JNER
JOURNAL OF NEUROENGINEERING
AND REHABILITATION
Effects of task-oriented robot training on arm
function, activity, and quality of life in chronic
stroke patients: a randomized controlled trial
Timmermans et al.
Timmermans et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:45
http://www.jneuroengrehab.com/content/11/1/45
Timmermans et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:45
http://www.jneuroengrehab.com/content/11/1/45
RESEARCH
JNER
JOURNAL OF NEUROENGINEERING
AND REHABILITATION
Open Access
Effects of task-oriented robot training on arm
function, activity, and quality of life in chronic
stroke patients: a randomized controlled trial
Annick AA Timmermans1,2,5*, Ryanne JM Lemmens1,2, Maurice Monfrance1, Richard PJ Geers1, Wilbert Bakx2,3,
Rob JEM Smeets1,2,4 and Henk AM Seelen1,2
Abstract
Background: Over fifty percent of stroke patients experience chronic arm hand performance problems,
compromising independence in daily life activities and quality of life. Task-oriented training may improve arm
hand performance after stroke, whereby augmented therapy may lead to a better treatment outcome.
Technology-supported training holds opportunities for increasing training intensity. However, the effects of
robot-supported task-oriented training with real life objects in stroke patients are not known to date. The aim
of the present study was to investigate the effectiveness and added value of the Haptic Master robot combined
with task-oriented arm hand training in chronic stroke patients.
Methods: In a single-blind randomized controlled trial, 22 chronic stroke patients were randomly allocated to
receive either task-oriented robot-assisted arm-hand training (experimental group) or task-oriented non-robotic
arm-hand training (control group). For training, the T-TOAT (Technology-supported Task-Oriented Arm Training)
method was applied. Training was provided during 8 weeks, 4 times/week, 2× 30 min/day.
Results: A significant improvement after training on the Action Research Arm Test (ARAT) was demonstrated in the
experimental group (p = 0.008). Results were maintained until 6 months after cessation of the training. On the
perceived performance measure (Motor Activity Log (MAL)), both, the experimental and control group improved
significantly after training (control group p = 0.008; experimental group p = 0.013). The improvements on MAL in
both groups were maintained until 6 months after cessation of the training. With regard to quality of life, only in
the control group a significant improvement after training was found (EuroQol-5D p = 0.015, SF-36 physical p = 0.01).
However, the improvement on SF-36 in the control group was not maintained (p = 0.012). No between-group
differences could be demonstrated on any of the outcome measures.
Conclusion: Arm hand performance improved in chronic stroke patients, after eight weeks of task oriented training.
The use of a Haptic Master robot in support of task-oriented arm training did not show additional value over the
video-instructed task-oriented exercises in highly functional stroke patients.
Clinical trial registration information: Current Controlled Trials ISRCTN82787126
Keywords: Stroke, Hemiplegia, Robotics, Computer-assisted therapy, Rehabilitation, Upper extremity, Arm, Hand,
Motor skills, Automation
* Correspondence: Annick.Timmermans@uhasselt.be
1
Adelante, Centre of Expertise in Rehabilitation and Audiology, Hoensbroek,
The Netherlands
2
Research School CAPHRI, Department of Rehabilitation Medicine, Maastricht
University, Maastricht, The Netherlands
Full list of author information is available at the end of the article
© 2014 Timmermans et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly credited.
Timmermans et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:45
http://www.jneuroengrehab.com/content/11/1/45
Background
Stroke is a leading cause of morbidity worldwide and the
first cause of motor impairment [1,2]. Chronic arm hand
performance problems are present in over 50% of the
stroke patients [3], limiting the use of their arm and
hand in everyday life activities [4,5], but also limiting engagement in social life, and quality of life in general [6].
Rehabilitation may have positive effects on the improvement of arm hand performance [7], in acute as well
as in chronic stages after stroke [8]. It is also known that
a higher therapy intensity and duration leads to a better
therapy outcome after stroke [9,10]. However, because of
the increasing incidence of stroke [11], the availability of
augmented therapy will be compromised. Technologysupported training is emerging as a solution to support
therapists in their efforts and to relieve pressures on the
health system.
Initial results of meta-analyses on clinical trial results
with robotics only showed the effectiveness of robotics for
the improvement of upper extremity function (muscle
strength, coordination, joint range of motion), and not for
the improvement on the ICF activity level (arm hand
skilled performance) [7,12-14]. One of the reasons for the
absence of activity-related training effects, was the absence
of activity related training input [12-14]. Recently, more
trial results have been published with positive effects of
robot supported arm training on the ICF activity level
outcome measures, e.g. a randomized clinical trial using
T-WREX [15], and MIT Manus [16] in stroke, and the
most recent systematic review of Mehrholz J. et al. [17]
also found positive effects of the use of robotics and
electromechanical devices on the upper extremity activity
performance after stroke. But also in other neurological
pathologies, such as MS, positive effects on the activity
level after the use of robotics have been registered [18,19].
The study of Carpinella et al. [19] uses task oriented training with object manipulation, finding additional effects
over reaching without object manipulation in MS patients.
While French et al. [20] did not find evidence for the
effectiveness of task-oriented training of the upper extremity in stroke, results of clinical trials with technology
supported task training look more promising. Several
authors [21-24] have combined the task-oriented training
method using real life objects with sensor-based technologies for the rehabilitation of arm hand performance after
stroke. Results from pilot studies show an improvement of
arm hand function [21], arm hand activity [21,25], and
health related quality of life [21] after stroke. But also
treatment credibility and patient motivation are quite high
for such training approaches [22]. Johnson et al. [26] were
the first to combine the task-oriented training approach
using real life objects with the Haptic Master robot in
stroke patients, enabling the handling of real life objects
during robot training. Carpinella et al. [19] used the
Page 2 of 11
Braccio di Ferro robot in combination with a functional
orthosis, allowing for the execution of functional tasks
with real life objects in MS patients. However, no results
on clinical effectiveness of task-oriented robot supported
training in stroke patients are available to date. Also, while
robot-supported training has been extensively investigated
and proven effective in chronic stroke patients with lower
functional levels [16,27], to the authors knowledge no evidence is available on the use of robot supported training
in highly functional chronic stroke patients.
The aim of the present study was to investigate the
effectiveness and added value of the Haptic Master robot
adjunct to task-oriented arm hand training in chronic
stroke patients on the ICF body function level, the ICF
activity level, and on the quality of life.
Methods
Participants and study protocol
Twenty-two chronic stroke patients were recruited from
Adelante Rehabilitation Centre (Hoensbroek, NL) to participate in a single-blind RCT. The sample size was determined after a power calculation based on results of a
sensor-based intervention that used the same training
method as the one in this study [21].
Inclusion criteria were 1) first ever stroke, 2) age
between 18–85 years 3) clinically diagnosed with a central paresis of the arm/hand (strength: Medical Research
Council grade 2–4 at entry into study), 4) post-stroke
time ≥ 12 months, 5) fair to good cognitive level (Mini
Mental State Examination (MMSE) score ≥ 26 [28]), 6)
able to read and understand the Dutch language, 7) unable
to fully perform at least two of the following skills: drinking from a cup, eating with knife and fork, taking money
from a purse and using a tray, 8) motivated to train at
least two of the abovementioned skills. At the start of the
last 6 months of the inclusion period, inclusion criterion 4
was adjusted to post-stroke time ≥ 8 months, to facilitate
patient inclusion. Exclusion criteria were: 1) severe neglect
(Bell Test [29], Letter Cancellation Test: minimum omission score of 15% [30]), 2) hemianopsia, 3) severe spasticity (Modified Ashworth Scale total arm > 3), 4) severe
additional neurological, orthopaedic or rheumatoid impairments prior to stroke which could interfere with task
performance, 5) Broca aphasia, Wernicke aphasia, global
aphasia (determined by the Akense Afasie Test [31], 6)
apraxia (apraxiatest of Van Heugten [32]) and 7) attending
another study or therapy to improve arm-hand function.
The participating rehabilitation physicians identified
potential participants based on their medical files. Letters
with information about the study and an invitation to participate were sent to potential participants. After receiving
informed consent, the rehabilitation physician screened
the patients willing to participate as to the inclusion
and exclusion criteria. After inclusion, participants were
Timmermans et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:45
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randomly allocated to either the experimental group
(robot-assisted training group) or the control group, using
blocked randomization (block size = 2). The randomization procedure was performed by an independent
researcher using 2 opaque envelopes with in each envelope a training condition code. Persons involved in data
collection were blinded for group allocation. During the
study period, participants were asked not to participate in
other studies involving arm-hand performance and not to
change the therapy they received next to this intervention.
Participants were reimbursed for transportation costs to
and from the rehabilitation centre. All procedures were
approved by the Medical Ethics Committee of Adelante.
All participants signed an informed consent prior to
participating in the study. A written informed consent
was also obtained for publication of the patient photos for scientific purposes. This trial was registered as
ISRCTN82787126.
Task-oriented training method and robotic system
The robotic system Haptic Master (MOOG, NieuwVennep, NL) was used in the present study for the armhand training in the experimental group (Figure 1). The
Haptic Master is a commercially available end-effector
based robot that permits training of real-life functional
tasks involving reach, grasp, as well as object transportation in a three dimensional space. The environment is
set-up to support seated as well as standing up task
performance. The Haptic-Master and the currently used
gimbal allow six degrees of freedom (DoF). Three actuated
(i.e. activated by the system) DoFs are for positioning and
Page 3 of 11
three non-actuated DoF’s are for orientation in the gimbal.
This configuration permits the person to freely orient their
hand as needed to manipulate an object.
For training, the T-TOAT (Technology-supported
Task-Oriented Arm Training) method [33] was applied.
The training method comprises of breaking down skills
into functional components that maintain a strong relationship with the original skill itself. For each of these
components exercises are offered at gradually increasing
levels of difficulty, based on progress criteria from the
fields of exercise physiology [34], and motor learning
[35]. With regard to principles from exercise physiology,
patients were instructed to not only focus on training
coordination with low weight objects, but also include
series where a number of repetitions was performed with
heavier objects, in order to train on endurance (e.g. 50%
of maximal weight, 3×15 repetitions) and strength (e.g.
submaximal weights, 8–10 repetitions) [36]. With regard
to motor learning, treatment variability was encouraged
as patients were asked to use as many different objects
as possible (e.g. different shapes and sizes of cutlery, different kinds of cups and glasses, several shapes and sizes
of purses, different coins, etc.). Also patients were explicitly asked to mix different exercises and different tasks
as random practise is known to support motor learning
and retention of training effects through high contextual
interference [37,38]. Participants are encouraged to
first train on components of a skill (e.g. reach out to
cup, grasp cup, lift cup, bring cup to mouth, empty
cup in mouth, place cup on table), after which the
complete action (e.g. drinking from a cup), sequencing all
Figure 1 Picture of patients training in the control group (left) and in the experimental group (right).
Timmermans et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:45
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components, is trained. The “Haptic-TOAT” software tool
[33] was used to enable the use of the T-TOAT method in
combination with the Haptic Master robot. Individual
movement trajectories can be recorded for each patient,
in order to train on the for each patient optimal path. The
3D positions (x,y,z coordinates) are logged with a sample
rate of 100 Hz, and can be saved and replayed. The number of repetitions can be adjusted per patient. Several
modes can be used: the passive mode (for patients with very
little strength or for learning the movement trajectory), or
the active mode. In the passive mode, the trajectory is
covered by the Haptic Master, taking the patient’s arm
along the recorded path. In the active mode, the patient
performs the arm movement along the recorded trajectory, whereby a deviation from the trajectory is corrected
by bouncing into a wall, the diameter of the tunnel around
the trajectory can be set by the therapist (a larger diameter
is more difficult). Also correction of trajectory deviation is
supported by a spring, with customizable strength, that
pulls the robot end effector (and thus the patient arm)
back to the optimal movement trajectory (stronger
spring = easier). In addition to the spring force, damping (allowing for strength training) and addition of a
force in the vertical direction (either supporting the
arm against gravity or addition of extra load, both possible
at the wrist and distal part of the forearm via the orthotic
arm-HM interface) may be set at each point of the trajectory with the same graphical user interface. The T-TOAT
method and its use with the Haptic Master are extensively
described in a paper of Timmermans et al. [33].
Arm-hand training program
Training was provided during 8 weeks, 4 times/week,
twice a day for 30 minutes (separated by 0.5 hour to
1 hour of rest). At the start of the training program participants in both groups chose a minimum of 2 out of 4
skills to train: ‘drinking from a cup’, ‘eating with knife
and fork’, ‘taking money from a purse’ or ‘using a tray’.
The reason for letting patients choose the tasks they
preferred most, was that we wanted to provide in both
groups, to some extent, goal oriented training where
patients would train towards personally set goals. This
to enhance patient compliance, patient motivation and
self-efficacy [39-41]. The tasks for which exercises were
provided are chosen out of a list of training preferences
for stroke patients [42]. Before training, participants
were educated about the principles of task-oriented armhand training and the importance of frequent training to
enhance therapy success. Video-instructions were used
to explain the exercises. The video-instructions were
organised per task, and within the task per skill component. Patients could easily select by themselves the
appropriate training content. For each skill component
at least 5 exercises were given in increasing difficulty
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level. The training program was similar for the experimental group and the control group, the only difference
being the use of technology in the experimental group.
Both groups received the same video-instructions, together
with a tool box filled with training objects and posters how
to use different objects with increasing difficulty levels
(e.g. round glass with anti-slip material → normal
glass→ wine glass). The exercises were done by (dis)
placing objects on pre-printed templates, which were
the same in both groups. The experimental group, by
combining the T-TOAT training with the use of a Haptic
Master robot received trajectory guidance through haptic
feedback; while the control group had to master all upper
extremity movements without support. Training in both,
the experimental and control group, was supervised by a
physiotherapist, occupational therapist or movement
scientist. The therapist was present during the training
(for both groups) to answer questions of the patients when
necessary, to assist with advice on the use of objects, or to
provide assistance in adaptations of the Haptic TOAT
software. Assistance or resistance during training was adjusted between training sessions, by changing the strength
of the spring force or damping in the Haptic Master software, in order to always maximally challenge the patient
according to his/her potential. The patients were offered
exercises that they could just about manage successfully
without an increase of their compensatory strategies during movement performance. This approach is supportive
of motor learning in stroke patients [33]. The therapists
used the assistance and damping facilities in the software
according to their clinical experience. No recordings were
made of how therapists actually used these training facilities. Both groups received equal attention of the therapist.
Compliance was determined as the percentage of training
sessions patients attended relative to the maximal total of
64 training sessions. Patients continued their usual care
outside of this intervention. The usual care therapy did
not consist of arm-hand activities at the ICF activity level.
Outcome measures
The demographical data obtained from the medical files
were, age, gender, date and type of stroke, side of hemiparesis, and hand dominance. Outcome measurements
were taken upon entry into the study (T0, baseline), after
4 weeks training (T1, only for primary outcome measures), at the end of the 8 weeks training program (T2),
and 6 months after finishing the training program (T3). A
diary was kept during training sessions to determine therapy compliance and to record which exercises were practised as well as the number of repetitions.
Primary outcome measures consisted of assessment at
the ICF body function level (Fugl Meyer Motor Assessment (FMMA) [43]), and at the ICF activity level (Action
Research Arm Test (ARAT) [44], Motor Activity Log
Timmermans et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:45
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(MAL) [45]). The upper extremity section of the FMMA
was used. FMMA has been found a reliable and valid
test for the assessment of arm hand function in stroke
patients [43]. The maximum score on the FMMA, upper
extremity section is 66. The ARAT has been proven to
be a reliable, valid and sensitive instrument for upper
limb activity measurement [44-46]. The maximum score
on ARAT is 57. The MAL is a semi-structured interview
to assess frequency of use (AU) (“How often did you
actually use your affected arm for this activity ?”, 6 point
likert scale: 0 = not used/5 = same as pre-stroke) and
quality of use (QU) (“How useful was the contribution
of your affected arm during this activity?”, 6 point likert
scale: 0 = no contribution/5 = same as pre-stroke) of the
affected limb for skill performance. The MAL has been
shown to be a reliable and valid tool for measurement of
arm hand activity in stroke patients [47].
Secondary outcome measurement, at the ICF participation level, consisted of quality of life assessment
(EuroQol-5D and SF-36). The EuroQol-5D (EQ-5D) is a
broad generic assessment tool for quality of life [48]. It
includes a Visual Analogue Scale (VAS) (0–100) to indicate the perceived health-state, as well as scoring of 5
sub-items (0–2 score) (mobility, self-care, usual activities,
pain, and anxiety). The EuroQol-5D has good psychometric properties [49]. The SF-36 Health Survey (SF-36) [50]
is a generic survey with a good reliability and validity. SF36 is composed of 36 questions and standardized response
choices, and yields physical and mental health summary
measures. All raw scale scores were linearly converted to a
0 to 100 scale, with higher scores indicating higher levels
of functioning or well-being [50]. SF-36 has a greater
sensitivity than EQ-5D, however EQ-5D shows higher
completion rates [51].
Persons who performed the assessments, were not
involved in training and data analysis. Intention to treat
analysis was applied: data were analysed according to the
original random assignments, regardless of whether the
patients actually received treatment.
Data processing and analysis
If between different test occasions only one of the test
results was missing for a person, the missing value was
substituted according to the last observation carried forward principle. No baseline data were missing.
As data did not follow a normal distribution pattern,
non-parametric statistics were used.
Data were analyzed using SPSS software (SPSS Inc.,
Chicago, IL). For testing of differences between groups
with regard to nominal data (side of hemiparesis, hand
dominance), a Chi Square Test was performed. Other
differences between the experimental and control group
were tested with the Wilcoxon-Mann–Whitney U test. A
Friedman analysis was performed to assess if significant
Page 5 of 11
progress was made over time (T0,(T1),T2,T3) within
either one of the treatment groups. Alpha was set at 0.05.
The progress made between two specific test occasions
within a group was tested with a Wilcoxon signed ranks
test, using a Bonferroni approach. The Bonferroni corrected alpha value equals 0.0125 (for data comparison T0-T2,
T2-T3, T0-T1, T1-T2) [52].
Results
The inclusion of patients into the study started in May
2009 and lasted till May 2011. Figure 2 gives an overview of the trial profile, i.e. the number of participants
throughout the study.
Error analysis
As for the primary outcome measures, only 1 T3 measurement was missing from the FMMA data. As for the
secondary outcome measures, only 1 T3 measurement was
missing for the SF-36 test. Both were from the same patient
who was not willing to stay as long as we needed to
complete the test because of conflicting agenda items.
Intention to treat analysis was applied. The missing T3 data
were substituted by the T2 data from the same person.
Patient characteristics
Participant characteristics are presented in Table 1. Five
patients were included with a post stroke time shorter
than the initially required 12 months (i.e. experimental
group: 1× 10 months and 1× 8 months, control-group:
2× 11 months, 1× 10 months post-stroke). No significant
differences were found for age and post-stroke time
between the experimental group and the control group
at baseline (p = 0.133, and p = 0.606 respectively). Also
no significant differences were found for the side of
hemiparesis (p = 0.08) and for dominant arm impairment
(p = 1.0). For patient characteristics at baseline, only for
hand dominance a significant difference was found between groups (p = 0.03). At baseline, arm-hand function
did not differ significantly between patients in the
experimental and the control group, as determined by
the FMMA (p = 0.401), ARAT (p = 0.365) and MAL
(p = 0.797). No patients dropped-out of the study. Compliance of the patients with attending the training scheme
was 96% in the experimental group and 96.2% in the
control group. One patient (in the experimental group)
fainted briefly once. However, after visiting the medical
specialist, the cause turned out to be a change of medication. No relationship with the intervention was found.
No adverse effects of the study were found.
Primary outcome measures
An overview of the test results can be found in Table 2.
An overview of the individual improvement over time
relative to baseline values (IIT) between the start of the
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Page 6 of 11
Enrolment
Patient files screened: (n = 650)
Excluded ( n = 553)
- Not meeting the inclusion criteria based on
medical files: (n = 553)
Letters sent to eligible patients: (n = 97)
Excluded ( n = 75)
- Not meeting the inclusion criteria after additional
screening by a rehabilitation specialist: (n = 11)
- Declined to participate (n = 64)
Allocation
Randomized: (n = 22)
Haptic Master group (n = 11)
Control group (n =11)
Training period
(8 weeks)
T0 measurement (baseline)
T1 measurement (after 4 weeks training)
Analysis
Followup
T2 measurement (after training period)
T3 measurement (follow-up, 6 months after T2)
Haptic Master group (n = 11)
Control group (n =11)
Figure 2 Flow chart representing the number of patients throughout the trial measures.
training and the cessation of the training for the different
tests can be found in Table 3.
Between group results
No between group differences were found with regard to
arm hand function (FMMA), arm hand capacity (ARAT),
and self-perceived arm-hand performance (MAL).
Within-group results
No within-group differences were detected between different test occasions for FMMA, neither in the control
group, nor in the experimental group.
With regard to the ARAT, a significant improvement
over time occurred in the experimental group (p = 0.004).
The progress was significant between the start of the
training and the end of the training (p = 0.008). There was
no difference found in ARAT results between cessation of
the training and the 6-month follow-up measurement.
With regard to the MAL (total test result), a significant
improvement over time was found in the control group
(p = 0.002), and in the experimental group (p = 0.005). The
progress was significant between the start of the training
and the end of the training in the control group (p =
0.008), and a trend towards significance was observed in
the experimental group (p = 0.013). Also the progress on
the MAL was significant between the start of the training
and the intermediate measurement for the control group
(p = 0.004). No difference was found in either group between the cessation of the training and the 6-month
follow-up measurement.
When looking at the MAL test results for AU and QU
separately, it was found that for AU a significant difference was found in the control group over time (p =
0.008), and specifically between T0 and T1 (p = 0.008).
With regard to the MAL test results for QU, a significant improvement over time was found for the control
group (p = 0.001) and for the experimental group (p =
0.006). Also significant differences were found for QU in
both groups between start and cessation of the training
(control group p = 0.006, experimental group p = 0.01).
For the control group, also significant differences in QU
were found between T0 and T1 (p = 0.004).
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Table 1 Patient characteristics at baseline
Control
Experimental
Between
group n = 11 group n = 1 group difference
Mean age in
years (SD)
56.8 (6.4)
61.8 (6.8)
Male
8
8
Female
3
3
3.7 (3.0)
2.8 (2.9)
p = 0.1
Gender
Mean time post-stroke
in years (SD)
p = 0.6
Side of hemiparesis
Left
8
7
Right
3
4
Left
1
5
Right
p = 0.08
Arm hand dominance
p = 0.03
10
6
Dominant arm hand
impaired
4
7
Non-dominant arm
hand impaired
7
4
MEdian [IQR] FMMA
53 [47, 57]
50 [39, 58]
p = 0.4
Median [IQR] ARAT
39 [28, 46]
31 [24, 40]
p = 0.3
Median [IQR] MAL
4.1 [2.4, 6.9]
3.7 [2.7, 4.9]
p = 0.7
p = 1.0
Abbreviations: SD Standard deviation, IQR Interquartile range, FMMA Fugl
Meyer Motor Assessment, ARAT Action Research Arm Test, MAL Motor
Activity Log.
No significant differences were found between cessation of training and the follow up measurement for any
of the primary outcome measures.
Secondary outcome measurements
An overview of the test results can be found in Table 2.
An overview of the individual improvement over time
(IIT) between the start of the training and the cessation
of the training for both quality of life measurements can
be found in Table 3.
Between group results
A significant difference was found between the control
and experimental group with regard to the delta physical
health scores of the SF-36 T2-T3 (p = 0.002). In the control group a deterioration in perceived physical health
scores was observed, while in the experimental group
physical health scores improved after cessation of the
training. No other between group differences were found
between the control and the experimental group with regard to quality of life, neither for EQ-5D results, nor for
SF-36 results.
Within-group results
In the control group, a trend for significant improvement in the EQ-5D health status (VAS) and a significant
Page 7 of 11
improvement in the SF-36 physical health component
was found between start and cessation of the training
(EQ-5D: p = 0.015; SF-36 physical health, p = 0.01).
For the control group, a significant decrease was shown
in physical health scores after cessation of the training
until 6 months follow up (p = 0.012).
No within-group differences were found between test
occasions for the different sub-items of the EQ-5D, nor
for the items of SF-36 relating to mental health.
Discussion
The aim of the present study was to investigate the effectiveness and added value of the use of the Haptic Master
robot adjunct to task-oriented arm hand training in
chronic stroke patients on the ICF (International Classification of functioning, disability and Health) function level,
the ICF activity level, and on the quality of life.
No differential effects could be demonstrated between
the control and experimental group on any of the primary outcome measures. The highly functional chronic
stroke patients in this study did not seem to benefit
from the addition of a Haptic Master robot to execute
task-oriented training with real life objects over training
with the same video instructed exercises and real life
objects alone. Hesse et al. have argued that stroke patients with lower functional levels may benefit more
from robot-supported training where actuator assistance
to movement may overcome problems, such as muscle
weakness [53]. In these patients, the robot assistance is
of great importance for obtaining the critical number of
repetitions that contribute to motor learning and to
improvement of arm hand performance, while higher
functional patients may obtain the critical number of repetitions for improvement without robot assistance. On the
other hand, an additional effect could be expected from
the haptic feedback in the robot supported training, as
feedback is known to be an important component for
motor learning [54]. Especially haptic feedback can bring
an additional and more immersive sensation than purely
visual environments, thereby increasing the person’s attention and motivation during repeated performance [55].
Although no differential effects were found, it should
be emphasized that in the current trial, both groups
improved with regard to activity based arm hand performance measures, indicating that the task-oriented
training method with real life objects that was used in
both groups resulted in the improvement of activity
based motor performance, rather than the robot support.
On Fugl Meyer no statistically significant improvements
were found in either group, and also individual improvement over time was smaller than 4% (Table 3). This
result can be explained by training specific effects of the
activity based training approach in both groups. Almost
all results on individual improvement over time (Table 3)
Control group
FMMA
ARAT
Experimental group
T0 MED [IQR]
T1 MED [IQR]
T2 MED [IQR]
T3 MED [IQR]
P values
T0 MED [IQR]
T1 MED [IQR]
T2 MED [IQR]
T3 MED [IQR]
53 [47, 57]
52 [44, 58]
54 [51, 59]
53 [50.7, 59.5]
NS
50 [39, 58]
54 [45, 59]
55 [46, 56]
52 [43, 59]
39 [28, 46]
43 [28, 49]
43 [32, 51]
50 [27, 54]
2.2 [1.5, 3.8]
2.8 [1.5, 4.6]
3.7 [2.5, 4.2]
3.8 [1.7, 4.3]
NS
31 [24, 40]
38 [24, 43]
34 [25, 41]
37 [25, 49]
P values
NS
**
††
T0-T2
MAL
AU
**
††T0-T1
2.2 [1.5, 2.8]
1.9 [1.4, 3.6]
2.2 [1.6, 4.2]
2.8 [2.2, 3.8]
1.5 [1.1, 2.1]
1.7 [0.9, 3.1]
1.6 [1.3, 3.4]
2 [1.6, 3.5]
3.7 [2.7, 4.9]
3.6 [3.6, 2.2, 7]
3.7 [3.6, 7.9]
5 [4, 7.4]
**
65 [63, 85]
-
80 [70, 80]
74 [70, 80]
NS
58.4 [38.9, 64.9]
-
58.4 [55, 70]
64 [58.4, 77]
NS
86.5 [81.6, 90.5]
-
86.7 [81.7, 91.3]
82.7 [76.3, 91.2]
NS
QU
1.8 [0.9, 3]
2.7 [1, 3.5]
3 [2.2, 3.5]
2.3 [1.3, 4]
**
†† T0-T2 ††T0-T1
TOT
4.1 [2.4, 6.9]
5.3 [2.5, 8.3]
6.1 [4.8, 7.8]
6.1 [3, 8.5]
**
††T0-T2 ††T0-T1
EQ-5D [VAS]
70 [64, 75]
-
78 [68, 90]
75 [60, 80]
Physical health
59 [519, 65.4]
-
71 [59.4, 92]
64 [52.8, 84.3]
Mental health
84.5 [74.8, 95.25]
-
87.6 [72.6, 98.7]
86.6 [75.2, 100]
NS
ns
**
††T0-T2
Timmermans et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:45
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Table 2 Results on primary outcome measures
SF-36
** ††T0-T2 ††
NS
T2-T3
Abbreviations: MED Median, IQR Interquartil range, FMMA Fugl Meyer Motor Assessment, ARAT Action Research Arm Test, MAL Motor Activity Log, AU Amount of use, QU Quality of use, TOT total, EQ-5D EuroQol-5D,
VAS Analogue Scale, IIT Individual improvement overtime between start and cessation of the training, relative to baseline values. ***Friedman p < 0.001, **Friedman p < 0.01, *Friedman p < 0.05, ††Wilcoxon
p < 0.0125, NS = non significant.
Page 8 of 11
Timmermans et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:45
http://www.jneuroengrehab.com/content/11/1/45
Table 3 Individual improvement over time for the primary
and secondary outcome measures
Control group
IIT T0-T2 MED [IQR]
Experimental group
IIT T0-T2 MED [IQR]
P-value
FMMA
3.5 [0, 8]
1.6 [−5.1, 19.1]
0.51
ARAT
16.1 [0, 35.8]
9.0 [3.8, 18.7]
0.79
MAL
AU
33.1 [9.6, 96]
9 [0.4, 51]
0.33
QU
46.5 [23, 78.2]
41.3 [14.28, 58.9]
0.4
TOT
44.9 [15.6,108.2]
24.3 [9.1, 55.2]
0.36
11.4 [0, 25]
7.8 [−4.9, 24]
0.5
Physical health
22.7 [2.1, 26.7]
13.4 [−5, 32.4]
1.0
Mental health
0 [0, 8.4]
0 [−1.9, 8.3]
0.6
EQ-5D
SF-36
Abbreviations: MED Median, IQR Interquartile Range, FMMA Fugl Meyer Motor
Assessment, ARAT Action Research Arm Test, MAL Motor Activity Log, AU
Amount of use, QU Quality of Use, TOT total, EQ-5D EuroQol-5D, IIT Individual
improvement over time between start and cessation of the training, relative to
baseline values.
of ARAT and MAL were above 10%. The results of this
study corroborate the findings of Lo et al. [16] and of
Kahn et al. [56] who found that in patients with long-term
upper limb deficits after stroke, robot-assisted therapy had
no additional effects over non-technology supported control interventions. But, as in our study, Lo et al. [16] and
Kahn et al. [56] did find evidence for gains in upper
extremity motor function after arm hand training in
chronic stroke patients.
In our study only the experimental group improved
statistically significantly on the ARAT, which is a capacity
test [57] on the ICF activity level. The ARAT is mostly
focusing on grasping and displacing several objects in a
confined workspace, whereby successful grasp and displace movements, as well as quality of movement performance with regard to the compensatory shoulder girdle
and arm movements during grasp, are evaluated. Patients
in the experimental group received haptic guidance as to
following the correct movement trajectory, thereby allowing them to focus on correct movement trajectories
during a grasp and displacement task. The statistically significant improvement on the ARAT in the experimental
group may be explained by Haptic Master guidance, leading to a) better ARAT results as patients in the Haptic
Master group were able to focus on correct trajectory
performance guided by extra proprioceptive feedback
from the Haptic Master; and b) a reduced within group
variability in treatment results due to more standardized
exercise performance, which is corroborated by the lower
IIT values on ARAT for the experimental group compared
with the control group (Table 3). The improvements on
the ARAT in the experimental condition alone, may therefore be attributable to training specific effects of the
Haptic Master.
Page 9 of 11
As for perceived arm hand performance (measured by
the MAL), both groups showed significant improvements after training, that were maintained until 6 months
after the training had stopped. Whereas the ARAT measures the highest level of performance in a test situation,
the MAL measures the functioning in daily life situations as perceived by the patient [58]. The perceived
individual improvement over time with regard to daily
life performance is lower for the experimental group
(see Table 3). This may be attributed to the fact that the
activity training in the control group was more similar
to performance of everyday life activities than the activity
training of the experimental group, leading to better
performance in everyday life and/or more confidence for
the performance of everyday life activities. In the control
group a significant improvement in health related quality
of life (SF-36) was found. This could be related to the increase of perceived performance (IIT MAL was higher in
the control group, see Table 3) in the everyday life situation. However, these gains could not be maintained at
6 months after cessation of the training.
Methodological considerations
Interactive systems that guide and support motor actions
depending on the needs of the patient, offer opportunities for motor learning through the prolonged active
involvement of the patient in a high number of exercise
repetitions, and through reducing effort and fatigue
[55,59]. It is indeed a strength of robotic systems to
allow for a high number of exercise repetitions, which is
known to contribute to more improvement of arm hand
performance after stroke [9]. Because it did take some time
(approximately 3 minutes for new recording, 1 minute
when old recording was used) to start up and adjust the
settings for each exercise in the Haptic TOAT software
environment, and because of the confined total exercise
duration (2 × 30 min/day), the current intervention may
not have made optimal use of the opportunity to reach a
high number of exercise repetitions. Given these potential
influences, software optimization and unlimited training
times could have resulted in higher training effects in the
same period of time.
The improvement on test results of the Motor Activity
Log was quite high in both groups. The Motor Activity
Log is a self-evaluation scale. Because of the effort participants in this study have invested into the training,
high expectations of the participants for the treatment
outcome may have influenced the results of these selfappraisal scales [60].
It could be argued that our study may have been
underpowered, which may have contributed to the lack
of significance between groups. Only 21% of the eligible
patients were willing to participate in this study. The most
important reason for not participating was the length and
Timmermans et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:45
http://www.jneuroengrehab.com/content/11/1/45
the intensity of the training period. This may have lead to
a selection bias towards recruitment of persons with a
high level of motivation to train, which may have influenced the results in both, the control and the experimental group.
The patients included in this study had a relatively high
functional level (see Table 1). It is not known whether the
results of this study can be generalized to patients with
more severe impairments.
Considerations for future research
It would be very interesting to repeat this trial for low
functional stroke patients, in order to assess and compare
the benefit of Haptic Master supported training across a
wider spectrum of stroke patients. It can be expected that
patients with a lower functional level may benefit more
from robot-assistance to movements that they cannot
perform actively. They may also benefit more from the
minimization of execution errors through haptic guidance
that has shown to be beneficial for motor learning [61].
Conclusion
Arm-hand performance improved in highly functional
chronic stroke patients, after eight weeks of task oriented
training, in both the robot- and in the non-technology
supported intervention. However, the use of a Haptic
Master robot in support of task-oriented arm training did
not seem to have an additional value over the videoinstructed task-oriented exercises.
Competing interests
The contributing authors guarantee that this manuscript has not been
submitted, nor published elsewhere. Each of the authors declares that
he/she does not have any financial or non-financial competing interests.
Authors’ contributions
All authors have proofread the manuscript and agree with the final
manuscript version. AT, HS, WB, and RG participated in the conception and
design of the study. MM, RL, AT participated in data collection. RS, HS, AT, RL
have been involved in data analysis and interpretation and also in drafting
and writing of the manuscript.
Acknowledgements
The patients, therapists and rehabilitation physicians of Adelante
Rehabilitation Centre (Hoensbroek, The Netherlands) are gratefully
acknowledged for their participation in the clinical trial.
Author details
1
Adelante, Centre of Expertise in Rehabilitation and Audiology, Hoensbroek,
The Netherlands. 2Research School CAPHRI, Department of Rehabilitation
Medicine, Maastricht University, Maastricht, The Netherlands. 3Adelante
Rehabilitation Centre, Hoensbroek, The Netherlands. 4Department of
Rehabilitation Medicine, Maastricht University Medical Centre, Maastricht, The
Netherlands. 5Reval - Rehabilitation Research Institute, Biomed – Biomedical
Research Institute, Faculty of Medicine and Life Sciences, Hasselt University,
Belgium.
Received: 20 September 2012 Accepted: 17 March 2014
Published: 31 March 2014
Page 10 of 11
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doi:10.1186/1743-0003-11-45
Cite this article as: Timmermans et al.: Effects of task-oriented robot
training on arm function, activity, and quality of life in chronic stroke
patients: a randomized controlled trial. Journal of NeuroEngineering and
Rehabilitation 2014 11:45.
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