the
virtual environment and carried out by the patient in the real world through mechanisms
such as synaptic pruning, Hebbian mechanism, or long term potentiation (LTP) [37].
There was also a pooled significant improvement in the balance performance of stroke
survivors in favour of virtual reality training [39]. The control of human balance is a
comprehensive process relying on the integration of visual, vestibular and somatosensory
inputs to the central nervous system. Balance performance can be therefore be affected by
a dysfunction in the proprioceptors, muscle weakness, joint immobility and instability,
506 E. N. D. Ekechukwu et al.
pain or visual deficits; these impairments characterizes post-stroke morbidity. Balance
as an outcome measure has been identified to be one of the key areas to be considered
during stroke rehabilitation. About 70–80% of stroke patients experience a fall as a
result of balance dysfunctions [30]. Virtual reality can be used to encourage long term
potentiation of the vertibular cortext and its pathways for balance functioning through
the visual feedback enhanced in a virtual environment; thus, “pathways that fire together,
wire together”. In an RCT to determine the effects of a community based virtual reality
training on the balance performance of chronic stroke survivors, Kim et al. found that
virtual reality significantly decreased the anterioposterior and total postural sway path
lengths as well as the postural sway speed [27].
A major advantage of virtual reality training over conventional neurorehabilitation
approach is adherence. Virtual reality is an entertaining, motivating and fun-therapy
and thus encourages patient-participation, repetition, attention and enjoyment which are
recipes for neuroplasticity. However, virtual reality is not without its own demerits. These
include problems of availability, affordability, acceptability and adaptability especially
in low and middle income countries where stroke morbidity and mortality is greatest.
5 Conclusion
Virtual reality is an effective neuroergonomic tool for the neurorehabilitation of stroke
survivors by harnessing its neuroplastic effects.
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