2018 Mental practice for upper limb rehabilitation after stroke-a systematic review and meta-analysis

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Mental practice for upper limb rehabilitation after stroke:
a systematic review and meta-analysis
Si-Woon Parka, Jae-Hyung Kima and Yun-Jung Yangb
Mental practice (MP) is usually provided in combination with
other therapies, and new developments for neurofeedback
to support MP have been made recently. The objectives of
this study were to evaluate the effectiveness of MP and to
investigate the intervention characteristics including
neurofeedback that may affect treatment outcome. The
Cochrane Central Register of Controlled Trials, PubMed,
Embase, KoreaMed, Scopus, Web of Science, PEDro, and
CIRRIE were searched from inception to March 2017 for
randomized controlled trials to assess the effect of MP for
upper limb rehabilitation after stroke. Fugl-Meyer
Assessment (FMA) was used as the outcome measure for
meta-analysis. Twenty-five trials met the inclusion criteria,
and 15 trials were eligible for meta-analysis. Among the
trials selected for meta-analysis, MP was added to
conventional therapy in eight trials or to modified constraint-
induced movement therapy in one trial. The other trials
provided neurofeedback to support MP: MP-guided
neuromuscular electrical stimulation (NMES) in four trials
and MP-guided robot-assisted therapy (RAT) in two trials.
MP added to conventional therapy resulted in significantly
higher FMA gain than conventional therapy alone.
MP-guided NMES showed superior result than conventional
NMES as well. However, the FMA gain of MP-guided RAT
was not significantly higher than RAT alone. We suggest that
MP is an effective complementary therapy either given with
neurofeedback or not. Neurofeedback applied to MP
showed different results depending on the therapy provided.
This study has limitations because of heterogeneity and
inadequate quality of trials. Further research is
requested. International Journal of Rehabilitation Research
00:000–000 Copyright © 2018 Wolters Kluwer Health, Inc.
All rights reserved.
International Journal of Rehabilitation Research 2018, 00:000–000
Keywords: brain–computer interface, imagery, neurofeedback,
rehabilitation, stroke
aDepartment of Rehabilitation Medicine and bInstitute for Integrative Medicine,
Catholic Kwandong University International St. Mary’s Hospital, Incheon, South
Korea
Correspondence to Si-Woon Park, MD, MSCR, Department of Rehabilitation
Medicine, Catholic Kwandong University International St. Mary’s Hospital, 25
Simgok-ro-100beon-gil, Seo-Gu, Incheon 22711, South Korea
Tel: + 82 322 903 114; fax: + 82 322 903 120; e-mail: seanpark05@gmail.com
Received 15 April 2018 Accepted 25 May 2018
Introduction
Stroke is a leading cause of acquired disability worldwide
(Hong et al., 2013), and the most common type of phy-
sical impairment after stroke is hemiparesis (Langhorne
et al., 2011; Pollock et al., 2014). In rehabilitation practice
for hemiparetic patients, it is well acknowledged that
functional recovery of the upper limb is much more dif-
ficult than the lower limb because of several reasons
including innervatory pattern of distal hand and the
nature of hand function itself (Nakayama et al., 1994;
Feys et al., 1998, 2000; Pollock et al., 2014). Many reha-
bilitation strategies have been developed to overcome
hemiparetic arm disability, such as constraint-induced
movement therapy (CIMT) (Kwakkel et al., 2015),
bilateral arm movement (Coupar et al., 2010), electrical
stimulation (Nascimento et al., 2014), robot-assisted arm
rehabilitation (Chang and Kim, 2013), etc. Each treat-
ment has limitations in application. For example, CIMT
is only applicable to those who meet highly functioning
hand movement criteria (Wolf et al., 2006; Park et al.,
2008), and robot-assisted rehabilitation requires very
expensive equipment (Lo et al., 2010). One of the upper
limb rehabilitation strategies that can be applied to a
wide range of patients without requiring complicate
equipment is mental practice (MP) (Butler and Page,
2006).
MP was originated from sports science theory, which states
that mental rehearsal can enhance motor skill acquisition
(Yaguez et al., 1998). It is known that the neural pathway
for task performance is activated during the mental
rehearsal of the same task (Gerardin et al., 2000). Many
researchers conducted clinical trials of MP in stroke
patients in which auditory and/or visual stimuli to guide
motor imagery were provided. Because MP alone is not
expected to produce motor learning effect, it is recom-
mended as a complementary therapy in combination with
other rehabilitative interventions. Most early trials used
MP in combination with conventional care (Page, 2000;
Page et al., 2001, 2005, 2007), whereas other trials com-
bined other interventions such as CIMT (Page et al., 2009),
neuromuscular electrical stimulation (NMES) (Hong et al.,
2012; Park and Choi, 2013; You and Lee, 2013; Li et al.,
2014), and robot-assisted arm training (Varkuti et al., 2013;
Ang et al., 2015a, 2015b). One of the practical issues in MP
is that it is difficult to know whether the participant is truly
paying attention to motor imagery enough to activate
related neural pathways. To overcome this pitfall, some
Review article 1
0342-5282 Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved. DOI: 10.1097/MRR.0000000000000298
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mailto:seanpark05@gmail.com
researchers adopted brain–computer interface (BCI) tech-
nology using electroencephalography (EEG) (Prasad et al.,
2010; Ramos-Murguialday et al., 2013) or near-infrared
spectroscopy (Mihara et al., 2013) to provide neurofeed-
back to ensure MP.
Although previous systematic reviews (Zimmermann-
Schlatter et al., 2008; Barclay-Goddard et al., 2011; Braun
et al., 2013) already suggested some evidence of MP in
hemiparetic arm recovery after stroke, its efficacy has been
challenged (Ietswaart et al., 2011), and new development for
neurofeedback has been made recently. We assumed there
may be differences in the effect of MP according to the
differences of intervention by which MP is performed as
well as the characteristics of the therapy combined with
MP. The purpose of this systematic review were to evaluate
the effectiveness of MP for upper limb motor recovery in
participants with hemiparesis after stroke, and to investigate
the intervention characteristics – classified by the type of
therapies combined with MP and whether neurofeedback
was provided or not – that might affect treatment outcome.
We reviewed randomized controlled trials that examined
the effect of MP on motor recovery of the upper limb in
patients with hemiparesis after stroke.
Materials and methods
Data sources
A literature search was performed using the following
databases: the Cochrane Central Register of Controlled
Trials, PubMed, Embase, KoreaMed, Scopus, Web of
Science, the Physiotherapy Evidence Database (PEDro)
and CIRRIE until March 2017.
Search strategy
The keywords used for the search were (motor imagery
OR mental practice OR mental preparation OR mental
imagery OR mental therapy) AND (stroke OR cere-
brovascular disorder OR intracranial hemorrhages OR
cerebral vascular accident OR cerebral hemorrhage OR
cerebral ischemia OR cerebrovascular accidents) AND
(upper limb OR upper extremity OR arm). MeSH terms
were used in relevant databases: (STROKE [MeSH] OR
stroke* OR cerebrovascular* OR cerebral*) AND
(Imagery [MeSH] OR mental* OR imagery*) AND
(Upper Extremity [MeSH] OR arm OR upper limb).
Selection criteria
Two reviewers independently screened, reviewed, and
selected literature according to the following criteria:
(a) randomized controlled trials; (b) participants included
were those who had suffered a stroke only; (c) trials that
compared MP of any type in the intervention group with
the control group in which all other treatments were the
same except MP; (d) outcome measure of upper extre-
mity motor function; (e) studies in English or Koreanlanguage. When there was a disagreement, the final
decision was made after thorough discussion between the
reviewers.
Data collection
Data extracted from the selected studies included parti-
cipants’ characteristics, sample size, intervention char-
acteristics, outcome measures, and results. Outcome
measures of the upper extremity motor function used in
the meta-analysis was Fugl-Meyer Assessment (FMA). It
is a widely used tool in poststroke hemiparetic arm
rehabilitation studies, and we intended to make relevant
comparisons by mean differences of FMA in the analysis.
Risk of bias assessment
Two reviewers independently assessed the quality of the
studies using the Cochrane Collaboration’s tool for
assessing risk of bias (Higgins and Altman, 2008). The
domains of assessment consisted of adequate sequence
generation, allocation concealment, blinding, incomplete
outcome data addressed, free of selective reporting, and
other bias. This tool is not a scale but a domain-based
evaluation. Disagreements between reviewers were cor-
rected after discussion.
Data analysis
Effect sizes were expressed as weighted mean differ-
ence, and 95% confidence interval (CI). The net mean
change of each study was calculated using the following
formula: (measures after intervention in the treatment
group−measure at baseline in the treatment group)−
(measure after intervention in the control group−
measure at baseline in the control group). The mean
difference of SD was calculated using the following
formula: SD= square root [(SD pretreatment)2+ (SD
post-treatment)2− (2r× SD pretreatment× SD post-
treatment)], assuming a correlation coefficient (r)= 0.5.
In case of reporting range, the SD was estimated (Hozo
et al., 2005).
The heterogeneity across studies was assessed using
Cochrane’s Q test and I2 statistic. It was considered that
P values less than 0.10 in Cochrane’s Q test and I2 values
greater than 50% showed significant heterogeneity
(Higgins et al., 2003). The fixed effects model was used
to carry out the meta-analysis. Meta-analysis was per-
formed using the STATA ver. 15 (StataCorp, College
Station, Texas, USA).
Results
Study selection
In total, 1143 articles were identified by the search strategy
mentioned above. Among them, 920 articles were not
relevant, and 172 articles were excluded on the basis of the
abstract. Full text of the remaining 51 articles were
reviewed, and 26 were excluded because of the following
reasons: three were not randomized trials, one included
participants who had a diagnosis other than stroke, 15 did
2 International Journal of Rehabilitation Research 2018, Vol 00 No 00
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not match the intervention criteria, three were in the
Chinese language, and four were duplicates or abstract only
materials. For meta-analysis, 10 of 25 articles were exclu-
ded, because the FMA of upper extremity (range: 0–66)
was not used as an outcome measure, or data needed for
the analysis were not available. The final 15 articles were
included in the meta-analysis. The flow diagram of the
study selection is depicted in Fig. 1.
Risk of bias assessment
Allocation sequence was adequately generated in most
studies, but two studies did not specify the method of
randomization. Allocation concealment was stated in only
one study. Assessor blinding was reported in six studies.
Incomplete outcome data were adequately addressed in
most studies. Table 1 shows the risk of bias assessment
summary of the included studies.
Study characteristics
All studies included participants who suffered from
stroke in various stages. Eight studies included stroke
patients in chronic stage; four studies in acute to subacute
stage; three studies in mixed stages. Sample size ranged
from 9 to 47. Severity of arm paresis at baseline measured
by FMA was 30.3 in average.
All studies provided MP in addition to other physiotherapy
and/or occupational therapy for intervention group. MP was
given by auditory and/or visual stimulation without neuro-
feedback in nine trials. Six trials used auditory stimulation,
whereas two trials used visual stimulation as well. The time
for MP was 10min in two trials, 20min in two trials, and
30min in four trials. In eight trials, MP was given in addition
to conventional therapy. Conventional therapy was specified
as occupational therapy in one trial and physical therapy in
another, but mostly included both physical and occupational
therapy or described as conventional rehabilitation as an
all-inclusive term. One trial added MP to modified CIMT
(Page et al., 2009). Six studies applied neurofeedback to
support MP. In four trials, neurofeedback was coupled with
NMES, so that MP could induce muscle stimulation. The
neurofeedback was provided through electromyogram
(EMG) in three studies (Hong et al., 2012; Park and Choi,
2013; You and Lee, 2013) and BCI-EEG in one study (Li
et al., 2014). The other two studies tried BCI-EEG neuro-
feedback, coupled with robot-assisted therapy (RAT), using
MIT-Manus robot (Varkuti et al., 2013; Ang et al., 2015a).
Control group participants in all studies received the same
treatment as the intervention group except MP. Treatment
frequency was three or five times per week in seven trials
each, and two times per week in one trial. The treatment
period varied from 2 to 10 weeks, but mostly lasted for 4 or
6 weeks.
All but one trial reported superior results in the inter-
vention group compared with the control group in terms
of arm motor function. Studies that used functional brain
imaging reported concomitant changes in the brain.
Details of study characteristics are summarized in
Table 2.
Meta-analysis
The meta-analysis was conducted using the same out-
come measurement tool (FMA), and its forest plot is
presented in Fig. 2. As MP is usually given as a com-
plementary therapy to other types of physiotherapy, the
types of MP interventions of the included studies were
classified into four groups according to the types of other
therapies given with MP: (a) MP plus conventional
therapy (eight studies); (b) MP plus modified CIMT (one
study); (c) MP-guided NMES (four studies); and (d) MP-
guided RAT (two studies). The last two types of MP
adopted neurofeedback by either EMG or EEG.
Mental practice plus conventional therapy versus
conventional therapy
MP resulted in significantly higher FMA gain with the
mean difference of 4.43 (95% CI: 2.72–6.14; P< 0.001).
There was no heterogeneity among studies (Higgins’
I2= 0.0%, Cochrane’s Q statistics P= 0.925).
Mental practice plus modified constraint-induced
movement therapy versus modified constraint-induced
movement therapy
Only one study was in this group, and the mean differ-
ence of FMA was 4.00 (95% CI: 2.60–5.40; P< 0.001).
Fig. 1
Records screened
(n = 223)
Records excluded
after abstract review
(n = 172)
Full-text articles assessed
for eligibility
(n = 51)
Full-text articles excluded (n = 26)
- by intervention criteria (n = 15)
- by participants criteria (n = 1)
- not RCT (n = 3)
- duplicates / abstracts only (n = 4)
- Chinese language (n = 3)
Studies included in
qualitative synthesis
(n = 25)
Studies included in
quantitative synthesis
(meta-analysis)
(n = 15)
Records identified through 
database searching 
(n = 1,143)
Not relevant records removed 
(n = 920) 
Data not available (n = 10)
Flow diagram of selection of studies included in the review.
Mental practice in stroke rehabilitation Park et al. 3
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Mental practice-guided neuromuscular electrical
stimulation versus neuromuscular electrical
stimulation
MP-guided NMES resulted in significantly higher FMA
gain than usual NMES with the mean difference of 6.11
(95% CI: 3.43–8.79; P< 0.001). There was no hetero-
geneity among studies (Higgins’ I2= 0.0%, Cochrane’s
Q statisticsP= 0.571).
Mental practice-guided robot-assisted therapy versus
robot-assisted therapy
MP-guided RAT and RAT alone did not show significant
difference in upper extremity motor function improvement.
The mean difference of FMAwas −1.42 (95% CI: −11.91 to
9.07; P=0.791). There was no heterogeneity among studies
(Higgins’ I2=0.0%, Cochrane’s Q statistics P=0.921).
As a whole, MP showed more favorable outcome than the
control group with the mean difference of 4.39 (95% CI:
3.39–5.39; P< 0.001; Fig. 2).
Discussion
The overall result of this review supports beneficial
effect of MP when given with other therapies, which is in
line with previously published systematic reviews. There
have been questions about adherence and compliance to
MP (Braun et al., 2013) as well as ideal dosage
(Barclay-Goddard et al., 2011). There were negative trials
of MP, and the timing of therapy was also questioned
(Ietswaart et al., 2011). These questions are yet to be
answered, and how to standardize the method of MP is
another challenging issue. We could not find notable
concomitant differences in results with regard to the
differences in dosage, frequency, and duration of MP in
this study. Studies with participants in acute to subacute
stage (Page et al., 2001; Sun et al., 2013; Uttam et al., 2015)
tend to show larger FMA changes, but the difference
between groups was similar to other studies with chronic
stroke. In one study (Oh et al., 2016) that did not show
significant effect of MP, the participants had stroke
within the past 6 months, and their arm motor function
was relatively good at baseline. It seems that motor
recovery in those patients was too good to get additional
benefit from adjuvant MP.
In this review, we included recently published trials that
applied neurofeedback and BCI technology to ensure
adherence to MP. Although it seems that these studies
with neurofeedback did not change the overall efficacy of
MP without neurofeedback, there were differences
according to the types of therapy with which MP is
combined. MP-guided NMES either by EMG or EEG
showed promising results. The difference of FMA gain
between intervention and control group is higher than
that of MP without neurofeedback (4.43 vs. 6.11; see
Fig. 2), which can be interpreted as clinically important
with respect to the estimates by Page et al. (2012). In
contrast, MP-guided RAT did not show better outcome
compared with RAT alone. However, it should not be
regarded as conclusive, as only two pilot trials were
included. It is noteworthy that the intensity of RAT (i.e.
the number of repetitive movements in the robot) was
lower in the intervention group than in the control group,
because of the nature of MP-guided RAT, in which the
robot had to wait until the MP-generated signal is arrived
(Ang et al., 2015a). Probably, the intensity of physical
practice matters more than MP.
BCI is an emerging technology applicable in neuroreh-
abilitation (Soekadar et al., 2015; van Dokkum et al., 2015).
It is not surprising that its applicability is tested pre-
ferentially in MP, which is a form of mind–body therapy.
As the essential nature of rehabilitation is motor learning,
the importance of awareness and active involvement of
patients cannot be overemphasized. Despite potential
benefit of BCI in neurorehabilitation, it seems that further
development is warranted for clinical use, because its
methodology and clinical efficacy varied between trials.
Not only the trials included in this review (Varkuti et al.,
2013; Li et al., 2014; Ang et al., 2015a) but also other studies
that are not included in this review (Prasad et al., 2010;
Table 1 Risk of bias summary of the selected studies
References
Adequate sequence
generation
Allocation
concealment Blinding
Incomplete outcome data
addressed
Free of selective
reporting
Free of other
bias
Page (2000) Yes Unclear Unclear Unclear Yes Unclear
Page et al. (2001) Yes Unclear Yes Yes Yes Unclear
Page et al. (2007) Yes Unclear Yes Unclear Yes Unclear
Page et al. (2009) Yes Unclear Yes Yes Yes Unclear
Hong et al. (2012) Yes Unclear Yes Unclear Yes No
Park and Choi (2013) Unclear Unclear Unclear Yes Yes No
Sun et al. (2013) Yes Unclear Yes Yes Yes Unclear
Varkuti et al. (2013) Unclear Unclear Unclear Yes Yes Unclear
You and Lee (2013) Yes Unclear Unclear Yes Yes Unclear
Li et al. (2014) Yes Unclear Unclear Yes Yes Unclear
Ang et al. (2015a) Yes No Yes Yes Yes Unclear
Kim and Lee (2015) Yes Unclear Unclear Yes Yes Unclear
Park et al. (2015) Yes Unclear Unclear Yes Yes Unclear
Uttam et al. (2015) Yes Unclear Unclear Yes Yes Unclear
Oh et al. (2016) Yes Yes Unclear Yes Yes Yes
4 International Journal of Rehabilitation Research 2018, Vol 00 No 00
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Table 2 Summary of findings of the selected studies
References Participants Intervention Outcome measure Results
Page (2000) N=16 (8 : 8)
Chronic (average time since
stroke=1.8 years)
Baseline mean FMA=22.1 ±3.4
Audiotape-recorded motor imagery for 20 min three times/week for
4 weeks with 30 min conventional occupational therapy
FMA Significantly greater improvement in FMA
Postmean FMA=29.97 ±4.4
Page et al. (2001) N=13 (8 : 5)
mean time since stroke 6.5 months
(4 weeks to 1 year)
Baseline mean FMA=29.3 ±11.9
Audiotape-recorded motor imagery for 10 min three times/week for
6 weeks as well as practicing imagery at home twice each week with
1 h of conventional therapy
FMA, ARA Intervention group displayed substantial increases in FMA and
ARA
Postmean FMA=43.0 ±10.1
FMA change=13.8
Page et al. (2007) N=32 (16 : 16)
Chronic (mean 3.6 years,
12–174 months)
Baseline mean FMA=33.0 ±8.4
Audiotape-recorded motor imagery for 30 min 2 days/week for 6 weeks
with conventional 30-min therapy
ARA, FMA Significantly larger changes in ARA and FMA
Postmean FMA=39.75 ±6.86
FMA change=6.72 ±3.68
Page et al. (2009) N=10 (5 : 5)
chronic stroke (13–42 months)
Baseline mean FMA=38.4 ±1.1
Audiotape-recorded motor imagery for 30 min 3 days/week for
10 weeks with modified constraint-induced therapy: 1/2-h repetitive
task practice and restrain 5 h/weekday
ARA, FMA Significantly larger changes in ARA and FMA
Postmean FMA=46.4 ± 0.89
FMA change=7.8
Hong et al. (2012) N=14 (7 : 7)
chronic stroke (≥12 months)
Baseline mean FMA=29 (22–33)
Mental imagery training combined with electromyogram-triggered
electric stimulation in two 20-min daily sessions 5 days a week for
4 weeks
FMA, MAL, MBI,
PET
Significant improvements in FMA and increased metabolism in
the contralesional cortex
FMA change=7 (5–8)
Park and Choi
(2013)
N=47 (23 : 24)
Chronic stroke (≥1 year)
Baseline mean FMA=26.6 ±13.3
Mental imagery training combined with electromyography-triggered
electrical stimulation for 6 weeks, 30-min session, 5 days/week
FMA, MFT, MBI Significant improvements in FMA and MFT
Postmean FMA=27.91 ± 13.46
Sun et al. (2013) N=18 (9 : 9)
Stroke ≥3 and ≤6 months
Baseline mean FMA=22.4 ±11.6
Motor imagery administered by therapist for 30 min 5 days/week for
4 weeks with conventional therapy
FMA, fMRI Significantly greater improvement in FMA
Postmean FMA=39.78 ±14.03
FMA change=17.33 ±7.71
Varkuti et al. (2013) N=9 (6 : 3)
Stroke more than 1 month
Baseline mean FMA=35.0 ±11.8
Motor imagery-BCI-based MANUS shoulder-elbow robotic
rehabilitation 12 sessions in 1 month
RS-fMRI, FMA FMA gain and functional connectivity changes were
numerically higher
Postmean FMA=40.64 ±15.08
FMA change=5.6
You and Lee (2013) N=18 (9 : 9)
Chronic stroke ≥12 months
Baseline mean FMA=25.6 ±10.0
Mental imagery training linked with electromyogram-triggered electrical
stimulation 20 times over 4 weeks
AROM, MAS, FMA,
MAL, MBI
Significant difference between groups in FMA
Postmean FMA=31.88 ±12.91
FMA change=6.13 ±3.4
Li et al. (2014) N=15 (8 : 7)
Stroke 1–6 months
Baseline mean FMA=13.6 ±4.7
Motor imagery-based BCI combined with functional electrical
stimulation training,three times per week for 8 weeks
FMA, ARA, ERD Significant improvement in FMA and ARA with the activation
of bilateral cerebral hemispheres
Postmean FMA=26.29 ± 11.97
Ang et al. (2015a) N=25 (11 : 14)
Poststroke duration >3 months
(mean 297; 4 days)
Baseline mean FMA=26.3 ±10.3
EEG-based motor imagery BCI system coupled with MANUS
shoulder-elbow robotic feedback over 4 weeks (1.5 h each, three
times/week)
FMA, rBSI More participants attained further gains in FMA. A negative
correlation between the rBSI and FMA score improvement
Postmean FMA=30.8 ± 13.8
Kim and Lee (2015) N=24 (12 : 12)
6–12 months since stroke onset
Baseline mean FMA=27.9 ±7.7
Mental imagery using computer monitor and speakers for 30 min five
times a week for 4 weeks with conventional therapy
FMA, WMFT Significantly higher changes in FMA
Postmean FMA=36.08 ±9.9
FMA change=8.17 ±2.55
Park et al. (2015) N=29 (14 : 15)
stroke over 6 months
Baseline mean FMA=42 ±7.5
Audiotape-recorded motor imagery for 10 min 5 days a week for
2 weeks with conventional therapy
ARA, FMA, MBI Significant improvements in ARA, FMA and MBI
Postmean FMA=46.5 ± 8.1
Uttam et al. (2015) N=26 (13 : 13)
Stroke between 1 and 6 months
Baseline mean FMA=29.5 ±4.0
Graded motor imagery consisted of 2 weeks each of left right
discrimination training, explicit motor imagery, and mirror therapy,
5 days a week for 6 weeks with conventional therapy
FMA, CAHAI,
SS-QOL
More significant improvement in all three outcome measures
Postmean FMA=50.69 ±2.213
FMA change=21.23 ± 4.166
Oh et al. (2016) N=10 (5 : 5)
stroke within the past 6 months
Baseline mean FMA=50 ±7.4
Audiotape-recorded motor imagery for 20 min, 3 days a week for
3 weeks with conventional therapy
Motion analysis,
FMA, MAL
No significant effect on upper limb function
Postmean FMA=54 ±9.51
ARA, action research arm test; AROM, active range of motion; BCI, brain–computer interface; CAHAI, Chedoke arm and hand activity inventory; ERD, event-related desynchronization; FMA, Fugl-Meyer Assessment; MAL, motor
activity log; MAS, modified Ashworth scale; MBI, modified Barthel index; MFT, manual function test; PET, positron emission tomography; rBSI, revised brain symmetry index; RS-fMRI, resting state functional magnetic resonance
imaging; SS-QOL, stroke specific-quality of life; WMFT, Wolf motor function test.
M
entalpractice
in
stroke
rehabilitation
P
ark
et
al.
5
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opyright
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2018
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olters
K
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er
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Inc.
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nauthorized
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of
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prohibited.
Mihara et al., 2013; Ramos-Murguialday et al., 2013; Sun
et al., 2016) are all showing varying results. A larger clinical
trial is requested.
There are limitations and cautions in the interpretation of
this review. Our purpose of review was to evaluate clin-
ical efficacy of MP, and we included the studies that
adopted BCI in MP, but there are other excluded studies
that compared MP with and without BCI. The overall
effect of BCI should not be inferred from the results of
this study. Another limitation is that we only included the
studies using the FMA as an outcome measure in order to
overcome the problem of heterogeneity and clarify clin-
ical meaning. It should be taken into account that other
trials of MP using different outcome measure might lead
to different clinical implications. In addition, the quality
of study was inadequate in many trials included in the
analysis, especially in terms of allocation concealment
and blinding. These aspects need to be carefully
addressed to avoid bias, as MP is usually given with other
physical practices, which might be more efficacious than
MP. More efforts should be made to lower the risk of bias
in future studies. It should also be noted that the quality
assessment in this study has limitations because a third
reviewer was not applied in cases of disagreement.
In conclusion, this systematic review and meta-analysis sug-
gests that MP is an effective complementary therapy, either
given with neurofeedback or not. However, a further well-
designed unbiased MP trial is encouraged. Neurofeedback
and BCI applied to MP showed different results depending
on the therapy provided. Considering the potential benefit of
BCI in clinical application of MP, further development and
investigation of BCI-based MP is requested.
Acknowledgements
Conflicts of interest
There are no conflicts of interest.
References
Ang K, Chua K, Phua K, Wang C, Chin Z, Kuah C, et al. (2015a). A randomized
controlled trial of EEG-based motor imagery brain-computer interface robotic
rehabilitation for stroke. Clin EEG Neurosci 46:310–320.
Ang K, Guan C, Phua K, Wang C, Zhao L, Teo W, et al. (2015b). Facilitating
effects of transcranial direct current stimulation on motor imagery brain-
computer interface with robotic feedback for stroke rehabilitation. Arch Phys
Med Rehabil 96:S79–S87.
Fig. 2
Mental practice effect on hemiparetic arm motor function: meta-analysis of subgroups classified by the types of intervention. CI, confidence interval;
mCIMT, modified constraint-induced movement therapy; MP, mental practice; NMES, neuromuscular electrical stimulation; RAT, robot-assisted
therapy; WMD, weighted mean difference.
6 International Journal of Rehabilitation Research 2018, Vol 00 No 00
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