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Effects of Virtual Reality Intervention on Cognition and Motor Function in Older Adults With Mild Cognitive Impairment or Dementia: A Systematic Review and Meta-Analysis

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Background: Virtual reality (VR) intervention is an innovative and efficient rehabilitative tool for patients affected by stroke, Parkinson's disease, and other neurological disorders. This meta-analysis aims to evaluate the effects of VR intervention on cognition and motor function in older adults with mild cognitive impairment or dementia. Methods: Seven databases were systematically searched for relevant articles published from inception to April 2020. Randomized controlled trials examining VR intervention in adults with mild cognitive impairment or dementia aged >60 years were included. The primary outcome of the study was cognitive function, including overall cognition, global cognition, attention, executive function, memory, and visuospatial ability. The secondary outcome was motor function, consisting of overall motor function, balance, and gait. A subgroup analysis was also performed based on study characteristics to identify the potential factors for heterogeneity. Results: Eleven studies including 359 participants were included for final analysis. Primary analysis showed a significant moderate positive effect size (ES) of VR on overall cognition ( g = 0.45; 95% confidence interval (CI) = 0.31–0.59; P < 0.001), attention/execution ( g = 0.49; 95% CI = 0.26–0.72; P < 0.001), memory ( g = 0.57; 95% CI = 0.29–0.85; P < 0.001), and global cognition ( g = 0.32; 95% CI = 0.06–0.58; P = 0.02). Secondary analysis showed a significant small positive ES on overall motor function ( g = 0.28; 95% CI = 0.05–0.51; P = 0.018). The ES on balance ( g = 0.43; 95% CI = 0.06–0.80; P = 0.02) was significant and moderate. The ES on visuospatial ability and gait was not significant. In the subgroup analysis, heterogeneity was detected in type of immersion and population diagnosis. Conclusions: VR intervention is a beneficial non-pharmacological approach to improve cognitive and motor function in older adults with mild cognitive impairment or dementia, especially in attention/execution, memory, global cognition, and balance. VR intervention does not show superiority on visuospatial ability and gait performance.
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SYSTEMATIC REVIEW
published: 05 May 2021
doi: 10.3389/fnagi.2021.586999
Frontiers in Aging Neuroscience | www.frontiersin.org 1May 2021 | Volume 13 | Article 586999
Edited by:
Elizabeta Blagoja
Mukaetova-Ladinska,
University of Leicester,
United Kingdom
Reviewed by:
Rocco Salvatore Calabrò,
Centro Neurolesi Bonino Pulejo
(IRCCS), Italy
Valérie Gyselinck,
Institut Français des Sciences et
Technologies des Transports, de
l’Aménagement et des Réseaux
(IFSTTAR), France
*Correspondence:
Tong Wang
wangtong60621@163.com
Chuan Guo
guochuanrehab@126.com
These authors have contributed
equally to this work and share first
authorship
Received: 24 July 2020
Accepted: 06 April 2021
Published: 05 May 2021
Citation:
Zhu S, Sui Y, Shen Y, Zhu Y, Ali N,
Guo C and Wang T (2021) Effects of
Virtual Reality Intervention on
Cognition and Motor Function in Older
Adults With Mild Cognitive Impairment
or Dementia: A Systematic Review
and Meta-Analysis.
Front. Aging Neurosci. 13:586999.
doi: 10.3389/fnagi.2021.586999
Effects of Virtual Reality Intervention
on Cognition and Motor Function in
Older Adults With Mild Cognitive
Impairment or Dementia: A
Systematic Review and
Meta-Analysis
Shizhe Zhu 1,2† , Youxin Sui 1, 2†, Ying Shen 1† , Yi Zhu 1, Nawab Ali 1, Chuan Guo 1
*and
Tong Wang 1,2
*
1Department of Rehabilitation, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China, 2School of
Rehabilitation Medicine, Nanjing Medical University, Nanjing, China
Background: Virtual reality (VR) intervention is an innovative and efficient rehabilitative
tool for patients affected by stroke, Parkinson’s disease, and other neurological disorders.
This meta-analysis aims to evaluate the effects of VR intervention on cognition and motor
function in older adults with mild cognitive impairment or dementia.
Methods: Seven databases were systematically searched for relevant articles published
from inception to April 2020. Randomized controlled trials examining VR intervention in
adults with mild cognitive impairment or dementia aged >60 years were included. The
primary outcome of the study was cognitive function, including overall cognition, global
cognition, attention, executive function, memory, and visuospatial ability. The secondary
outcome was motor function, consisting of overall motor function, balance, and gait.
A subgroup analysis was also performed based on study characteristics to identify the
potential factors for heterogeneity.
Results: Eleven studies including 359 participants were included for final analysis.
Primary analysis showed a significant moderate positive effect size (ES) of VR on
overall cognition (g=0.45; 95% confidence interval (CI) =0.31–0.59; P<0.001),
attention/execution (g=0.49; 95% CI =0.26–0.72; P<0.001), memory (g=0.57;
95% CI =0.29–0.85; P<0.001), and global cognition (g=0.32; 95% CI =0.06–0.58;
P=0.02). Secondary analysis showed a significant small positive ES on overall motor
function (g=0.28; 95% CI =0.05–0.51; P=0.018). The ES on balance (g=0.43;
95% CI =0.06–0.80; P=0.02) was significant and moderate. The ES on visuospatial
ability and gait was not significant. In the subgroup analysis, heterogeneity was detected
in type of immersion and population diagnosis.
Conclusions: VR intervention is a beneficial non-pharmacological approach to improve
cognitive and motor function in older adults with mild cognitive impairment or dementia,
especially in attention/execution, memory, global cognition, and balance. VR intervention
does not show superiority on visuospatial ability and gait performance.
Keywords: meta-analysis, cognition, motor, virtual reality, mild cognitive impairment, dementia
Zhu et al. VR in MCI or Dementia
INTRODUCTION
Dementia is a collective name for a heterogeneous group of
chronic neurodegenerative diseases characterized by progressive
deterioration of goal-directed behaviors and cognitive function
(Aruanno and Garzotto, 2019; D’Cunha et al., 2019). As the
“symptomatic predementia stage” (Langa and Levine, 2014),
individuals with mild cognitive impairment (MCI) display mild
impairment in cognitive function with preserved independent
functional abilities and with no obvious deficits in social
and occupational functioning (Albert et al., 2011). However,
these individuals typically have a higher risk of dementia than
age-matched individuals without MCI (Petersen et al., 2018;
Aruanno and Garzotto, 2019). According to the World Health
Organization, 47 million people worldwide are afflicted by
dementia; this number is expected to increase to 75 million by
2030 and nearly 131 million by 2050 (Arvanitakis et al., 2019). In
people aged 60 years and older, the reported prevalence of MCI
ranges from 6.7 to 25.2% (Petersen et al., 2018).
Important breakthroughs in the pharmacological treatment
of dementia have not been achieved, resulting in gravitation
toward non-pharmacological approaches (D’Cunha et al., 2018).
Updated practice guidelines have noted that exercises (Level
B) and cognitive interventions (Level C) may be beneficial to
improve measures of cognitive function in patients with MCI
(Petersen et al., 2018) and stress that exercise training for
6 months is likely to improve cognitive outcome (moderate
confidence in the evidence based on two Class II studies).
Virtual reality (VR) is a new technology for implementing
rehabilitation of cognitive and motor function (Tieri et al., 2018).
VR technology is defined as a “high-end computer interface
that involves real time simulation and interactions through
multiple sensory channels.” All VR applications possess two key
features, immersion and interaction, which means that VR can
bring the subject inside a virtual environment and to respond
in real time to movements of the body in a naturalistic way
(Tieri et al., 2018). VR can be categorized into three types
according to the level of immersion: low immersion, semi-
immersion, and full immersion (García-Betances et al., 2015).
In a low immersive system, the patient interacts with the
virtual environment using conventional graphic workstations
such as PC monitor, keyboard and mouse. A semi-immersive VR
system typically consists of more complicated interactive devices
such as motion tracker, haptic gloves, and balance platform.
On the other hand, a full immersive VR can be defined as
an immersive experience delivered through a combination of
more sophisticated graphic systems for example head-mounted
display, surrounded screen, along with input of other sensory
information such as sound, touch or even smell to let participants
fully sink into virtual environment. VR intervention can combine
exercises and cognitive training together making it a good option
for patients with MCI or dementia.
According to recent meta-analyses, VR has significant
beneficial effects on cognitive function in individuals who have
sustained a stroke and evidence supports its use as an adjunct
strategy for stroke rehabilitation at different stages of recovery
using numerous platforms and training parameters (Aminov
et al., 2018). VR can not only achieve the same effect as
conventional rehabilitation training, but also improve gait and
balance performance in patients with Parkinson’s disease (Lei
et al., 2019; Santos et al., 2019). To the best of our knowledge,
there is only one systematic review regarding VR intervention
in MCI or dementia (Kim et al., 2019). However, through
analyzing the papers from Kim et al. (2019), we found that the
quality of the included studies varied according to the Cochrane
Collaboration’s tool and the PEDro scale.
Due to the inclusion of many non-randomized controlled
trials (RCTs) in Kim et al. (2019), the included articles are very
different in study design, setting of control groups, and the
quality of studies, all of which may increase the risk of bias. In this
case, they only performed a sub-analysis of randomized vs. non-
randomized studies. We believed this is not enough although it is
difficult to analyze all potential risk of bias. Kim et al. (2019) only
performed an overall effect size on cognition and physical fitness.
Therefore, we perform an updated quantitative review of RCTs
focusing on the efficacy of VR intervention on specific domains
of cognition in older adults with MCI or dementia. In addition,
knowing that MCI and dementia patients often have an increased
risk of fall (Allali and Verghese, 2017), we also identify changes
in motor function such as gait and balance.
The primary objective of this meta-analysis was to assess
the effect of VR intervention on cognitive function, including
overall cognition, global cognition, attention, executive function,
memory, and visuospatial ability. The secondary objective was
to identify the effect of VR on motor function, which comprises
balance and gait.
MATERIALS AND METHODS
The results of this meta-analysis are reported in accordance
with the Preferred Reporting Items for Systematic Reviews and
Meta-Analysis (PRISMA) guidelines (Moher et al., 2009).
Search Strategy
We searched the Cochrane Library, EMBASE, EBSCO, Ovid,
PubMed, Scopus, and Web of Science databases from inception
to April 2020 for RCTs that examined the effects of VR on one or
more cognitive or behavioral outcomes in older adults with MCI
or dementia without time limit. The specific search syntax, such
as PubMed, can be available in the Supplementary Material.
Eligibility Criteria
Eligibility criteria were formulated based on the PICOS
framework (Moher et al., 2009):
(1) Participants: The age of the participants was >60 years, with
a diagnosis of MCI or dementia (of any etiology).
(2) Intervention: We included studies using VR interventions
that met the following definition: “The VR intervention should
be the use of interactive simulations created with computer
hardware and software to present users with a virtual figure
to engage in environments that appear and feel similar to
real world objects and events.” We did not limit the type of
Frontiers in Aging Neuroscience | www.frontiersin.org 2May 2021 | Volume 13 | Article 586999
Zhu et al. VR in MCI or Dementia
VR according to level of immersion (low immersive, semi-
immersive, or full immersive). Interventions needed to be
implemented for 5 h using standardized computerized tasks
or interactive video games.
(3) Control: The comparison group received either an
alternative intervention or no intervention. Alternative
interventions included any activity designed to be therapeutic
at the impairment, activity, or participation level that did not
include the use of VR, ranging from motor and/or cognitive
training to health education.
(4) Outcome: The primary outcome of this study is cognitive
function, including overall cognition, global cognition,
attention, executive function, memory, and visuospatial
ability. The secondary outcome is motor function, consisting
of balance and gait. All outcome measures had to be evaluated
at baseline and immediately after the intervention period.
Long-term follow-up data was not included in data synthesis,
as only a small number of studies reported this information.
(5) Study: Eligible studies were peer-reviewed articles reporting
results from RCTs that examined the effects of VR on one
or more cognitive outcomes and motor function in adults
aged 60 years with MCI or dementia. The following types of
articles were excluded: (1) prospective or retrospective cohort
studies; (2) case reports; (3) conference abstracts; and (4) not
written in English.
Study Selection
Two reviewers (SZ and YS) conducted the initial online search
independently to avoid selection bias. After excluding duplicate
studies, article titles, and abstracts were reviewed. If an abstract
was considered relevant or ambiguous, the full text was reviewed
and inclusion and exclusion criteria were applied. Disagreements
regarding study eligibility were resolved by CG, who approved
the final list of included studies.
Data Extraction and Analysis
Data regarding study characteristics (nation, year, type of study,
sample size, mean age, intensity, number of sessions, session
length, immersion type of VR, comparison group, and outcome
measures) of each article were extracted. We also summarized
the components of the intervention in the two groups and
extracted differences between them based on the description of
interventions and the purpose of trials which could contribute
to the superiority of VR. In the control group, a blank control
group was defined as passive interventions while the same total
training time in the experimental group was defined as active
interventions. Most data was entered as mean with standard
deviation (SD) for the VR and control groups at baseline
and immediately after training. When mean and SD were
not available, mean changes from baseline with SD or mean
differences with 95% confidence interval (CI) were extracted.
In addition, we contacted the authors to request raw summary
data if there was no data available. For studies that included
several tests of the same domain, each domain was averaged to
one pooled effect size (ES) using Comprehensive Meta-Analysis
(CMA) software version 2.0 (Biostat, Inc., Englewood, NJ, USA).
For example, if a study included several attention tests, then these
tests were summarized to generate one pooled effect size (Li et al.,
2011). CMA allows for each of these different study outcomes
to be flexibly entered into the model. A random effects model
was used to correct for variable effect sizes across the studies
if these studies show heterogeneity in their intervention (e.g.,
intervention type, duration, outcome measures) (Rosenblad,
2009). We chose the Hedges’ g of the ES to estimate the efficacy of
VR intervention. Hedge’s g estimates of <0.3 were considered as
small, 0.3 and <0.6 as moderate, and 0.6 as large, respectively
(Hill et al., 2017). The calculation equation (Newton et al., 1998)
of hedges’ g is shown as follows:
gi=((m1im2i)/si)(1 3/(4Ni9)) (1)
The collected data were utilized to calculate a combined effect
size (Hedges’ g) and 95% CI of changes in outcome measures
between the experimental group (EG) and control group (CG)
from pre- to post-test. Estimate variance was scaled up based on
an assumed inter-correlation between the tests of 0.7 (Lampit
et al., 2014; Hill et al., 2017). For Hedges’ g, the direction of
ES was positive if post-test performance was better than pre-test
performance. In addition, a random effects model was chosen
to accommodate heterogeneity for this analysis (Higgins et al.,
2020). A subgroup analysis was also conducted based on study
characteristics (population diagnosis, type of immersion, training
time, and effectiveness of control group).
Risk of Bias and Study Quality Assessment
The Cochrane Collaboration’s tool (Higgins et al., 2011) was used
to assess risk of bias in each individual study. The tool contains
the domains of sequence generation; allocation concealment;
blinding of participants, personnel, and outcome assessors;
incomplete outcome data; selective outcome reporting; and other
sources of bias. We classified items as “low risk,” “high risk,” or
“unclear risk” of bias. The risk of bias is presented in Table 1.
We also used the PEDro scale (Physiotherapy Evidence
Database Rating Scale) to assess the quality of included studies
using a score (Table 1) (Maher et al., 2003). The scale includes
11 items to rate study quality (Maher et al., 2003); the maximum
score is 11. Studies that scored seven or higher were considered
high quality, while those that scored six or lower were considered
low quality. The scoring process was conducted by two authors
(SZ and YS). CG established consensus scores and resolved
any disagreements.
Publication bias was assessed by a funnel plot that displayed
the relationship between sample size and ES. Small sample studies
with a relatively large variance scatter at the bottom and large
sample studies appear toward the top clustering around the mean
ES. Studies that fall outside the funnel shape have a high risk of
bias (Rosenblad, 2009).
RESULTS
Identification of Studies
Figure 1 shows the flow diagram of study selection. The initial
search yielded 1,859 articles from seven databases and three
additional articles were identified through other sources. After
Frontiers in Aging Neuroscience | www.frontiersin.org 3May 2021 | Volume 13 | Article 586999
Zhu et al. VR in MCI or Dementia
TABLE 1 | Study characteristics.
Study Country Study design Sample VR intervention design Additional
therapy
PEDro
score
N (EG/CG) Population
diagnosis
Mean
age
%Sex
(Female)
Session
Length/
Per Week/N
Cost of
time
Immersive
type
VR content Interactive
medium
Delbroek et al.
(2017)
Belgium RCT 17 (8/9) MCI 87.2 65 30 min/2/12 6 h Semi Task BioRescue No 8
Hughes et al.
(2014)
America RCT 20 (10/10) MCI 77.35 70 90 min/1/24 36h Semi Task Wii/Motion
tracking
No 7
Hwang and
Lee (2017)
Korea RCT 24 (12/12) MCI 72.1 70.83 30 min/5/20 10 h Semi Task Motion tracking No 7
Liao et al.
(2019)
China RCT 34 (18/16) MCI 74.37 67.64 60 min/3/36 36 h Full Task HMD/stick/
Kinect
No 9
Man et al.
(2012)
China RCT 44 (20/24) MCI 80.29 88.64 30 min/2or3/10 5 h Low Task Joystick or a
keyboard
No 7
Padala et al.
(2012)
America RCT 22 (11/11) Mild AD 80.45 72.73 30 min/5/40 20 h Semi Task Wii-Fit Encourage
self-training
7
Park et al.
(2019)
Korea RCT 21 (10/11) MCI 72.04 80.95 30 min/3/18 9 h Full Task HMD/depth
camera/sensor
motion tracker
No 8
Schwenk et al.
(2016)
America RCT 20 (11/9) MCI 78.34 54.54 45 min/2/8 6 h Semi Task Inertial sensors No 8
Serino et al.
(2017)
Italy RCT 20 (10/10) AD 87.65 85 20 min/3/10 200 min Semi Task Gamepad No 7
Tarnanas et al.
(2014)
Greece RCT 71 (32/39) MCI 70.06 60.56 90 min/2/40 60 h Semi Task Touching screen No 9
Thapa et al.
(2020)
Korea RCT 66 (33/33) MCI 72.65 76.47 100 min/3/24 40 h Full Task HMD/headset/
sticks
Health
education
8
EG, experimental group; CG, control group; N, number; AD, Alzheimer’s disease; MCI, mild cognitive impairment; VR, virtual reality; RCT, randomized controlled trial; HMD, Head Mount Display.
Frontiers in Aging Neuroscience | www.frontiersin.org 4May 2021 | Volume 13 | Article 586999
Zhu et al. VR in MCI or Dementia
FIGURE 1 | Flow chart of the literature search.
removing duplicates, 1,160 eligible records were retrieved. After
reading titles and abstracts, 1,098 articles were excluded. After
readinging full text, the remaining 62 articles met the inclusion
criteria. Among these 62 articles, 21 were excluded because
study design was not RCT, 17 because VR was not used as
the intervention, nine because participants did not meet the
inclusion criteria, and four were excluded because exact outcome
values were not reported and we contacted the authors, but
there was no response. Finally, 11 original research articles were
selected for further analysis.
Participant and Study Characteristics
Table 1 summarizes the characteristics of the included studies.
All 11 studies were RCTs. Nine studies reported older adults
with MCI (n=317) and two reported Alzheimer’s dementia (n
=42). Thus, the analysis involved a total of 359 participants
(experimental group =175, mean group size =16; control group
=184, mean group size =17). Mean patient age was 75.84 years
and 71.82% of the patients were female. Three studies reported
full immersive VR, seven reported semi-immersive VR, and the
remaining one reported low immersive VR. The VR methodology
in all studies was task-oriented training. The interactive devices
included head-mounted device, stick, Kinect, depth camera,
sensor motion tracker, Nintendo Wii, touchscreen, keyboard, Bio
Rescue, or gamepad. Number of intervention sessions ranged
from 10 to 40 sessions. Training frequency varied from 1 to 5
sessions per week and the duration per session varied from 20
to 90 min. Three interventions in the control group were passive
and eight interventions were active. The brief components of
interventions in the two groups are listed in Table 2.
Table 3 provides an overview of the outcome measures used
in the different studies.
(1) Attention/Executive Function: Trail Making Test (Arnett
and Labovitz, 1995), Stroop Color and Word Test (Scarpina
and Tagini, 2017), verbal fluency test (Park et al., 2019), verbal
categorical test (Serino et al., 2017), Frontal Assessment Battery
(Appollonio et al., 2005), Attentional Matrices Test (Serino et al.,
2017), letter fluency test (Tarnanas et al., 2014), category fluency
test (Tarnanas et al., 2014), and digit symbol substitution test
(Makizako et al., 2013).
(2) Memory: visual span test (Hwang and Lee, 2017), Fuld
Object Memory Evaluation (Monaco et al., 2015), Multifactorial
Frontiers in Aging Neuroscience | www.frontiersin.org 5May 2021 | Volume 13 | Article 586999
Zhu et al. VR in MCI or Dementia
TABLE 2 | Comparison of interventions.
Study VR group Control group Effectiveness of
control group
Between-group
difference
Delbroek et al.
(2017)
The BioRescue:
containing the nine exercises which were
used to train balance, weight bearing,
memory, attention, and dual tasking, like
walking through a street without touching
barriers by performing weight shifts to the
right and left side at a specific moment.
A blank control group. Passive Active training
Hughes et al. (2014) Nintendo Wii group:
including bowling, golf, tennis, and
baseball.
Healthy aging education
program:
learning about and discussing
age-specific health-related topics
with professionals from the local
academic and health care
communities.
Active Intensity and interest of
training;
The element of motor
training;
VE
Hwang and Lee
(2017)
The virtual reality program:
confirming a self-image and solving the
problems presented through the screen,
enhancing the motivation and active
participation of users through various
sensory feedback.
Traditional occupational therapy. Active Interest of training;
Feedback system;
VE
Liao et al. (2019) Virtual reality-based physical and cognitive
training:
a simplified 24-form Yang-style Tai Chi,
resistance exercise, aerobic exercise, and
functional tasks in the forms of window
cleaning, goldfish scooping and other
tasks relevant to daily activities.
Combined physical and cognitive
training:
walking while reciting poems,
naming flowers and animals
while crossing obstacles, solving
math questions during the
resistance training, and so on.
Active Different training items;
Instant adjustment of
difficulty;
VE
Man et al. (2012) VR programme:
training in virtual environment of a home
setting and a convenience shop, such as
moving around, reading, and memorizing
the items on a memo pad placed on the
table within the living room.
Therapist-led programme:
a sample memory training sheet
showing the objects to be
memorized and a sample answer
sheet showing the objects to be
memorized and distracters.
Active Instant adjustment of
difficulty;
Variety in training;
VE
Padala et al. (2012) Wii-Fit group:
including yoga, strength training, aerobics,
and balance games.
Walking group:
walking at their own pace as a
group of three or four subjects at
any given time with research
personnel.
Active ADL-oriented tasks;
Intensity and interest of
training;
VE
Park et al. (2019) MR-based cognitive training system:
15 training tasks that reflected daily
activities where the study participants are
likely to participate in a home setting, such
as caring for a grandchild.
Computer-assisted training
system:
providing 10 training activities
(two visual processing tasks that
assess response time during
visual stimulation, two auditory
processing tasks that assess
response time during auditory
stimulation and so on).
Active ADL-oriented tasks;
Instant adjustment of
difficulty;
VE
Schwenk et al.
(2016)
Exercise Training Technology:
including ankle point-to-point reaching
task (requiring anterior, posterior, and
lateral leaning and partial weight transfer in
order to improve postural balance during
standing) and virtual obstacle-crossing
task (crossing virtual obstacles moving on
the computer screen from the left to the
right side).
A blank control group. Passive Active training
Serino et al. (2017) VR-Based Training Program: asking
participants to enter this virtual city starting
from the center of the scene to discover
one, two or three hidden objects.
Traditional cognitive rehabilitative
activities:
cards games, naming, fluency,
and music listening.
Active Instant adjustment of
difficulty;
Different training items;
VE
(Continued)
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Zhu et al. VR in MCI or Dementia
TABLE 2 | Continued
Study VR group Control group Effectiveness of
control group
Between-group
difference
Tarnanas et al.
(2014)
Virtual reality museum cognitive exercises:
containing three tasks encompassing
several tasks from each cognitive domain
(memory, attention, execution), such as
following instructions to locate and find
items in an order and so on.
Learning-based memory training:
Including viewing DVD-based
educational programs on history,
art and literature or participated
at puzzle solving exercises.
Active Reward-based daily
training;
VE
Thapa et al. (2020) The VR training:
consisting of four series of cognitive
games (juice making, crow shooting,
finding the fireworks number, memory
object at the house).
A blank control group. Passive Active training
VR, virtual reality; VE, virtual environment; ADL, activity of daily living.
Memory Questionnaire (Troyer and Rich, 2002), Boston Naming
Test (Erdodi et al., 2018), word list test (Lee et al., 2002),
constructional recall (Lee et al., 2004), digit span test (Monaco
et al., 2013), Corsi block test (Serino et al., 2017), and Rey
Auditory Verbal Learning Test (Lezak, 1998).
(3) Visuospatial ability: constructional praxis (Park et al.,
2019) and Rey complex figure copy (Shin et al., 2006).
(4) Global Cognition: Montreal Cognitive Assessment
(Nasreddine et al., 2005), Mini-Mental State Examination
(Folstein et al., 1975), The Computerized Assessment of MCI
(Saxton et al., 2009), and Cognitive Self-Report Questionnaire
(Hughes et al., 2014).
(5) Gait: Gait speed, stride time, gait system (Liao et al., 2019),
Instrument Timed Up and Go (Podsiadlo and Richardson, 1991),
and 8-feet Up and Go (Thapa et al., 2020).
(6) Balance: Tinetti test (Tinetti et al., 1986), Balance Limit of
Stability (Hwang and Lee, 2017), Berg Balance Scale (Muir et al.,
2008), and center of mass (Pai and Patton, 1997).
Primary and Secondary Analyses
Primary analysis using the random effects model showed a
significant moderate positive ES of VR on overall cognition
(g=0.45; 95% CI =0.31–0.59; P<0.001; I2=0%),
attention/execution (g=0.49; 95% CI =0.26–0.72; P<0.001; I2
=31.45%), memory (g=0.57; 95% CI =0.29–0.85;P<0.001; I2
=0%), and global cognition (g=0.32; 95% CI =0.06–0.58; P=
0.02; I2=0%) (Figure 2;Table 4). The ES of VR on visuospatial
ability was not significant (g=0.33; 95% CI = −0.08–0.74;
P=0.11; I2=0%).
Secondary analysis showed a significant small positive ES of
VR on overall motor function (g=0.28; 95% CI =0.05–0.51; P
=0.018; I2=0%). Its ES on balance (g=0.43; 95% CI =0.06–
0.80; P=0.02; I2=0%) was significant and of moderate efficacy.
However, the ES of VR on gait was not significant (g=0.18; 95%
CI = −0.11–0.47; P=0.21; I2=0%) (Figure 3;Table 4).
Subgroup Analyses
The results of a subgroup analysis according to study
characteristics are shown in Table 5. VR interventions for
patients with MCI resulted in greater efficacy (g=0.46; 95%
CI =0.25–0.68; P<0.0001) than for patients with dementia (g
=0.11; 95% CI = −0.47–0.70; P=0.706). Regarding type of
immersion, studies using full immersive VR (g=0.47; 95% CI
=0.10–0.83; P=0.012) had a greater efficacy than studies using
semi-immersive VR (g=0.38; 95% CI =0.11–0.64; P=0.005),
but did not reach statistical significance on low immersive VR
(g=0.54; 95% CI = −0.06–1.15; P=0.077). For the training
time, more than 20 h showed moderate effect size (g=0.43; 95%
CI =0.15–0.70; P=0.002), which were almost equal to those
<20 h (g=0.42; 95% CI =0.12–0.72; P=0.006). In terms of the
effectiveness of interventions in the control group, active (g=
0.40; 95% CI =0.15–0.64; P=0.001) and passive (g=0.55; 95%
CI =0.15–0.95; P=0.008) interventions both showed moderate
effect sizes.
Risk of Bias and Study Quality
Figure 4 and Table 6 show the risk of bias in the 11 included
studies. All studies used random sequence generation (Man et al.,
2012; Padala et al., 2012; Hughes et al., 2014; Tarnanas et al., 2014;
Schwenk et al., 2016; Delbroek et al., 2017; Hwang and Lee, 2017;
Serino et al., 2017; Liao et al., 2019; Park et al., 2019; Thapa et al.,
2020). Participants and assessments were blinded in only one
study (Padala et al., 2012). Four studies had adequate blinding of
outcome assessment (Tarnanas et al., 2014; Delbroek et al., 2017;
Liao et al., 2019; Park et al., 2019). All studies had a low risk of
attrition bias on incomplete outcome data and selective reporting
(Man et al., 2012; Padala et al., 2012; Hughes et al., 2014; Tarnanas
et al., 2014; Schwenk et al., 2016; Delbroek et al., 2017; Hwang
and Lee, 2017; Serino et al., 2017; Liao et al., 2019; Park et al.,
2019; Thapa et al., 2020). Study quality is shown in Table 1. All
11 studies were high quality and the mean PEDro score was 7.67.
Figure 5 shows the funnel plot of the studies. Distribution was
fairly symmetric, indicating no hint of publication bias. Orwin’s
fail-safe N was calculated only for the measures that showed
significant differences between the experimental and control
groups. For overall cognition, 71 studies would be required to
reduce the observed effect to an ES <0.1.
DISCUSSION
Based on the results from 11 high quality RCTs, this meta-
analysis showed that VR intervention exhibited a moderate effect
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Zhu et al. VR in MCI or Dementia
TABLE 3 | Outcome measures used in the included studies.
Article Attention/
Execution
Memory Visuospacial
ability
Global cognition Gait Balance
Delbroek et al.
(2017)
MoCA Instrument timed
up and go
TT
Hughes et al.
(2014)
TMT The computerized
assessment of mild
cognitive
impairment,
Cognitive Self-report
questionnaire
Gait speed
Hwang and Lee
(2017)
Word color test Visual span test Balance limit of
stability
Liao et al. (2019) TMT,
SWCT
Gait system
Man et al. (2012) Fuld object
memory
evaluation,
Multifactorial
memory
questionnaire
Padala et al.
(2012)
MMSE Berg balance
scale, TT
Park et al. (2019) TMT,
Verbal fluency test
Korean-BNT,
word list test,
Constructional
recall
Constructional
praxis
Schwenk et al.
(2016)
TMT MoCA Gait speed,
Stride time
Center of mass
Serino et al.
(2017)
Verbal fluency test,
Verbal categorical
test,
Frontal assessment
battery,
Attentional matrices
test
Digit span test,
Corsi block test
Tarnanas et al.
(2014)
SWCT,
Letter fluency test,
TMT,
Category fluency
test
Rey auditory
verbal learning
test,
BNT,
Digit span test
Rey complex
figure copy
MMSE
Thapa et al.
(2020)
TMT,
Symbol digit
substitution test
MMSE Gait speed,
8-feet up and go
MoCA, Montreal Cognitive Assessment; TT, Tinetti Test; TMT, Trail Making Test; SWCT, Stroop Color and Word Test; BNT, Boston Naming Test; MMSE, Mini-Mental State Examination.
on several measures of cognitive and motor function, including
attention/execution, memory, global cognition, and balance.
This indicates VR is a promising effective non-pharmacological
therapy for older adults with MCI or dementia. However, VR
intervention did not show a significant effect on visuospatial
ability or gait.
Interpretation of Results and Comparison
With Previous Research
To the best of our knowledge, this is the first meta-analysis to
focus on the effect of VR intervention on specific cognitive and
motor function domains in older adults with MCI or dementia.
Recently, Kim et al. (2019) investigated the benefits of VR
intervention in older adults with MCI or dementia. In their
meta-analysis, four of the 11 included studies were not RCTs.
However, by including only RCTs, our meta-analysis was able
to quantify the magnitude of the effect, confirming the efficacy
of VR intervention in MCI or dementia patients (Karssemeijer
et al., 2017). Moreover, our exploration of the effects of VR
intervention on specific cognitive and motor function domains
can provide a framework for future clinical practice.
Our results are comparable with those of Kim et al. (Cohen’
d=0.42; 95% CI =0.24–0.60), demonstrating a moderate
effect of VR on cognitive function (Kim et al., 2019). However,
they did not perform an analysis of specific cognitive domains
and found that VR had no significant effect on execution
Frontiers in Aging Neuroscience | www.frontiersin.org 8May 2021 | Volume 13 | Article 586999
Zhu et al. VR in MCI or Dementia
FIGURE 2 | Forest plot for the efficacy of VR intervention on cognitive functions compared with the control group.
TABLE 4 | Mean weighted effect sizes, confidence interval, and heterogeneity for primary and secondary outcome measures.
K N Standard
error
95% CI Q P(Q) I2
Cognitive functions Global cognition 6 216 0.14 0.06–0.58 3.54 0.62 0.00
Execution/Attention 8 300 0.12 0.26–0.72 10.21 0.18 31.45
Memory 5 204 0.14 0.29–0.85 0.66 0.96 0.00
Visuospatial ability 2 92 0.21 0.08–0.74 0.16 0.69 0.00
Overall cognition 11 359 0.07 0.31–0.59 16.71 0.67 0.00
Motor functions Gait 6 179 0.15 0.11–0.47 4.05 0.54 0.00
Balance 4 107 0.19 0.06–0.80 1.37 0.71 0.00
Overall motor function 7 203 0.12 0.05–0.51 6.47 0.69 0.00
k, number of studies; N, number of patients; CI, confidence interval; Q, within domain heterogeneity; p(Q), p-value for heterogeneity; I2, percentage of heterogeneity due to true
differences within studies, p <0.
(Cohen’ d=0.07; 95% CI = −0.34–0.49), in contrast to our
findings. Another recent meta-analysis that evaluated the effect
of combined cognitive and physical exercise training on cognitive
function in older adults with MCI or dementia did not find a
significant effect on the attention/execution cognitive domain
(SMD =0.38; 95% CI = −0.21–0.97) or memory (SMD =0.02;
95% CI = −0.35–0.39) either (Karssemeijer et al., 2017), which is
inconsistent with our findings. However, the combined ES data in
their study were based on only two studies. Furthermore, whether
VR intervention utilizes its unique features of immersion and
interaction to improve these cognitive domains remains to be
explored. A meta-analysis conducted by Hill et al. (2017) showed
a positive effect of computerized cognitive training on working
memory (Hedges’ g=0.74; 95% CI =0.32–1.15), verbal memory
(g=0.42; 95% CI =0.21–0.63), and attention (g=0.44; 95% CI
=0.20–0.68) in older adults with MCI or dementia. They found
no significant effect on visuospatial ability (g=0.18; 95% CI
= −0.23–0.60), which is consistent with our findings. Another
recent review noted that VR was more effective in improving
attention, visuospatial deficits, and motor impairment in stroke
patients (Maggio et al., 2019). Of all the studies included in our
meta-analysis, only two focused on MCI subjects and provided
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Zhu et al. VR in MCI or Dementia
FIGURE 3 | Forest plot for the efficacy of VR intervention on motor functions compared with the control group.
TABLE 5 | Effect sizes of subgroup according to study characteristics.
Categories k ES (Hedges’s g) +95% CI p SE
Population diagnosis AD 2 0.11 0.47 0.70 0.706 0.30
MCI 9 0.46 0.25 0.68 <0.0001 0.11
Type of immersion Full 3 0.47 0.10 0.83 0.012 0.19
Semi 7 0.38 0.11 0.64 0.005 0.14
Low 1 0.54 0.06 1.15 0.077 0.31
Training time 20 h 5 0.43 0.15 0.70 0.002 0.14
>20 h 6 0.42 0.12 0.72 0.006 0.15
Effectiveness Active 8 0.40 0.15 0.64 0.001 0.12
Passive 3 0.55 0.15 0.95 0.008 0.21
k, number studies; CI, confidence interval; ES, effect size; SE, standard error.
visuospatial ability data. Therefore, future studies are needed to
determine if VR intervention can improve visuospatial ability
in older adults with MCI or dementia. Meanwhile, tasks of VR
interventions in these two studies were not especially designed
for the visuospatial ability, so the effects could not be translated
into the significant improvement of a general visuospatial ability
compared with the interventions in the control group. Our
meta-analysis showed a moderate ES of VR intervention on
global cognition (SMD =0.32; 95% CI =0.17–0.47), which
is in agreement with the results of Karssemeijer et al. (2017)
and Hill et al. (2017).
Kim et al. (2019) also reported a moderate ES of VR
intervention on overall motor function (Cohen’ d=0.41; 95%
CI =0.16–0.65), but did not evaluate the effect on measures
of gait, balance, or falls. Balance improvement (MD =2.99;
95% CI =1.80–4.18) was in accordance with the efficacy of
VR intervention in community-dwelling older adults found
by Neri et al. (2017). Their study also showed no significant
effect on gait performance (MD = −1.20; 95% CI = −1.62 to
0.77), which is consistent with our study. Indeed, in addition
to cognitive impairment, patients with MCI or dementia likely
have impaired motor function, which is often ignored in current
studies. Poor performance in motor function is seldom seen
in a single walking or motor task, since patients may sacrifice
their efficiency in cognitive tasks to compensate for deficits in
motor performance; however, this relationship is broken when a
complex cognitive task is added to complete concurrently (Bloem
et al., 2006). Therefore, in future studies, researchers should take
dual tasking into consideration when designing measurements of
motor outcomes.
In order to understand what makes the VR intervention
efficient, we did a subgroup analysis according to study
Frontiers in Aging Neuroscience | www.frontiersin.org 10 May 2021 | Volume 13 | Article 586999
Zhu et al. VR in MCI or Dementia
FIGURE 4 | Risk of bias assessment per domain across studies with domains of bias on the Y-axis and % of studies having a high, unclear, or low risk of bias in each
domain on the X-axis. The total score is the final author judgment of the total risk of bias.
characteristics, as follows: (1) population diagnosis (e.g., MCI.,
AD.); (2) type of immersion (e.g., full immersion, semi-
immersion, low immersion); (3) training time (e.g., less or more
than 20 h); (4) effectiveness of control group (e.g., active or
passive interventions). We have found positive effect of VR
intervention in the MCI group while no statistical significance
in AD. It may be related to the progression of MCI to dementia
that MCI as the “predementia stage” is less severe compared to
AD (Langa and Levine, 2014; Roberts et al., 2014). Consequently,
we assumed that VR may be a beneficial intervention for
patients with MCI and the intervention in the early stage of
cognitive impairment is very important. With different types
of immersion, a deeper sense of immersion can provide the
users with more real experience. This might explain why full
immersive and semi-immersive VR showed efficacy. Previous
studies demonstrated that the more sophisticated VR technology
used, in terms of the degree of immersion, their participants
would get deeper experience of the virtual environment (Tieri
et al., 2018). One thing we have noticed is that there is no
statistical significance in low immersive VR type, however, it is
highly accepted by the older adults, due to low “cibersikness”
symptomatology (An and Park, 2018). The subgroup analysis did
not show obvious differences based on training time. However,
we believe that total training time may be a factor affecting the
outcome. Therefore, more attention should be paid to training
time in future studies. We found VR interventions had better
effectiveness than traditional strategies in the control group, no
matter whether the intervention in the control group was active
or passive.
Mechanism and Shortcomings of VR
The mechanism behind VR technology for patients with MCI
or dementia is still unclear and few studies have tried to
explore it. As mentioned before, VR intervention has two unique
features, immersion and interaction, which are thought to be the
mechanisms behind the beneficial effect of VR technology. The
feature of immersion can bring the sense of embodiment (Tieri
et al., 2018), just likes “Avatar” to simulate a virtual body that
substitutes the real one is able to elicit illusory sensations that
virtual body itself belongs to the observer (Slater et al., 2008).
According to neuroscience, to regulate and control the body in
the world effectively, the brain creates an embodied simulation
of the body in the world used to represent and predict actions,
concepts, and emotions (Barrett, 2017), and VR just shares the
similar basic mechanism to the brain to bring the simulated
body into the virtual environment. The feature of immersion
can elicit real physiological and psychological reactions (Meehan
et al., 2005; Miller et al., 2008). For example, Salter (Slater et al.,
2008) showed electromyography activity on the right arm of
the participants when they observed an embodied virtual limb
rotating on itself. At the same time, exposure to an immersive
virtual environment having a whole hole on the floor has
been shown to be able to elicit a stressful state determined
by an increase in heart rate (Meehan et al., 2005). In our
subgroup analysis, we have found that low immersive VR has
no statistical significance in patients with MCI or dementia, but
semi- and full immersive VR have, indicating that immersion
plays an important role in the improvement of function in
these patients.
Another unique feature is interaction which plays an
important role in the immersion, feeds back the information
in real time and has a reward system. By combining different
external equipment such as motion tracker, headphone, haptic
gloves, we can deliver the information of changes in the real
body, resulting from the reactions to virtual environment, to
the virtual body and easily set up the association of the virtual
body with the virtual environment (Slater, 1999). In addition,
the feedback system can provide the participants with the
instant information that can be used to reinforce control of
movement parameters and to reduce compensation movements
(Subramanian et al., 2013) from judging these details. Finally,
the reward system can arise the motivation of the participants
to facilitate the repetition of body movement as well as improve
patient compliance, treatment endurance, and happiness (Burdea
et al., 2015; De Luca et al., 2018). Schmidt et al. (2012) also
found that neuroplasticity process in the ventral striatum by
Frontiers in Aging Neuroscience | www.frontiersin.org 11 May 2021 | Volume 13 | Article 586999
Zhu et al. VR in MCI or Dementia
TABLE 6 | Risk of bias assessment in included studies: the authors’ judgments on each risk of bias item for all included studies.
References Sequence
generation
Allocation
concealment
Blinding Incomplete
outcome data
Selective
outcome reporting
Other
sources of bias
Therapist and
participants
Outcome
assessors
Delbroek et al. (2017) Low risk Unclear Unclear Low risk Low risk Low risk Low risk
Hughes et al. (2014) Low risk Unclear Unclear Unclear Low risk Low risk Low risk
Hwang and Lee (2017) Low risk Unclear High risk Unclear Low risk Low risk Low risk
Liao et al. (2019) Low risk Low risk Unclear Low risk Low risk Low risk Low risk
Man et al. (2012) Low risk Unclear Unclear Unclear Low risk Low risk Low risk
Padala et al. (2012) Low risk Unclear Low risk High risk Low risk Low risk Low risk
Park et al. (2019) Low risk Unclear Unclear Low risk Low risk Low risk Low risk
Schwenk et al. (2016) Low risk Low risk Unclear Unclear Low risk Low risk Low risk
Serino et al. (2017) Low risk Unclear Unclear High risk Low risk Low risk Low risk
Tarnanas et al. (2014) Low risk Unclear Unclear Low risk Low risk Low risk Low risk
Thapa et al. (2020) Low risk Low risk Unclear Unclear Low risk Low risk Low risk
FIGURE 5 | Funnel plot for overall cognition with Hedge’s g on the X-axis and the standard error on the Y-axis.
connecting the motor and cognitive circuits can be enhanced
by rewards.
However, these two features of VR technology are not
independent and they influence each other. Therefore, to avoid
one and explore the mechanism of the other is difficult. The
above mechanism is our speculation, so future trials should try
to set up a reasonable comparable group to explore these features
respectively and help clarify the underlying mechanism of VR.
Furthermore, among current trials of VR interventions for
patients with MCI or dementia, there are still few limitations.
First, all of 11 included studies are goal-orientated that could
be difficult for patients with severe cognitive impairment, like
dementia, to finish such tasks. Our subgroup analysis showing no
statistical significance in dementia may be the result of training
difficulty. The efficacy of VR may reduce because the majority of
the VR devices used are commercialized and few have adjusted
their components to fit the needs of these patients. Second,
although VR as a non-invasive, non-pharmacological cognitive
rehabilitation intervention has gained increasing attention in
recent years, health and safety must be taken into consideration,
especially when intended for the use in older adults with
neurodegenerative diseases. Cyber-sickness, a visually induced
motion sickness reaction (Nooij et al., 2018) that can arise
during or after immersion with a virtual environment depending
on the level of immersion, should be a matter of concern in
clinical settings (Bohil et al., 2011). Third, the conditions of
the intervention between the groups are not the same among
the included trials. By comparing differences between VR and
control groups, we found there were more than one difference
among these trials such as instant adjustment of difficulty,
feedback system, and virtual environment. The question arises
that whether one of them, like the element of the task, will
produce the same efficacy as VR or no. The best way to answer
this question is to find articles where the VR group set up a
Frontiers in Aging Neuroscience | www.frontiersin.org 12 May 2021 | Volume 13 | Article 586999
Zhu et al. VR in MCI or Dementia
task and the control group performed the same task without VR
at the same time. However, there is still a lack of such articles.
The goal-oriented training has been proved to be effective
in the recovery of many neurological diseases and all of the
included studies used it in their VR training. At the same time,
an advantage of VR is to combine such elements into their
training. In future trials, it would be better to design a protocol
to explore a single variable between experimental and control
groups which could help us to learn more about the efficacy of
that specific variable.
Strengths and Limitations
The strength of this meta-analysis is that only RCTs were
included. Furthermore, to the best of our knowledge, this is
the first meta-analysis to focus on both cognitive and motor
functions as well as specific cognitive domains. However,
several limitations should be addressed. First, the intervention
characteristics varied between studies. The optimal frequency
and duration of intervention remain to be explored to maximize
intervention effects. Second, as the training sessions in each
study are diverse, we only evaluated the immediate results
after VR intervention to avoid bias. Therefore, the follow-
up effect of VR in MCI or dementia was not analyzed so
we could not estimate whether VR can prevent long-term
worsening of dementia or conversion of MCI to dementia
without persistent training. Third, due to the relatively small
number of participants in the included studies, it is statistically
inappropriate to analyze the impact of varied intervention
components or different subtypes and severities of MCI
or dementia.
Implications for Future Studies
Further research is needed to explore the most effective
characteristics of VR intervention, specifically examining
type, frequency, and duration of intervention as well
as immersion. In addition, large sample studies are
needed and long-term effects should be studied to
gain insight into possible maintenance effects. Finally,
future studies should investigate the effects of VR
intervention on changes in neuroimaging findings and
molecular markers.
CONCLUSION
Our meta-analysis shows that VR intervention is a beneficial
non-pharmacological approach to improve cognitive and motor
function in older adults with MCI or dementia, especially in
attention/execution, memory, global cognition, and balance.
Moreover, VR intervention does not show superiority on
visuospatial ability and gait performance. The clinical relevance
of our findings remains to be confirmed in future research.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
AUTHOR CONTRIBUTIONS
SZ, YSu, YSh, and YZ chose the topic. SZ, YSu, and YSh
performed the analysis. SZ, YSu, and YZ analyzed the data. CG,
TW, and NA participated in the whole process. CG and TW
made final decisions. All authors contributed to writing of this
manuscript.
FUNDING
This work was supported by the National Key R&D Program of
China [Grant No.: 2018YFC2001600 and 2018YFC2001603].
ACKNOWLEDGMENTS
We thank the authors of the primary studies for providing their
data and other critical information. Additionally, the authors
would like to thank the researchers and participants for their
valuable contributions to this article.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fnagi.
2021.586999/full#supplementary-material
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... [16] Virtual reality is currently used in the fields of medicine, [17] engineering, [18] education and entertainment. [19,20] It has been widely used in the medical field for home rehabilitation exercises in patients with stroke, [21,22] total hip replacement, [23] cancer, [24] pain, [25] dementia, [26] and other conditions. Virtual reality has been recognized as a cost-effective healthcare tool due to its several advantages over traditional methods, including lower costs, engaging environments, real-time exercise personalization, greater adaptability to a patient's clinical needs and progress, and the ability to record and provide real-time feedback on exercise performance. ...
... Heterogeneity test results showed that there was homogeneity Maciołek et al [40] 2020 Poland CR patients 32 33 VR relaxation training CT Anxiety Jóźwik et al [41] 2021 Poland Female CVD patients 17 26 VR for 3 wk SAT for 3 wk Anxiety, depression, stress Szczepańska-Gieracha et al [42] 2021 Poland CAD patients 15 17 VR therapy for 4 wk SAT for 4 wk Anxiety, depression, stress Vieira et al [43] 2018 Portugal CR patients 11 10 Kinect training for 6 mo CT for 6 mo Anxiety, depression, stress Wang et al [44] 2021 China HF patients 32 32 VR training for 2 wk CT for 2 wk Anxiety, depression; cardiac function CAD = coronary artery disease, CR = cardiac rehabilitation, CT = conventional therapy, CVD = cardiovascular disease, HF = heart failure, SAT = Schultz's autogenic training, VR = virtual reality. among all studies (I 2 = 0%, P = .69), ...
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... Как и в случае с моторным статусом, многие исследования указывают на эффективность использования реабилитации в виртуальной среде [23,[44][45][46][47]. Обзор, проведенный в 2022 г., продемонстрировал положительное влияние этой терапии на когнитивные способности пациентов с расстройствами аутистического спектра [48]. Отмечено улучшение таких функций, как внимание, память и общие когнитивные функции [49][50][51][52]. ...
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Introduction . According to WHO, nowadays there is a tendency towards an increase in the average age of the population. This creates preconditions for the prevalence of cognitive impairment increase. So, the problem of cognitive disorders early diagnosis is currently relevant. The aim of the study is to analyze the current state of virtual reality technologies using in the field of neurorehabilitation, and to evaluate the clinical effectiveness of rehabilitation of cognitive abilities in a virtual environment in comparison with traditional methods. Material and methods . 245 publications were found in the PubMed database using keywords. After selection, 25 publications were included in the final sample. Results. During the publications analyzing, it was revealed that traditional methods of neurorehabilitation affect only socially significant components. Despite the topic relevance, there are only 6 programs for cognitive abilities rehabilitation. But, existing programs are a computer interpretation of traditional methods. However different studies indicate different clinical effectiveness degrees, which makes it difficult to draw a clear conclusion. Discussion . The use of virtual reality for the cognitive abilities rehabilitation is a promising direction in modern restorative neurology. Among other things VR opens up opportunities for mount rehabilitation in a remote format. Different clinical efficacy levels in different publications is due to the small number of studies and differences in the research methodology. Conclusion . It has been revealed that the problem of using virtual reality in the field of neurorehabilitation is currently relevant. However, more studies are needed to assess the clinical effectiveness of such methods.
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