ArticlePDF Available

Effects of Specific Virtual Reality-Based Therapy for the Rehabilitation of the Upper Limb Motor Function Post-Ictus: Randomized Controlled Trial

Authors:

Abstract and Figures

This research analyzed the combined effect of conventional treatment and virtual reality exposure therapy on the motor function of the upper extremities in people with stroke. We designed a randomized controlled trial set in the rehabilitation and neurology departments of a hospital (Talavera de la Reina, Spain). The subjects included 43 participants, all randomized into experimental (conventional treatment + virtual reality exposure therapy) and control group (conventional treatment).; The main measures were Fugl-Meyer Assessment for upper extremity, Modified Ashworth Scale, and Stroke Impact Scale 3.0. The results included 23 patients in the experimental (62.6 ± 13.5 years) and 20 in the control group (63.6 ± 12.2 years) who completed the study. After the intervention, muscle tone diminished in both groups, more so in the experimental group (mean baseline/post-intervention: from 1.30 to 0.60; η2 = 0.237; p = 0.001). Difficulties in performing functional activities that implicate the upper limb also diminished. Regarding the global recovery fromstroke, both groups improved scores, but the experimental group scored significantly higher than the controls (mean baseline/post-intervention: from 28.7 to 86.5; η2 = 0.633; p = 0.000). In conclusion, conventional rehabilitation combined with specific virtual reality seems to be more efficacious than conventional physiotherapy and occupational therapy alone in improving motor function of the upper extremities and the autonomy of survivors of stroke in activities of daily living.
Content may be subject to copyright.
brain
sciences
Article
Effects of Specific Virtual Reality-Based Therapy for the
Rehabilitation of the Upper Limb Motor Function Post-Ictus:
Randomized Controlled Trial
Marta Rodríguez-Hernández 1, Begoña Polonio-López 1,* , Ana-Isabel Corregidor-Sánchez 1,
JoséL. Martín-Conty 1, Alicia Mohedano-Moriano 1and Juan-JoséCriado-Álvarez 1,2
!"#!$%&'(!
!"#$%&'
Citation: Rodríguez-Hernández, M.;
Polonio-López, B.;
Corregidor-Sánchez, A.-I.;
Martín-Conty, J.L.;
Mohedano-Moriano, A.;
Criado-Álvarez, J.-J. Effects of Specific
Virtual Reality-Based Therapy for the
Rehabilitation of the Upper Limb
Motor Function Post-Ictus:
Randomized Controlled Trial. Brain
Sci. 2021,11, 555. https://doi.org/
10.3390/brainsci11050555
Academic Editor: Hannes Devos
Received: 6 April 2021
Accepted: 24 April 2021
Published: 28 April 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Faculty of Health Sciences, University of Castilla La Mancha, 45600 Talavera de la Reina, Spain;
Marta.RHernandez@uclm.es (M.R.-H.); AnaIsabel.Corregidor@uclm.es (A.-I.C.-S.);
JoseLuis.MartinConty@uclm.es (J.L.M.-C.); Alicia.Mohedano@uclm.es (A.M.-M.);
jjcriado@sescam.jccm.es (J.-J.C.-Á.)
2Department of Public Health, Institute of Health Sciences, 45600 Talavera de la Reina, Spain
*Correspondence: Begona.Polonio@uclm.es
Abstract:
This research analyzed the combined effect of conventional treatment and virtual real-
ity exposure therapy on the motor function of the upper extremities in people with stroke. We
designed a randomized controlled trial set in the rehabilitation and neurology departments of a
hospital (Talavera de la Reina, Spain). The subjects included 43 participants, all randomized into
experimental (conventional treatment + virtual reality exposure therapy) and control group (conven-
tional treatment).; The main measures were Fugl-Meyer Assessment for upper extremity, Modified
Ashworth Scale, and Stroke Impact Scale 3.0. The results included 23 patients in the experimental
(
62.6 ±13.5 years
) and 20 in the control group (63.6
±
12.2 years) who completed the study. After
the intervention, muscle tone diminished in both groups, more so in the experimental group (mean
baseline/post-intervention: from 1.30 to 0.60;
h2
= 0.237; p= 0.001). Difficulties in performing func-
tional activities that implicate the upper limb also diminished. Regarding the global recovery from
stroke, both groups improved scores, but the experimental group scored significantly higher than the
controls (mean baseline/post-intervention: from 28.7 to 86.5;
h2
= 0.633; p= 0.000). In conclusion,
conventional rehabilitation combined with specific virtual reality seems to be more efficacious than
conventional physiotherapy and occupational therapy alone in improving motor function of the
upper extremities and the autonomy of survivors of stroke in activities of daily living.
Keywords:
stroke; rehabilitation; motor recovery; upper limb; virtual reality exposure therapy;
occupational therapy; randomized controlled trial
1. Introduction
Stroke is one of the main causes of acquired disability in adulthood. The stroke
epidemic is primarily driven by the aging of the world population, globalization and the
urbanization of community settings [
1
,
2
]. The Stroke Alliance for Europe states that, every
20 s, a new case of stroke is detected in the adult population and predicts that the number
of people affected will increase by 35% to 12 million people in 2040. As a result, it is
estimated that the health and social costs for stroke diagnosis will increase to 75 million in
2030 (26% more than in 2017). In Spain, 550,941 people were diagnosed with stroke in 2017,
generating a health expenditure of 1700 million euros and a total cost to the Spanish state
of 3557 million euros [3].
Around 80% of survivors present motor difficulties in the upper extremities, affect-
ing the carrying out of activities of daily living (ADLs), the performance of roles in the
community and the health-related quality of life (HRQoL) [46].
Complications after stroke diagnosis can persist over time. Two-thirds of survivors
are disabled 15 years later, two out of five are immersed in depressive states and more than
Brain Sci. 2021,11, 555. https://doi.org/10.3390/brainsci11050555 https://www.mdpi.com/journal/brainsci
Brain Sci. 2021,11, 555 2 of 15
a quarter develop cognitive impairment [
7
]. The costs derived from stroke diagnosis are
high for survivors and their families, making their rehabilitation and survival processes a
great challenge for health policymakers [
8
,
9
]. On average, an informal (non-professional)
caregiver in Spain invests 2833 h per year in caring for the person affected by stroke and
with limitations in ADLs [3].
The general objective of neurological rehabilitation is to promote a rapid recovery
from the multiple deficits after a stroke and the achievement of a lifestyle similar to the
premorbid state [
10
,
11
]. Of all people diagnosed with stroke, only 30–40% regain certain
skills in the upper limb after six months of intervention [
12
]. The upper limb remains
non-functional for ADLs in up to 66% of survivors [
13
], constituting the most disabling of
all residual disorders.
In recent years, the use of neurorehabilitation approaches based on technology and
virtual reality has increased, allowing the creation of effective rehabilitation environments
and providing multimodal, controllable, and customizable stimulation [
14
], in which the
recreation of virtual objects maximize visual feedback [
15
] and high intensity and high
number of repetitions are key factors that influence neuroplasticity and functional im-
provement in patients [
16
]. Rehabilitation based on virtual reality offers the possibility
of individualizing treatment needs, and at the same time, standardizing evaluation and
training protocols [
17
,
18
]. In this sense, specific virtual reality technology for rehabilitation
processes of people with neurological pathology allows working in a functional way and
with specific intervention objectives, in addition to easily qualifying and documenting
progress during the session [
19
]. Taking advantage of these characteristics, several re-
searchers have used virtual reality exposure therapy (VRET) to recover motor function
after stroke. In the treatment of the upper limb, studies indicate that this rehabilitation
approach produces better motor and functional results than conventional therapy [20,21].
The increasing clinical use of neurorehabilitation approaches based on technology and
virtual reality leads to the assumption that spatial representations in virtual environments
may vary slightly from the perceptions that the patient would experience in real spaces. In
this sense, the team of Hruby et al. [
22
] insisted that spatial representations based in virtual
reality systems should be realistic 1:1 replicas with regard to the individual characteristics
of the subjects interacting with both virtual and real environments. This demand increases
the validity of virtual reality techniques for therapeutic purposes, since interaction with a
virtual space is safer and more profitable in the early phases of rehabilitation processes [
23
].
However, it is important for clinicians and researchers to consider that the interaction
with a virtual environment continues to be different from the relationship that the subject
maintains with the real environment [
24
] because people gradually build a mental repre-
sentation of the geographic space that we work with or are immersed in. The locomotion
techniques applied in the virtual model (software or hardware) can influence the cognitive
representations of the person experiencing them [25].
The present study aimed to analyze the combined effect of conventional treatment
and VRET on motor function of the upper limb in people diagnosed with stroke in the
acute phase and its evolution at three months in the Integrated Health Area of Talavera de
la Reina.
2. Materials and Methods
2.1. Study Design
We began the study in April 2018 and completed the evaluation of the three-month
follow-up in March 2020. The study followed the standards of the Declaration of Helsinki
and was approved by the Research and Medicines Ethics Committee (CEIm) of the Inte-
grated Area of Talavera de la Reina (protocol code: 12/2018). It is registered in the ISRCTN
trial registry (ISRCTN27760662) [26].
All participants received verbal and written information about the study and gave
their written informed consent.
Brain Sci. 2021,11, 555 3 of 15
This randomized controlled trial compared the conventional rehabilitation of physio-
therapy and occupational therapy (control group) and the combination of conventional
rehabilitation with the use of specific virtual reality (SVR) technology (experimental group),
following the Consolidated Standards of Reporting Trials (CONSORT) guidelines [
27
] and
CONSORT-artificial intelligence extension [
28
]. Change in upper limb motor skills and its
impact on ADLs (baseline, post-intervention, and three-month follow-up) were used as
primary outcome. The evaluation of the post-intervention variables was completed three
weeks after the start of treatment in both groups (after 15 intervention sessions).
The participants were recruited from the neurology and rehabilitation units of the
University General Hospital of Talavera de la Reina, Spain. They were randomly assigned
to the control or experimental group by a researcher who did not participate in the inter-
vention and the evaluation process (allocation ratio of 1:1). The conventional rehabilitation
therapists were blinded to the study, but neither the participants nor the therapist who
applied the VRET could be blinded to the intervention.
2.2. Participants and Setting
The study included 46 patients (43 of whom completed the intervention period and
follow-up evaluation) who were attended in the neurology and hospital rehabilitation
units (mean age 63.1, 18.6% were women) after being diagnosed with stroke. Inclusion
criteria were: (1) age: 18 to 85 years; (2) maximum evolution time of six months; (3) upper
limb motor involvement (Fugl-Meyer Assessment and Modified Ashworth Scale); (4) de-
pendence in ADLs (Stroke Impact Scale, version 3.0); (5) life expectancy greater than six
months (absence of life-threatening diagnoses, such as end-stage cancer); and (6) absence
of other serious and disabling pathology. Four exclusion criteria were defined: presence of
another neurological diagnoses, severe hemineglect, psychiatric pathology, and signature
of the revocation of informed consent.
2.3. Outcome Measures
The primary outcome variables for this study were upper limb motor function and the
impact of stroke diagnosis on ADL involving the use of the upper limb. To quantify these
variables, we used the Fugl-Meyer Assessment for upper limb (FMA-UE), the Modified
Ashworth Scale for the evaluation of muscle tone and the Stroke Impact Scale (SIS 3.0).
A large number of international guidelines and research in the field of neurorehabili-
tation and brain damage suggest that the FMA-UE is a valid instrument, given its excellent
psychometric properties and its adequate scale to assess the functionality and motor func-
tion of the upper limb after a stroke. Furthermore, its use has been validated with virtual
reality technology, specifically with the Kinect sensor, which is widely known and was
part of the evaluation and intervention processes of this study [
29
32
]. The full version
has 113 items, while the subscale for the evaluation of the upper limb examines 63 items
(55.75%). Regarding the characteristics of the upper limb, 33 items (29.20%) evaluate motor
function, 6 items (5.31%) the sensitivity and proprioception, and the last 24 points (38.09%)
correspond to pain and joint mobility. Each item on the evaluation scale responds to an
ordinal level of 0 to 2 points: 0 corresponds to an inability to carry out movement and 2 to
a capacity to carry it out completely and adequately [
29
]. We applied the Spanish version
of the FMA-EU [
33
] with a Spearman coefficient of 0.946 (p= 0.000) for the domain of the
upper limb, excellent reliability (ICC of 0.987; p= 0.000), and a Cronbach’s alpha of 0.98 for
motor balance of the upper limb.
The Modified Ashworth Scale measures resistance to passive movement according to
a scale of 0 to 4, in which 0 corresponds to no increase in muscle tone and 4 to the affected
part being rigid in flexion or extension (Kendall W of 0.765; p= <0.001 for elbow and a
reliability of 0.4 to 0.75 for 95% of the assessments) [34].
The SIS 3.0 contains 59 items that conceptually evaluate eight important domains:
strength, hand function, ADLs and instrumental activities of daily living (IADLs), mobility,
communication, emotion, memory, and thinking and participation [
35
]. The new structure
Brain Sci. 2021,11, 555 4 of 15
of four domains (physical, cognitive, emotional, and social participation) has conferred the
SIS 3.0 a good reliability of internal consistency (Cronbach’s alpha of 0.98 for the physical
domain) and test-retest (ICC of 0.79 for global recovery from stroke), concurrent validity,
and responsiveness, which recommend its use in clinical practice and research [3639].
The motor function of the upper limb, the impact of stroke on ADLs and muscle
tone were evaluated and recorded before the start of treatment (baseline), at three weeks
(post-intervention) and three months after its completion (follow-up). The entire evaluation
process was carried out by the same researcher in both groups (an experienced occupational
therapist trained for this research). In addition, we recorded sociodemographic and clinical
data, such as age, sex, time elapsed since diagnosis, location of the lesion, risk factors,
dominance, pain, self-perceived quality of life, or hemineglect syndrome.
2.4. Intervention
All study participants received 15 treatment sessions lasting 150 min per session and
distributed over five consecutive days a week. In total, the intervention lasted three weeks
per participant. The patients assigned to the experimental group combined conventional
upper and lower limb strength and motor training (50 min physiotherapy and 50 min
occupational therapy; administered by the hospital’s physiotherapy and occupational
therapy team) with SVR technology devices (50 min), while participants from the control
group received only conventional training in physiotherapy (75 min) and occupational
therapy (75 min).
The conventional intervention protocol consisted of performing manual therapy
techniques (massage), passive and active-assisted mobilizations of the upper and lower
limbs, march in parallel, slope and stairs, exercises with and against resistance with balls,
elastic bands and dumbbells in therapeutic cage and trellises, active-assisted mobility
exercises of the upper limb and fingers in a sitting position, moving objects horizontally on
the table, elevation and superposition of objects in the vertical plane, biomechanical tasks
that simulated flexion-extension and abduction-adduction of the shoulder and flexion-
extension of the wrist and fingers.
The motor training protocol with SVR devices consisted of the application of three
systems: (1) HandTutor
©
glove [
40
,
41
], 3DTutor
©
[
42
], and Rehametrics
©
[
43
]. All systems
are based on intensive and repetitive practice through movement instructions and feedback
provided by software with virtual environments and tasks that simulate the movements
that the stroke survivor requires for daily life [4446].
In this work, we will address the clinical and functional effects of the use of Rehamet-
rics© (30 min of intervention/session per participant).
The Rehametrics
©
software [
43
] and Microsoft Kinect sensor [
47
50
] work the upper
limb (shoulder and elbow), trunk and lower body. The technology simulates ADLs and
mobility in the community with virtual environments and in combination with the use
of gamification. It monitors and captures the user’s movement of body, joints in real
time through the Kinect sensor. In addition, the sensor calibrates the patient’s position at
the beginning of each session and during exercise execution, providing visual feedback
for movement and posture correction during treatment sessions. During the study, the
Rehametrics© software was updated to the 2019 and 2020 versions.
The software requires the physical presence of the therapist to assess the AROM at
the beginning of the session, determine the tolerance level, and adjust and customize the
difficulty, duration, range of motion, and number of distracting elements or visual aids.
Rehametrics
©
has two types of ‘exergames’: (1) analytical exergames that work iso-
lated movements necessary to complete an ADL-inspired flexion-extension of the elbow
or abduction and adduction of the shoulder (Figure 1a) and (2) functional exergames that
involve motor control, coordination of movements, contraction dynamics and displace-
ments (Figure 1b). Before starting the treatment session, the therapist selects the exergames,
the duration of each, breaks between exercises, the range of motion for each of the joints,
the time pressure for the patient, the number of distractors (night or fog) and the number
Brain Sci. 2021,11, 555 5 of 15
of visual aids (arrows that indicate if the obstacle appears on one side of the screen or
another) (Figure 1c). The software automatically adjusts the level of difficulty based on
the patient’s progress during each exergame. In addition, Rehametrics
©
provides result
graphs to indicate the progress of the patient during the different treatment sessions for a
given exergame, the number of failures or the ability to react to obstacles in seconds. This
allows patients to visualize their progress and access a quantitative evaluation of failures,
successes, and times (Figure 1d).
Brain Sci. 2021, 11, x FOR PEER REVIEW 5 of 16
Rehametrics©
has two types of ‘exergames’: (1) analytical exergames that work iso-
lated movements necessary to complete an ADL-inspired flexion-extension of the elbow
or abduction and adduction of the shoulder (Figure 1a) and (2) functional exergames that
involve motor control, coordination of movements, contraction dynamics and displace-
ments (Figure 1b). Before starting the treatment session, the therapist selects the exer-
games, the duration of each, breaks between exercises, the range of motion for each of the
joints, the time pressure for the patient, the number of distractors (night or fog) and the
number of visual aids (arrows that indicate if the obstacle appears on one side of the screen
or another) (Figure 1c). The software automatically adjusts the level of difficulty based on
the patient’s progress during each exergame. In addition, Rehametrics© provides result
graphs to indicate the progress of the patient during the different treatment sessions for a
given exergame, the number of failures or the ability to react to obstacles in seconds. This
allows patients to visualize their progress and access a quantitative evaluation of failures,
successes, and times (Figure 1d).
The exergames used were personalized according to the functional capacity of the
patient, dividing them into low, medium, or high difficulty. In the first sessions, we focus
on analytical exergames to increase the joint range of the upper limb. In a second phase,
we selected exergames that allowed us to work in several planes and required elaborate
and coordinated movements with the trunk and lower limb, lateral, and anteroposterior
movements. In the final treatment sessions, elements were incorporated that allowed
greater destabilization and high motor control (exergames in a sitting position on bobath
balls or in a standing position on a trampoline or destabilizing base). In addition, weights
were added to increase muscle strength.
Changes in active range of motion (AROM), patient position, movement correction
during activity, and scheduled task execution level were extracted from Rehametrics©
software and Microsoft’s Kinect sensor. These changes were not used as an outcome meas-
ure in the study. The software automatically stores these variables for each patient and
exergames.
(a) (b)
(c) (d)
Figure 1.
(
a
) Selection of analytical exergames; (
b
) selection of functional exergames; (
c
) adaptation of exergames at the
beginning of a treatment session; (d) graphical representation of results or progress of the patient.
The exergames used were personalized according to the functional capacity of the
patient, dividing them into low, medium, or high difficulty. In the first sessions, we focus
on analytical exergames to increase the joint range of the upper limb. In a second phase,
we selected exergames that allowed us to work in several planes and required elaborate
and coordinated movements with the trunk and lower limb, lateral, and anteroposterior
movements. In the final treatment sessions, elements were incorporated that allowed
greater destabilization and high motor control (exergames in a sitting position on bobath
balls or in a standing position on a trampoline or destabilizing base). In addition, weights
were added to increase muscle strength.
Changes in active range of motion (AROM), patient position, movement correction
during activity, and scheduled task execution level were extracted from Rehametrics
©
software and Microsoft’s Kinect sensor. These changes were not used as an outcome
measure in the study. The software automatically stores these variables for each patient
and exergames.
2.5. Statistical Analysis
The sample size was calculated with the Epidat 4.2 program. An effectiveness ratio
of 90% was estimated for the experimental group and 50% for the control group. Using
a power of 80% and a confidence level of 95% (p< 0.05), a minimum sample size of
Brain Sci. 2021,11, 555 6 of 15
20 participants was obtained in each group. The data were analyzed with the IBM SPSS
statistical package (version 24.0) (IBM Spain, S.A., Madrid, Spain). To compare the clinical
and sociodemographic variables of the groups, Student’s T and chi-square tests were used.
Differences in the Ashworth, SIS 3.0 and FMA-UE scales at baseline, post-intervention,
and 3-month follow-up were analyzed with inter- and intra-group ANOVA and Student’s
ttest. For the FMA-UE, a score of 7.35 in the upper limb subscale was maintained as the
minimum detectable change in the three-month follow-up [
33
]. Statistical significance was
set at a p-value of less than 0.05.
The analysis of missing data from the control group was carried out with multiple
imputation in the analysis (expectation maximization and regression method), with a little’s
chi-square statistic of 31,370 (degree freedom = 48; p= 0.970).
The investigator performing the statistical analysis was unaware of the random alloca-
tion of participants to the intervention groups.
3. Results
Forty-six patients were randomly allocated, 43 of whom completed the study period
and the follow-up evaluation. Twenty-three participants were assigned to the experimental
group (EG) and 23 to the control group (CG). Three participants were lost in the control
group when the COVID-19 pandemic began in Spain (Figure 2).
Brain Sci. 2021, 11, x FOR PEER REVIEW 7 of 16
Figure 2. CONSORT flow diagram for participant recruitment, allocation, follow-up, and analysis.
CRT: conventional rehabilitation treatment. * Post-intervention evaluation.
Table 1. Characteristics of the participants of both groups (n = 43).
Characteristics: Study Variables Experimental Group (EG) Control Group (CG) Difference of Mean
between Groups
(n = 23) (n = 20) (p-Value)
Age –0.9 (0.812)
Mean (SD) 62.6 (13.5) 63.6 (12.2) 0.566
Under 55 years (%) 26.1 25
55 to 70 years (%) 30.4 45
Over 70 years (%) 43.5 30
Sex (%)
Male 78.3 85 0.571
Female 21.7 15
Main diagnostic (%)
0.883
Ischemic/thrombotic 91.3 90
Hemorrhagic 8.7 10
Middle cerebral artery lesion (%) 60.9 55 0.697
Location of the brain injury (%)
0.832
Right 82.6 85
Left 17.4 15
Time since diagnostic (days) *
Baseline (pre-intervention) 55.3 (34.3) 54.2 (30.4) 1.1 (0.909)
Post-intervention (3-week follow-up) 75.3 (34.3) 74.2 (30.4) 1.1 (0.909)
Follow-up (3 months) 162.3 (36.9) 157.2 (36.1) 5.1 (0.650)
Hemispatial neglect syndrome (%) 13 10 0.756
Presence of pain in extremities (%)
Baseline (pre-intervention) 43.5 50 0.669
Figure 2.
CONSORT flow diagram for participant recruitment, allocation, follow-up, and analysis. CRT: conventional
rehabilitation treatment. * Post-intervention evaluation.
Sociodemographic and clinical characteristics of the participants are shown in
Table 1
.
Significant differences are observed in the evolution of pain between both groups, decreas-
ing considerably after the intervention in the experimental group. Fifteen percent (n= 3)
of the participants in the control group registered a change in dominance (from right to
left) in the first follow-up (post-intervention), while the experimental group maintained
baseline dominance. Most participants in both groups coincided in the location of the
lesion in the right hemisphere (EG: 82.6% and CG: 85.0%), and more than 90% of the study
Brain Sci. 2021,11, 555 7 of 15
participants were diagnosed with ischemic stroke (EG: 91.3% vs. CG: 90.0%). Regarding
the results obtained in the visual analog scale of the EuroQoL instrument (EQ-VAS) for
the self-perceived measurement of HRQoL, statistically significant differences were ob-
served in the evolution of both groups and after the experimental intervention (EG: 86.5 vs.
CG:57.0; p= 0.000) [
51
]. Rodríguez-Hernández et al. [
51
], authors of this study, analyzed
the differences the effect of a combined treatment of conventional therapy with virtual
reality on HRQoL of this sample of participants. For the analysis and its quantification,
they used the following as outcome measures the EuroQoL instrument (EQ-5D-5L) and
EQ-VAS.
Table 1. Characteristics of the participants of both groups (n= 43).
Characteristics: Study Variables
Experimental Group (EG) Control Group (CG) Difference of Mean between Groups
(n= 23) (n= 20) (p-Value)
Age 0.9 (0.812)
Mean (SD) 62.6 (13.5) 63.6 (12.2) 0.566
Under 55 years (%) 26.1 25
55 to 70 years (%) 30.4 45
Over 70 years (%) 43.5 30
Sex (%)
Male 78.3 85 0.571
Female 21.7 15
Main diagnostic (%)
0.883
Ischemic/thrombotic 91.3 90
Hemorrhagic 8.7 10
Middle cerebral artery lesion (%) 60.9 55 0.697
Location of the brain injury (%)
0.832
Right 82.6 85
Left 17.4 15
Time since diagnostic (days) *
Baseline (pre-intervention) 55.3 (34.3) 54.2 (30.4) 1.1 (0.909)
Post-intervention (3-week follow-up) 75.3 (34.3) 74.2 (30.4) 1.1 (0.909)
Follow-up (3 months) 162.3 (36.9) 157.2 (36.1) 5.1 (0.650)
Hemispatial neglect syndrome (%) 13 10 0.756
Presence of pain in extremities (%)
Baseline (pre-intervention) 43.5 50 0.669
Post-intervention (3-week follow-up) 21.7 80 0
Follow-up (3 months) 82.6 100 0.05
EuroQoL visual analog scale (EQ-VAS) *
Baseline 29.1 (12.8) 25.5 (5.1) 0.241
Post-intervention 86.5 (7.1) 57.0 (4.7) 0
Follow-up 78.3 (10.7) 58.5 (5.9) 0
FMA-UE (Total A. Upper extremity) *
Baseline 12.6 (3.4) 12.7 (3.3) 0.1 (0.930)
Post-intervention 30.1 (3.0) 24.7 (3.7) 5.4 (0.000)
Follow-up 31.1 (4.3) 26.9 (4.1) 4.2 (0.002)
Dominance (change
baseline/post-intervention) (%)
Right 100.0/100.0 100.0/85.0 0.054
Left 0 0/15.0
* Mean (SD). p-value: Student’s t-test for independent samples in continuous variables/Pearson’s chi-squared test.
Table 2shows differences in the evolution of the results on the Modified Ashworth
Scale between intervention groups. The decrease in muscle tone is observed in both groups,
being notably higher in the experimental group (mean baseline/post-intervention: from
1.30 to 0.60). At follow-up, the muscle tone of participants in the experimental group
increased slightly, while those in the control group had a more pronounced increase in
muscle tone in most range of motion (mean post-intervention/follow-up: 1.05 to 1.75). The
Brain Sci. 2021,11, 555 8 of 15
effect size of the experimental intervention was large (greater than 0.14) and statistically
significant (p= 0.001).
Table 2. Linear model. Effect of the intervention on Modified Ashworth Scale results.
Intervention Group Difference Baseline/Follow-Up
Modified Ashworth
Scale (T-Score)
Baseline
Mean (SD)
Post-Intervention
Mean (SD)
Follow-Up
Mean (SD) Mean (CI95%)
ANOVA
Fph2Parcial
Experimental group 1.30 (0.70) 0.60 (0.50) 0.87 (0.46) 0.43 (0.07/0.79) *
Control group 1.22 (0.67) 1.05 (0.21) 1.75 (0.44) 0.45
(0.85/0.04) * 12.7 0.001 0.237
p0.670 0.001 0.000
SD: Standard deviation. CI95%: 95% confidence interval. Partial
h2
: magnitude of effect. * The difference in means is significant at level
0.014 (experimental group) and 0.026 (control group). p-value: italics.
The differences in the SIS 3.0 scores that reveal the impact of stroke by intervention
group are shown in Tables 3and 4. The effect of the experimental intervention is observed
in the distribution of means in both the physical dimension of force and the functional
dimension of ADLs/IADLs, with statistically significant differences at baseline, post-
intervention and follow-up. Exceptions were found in the evolution of strength in the
most affected leg, foot, and ankle (baseline: 0.623 and 0.608; post-intervention: 0.086,
respectively) and in the difficulty to reach the bathroom on time and control bladder and
intestines. Marked is the decrease in the difficulty to perform functional activities that
involve the use of the upper limb after the intervention in the experimental group.
Table 3.
Difference in means of physical domain (strength) according to intervention group as
measured by SIS 3.0 (n= 43).
SIS 3.0. Physical
Domain.
Experimental Group
(EG) Control Group (CG) Difference of Mean
between Groups
Strength (n= 23) (n= 20) (p-Value)
1a. Of the arm that was
most affected by your
stroke
Baseline 1.6 (0.7) 1.7 (0.7) 0.04 (0.838)
Post-intervention 4.3 (0.5) 3.4 (0.5) 0.92 (0.000)
Follow-up 4.3 (0.5) 3.4 (0.5) 0.95 (0.000)
1b. Grip of your hand
that was most affected
by your stroke
Baseline 1.3 (0.6) 1.4 (0.6) 0.04 (0.800)
Post-intervention 4.2 (0.4) 3.0 (0.0) 1.22 (0.000)
Follow-up 4.3 (0.5) 3.0 (0.0) 1.30 (0.000)
1c. Of the leg that was
most affected by your
stroke
Baseline 2.1 (1.0) 2.3 (0.8) 0.13 (0.623)
Post-intervention 4.4 (0.5) 4.2 (0.4) 0.24 (0.086)
Follow-up 4.2 (0.7) 3.8 (0.4) 0.37 (0.046)
1d. Of the foot/ankle
that was most affected
by your stroke
Baseline 2.0 (0.9) 2.2 (0.8) 0.13 (0.608)
Post-intervention 4.4. (0.5) 4.2 (0.4) 0.24 (0.086)
Follow-up 4.2 (0.7) 3.8 (0.4) 0.37 (0.046)
Mean (SD). p-value: Student’s t-test for independent samples in continuous variables. Values of the SIS 3.0
(physical domain; strength): 1 (no strength at all); 2 (a little strength); 3 (some strength); 4 (quite a bit of strength)
and 5 (a lot of strength).
Brain Sci. 2021,11, 555 9 of 15
Table 4.
Difference in means of physical domain (ADL/IADL) according to intervention group as
measured by SIS 3.0 (n= 43).
SIS 3.0. Physical Domain. Experimental Group
(EG)
Control Group
(CG)
Difference of Mean
between Groups
ADL/IADL (n= 23) (n= 20) (p-Value)
5a. Can you cut your food
with a knife and fork?
Baseline 1.5 (0.6) 1.7 (0.6) 0.17 (0.317)
Post-intervention 4.6 (0.5) 3.3 (0.5) 1.28 (0.000)
Follow-up 4.6 (0.5) 3.3. (0.5) 1.27 (0.000)
5b. Can you dress the top
part of your body?
Baseline 1.7 (0.8) 1.7 (0.6) 0.04 (0.836)
Post-intervention 4.9 (0.3) 3.4 (0.5) 1.49 (0.000)
Follow-up 5.0 (0.0) 3.4 (0.5) 1.60 (0.000)
5c. Can you bathe yourself?
Baseline 1.6 (0.7) 1.7 (0.6) 0.09 (0.667)
Post-intervention 4.5 (0.5) 3.3 (0.5) 1.14 (0.000)
Follow-up 4.6 (0.5) 3.4 (0.5) 1.22 (0.000)
5d. Can you clip your
toenails?
Baseline 1.3 (0.5) 1.3 (0.6) 0.09 (0.599)
Post-intervention 3.7 (0.8) 3.2 (0.4) 0.51 (0.010)
Follow-up 3.7 (0.8) 3.2 (0.4) 0.50 (0.013)
5e. Can you get to the toilet
on time?
Baseline 3.2 (1.0) 3.2 (0.8) 0.04 (0.870)
Post-intervention 4.8 (0.4) 4.5 (0.5) 0.26 (0.073)
Follow-up 4.8 (0.4) 4.6 (0.5) 0.23 (0.109)
5f. Can you control your
bladder (not have an
accident)?
Baseline 5.0 (0.0) 5.0 (0.2) 0.04 (0.323)
Post-intervention 5.0 (0.0) 5.0 (0.2) 0.05 (0.301)
Follow-up 5.0 (0.0) 5.0 (0.2) 0.05 (0.289)
5g. Can you control your
bowels (not have an
accident)?
Baseline 5.0 (0.0) 5.0 (0.2) 0.04 (0.323)
Post-intervention 5.0 (0.0) 5.0 (0.2) 0.05 (0.301)
Follow-up 5.0 (0.0) 5.0 (0.2) 0.05 (0.289)
5h. Can you do light
household tasks/chores (e.g.,
dust, make the bed, take out
garbage, do the dishes)?
Baseline 1.7 (0.6) 1.7 (0.4) 0.00 (1.000)
Post-intervention 4.7 (0.5) 3.8 (0.4) 0.93 (0.000)
Follow-up 4.7 (0.5) 3.8 (0.4) 0.95 (0.000)
5i. Can you go shopping?
Baseline 1.7 (0.5) 1.7 (0.4) 0.09 (0.532)
Post-intervention 4.4 (0.5) 3.1 (0.4) 1.34 (0.000)
Follow-up 4.5 (0.5) 3.1 (0.4) 1.38 (0.000)
5j. Can you do heavy
household chores (e.g.,
vacuum, laundry, or yard
work)?
Baseline 1.7 (0.5) 1.7 (0.4) 0.09 (0.532)
Post-intervention 4.3 (0.6) 3.0 (0.4) 1.21 (0.000)
Follow-up 4.3 (0.6) 3.1 (0.4) 1.25 (0.000)
Mean (SD). p-value: Student’s t-test for independent samples in continuous variables. Values of the SIS 3.0
(physical domain; ADL/AIDL): 1 (could not do at all); 2 (very difficult); 3 (somewhat difficult); 4 (a little difficult)
and 5 (not difficult at all).
Brain Sci. 2021,11, 555 10 of 15
The graphic representation of the effect of the experimental intervention on the total
strength scores and ADLs/IADLs (physical domain SIS 3.0) are shown in Figure 3.A
progressive and positive increase is observed in the means of both groups, being more
marked in the experimental group (p= 0.000 in post-intervention and follow-up). The
means between post-intervention and follow-up do not differ in any of the groups.
Brain Sci. 2021, 11, x FOR PEER REVIEW 11 of 16
Figure 3. Graphic representation of the intervention’s effect on the total scores of strength domain (minimum: 4; maximum:
20) and ADLs/IADLs (minimum: 10; maximum: 50) of the SIS 3.0 instrument.
Table 5 shows the differences in the evolution of the results in global stroke recovery
by intervention group. The increase in SIS 3.0 score is observed in both groups, but is
notably higher in the experimental group (baseline/post-intervention mean: from 28.7 to
86.5). During follow-up, the control group remained stable, while scores in the experi-
mental group decreased slightly (mean post-intervention/follow-up: from 86.5 to 78.3).
The magnitude of the effect of the experimental intervention is large (0.633) and statisti-
cally significant (p = 0.000).
Table 5. Linear model. Effect of the intervention on global recovery from stroke (SIS 3.0).
Intervention Group Difference Baseline/Follow-Up
Baseline
Mean (SD)
Post-Intervention
Mean (SD)
Follow-Up
Mean (SD) Mean (CI95%)
ANOVA
F p η2 Parcial
Global Recovery SIS 3.0
(T-Score)
Experimental group 28.7 (12.5) 86.5 (7.1) 78.3 (10.7) 49.5 (56.4/42.8) *
Control group 26.1 (4.5) 56.7 (4.8) 59.0 (6.4) 33.0 (36.8/29.1) * 70.6 0.000 0.633
p 0.359 0.000 0.000
SD: Standard deviation. CI95%: 95% confidence interval. Partial η2: magnitude of effect. * The difference in means is sig-
nificant at level 0.000. p-value: italics.
4. Discussion
The results of this study suggest that VRET with Rehametrics© in combination with
conventional physiotherapy and occupational therapy is effective in the rehabilitation of
the upper limb in survivors of stroke in the subacute phase.
Objective and validated outcome measures were used to assess muscle tone [34], mo-
tor function of the upper limb [29], and the impact of stroke diagnosis in the physical
domain and in ADLs [38], in comparison with the control group.
Several research studies have used the FMA-UE to assess the effects of the interven-
tion on motor function in the upper limb. Their results suggest a significant difference
between intervention groups, while the improvement of muscle strength has not shown
significant differences before and after the intervention [17,52,53]. In our study, the in-
Figure 3.
Graphic representation of the intervention’s effect on the total scores of strength domain (minimum: 4; maximum:
20) and ADLs/IADLs (minimum: 10; maximum: 50) of the SIS 3.0 instrument.
Table 5shows the differences in the evolution of the results in global stroke recovery by
intervention group. The increase in SIS 3.0 score is observed in both groups, but is notably
higher in the experimental group (baseline/post-intervention mean: from 28.7 to 86.5).
During follow-up, the control group remained stable, while scores in the experimental
group decreased slightly (mean post-intervention/follow-up: from 86.5 to 78.3). The
magnitude of the effect of the experimental intervention is large (0.633) and statistically
significant (p= 0.000).
Table 5. Linear model. Effect of the intervention on global recovery from stroke (SIS 3.0).
Intervention Group Difference Baseline/Follow-Up
Baseline
Mean (SD)
Post-Intervention
Mean (SD)
Follow-Up
Mean (SD) Mean (CI95%) ANOVA
Fp
h2Parcial
Global Recovery SIS 3.0
(T-Score)
Experimental group 28.7 (12.5) 86.5 (7.1) 78.3 (10.7) 49.5
(56.4/42.8) *
Control group 26.1 (4.5) 56.7 (4.8) 59.0 (6.4) 33.0
(36.8/29.1) * 70.6 0.000 0.633
p0.359 0.000 0.000
SD: Standard deviation. CI95%: 95% confidence interval. Partial
h2
: magnitude of effect. * The difference in means is significant at level
0.000. p-value: italics.
4. Discussion
The results of this study suggest that VRET with Rehametrics
©
in combination with
conventional physiotherapy and occupational therapy is effective in the rehabilitation of
the upper limb in survivors of stroke in the subacute phase.
Objective and validated outcome measures were used to assess muscle tone [
34
],
motor function of the upper limb [
29
], and the impact of stroke diagnosis in the physical
domain and in ADLs [38], in comparison with the control group.
Several research studies have used the FMA-UE to assess the effects of the intervention
on motor function in the upper limb. Their results suggest a significant difference between
intervention groups, while the improvement of muscle strength has not shown significant
Brain Sci. 2021,11, 555 11 of 15
differences before and after the intervention [
17
,
52
,
53
]. In our study, the increase in muscle
strength in the upper limb and the differences found between the experimental and control
groups can be explained by the placement of weights on the wrists during SVR training,
coinciding with findings by Miclaus et al. [54].
The use of VRET in treatments leading to functional recovery of the upper limb in
stroke patients is based on a new, enriched, and interactive environment that influences
neuroplasticity, especially in subacute phases of the disease, leading to participants mani-
festing the maximum level of brain reorganization [
55
]. No significant differences were
found in the baseline evaluation of the intervention groups, so we can assume that the
results are a consequence of the use of SVR technology in active and intensive training
and the demands and challenges of the exergames [
54
]. The use of SVR as a rehabilitation
method represents an alternative therapeutic concept that is attractive to patients as they
focus on the demands of the exergames and not on the repetitions or the degree of difficulty
of the exercise. It facilitates the relearning of coordinated motor patterns and allows us to
create an environment in which the intensity of visual and auditory feedback and training
can be manipulated and systematically enhanced to create individualized motor learning
paradigms. This personalization increases treatment compliance and generates a positive
effect on the emotional state of patients [56].
We used the SIS 3.0 scale and physical domain subscale to examine the differences in
the ability to perform ADLs after the intervention. This revealed statistically significant
differences in activities involving the use of the upper limb, such as eating and dressing
(p= 0.000). Saposnik et al. [
57
] examined the effects of virtual reality training in stroke
patients using SIS 3.0 and reported the same significant differences as our study, despite
their use of nonspecific virtual reality (NSVR). In this sense, Maier et al. [
58
] have recently
published a meta-analysis in which they analyze the differences between NSVR and SVR
in relation to motor recovery after stroke diagnosis. They conclude that studies with NSVR
focus on three principles: variable practice, promotion of paretic limb use, and dosage,
while more than 50% of studies with SVR include the same three principles of NSVR plus
explicit and implicit feedback, increased difficulty and specific practice focused on the
task. They compared the subsets of studies (NSVR vs. SVR) and observed a greater impact
on motor function and activity of the upper limbs in the case of SVR with statistically
significant differences.
The results of other studies coincide with the significant improvements of our study
in the IADLs (shopping; p= 0.000). In their case, and unlike Rehametrics
©
, they used
exergames that simulated the task of purchasing specifically [
17
,
59
]. Schuster et al. [
55
]
explain that the differences found in the improvement of strength measured with the SIS 3.0
tool could be explained by the high number of repetitions in the movements that involve
the upper limb during training with the exergames of the SVR system, which coincides
with our results (p= 0.000).
Young-Bin et al. [
60
] found no statistically significant differences in the Modified
Ashworth Scale after the combined intervention of virtual reality and real objects. However,
in our study, the experimental group significantly decreased in muscle tone and the
magnitude of the effect of the experimental intervention on this variable is large (
h2= 0.237
).
The differences in the findings may be due to the number of weekly sessions and the time
elapsed between the diagnosis and the start of the experimental intervention.
The above results suggest that SVR exercise-based intervention programs positively
affect the recovery of upper limb motor function and the ability to independently perform
ADL in people with stroke.
Limitations
The findings of the present study may not coincide with results found in stroke patients
in other phases of the disease or whose onset is prolonged over time or in institutions with
less intensive rehabilitation programs.
Brain Sci. 2021,11, 555 12 of 15
The onset of the COVID-19 pandemic hindered the monitoring of the participants.
The study design initially included an evaluation of the evolution at six months since
completion of the combined treatment (VRET+conventional treatment) in the experimental
group or conventional therapy in the control group.
The study was limited to a single center, and the results found may differ from other
multicenter studies with comparable design and methodology [
61
,
62
]. The participants
could not be blinded because they agreed on the conventional rehabilitative treatment.
In addition, virtual reality software developers should expand their research to help
clinicians estimate the differences that these systems have in the cognitive and spatial
representations of the individual. For example, Coomer et al. [
63
] suggest that swinging
of the upper limbs is a locomotion technique that causes a high spatial awareness in the
person who experiences it through virtual reality-based technology, and other researchers
argue that the teleportation in the exergames can cause spatial disorientation due to the
lack of self-movement signals [64,65].
5. Conclusions
The conventional rehabilitation approach combined with SVR appears to be more
effective than conventional physiotherapy and occupational therapy alone in improving
upper limb motor function and execution and autonomy in ADL in stroke survivors.
Virtual reality as a complement to conventional rehabilitation treatment is associated with
a decrease in muscle tone and greater overall recovery after stroke diagnosis.
Author Contributions:
Conceptualization, M.R.-H., B.P.-L., and J.-J.C.-Á.; methodology, M.R.-H. and
J.-J.C.-Á.; software, M.R.-H. and J.-J.C.-Á.; validation, M.R.-H., B.P.-L., and J.-J.C.-Á.; formal analysis,
M.R.-H. and J.-J.C.-Á.; investigation, M.R.-H.; writing—original draft preparation, M.R.-H., B.P.-L.,
and J.-J.C.-Á.; writing—review and editing, A.M.-M., A.-I.C.-S., and J.L.M.-C.; supervision, A.M.-M.,
A.-I.C.-S., and J.L.M.-C. All authors have read and agreed to the published version of the manuscript.
Funding: Sponsored by the University of Castilla La Mancha (grant # 2020-GRIN-29192).
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Research Ethics and Medicines Committee (CEIm) of
the integrate health area of Talavera de la Reina (protocol code: 12/2018 and date of approval: 17
April 2018).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Conflicts of Interest:
The authors declare no conflict of interest with respect to the research, author-
ship, and/or publication of this article.
References
1.
Mendis, S. Stroke Disability and Rehabilitation of Stroke: World Health Organization Perspective. Int. J. Stroke
2013
,8, 3–4.
[CrossRef][PubMed]
2.
Mathers, C.D.; Loncar, D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med.
2006
,3, e442.
[CrossRef][PubMed]
3.
SAFE: The Economic Impact of Stroke. Available online: https://www.safestroke.eu/economic-impact-of-stroke/ (accessed on
14 March 2021).
4.
Duncan, J.; Van Wijck, F.; Pollock, A.; Ali, M. Outcome measures in post-stroke arm rehabilitation trials: Do existing measures
capture outcomes that are important to stroke survivors, carers, and clinicians? Clin. Rehabil.
2019
,33, 737–749. [CrossRef]
[PubMed]
5.
Bosomworth, H.; Rodgers, H.; Shaw, L.; Smith, L.; Aird, L.; Howel, D.; Wilson, N.; Alvarado, N.; Andole, S.; Cohen, D.L.; et al.
Evaluation of the enhanced upper limb therapy programme within the Robot-Assisted Training for the Upper Limb after Stroke
trial: Descriptive analysis of intervention fidelity, goal selection and goal achievement. Clin. Rehabil.
2020
,35, 119–134. [CrossRef]
6.
Schnabel, S.; Van Wijck, F.; Bain, B.; Barber, M.; Dall, P.; Fleming, A.; Kerr, A.; Langhorne, P.; McConnachie, A.; Molloy, K.; et al.
Experiences of augmented arm rehabilitation including supported self-management after stroke: A qualitative investigation.
Clin. Rehabil. 2020,35, 288–301. [CrossRef]
7.
Crichton, S.L.; Bray, B.D.; McKevitt, C.; Rudd, A.G.; Wolfe, C.D.A. Patient outcomes up to 15 years after stroke: Survival, disability,
quality of life, cognition and mental health. J. Neurol. Neurosurg. Psychiatry 2016,87, 1091–1098. [CrossRef]
Brain Sci. 2021,11, 555 13 of 15
8.
Mukherjee, D.; Patil, C.G. Epidemiology and the Global Burden of Stroke. World Neurosurg.
2011
,76, S85–S90. [CrossRef]
[PubMed]
9.
Cheiloudaki, E.; Alexopoulos, E.C. Adherence to Treatment in Stroke Patients. Int. J. Environ. Res. Public Health
2019
,16, 196.
[CrossRef][PubMed]
10.
Turolla, A.; Dam, M.; Ventura, L.; Tonin, P.; Agostini, M.; Zucconi, C.; Kiper, P.; Cagnin, A.; Piron, L. Virtual reality for the
rehabilitation of the upper limb motor function after stroke: A prospective controlled trial. J. Neuroeng. Rehabil.
2013
,10, 85.
[CrossRef][PubMed]
11.
Kaplan, P.E.; Cailliet, R.; Kaplan, C.P. Rehabilitation of Stroke; Kaplan, P.E., Cailliet, R., Kaplan, C.P., Eds.; Elsevier Science,
Butterworth-Heinemann: Burlington, MA, USA, 2003; p. 180.
12.
Buma, F.E.; Lindeman, E.; Ramsey, N.F.; Kwakkel, G. Review: Functional Neuroimaging Studies of Early Upper Limb Recovery
After Stroke: A Systematic Review of the Literature. Neurorehabil. Neural Repair 2010,24, 589–608. [CrossRef]
13.
Kwakkel, G.; Kollen, B.J.; Van der Grond, J.; Prevo, A.J.H. Probability of Regaining Dexterity in the Flaccid Upper Limb. Stroke
2003,34, 2181–2186. [CrossRef][PubMed]
14.
Da Silva, M.; Bermúdez, S.; Duarte, E.; Verschure, P.F.M.J. Virtual reality based rehabilitation speeds up functional recovery of the
upper extremities after stroke: A randomized controlled pilot study in the acute phase of stroke using the rehabilitation gaming
system. Restor. Neurol. Neurosci. 2011,29, 287–298. [CrossRef]
15.
Mekbib, D.B.; Han, J.; Zhang, L.; Fang, S.; Jiang, H.; Zhu, J.; Roe, A.W.; Xu, D. Virtual reality therapy for upper limb rehabilitation
in patients with stroke: A meta-analysis of randomized clinical trials. Brain Inj. 2020,34, 456–465. [CrossRef][PubMed]
16.
Thomson, K.; Pollock, A.; Bugge, C.; Brady, M. Commercial Gaming Devices for Stroke Upper Limb Rehabilitation: A Systematic
Review. Int. J. Stroke 2014,9, 479–488. [CrossRef][PubMed]
17.
Ahmad, M.A.; Singh, D.K.A.; Mohd, N.A.; Hooi, K.; Ibrahim, N. Virtual Reality Games as an Adjunct in Improving Upper Limb
Function and General Health among Stroke Survivors. Int. J. Environ. Res. Public Health 2019,16, 5144. [CrossRef][PubMed]
18.
Akinladejo, F.O. Virtual Environments in Physical Therapy. In Virtual Reality and Environments;SíkLányi, C., Ed.; IntechOpen,
University of Pannonia: Veszprém, Hungary, 2012; pp. 1–10. [CrossRef]
19. Sveistrup, H. Motor rehabilitation using virtual reality. J. Neuroeng. Rehabil. 2004,1, 10. [CrossRef]
20.
Laver, K.E.; George, S.; Thomas, S.; Deutsch, J.E.; Crotty, M. Virtual reality for stroke rehabilitation. Cochrane Database Syst. Rev.
2015,2015, CD008349. [CrossRef]
21.
Laver, K.E.; Lange, B.; George, S.; Deutsch, J.E.; Saposnik, G.; Crotty, M. Virtual reality for stroke rehabilitation. Cochrane Database
Syst. Rev. 2017,11, CD008349. [CrossRef]
22.
Hruby, F.; Castellanos, I.; Ressl, R. Cartographic Scale in Immersive Virtual Environments. J. Cartogr. Geogr. Inf.
2020
,21, 399.
[CrossRef]
23.
Liang, Z.; Zhou, K.; Gao, K. Development of Virtual Reality Serious Game for Underground Rock-Related Hazards Safety
Training. IEEE Access 2019,2019, 118639–118649. [CrossRef]
24.
O’Meara, D.; Korte, A.; Dickmann, F. Effects of Virtual Reality Locomotion Techniques on Distance Estimations. ISPRS Int. J.
Geo-Inf. 2021,10, 150. [CrossRef]
25.
Ruddle, R.A.; Volkova, E.; Bülthoff, H.H. Walking improves your cognitive map in enviroments that are large-scale and large in
extent. ACM Trans. Comput. Hum. Interact. 2011,18, 1–20. [CrossRef]
26.
ISRCTN—ISRCTN27760662: Effectiveness of Virtual Reality Devices in the Rehabilitation of Adults with Stroke. Available online:
http://www.isrctn.com/ISRCTN27760662 (accessed on 8 January 2021).
27.
Schulz, K.F.; Altman, D.G.; Moher, D.; CONSORT Group. CONSORT 2010 Statement: Updated guidelines for reporting parallel
group randomised trials. BMC Med. 2010,8, 18. [CrossRef]
28.
Liu, X.; Cruz, S.; Moher, D.; Calvert, M.J.; Denniston, A.K.; The SPIRIT-AI and CONSORT-AI Working Group. Reporting
guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nat. Med.
2020
,
26, 1364–1374. [CrossRef]
29.
Roman, N.; Miclaus, R.; Repanovici, A.; Nicolau, C. Equal Opportunities for Stroke Survivors’ Rehabilitation: A Study on the
Validity of the Upper Extremity Fugl-Meyer Assessment Scale Translated and Adapted into Romanian. Medicina
2020
,56, 409.
[CrossRef]
30.
Kim, W.S.; Cho, S.; Baek, D.; Bang, H.; Paik, N.J. Upper Extremity Functional Evaluation by Fugl-Meyer Assessment Scoring
Using Depth-Sensing Camera in Hemiplegic Stroke Patients. PLoS ONE 2016,11, e0158640. [CrossRef][PubMed]
31.
Winstein, C.J.; Stein, J.; Arena, R.; Bates, B.; Cherney, S.C.; Deruyter, F.; Eng, J.J.; Fisher, B.; Harvey, R.L.; Lang, C.E.; et al.
Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals From the American Heart
Association/American Stroke Association. Stroke 2016,47.[CrossRef]
32.
Woodbury, M.L.; Velozo, C.A.; Richards, L.G.; Duncan, P.W.; Studenski, S.; Lai, S.M. Longitudinal Stability of the Fugl-Meyer
Assessment of the Upper Extremity. Arch. Phys. Med. Rehabil. 2008,89, 1563–1569. [CrossRef][PubMed]
33.
Ferrer, B.M. Adaptación y Validación al Español de la Escala Fugl-Meyer en el Manejo de la Rehabilitación de Pacientes con
Ictus. Ph.D. Thesis, Sevilla University, Sevilla, Spain, 2016. Available online: http://hdl.handle.net/11441/40335 (accessed on 9
January 2021).
Brain Sci. 2021,11, 555 14 of 15
34.
Brashear, A.; Zafonte, R.; Corcoran, M.; Galvez-Jimenez, N.; Gracies, J.M.; Gordon, M.F.; Mcafee, A.; Ruffing, K.; Thompson, B.;
Williams, M.; et al. Inter- and intrarater reliability of the Ashworth Scale and the Disability Assessment Scale in patients with
upper-limb poststroke spasticity. Arch. Phys. Med. Rehabil. 2002,83, 1349–1354. [CrossRef]
35.
Duncan, P.W.; Bode, R.K.; Min, S.; Perera, S. Rasch analysis of a new stroke-specific outcome scale: The stroke impact scale11No
commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit
upon the author(s) or upon any organization with which the author(s) is/are associated. Arch. Phys. Med. Rehabil.
2003
,84,
950–963. [CrossRef]
36.
Lin, K.C.; Fu, T.; Wu, C.Y.; Hsieh, Y.W.; Chen, C.L.; Lee, P.C. Psychometric comparisons of the Stroke Impact Scale 3.0 and
Stroke-Specific Quality of Life Scale. Qual. Life Res. 2010,19, 435–443. [CrossRef]
37.
Chou, C.Y.; Ou, Y.C.; Chiang, T.R. Psychometric comparisons of four disease-specific health-related quality of life measures for
stroke survivors. Clin. Rehabil. 2015,29, 816–829. [CrossRef][PubMed]
38.
Vellone, E.; Savini, S.; Fida, R.; Dickson, V.V.; Melkus, G.D.; Carod-Artal, F.J.; Rocco, G.; Alvaro, R. Psychometric Evaluation of the
Stroke Impact Scale 3.0. J. Cardiovasc. Nurs. 2015,30, 229–241. [CrossRef][PubMed]
39.
Lee, S.C.; Lin, G.H.; Huang, Y.J.; Huang, S.L.; Chou, C.Y.; Chiang, H.Y.; Hsieh, C.L. Cross-Validation of the Factorial Validity of
the Stroke Impact Scale 3.0 in Patients With Stroke. Am. J. Occup. Ther. 2021,75, 7502205070. [CrossRef][PubMed]
40.
Carmeli, E.; Peleg, S.; Bartur, G.; Elbo, E.; Vatine, J.J. HandTutorTM enhanced hand rehabilitation after stroke—A pilot study.
Physiother. Res. Int. 2011,16, 191–200. [CrossRef][PubMed]
41.
Rodríguez-Hernández, M.; Fernández-Panadero, C.; López-Martín, O.; Polonio-López, B. Hand Rehabilitation after Chronic Brain
Damage: Effectiveness, Usability and Acceptance of Technological Devices: A Pilot Study. In Physical Disabilities—Therapeutic
Implications; Tan, U., Ed.; IntechOpen, Cukurova University: Sarıçam/Adana, Turkey, 2017; pp. 57–72. [CrossRef]
42.
Chua, K.S.G.; Kuah, C.W.K. Innovating With Rehabilitation Technology in the Real World: Promises, Potentials, and Perspectives.
Am. J. Phys. Med. Rehabil. 2017,96, S150–S156. [CrossRef][PubMed]
43.
Rehametrics|Cuantificando la RehabilitaciónFísica y Cognitiva. Available online: https://rehametrics.com/ (accessed on 9
January 2021).
44.
Grefkes, C.; Fink, G.R. Connectivity-based approaches in stroke and recovery of function. Lancet Neurol.
2014
,13, 206–216.
[CrossRef]
45.
Saposnik, G.; Cohen, L.G.; Mamdani, M.; Pooyania, S.; Ploughman, M.; Cheung, D.; Shaw, J.; Hall, J.; Nord, P.; Dukelow, S.; et al.
Efficacy and safety of non-immersive virtual reality exercising in stroke rehabilitation (EVREST): A randomised, multicentre,
single-blind, controlled trial. Lancet Neurol. 2016,15, 1019–1027. [CrossRef]
46.
Sampaio-Baptista, C.; Sanders, Z.B.; Johansen-Berg, H. Structural Plasticity in Adulthood with Motor Learning and Stroke
Rehabilitation. Annu. Rev. Neurosci. 2018,41, 25–40. [CrossRef]
47.
Martínez, M.C.; Parejo, M.T.; Laiz, N.M. Valoración del uso de las nuevas tecnologías en personas con esclerosis múltiple. REDIS
2018,6, 149–171. [CrossRef]
48.
Abreu, J.; Rebelo, S.; Paredes, H.; Barroso, J.; Martins, P.; Reis, A.; EuricoVasco, A. Assessment of Microsoft Kinect in the
Monitoring and Rehabilitation of Stroke Patients. In Recent Advances in Information Systems and Technologies; Rocha, Á., Correia,
A.M., Adeli, H., Reis, L.P., Costanzo, S., Eds.; Springer International Publishing: Berlin/Heidelberg, Germany, 2017; pp. 167–174.
[CrossRef]
49.
Ashwini, K.; Amutha, R.; Nagarajan, K.K.; Raj, S.A. Kinect based Upper Limb Performance Assessment in Daily Life Activities.
In Proceedings of the International Conference on Wireless Communications Signal Processing and Networking (WiSPNET),
Chennai, India, 21–23 March 2019; pp. 201–205. [CrossRef]
50.
Mousavi, H.; Khademi, M. A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation.
J. Med. Eng. 2014,2014, 846514. [CrossRef]
51.
Rodríguez-Hernández, M.; Criado-Álvarez, J.J.; Corregidor-Sánchez, A.I.; Martín-Conty, J.L.; Mohedano-Moriano, A.; Polonio-
López, B. Effects of Virtual Reality-Based Therapy on Quality of Life of Patients with Subacute Stroke: A Three-Month Follow-Up
Randomized Controlled Trial. Int. J. Environ. Res. Public Health 2021,18, 2810. [CrossRef]
52.
Kim, J.H. Effects of a virtual reality video game exercise program on upper extremity function and daily living activities in stroke
patients. J. Phys. Ther. Sci. 2018,30, 1408–1411. [CrossRef]
53.
Norouzi-Gheidari, N.; Hernandez, A.; Archambault, P.S.; Higgins, J.; Poissant, L.; Kairy, D. Feasibility, Safety and Efficacy of a
Virtual Reality Exergame System to Supplement Upper Extremity Rehabilitation Post-Stroke: A Pilot Randomized Clinical Trial
and Proof of Principle. Int. J. Environ. Res. Public Health 2020,17, 113. [CrossRef]
54.
Miclaus, R.; Roman, N.; Caloian, S.; Mitoiu, B.; Suciu, O.; Onofrei, R.R.; Pavel, E.; Neculau, A. Non-Immersive Virtual Reality for
Post-Stroke Upper Extremity Rehabilitation: A Small Cohort Randomized Trial. Brain Sci. 2020,10, 655. [CrossRef][PubMed]
55.
Schuster-Amft, C.; Eng, K.; Suica, Z.; Thaler, I.; Signer, S.; Lehmann, I.; Schmid, L.; McCaskey, M.; Hawkins, M.; Verra, M.L.; et al.
Effect of a four-week virtual reality-based training versus conventional therapy on upper limb motor function after stroke: A
multicenter parallel group randomized trial. PLoS ONE 2018,13, e0204455. [CrossRef][PubMed]
56.
Bevilacqua, R.; Maranesi, E.; Riccardi, G.R.; Donna, V.D.; Pelliccioni, P.; Luzi, R.; Lattanzio, F.; Pelliccioni, G. Non-Immersive
Virtual Reality for Rehabilitation of the Older People: A Systematic Review into Efficacy and Effectiveness. J. Clin. Med.
2019
,
8, 1882. [CrossRef][PubMed]
Brain Sci. 2021,11, 555 15 of 15
57.
Saposnik, G.; Teasell, R.; Mamdani, M.; Hall, J.; McIlroy, W.; Cheung, D.; Thorpe, K.E.; Cohen, L.G.; Bayley, M. Effectiveness of
Virtual Reality Using Wii Gaming Technology in Stroke Rehabilitation: A Pilot Randomized Clinical Trial and Proof of Principle.
Stroke 2010,41, 1477–1484. [CrossRef]
58.
Maier, M.; Rubio, B.; Duff, A.; Duarte, E.; Verschure, P.F.M.J. Effect of Specific Over Nonspecific VR-Based Rehabilitation on
Poststroke Motor Recovery: A Systematic Meta-analysis. Neurorehabil. Neural Repair 2019,33, 112–129. [CrossRef][PubMed]
59.
Rand, D.; Weiss, P.L.; Katz, N. Training Multitasking in a Virtual Supermarket: A Novel Intervention after Stroke. Am. J. Occup.
Ther. 2009,63, 535–542. [CrossRef]
60.
Oh, Y.B.; Kim, G.W.; Han, K.S.; Won, Y.H.; Park, S.H.; Seo, J.H.; Ko, M.H. Efficacy of Virtual Reality Combined With Real
Instrument Training for Patients With Stroke: A Randomized Controlled Trial. Arch. Phys. Med. Rehabil.
2019
,100, 1400–1408.
[CrossRef][PubMed]
61.
Ho, T.H.; Yang, F.C.; Lin, R.C.; Chien, W.C.; Chung, C.H.; Chiang, S.L.; Chou, C.-H.; Tsai, C.-K.; Lin, Y.-K.; Lee, J.-T. Impact of
virtual reality-based rehabilitation on functional outcomes in patients with acute stroke: A retrospective case-matched study. J.
Neurol. 2019,266, 589–597. [CrossRef]
62.
Cano-Mañas, M.J.; Collado-Vázquez, S.; Rodríguez, J.; Muñoz, A.J.; Cano-de-la-Cuerda, R. Effects of Video-Game Based Therapy
on Balance, Postural Control, Functionality, and Quality of Life of Patients with Subacute Stroke: A Randomized Controlled Trial.
J. Healthc. Eng. 2020,2020, 5480315. [CrossRef][PubMed]
63.
Coomer, N.; Bullard, S.; Clinton, W.; Williams-Sanders, B. Evaluating the effects of four VR locomotion methods: Joystick,
arm-cycling, point-tugging, and teleporting. In Proceedings of the SAP’18: 15th ACM Symposium on Applied Perception,
Vancouver, BC, Canada, 10–11 August 2018; Grimm, C., Willemsen, P., Eds.; Association for Computing Machinery: New York,
NY, USA, 2018; pp. 1–8.
64.
Cherep, L.A.; Lim, A.F.; Kelly, J.W.; Acharya, D.; Velasco, A.; Bustamante, E.; Ostrander, A.G.; Gilbert, S.B. Spatial cognitive
implications of teleporting through virtual environments. J. Exp. Psychol. Appl. 2020,26, 480–492. [CrossRef][PubMed]
65.
Kelly, J.W.; Ostrander, A.G.; Lim, A.F.; Cherep, L.A.; Gilbert, S.B. Teleporting through virtual environments: Effects of path scale
and environment scale on spatial updating. IEEE Trans. Vis. Comput. Graph. 2020,26, 1841–1850. [CrossRef][PubMed]
... In this sense, new technologies would allow the incorporation of elements, such as intensity and repetition of functional tasks, that are considered key in stroke rehabilitation. Among them, robotics [11] and virtual reality [12] are positioned as widely accepted complementary tools in stroke rehabilitation [13]. ...
Article
Full-text available
Background: Sensory–motor deficits are frequent and affect the functionality after stroke. The use of robotic systems to improve functionality and motor performance is advisable; therefore, the aim of the present study was to evaluate the effects of intensive, high-frequency vibration treatment administered with a robotic system in subacute and chronic stroke patients in terms of upper limb sensitivity, motor function, quantity and quality of movement, and quality of life. Methods: A simple-blind, non-randomized controlled trial was conducted. The control group received conventional rehabilitation treatment and the experimental group received robotic treatment with an Amadeo® robot in addition to their conventional rehabilitation sessions. Results: Intragroup analysis identified significant improvements in the experimental group in hand (p = 0.012), arm (p = 0.018), and shoulder (p = 0.027) sensitivity, as well as in motor function (FMA-UEmotor function, p= 0.028), integration of the affected limb (MAL-14amount scale, p = 0.011; MAL-14How well scale, p = 0.008), and perceived quality of life (SIS-16, p = 0.008). The measures between the control and experimental groups showed statistically significant differences in motor performance and spontaneous use of the affected limb (MAL-14amount scale, p = 0.021; MAL-14How well scale, p = 0.037). Conclusion: Intensive, high-frequency vibration with a robotic system, in combination with conventional intervention, improves the recovery of upper limb function in terms of quantity and quality of movement in patients with subacute and chronic stroke.
... Although new technologies such as robots, virtual reality, or other sensor-based technologies have been developed for rehabilitation in post-stroke or central nervous system impairments with motion disorders, each device is used with its own type of evaluation [58][59][60][61][62][63]. Due to the heterogeneity of these devices and the types of assessment provided, it is difficult for researchers and clinicians to use them properly and include them in daily practice. ...
Article
Full-text available
In neuro-rehabilitation, the assessment of post-stroke patients’ motor function of damaged upper extremities (UEs) is essential. Clinicians need clear and concise assessment instruments to monitor progress recorded in intensive rehabilitation sessions. One such instrument is Manual Muscle Testing (MMT), which, in our view, requires a modified scoring model aimed at improving the assessment process of patients’ motor and functional UE status, and recording their step-by-step-progress, especially if patients undergo a short length of hospitalization (of about 10 therapy days). Hence, this paper presents a new scoring system developed by the authors. This system results in a more precise MMT grading scale, which has more grades and can provide a more specific muscular assessment, while offering more clarity in quantifying patients’ progress after physical therapy. A prospective study was made of 41 post-stroke patients with upper extremity (UE) impairments. To determine the validity of the assessment tool for hypothesizing, and the unidimensionality and internal consistency of the customized model, exploratory and confirmatory factor analysis (CFA) with a structural equation model (SEM), Cronbach’s Alpha, and Pearson correlation coefficients were used with Fugl–Meyer (FM) assessments, the Modified Ashworth Scale (MAS), AROM, and the Modified Rankin Scale (MRS). Considering the unidimensionality of the instrument used, we performed a linear regression to identify whether certain movements performed segmentally by the manually evaluated muscles influence the measured manual score of the whole UE. All indices suggested a good model fit, and a Cronbach’s Alpha of 0.920 suggested strong internal consistency. The Pearson correlation coefficient of the MMT-customized score with AROM was 0.857, p < 0.001; that with FMUE was 0.905, p < 0.001; that with MRS was −0.608, p = 0.010; and that with MAS was −0.677, p < 0.001. The linear regression results suggest that wrist extensors, shoulder abductors, and finger flexors can influence the manual assessment of the muscle strength of the whole UE, thereby improving post-stroke patient management. The results of our research suggest that, using the proposed scoring, MMT may be a useful tool for UE assessment in post-stroke patients.
... An advanced method for DNS research is functional magnetic resonance imaging (fMRI), which can be used to explore the neuroplasticity of neural rehabilitation patients. Orakpo et al. [18] argued that the promising behavioral improvements demonstrated in clinical trials must be associated with an understanding of cortical and subcortical changes that form the biological basis of rehabilitation. The compatibility between VR and such imaging technology has enabled researchers to present multimodal stimuli with high ecological effectiveness while recording changes in brain activity, which is also beneficial for therapists [154]. ...
Article
Full-text available
Health 4.0 aligns with Industry 4.0 and encourages the application of the latest technologies to healthcare. Virtual reality (VR) is a potentially significant component of the Health 4.0 vision. Though VR in health care is a popular topic, there is little knowledge of VR-aided therapy from a macro perspective. Therefore, this paper was aimed to explore the research of VR in aiding therapy, thus providing a potential guideline for futures application of therapeutic VR in healthcare towards Health 4.0. A mixed research method was adopted for this research, which comprised the use of a bibliometric analysis (a quantitative method) to conduct a macro overview of VR-aided therapy, the identification of significant research structures and topics, and a qualitative review of the literature to reveal deeper insights. Four major research areas of VR-aided therapy were identified and investigated, i.e., post-traumatic stress disorder (PTSD), anxiety and fear related disorder (A&F), diseases of the nervous system (DNS), and pain management, including related medical conditions, therapies, methods, and outcomes. This study is the first to use VOSviewer, a commonly used software tool for constructing and visualizing bibliometric networks and developed by Center for Science and Technology Studies, Leiden University, the Netherlands, to conduct bibliometric analyses on VR-aided therapy from the perspective of Web of Science core collection (WoSc), which objectively and visually shows research structures and topics, therefore offering instructive insights for health care stakeholders (particularly researchers and service providers) such as including integrating more innovative therapies, emphasizing psychological benefits, using game elements, and introducing design research. The results of this paper facilitate with achieving the vision of Health 4.0 and illustrating a two-decade (2000 to year 2020) map of pre-life of the Health Metaverse.
Article
Objective To investigate the efficacy and acceptability of virtual reality (VR) with time-dose-matched conventional therapy (CT) in patients post-stroke with upper limb dysfunction. Data Sources Cochrane, PubMed, Web of Science, Embase, and ProQuest were systematically searched up to 24 May 2021. Study Selection Randomized controlled trials (RCTs) comparing VR with time-dose-matched CT in patients post-stroke with upper limb dysfunction were included. Data Extraction The extracted data included efficacy (mean change in Structure/Function, Activity, and Participation scores), acceptability (dropouts for all reasons), adverse events and characteristics of the included studies. The Cochrane risk of bias assessment tool was used to assess the risk of bias. Data Synthesis 31 RCTs were included. VR was superior to time-dose-matched CT in terms of ICF-WHO Structure/Function, with a standardized mean difference (SMD) of 0.35, but not Activity and Participation. Subgroup analyses demonstrated that virtual environment was superior to CT in Structure/Function (SMD=0.38) and Activity (SMD=0.27), while there were no significant differences between commercial gaming and CT in any ICF-WHO domain. VR mixed with CT was more effective than time-dose-matched CT in Structure/Function (SMD=0.56), while VR only was not significantly different from CT. There were no significant differences in the incidence of adverse events and dropout rates between VR and CT. Conclusions The results suggest that VR is superior to time-dose-matched CT in terms of recovery of upper extremity motor function, especially when virtual environment is used, or VR is mixed with CT. However, VR (VR only or mixed with CT) does not improve patients' daily activity performance and participation compared with CT. Overall, VR appears to be safe and acceptable as CT. Large-scale definitive trials are needed to verify or refute these findings.
Article
Full-text available
Objective: To evaluate the influence of conventional rehabilitation combined with virtual reality on improving quality of life related to post-stroke health. Design: Randomized controlled trial. Setting: Rehabilitation and neurology departments of a general hospital (Talavera de la Reina, Spain). Subjects: A total of 43 participants with subacute stroke. Intervention: Participants were randomized into experimental group (conventional treatment + virtual reality) and control (conventional treatment). Main measures: Health-related quality of life as measured by the EuroQoL-5 dimensions instrument (EQ-5D-5L) and EuroQoL visual analog scale (EQ-VAS). Results: A total of 23 patients in the experimental group (62.6 ± 13.5 years) and 20 in the control (63.6 ± 12.2 years) completed the study. In the experimental group, EQ-VAS score was 29.1 ± 12.8 at baseline, 86.5 ± 7.1 post-intervention, and 78.3 ± 10.3 at the three-month follow-up. The control group obtained scores of 25.5 ± 5.1, 57.0 ± 4.7, and 58.5 ± 5.9, respectively. We identified significant differences at the post-intervention and follow-up timepoints (p = 0.000) and a partial η2 of 0.647. In EQ-5D-5L, the severity of issues decreased after intervention in the experimental group, while pain and anxiety dimensions increased between post-intervention and follow-up. Conclusions: The conventional rehabilitative approach combined with virtual reality appears to be more effective for improving the perceived health-related quality of life in stroke survivors.
Article
Full-text available
Mental representations of geographic space are based on knowledge of spatial elements and the spatial relation between these elements. Acquiring such mental representations of space requires assessing distances between pairs of spatial elements. In virtual reality (VR) applications, locomotion techniques based on real-world movement are constrained by the size of the available room and the used room scale tracking system. Therefore, many VR applications use additional locomotion techniques such as artificial locomotion (continuous forward movement) or teleporting (“jumping” from one location to another). These locomotion techniques move the user through virtual space based on controller input. However, it has not yet been investigated how different established controller-based locomotion techniques affect distance estimations in VR. In an experiment, we compared distance estimations between artificial locomotion and teleportation before and after a training phase. The results showed that distance estimations in both locomotion conditions improved after the training. Additionally, distance estimations were found to be more accurate when teleportation locomotion was used.
Article
Full-text available
Scale has been a defining criterion of mapmaking for centuries. However, this criterion is fundamentally questioned by highly immersive virtual reality (VR) systems able to represent geographic environments at a high level of detail and, thus, providing the user with a feeling of being present in VR space. In this paper, we will use the concept of scale as a vehicle for discussing some of the main differences between immersive VR and non-immersive geovisualization products. Based on a short review of diverging meanings of scale we will propose possible approaches to the issue of both spatial and temporal scale in immersive VR. Our considerations shall encourage a more detailed treatment of the specific characteristics of immersive geovisualization to facilitate deeper conceptual integration of immersive and non-immersive visualization in the realm of cartography.
Article
Full-text available
Immersive and non-immersive virtual reality (NIVR) technology can supplement and improve standard physiotherapy and neurorehabilitation in post-stroke patients. We aimed to use MIRA software to investigate the efficiency of specific NIVR therapy as a standalone intervention, versus standardized physiotherapy for upper extremity rehabilitation in patients post-stroke. Fifty-five inpatients were randomized to control groups (applying standard physiotherapy and dexterity exercises) and experimental groups (applying NIVR and dexterity exercises). The two groups were subdivided into subacute (<six months post-stroke) and chronic (>six months to four years post-stroke survival patients). The following standardized tests were applied at baseline and after two weeks post-therapy: Fugl-Meyer Assessment for Upper Extremity (FMUE), the Modified Rankin Scale (MRS), Functional Independence Measure (FIM), Active Range of Motion (AROM), Manual Muscle Testing (MMT), Modified Ashworth Scale (MAS), and Functional Reach Test (FRT). The Kruskal-Wallis test was used to determine if there were significant differences between the groups, followed with pairwise comparisons. The Wilcoxon Signed-Rank test was used to determine the significance of pre to post-therapy changes. The Wilcoxon Signed-Rank test showed significant differences in all four groups regarding MMT, FMUE, and FIM assessments pre- and post-therapy, while for AROM, only experimental groups registered significant differences. Independent Kruskal-Wallis results showed that the subacute experimental group outcomes were statistically significant regarding the assessments, especially in comparison with the control groups. The results suggest that NIVR rehabilitation is efficient to be administered to post-stroke patients, and the study design can be used for a further trial, in the perspective that NIVR therapy can be more efficient than standard physiotherapy within the first six months post-stroke.
Article
Full-text available
Objective: To report the fidelity of the enhanced upper limb therapy programme within the Robot-Assisted Training for the Upper Limb after stroke (RATULS) randomized controlled trial, the types of goals selected and the proportion of goals achieved. Design: Descriptive analysis of data on fidelity, goal selection and achievement from an intervention group within a randomized controlled trial. Setting: Out-patient stroke rehabilitation within four UK NHS centres. Subjects: 259 participants with moderate-severe upper limb activity limitation (Action Research Arm Test 0-39) between one week and five years post first stroke. Intervention: The enhanced upper limb therapy programme aimed to provide 36 one-hour sessions, including 45 minutes of face-to-face therapy focusing on personal goals, over 12 weeks. Results: 7877/9324 (84%) sessions were attended; a median of 34 [IQR 29-36] per participant. A median of 127 [IQR 70-190] repetitions were achieved per participant per session attended. Based upon the Canadian Occupational Performance Measure, goal categories were: self-care 1449/2664 (54%); productivity 374/2664 (14%); leisure 180/2664 (7%) and 'other' 661/2664 (25%). For the 2051/2664 goals for which data were available, 1287 (51%) were achieved, ranging between 27% by participants more than 12 months post stroke with baseline Action Research Arm Test scores 0-7, and 88% by those less than three months after stroke with scores 8-19. Conclusions: Intervention fidelity was high. Goals relating to self-care were most commonly selected. The proportion of goals achieved varied, depending on time post stroke and baseline arm activity limitation.
Article
Full-text available
The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
Article
Full-text available
Background and objectives: The Upper Extremity Fugl-Meyer Assessment (UEFMA) is one of the most recommended and used methods of clinical evaluation not only for post-stroke motor function disability conditions but also for physiotherapy goal-setting. Up to the present, an official Romanian version has not been officially available. This study aims to carry out a translation, adaptation, and validation of UEFMA in Romanian, thus giving both patients and medical practitioners the equal opportunity of benefiting from its proficiency. Material and methods: The English version of the motor component of UEFMA was back and forth translated in the assent of best practice translation guidelines. The research was performed on a group of 64 post-stroke in-patients regarding psychometric properties for content validation and an exploratory and confirmatory factorial analysis was performed using the Bayesian model. To assess internal consistency and test–retest reliability, we used the Cronbach Alpha index and Intraclass Correlation Coefficient (ICC). We used Pearson correlation with the Functional Independence Measure (FIM) and Modified Rankin Scale (MRS) to determine concurrent validation. Standardized response mean (SRM) was applied to determine the responsiveness of the instrument used. Results: After performing the exploratory factor analysis, a single factor was extracted, with an Eigenvalue of 19.363, which explained 64.543% of the variation. The model was confirmed by Bayesian exploration, with Root Mean Square Residual (RMR) 0.051, Goodness-of-fit Index (GFI) 0.980, Normed-Fit Index (NFI) 0.978 and Relative Fit Index (RFI) 0.977. The Cronbach Alpha value was 0.981, the Intraclass Correlation Coefficient (ICC) index for average measures was 0.992, the Pearson correlation with FIM 0.789, and MRS −0.787, while the SRM was 1.117. Conclusions: The Romanian version of the UEFMA scale is a reliable, responsive and valid tool which can be used as a standardized assessment in post-stroke patients across Romania.
Article
Importance: The Stroke Impact Scale 3.0 appears to be a promising outcome measure of health-related quality of life (HRQOL) for clients with stroke. However, because the factorial validity of the Stroke Impact Scale 3.0 remains unclear, its validity is limited. Objective: To examine the underlying structure of the Stroke Impact Scale 3.0 by comparing the currently available eight- and four-domain structures simultaneously. Design: Secondary data analysis of responses to the Stroke Impact Scale 3.0 from a previous psychometric validation study. Setting: Five general hospitals in northern and southern Taiwan. Participants: Two hundred sixty-three patients with stroke from rehabilitation wards (inpatients) and neurology and rehabilitation clinics (outpatients). Outcomes and Measures: Confirmatory factor analysis was used to examine the eight- and four-domain structures of the Stroke Impact Scale 3.0. Four fit indices were considered simultaneously to examine the model fits of both structures. Results: The eight- and four-domain structures of the Stroke Impact Scale 3.0 were not supported by all four indices (χ2/df = 2.7 and 5.0, comparative fit index = .79 and .86, root mean square error of approximation = .08 and .12, standardized root mean square residual = .08 and .08, respectively). The unidimensionality of each domain in the two structures was not supported. Conclusions and Relevance: Neither the eight- nor the four-domain structure of the Stroke Impact Scale 3.0 was supported, suggesting that scores may not provide valid assessments of HRQOL in clients with stroke. Further modification and validation of the Stroke Impact Scale 3.0 are warranted. What This Article Adds: Our findings suggest that the eight- and four-domain scores of the Stroke Impact Scale 3.0 may not be valid. Therefore, until more supporting evidence is developed, these scores should be interpreted cautiously in regard to clients’ HRQOL; alternatively, other measures could be used.
Article
Objective: To explore the experiences of stroke survivors and their carers of augmented arm rehabilitation including supported self-management in terms of its acceptability, appropriateness and relevance. Design: A qualitative design, nested within a larger, multi-centre randomized controlled feasibility trial that compared augmented arm rehabilitation starting at three or nine weeks after stroke, with usual care. Semi-structured interviews were conducted with participants in both augmented arm rehabilitation groups. Normalization Process Theory was used to inform the topic guide and map the findings. Framework analysis was applied. Setting: Interviews were conducted in stroke survivors' homes, at Glasgow Caledonian University and in hospital. Participants: 17 stroke survivors and five carers were interviewed after completion of augmented arm rehabilitation. Intervention: Evidence-based augmented arm rehabilitation (27 additional hours over six weeks), including therapist-led sessions and supported self-management. Results: Three main themes were identified: (1) acceptability of the intervention (2) supported self-management and (3) coping with the intervention. All stroke survivors coped well with the intensity of the augmented arm rehabilitation programme. The majority of stroke survivors engaged in supported self-management and implemented activities into their daily routine. However, the findings suggest that some stroke survivors (male >70 years) had difficulties with self-management, needing a higher level of support. Conclusion: Augmented arm rehabilitation commencing within nine weeks post stroke was reported to be well tolerated. The findings suggested that supported self-management seemed acceptable and appropriate to those who saw the relevance of the rehabilitation activities for their daily lives, and embedded them into their daily routines.