Design strategies to improve patient motivation during robot-aided rehabilitation
Journal of NeuroEngineering and Rehabilitation
Design strategies to improve patient motivation during robot-aided rehabilitation
Roberto Colombo 2
Fabrizio Pisano 1
Alessandra Mazzone 2
Carmen Delconte 1
Silvestro Micera 0
M Chiara Carrozza 0
Paolo Dario 0
Giuseppe Minuco 2
0 ARTS Lab Scuola Superiore Sant'Anna V.le Piaggio 34 , 56025 Pontedera (PI) , Italy
1 Division of Neurology, Salvatore Maugeri Foundation , IRCCS Via Revislate 13, 28010 Veruno (NO) , Italy
2 Service of Bioengineering , Salvatore Maugeri Foundation, IRCCS Via Revislate 13, 28010 Veruno (NO) , Italy
Background: Motivation is an important factor in rehabilitation and frequently used as a determinant of rehabilitation outcome. Several factors can influence patient motivation and so improve exercise adherence. This paper presents the design of two robot devices for use in the rehabilitation of upper limb movements, that can motivate patients during the execution of the assigned motor tasks by enhancing the gaming aspects of rehabilitation. In addition, a regular review of the obtained performance can reinforce in patients' minds the importance of exercising and encourage them to continue, so improving their motivation and consequently adherence to the program. In view of this, we also developed an evaluation metric that could characterize the rate of improvement and quantify the changes in the obtained performance. Methods: Two groups (G1, n = 8 and G2, n = 12) of patients with chronic stroke were enrolled in a 3-week rehabilitation program including standard physical therapy (45 min. daily) plus treatment by means of robot devices (40 min., twice daily) respectively for wrist (G1) and elbow-shoulder movements (G2). Both groups were evaluated by means of standard clinical assessment scales and the new robot measured evaluation metric. Patients' motivation was assessed in 9/12 G2 patients by means of the Intrinsic Motivation Inventory (IMI) questionnaire. Results: Both groups reduced their motor deficit and showed a significant improvement in clinical scales and the robot measured parameters. The IMI assessed in G2 patients showed high scores for interest, usefulness and importance subscales and low values for tension and pain subscales. Conclusion: Thanks to the design features of the two robot devices the therapist could easily adapt training to the individual by selecting different difficulty levels of the motor task tailored to each patient's disability. The gaming aspects incorporated in the two rehabilitation robots helped maintain patients' interest high during execution of the assigned tasks by providing feedback on performance. The evaluation metric gave a precise measure of patients' performance and thus provides a tool to help therapists promote patient motivation and hence adherence to the training program.
Recent epidemiological data point to an increasing trend
in prevalence of stroke and this fact has prompted novel
treatment approaches based on robot-aided
neurorehabilitation. Many researchers using these new rehabilitation
tools have investigated upper limb rehabilitation effects
by means of detailed kinematic analyses before and after
treatment. In particular the MIT-Manus [1-3] and
MirrorImage Motion Enabler (MIME) robots [4,5], which were
developed for unrestricted unilateral or bilateral shoulder
and elbow movement, show that recovery can be
improved through additional therapy aided by robot
technology. The ARM guide , which assists reaching in a
straight-line trajectory, and the Bi-Manu-Track , which
enables active and passive bilateral forearm and wrist
movement, show also that use of simple devices makes
possible intensive training of chronic post stroke subjects
with positive results in terms of reduction in spasticity,
easier hand hygiene, and pain relief. The Gentle/s system
 is an appealing device that, by coupling models for
human arm movement with haptic interfaces and virtual
reality technology, can provide robot mediated motor
tasks in a three dimensional space. Finally, a robot device
based on recent studies of neuro-adaptive control, has
been used to generate custom training forces to "trick"
subjects into altering their target-directed reaching
movements to a prechosen movement as an after-effect of
adaptation . This system applies a form of "implicit
learning" for teaching motor skills, so demonstrating that
it is possible to learn at a quasi-subconscious level with
minimal attention and less motivation than more explicit
types of practice like pattern tracing.
Motivation is an important factor in rehabilitation and is
frequently used as a determinant of rehabilitation
outcome . In particular, active engagement towards a
treatment/training intervention is usually equated with
motivation, and passivity with lack of motivation.
Consequently, high adherence to a rehabilitation program is
seen as indicative of motivation . In addition to
personality and social factors the motivation and adherence
of patients to robot-aided treatments can be greatly
influenced by the design features of the biomedical robot. In
particular the difficulty level of the motor task, the
awareness of the performance obtained, and the quantity and
quality of feedbacks presented to the patient can influence
patient motivation and produce different ways of acting
and different performances. Environmental demands play
a critical role in the determination of how people execute
purposeful actions. Environmental features usually
influence the choice of motor strategies. These environmental
features are referred to as "regulatory conditions". Often
in rehabilitation therapy, patients are asked to perform
one or two movement patterns repetitively, the goal being
to improve motor performance. Persons with hemiplegia
need opportunities to practise skills in situations with
varying regulatory conditions so that they can develop motor
schemata that are versatile enough to meet the situations
they encounter in daily life . Therefore, robot-aided
rehabilitation, even if it involves practising only a few
articular movements with simple motor tasks, may be
considered a tool to help the therapist motivate patients
to do voluntary activity with the affected limb when the
practice of daily living activities (ADL) is hindered by
disability. Robot devices used in neurorehabilitation can
offer the patient various different types of feedback and
modes of interaction, so influencing the learning process
at different levels. It is worth noting that the possibility of
assessing patients' performance in a repeatable, objective
manner is of great advantage in stroke rehabilitation, and
in evaluating treatment effects.
The aim of this paper is to present two rehabilitation
robots and the design strategies we implemented in order
to boost patient motivation and improve adherence. In
addition, we outline a new evaluation metric for
quantifying the patient's rate of improvement and allowing a
regular review of the performance.
A one degree of freedom (DoF) wrist manipulator and a 2
DoF elbow-shoulder manipulator were designed for the
treatment of our patients (figure 1). Both include an
endeffector, normally consisting of a sensorized handle which
is grasped by the patient and moved through the
workspace of the device (i.e. the horizontal plane). A force/
torque transducer is located at the base of the handle near
the fixation point so as to provide an estimation of the
patient's exerted force/torque in the movement direction.
Both devices we developed are admittance control based;
this means that the robot detects the force exerted by the
patient on the handle and produces a movement in the
force direction with a speed proportional to the force
amplitude. Three possible control strategies were
1. completely servo-assisted movements;
2. shared control of the movements (i.e. the system helps
the subject to carry out the part of the task he/she is not
able to do autonomously);
3. completely voluntary movements.
The devices were applied in the upper limb rehabilitation
of two groups of patients with chronic stroke admitted to
our Institute for a rehabilitation program. Eight patients
(Group 1; aged 66 15 years) were treated using the wrist
rehabilitation device and 12 patients (Group 2; aged 55
aF)igOunree d1egree of freedom (DoF) robot device for wrist rehabilitation
a) One degree of freedom (DoF) robot device for wrist rehabilitation. b) Two DoF robot device for elbow-shoulder
13 years) with the shoulder-elbow device. A detailed
description of the systems can be found in [13,14].
Subjects in both groups were moderate to mildly
impaired: inclusion criteria were the presence of a single
unilateral cerebrovascular accident and the presence of at
least 10 of motion in the treated joints. Mild sensory and
visual field impairment and aphasia were not exclusion
criteria. Subjects needed to be able to follow the simple
instructions of the assigned motor tasks. Patients meeting
the inclusion criteria were seen by a professional
neurologist who evaluated the patient's neurological status and
determined if the patient was medically capable of
participating in the study.
The treatment consisted of four cycles of exercise lasting 5
min. each followed by a 3 min. resting period. Subjects
were trained twice a day, 5 days a week for three weeks. A
practice session preceded the treatment, during which
detailed instructions were given to shorten the exercise
learning phase. The robot session was fully supervised by
the therapist only during the learning phase. Following
this, supervision was limited to the patient's connection
and disconnection the device and during changes in the
difficulty level of the motor task. Patients were seated at
the robot desk with their trunk fastened to the back of the
chair by a special jacket in order to limit compensation
phenomena. A video screen in front of them provided
visual feedback in the form of three coloured circles as
follows: a) a yellow circle indicated the task's starting
position; b) a red circle, the task's target position; c) a
green circle, the current position of the handle. The path
to follow was a circular arc for the wrist device and a
square or a more complex path for the shoulder-elbow
device. If, during execution, the patient could not
complete the task autonomously, the robot evaluated the
current position and, after a resting period of three seconds
in the same place, guided the patient's arm to the target
position. During the treatment the device provided visual
and auditory feedback to the patient to signal the start, the
resting phase and the end conditions of the exercise.
Patient cooperation and satisfaction with a training
program is essential to achieve successful rehabilitation
results . In spite of this, little research has been carried
out on motivation in patients with stroke . Several
factors can influence patients' motivation and so improve
exercise adherence . These include features inherent in
the prescribed regimen as well as characteristics related to
the patient, physician and therapist . In particular, the
major contributors to exercise adherence include
simplicity and short duration of treatment [18,19]. Patients who
believe that health depends on their own behaviour
appear to be more motivated and compliant that those
who think that they can do little by themselves to improve
their condition and rely on fate, the institution, physician
or therapist . Health care providers can usually greatly
influence the patient's intrinsic motivation and make
exercising more effective . In fact, the patient's
perception of therapy, in terms of its relevance to daily needs, the
perceived potential to reduce disability and improve
quality of life play a role in motivation. Consequently
adherence to training is more likely when the therapist gives
clear instructions and when the patient understands the
rationale and benefits of the prescribed regimen .
The introduction of new technologies such as robot
devices and virtual reality devices, that partly reduce the
patient-therapist interaction, could negatively influence
the patient's motivation and hence the crucial questions
that arise are: how are these technologies accepted by the
patient, and what design and treatment features can
positively influence patient motivation? First of all, the initial
exercise load should be minimized in order to reduce the
start-up effort and decrease the amount of time required
for exercise learning. For this reason we developed a
special front-end robot interface, thanks to which the
therapist could easily select different sequences of targets in the
robot workspace so as to propose exercises of a difficulty
level tailored to the patient's disability. In addition the
front-end interface made it possible to demonstrate the
exercise, test the movement range, verify safety of the
required movement and adjust robot stiffness. During the
learning phase, patients were instructed to make sure they
understood how and why the robot-aided exercise needed
to be done, and what benefits were expected overall in
terms of improvement in daily life activities. No
restrictions were placed on the movement in the robot
workspace, so that patients could guide the robot handle
anywhere their spared function allowed. If the patient
could not complete the task the robot assisted in reaching
The robot devices were developed to offer the patient
various different types of feedback and modes of interaction,
so influencing the learning process at different levels. In
fact, in addition to feedback about the position of the
handle (green circle), two scores were displayed on the
video screen facing the patient during task execution: the
first was the score obtained during a single task, the
second the score for each 5 min. cycle of exercise. Scores
increased only during the patient's voluntary activity,
reflecting the proportion of the path travelled by the
handle (expressed as a tenth of the total distance between the
starting point and the target). They remained unchanged
during robot assisted movements. Scores may be very
useful in maintaining the patient's motivation high
throughout the session, simulating a video-game experience (a
higher score indicates a better performance). They are also
useful for a quantitative evaluation of the patient's
performance. A regular review of performance results also
reinforces in patients' minds the importance of exercising
and encourages them to continue, so improving their
motivation and, hence, adherence to the program. For this
reason we developed an evaluation metric that could
characterize the rate of improvement and quantify the
changes in the obtained performance.
The Intrinsic Motivation Inventory (IMI) is a
multidimensional measurement method designed to assess
participants' subjective experience related to a target activity in
laboratory experiments [22-24]. It consists of a multi-item
questionnaire assessing the subject's interest/enjoyment,
perceived competence, effort, value/usefulness, felt
pressure and tension, and perceived choice while performing
a given activity. The interest/enjoyment subscale is
considered a self-report measure of intrinsic motivation. The
perceived choice and competence concepts are regarded as a
positive predictor of intrinsic motivation. The pressure/
tension is theorized to be a negative predictor of intrinsic
motivation. Past research suggests that order effects of
item presentation appear to be negligible. Furthermore,
the inclusion or exclusion of specific subscales appears to
have no impact on the others . Another important
issue of the IMI is that of item redundancy. In fact, items
within the same subscale overlap considerably, although
randomizing their presentation makes this not relevant to
most patients . The full version of the questionnaire
includes 45 items and 7 subscales; shorter versions have
been used and found to be apparently reliable [26,27].
McAuley et al. assessed the psychometric properties of an
18-item version of the IMI in a competitive sport setting,
and found it adequately reliable .
In order to evaluate the intrinsic motivation of our
patients, we administered a 17-item version to our
patients at the end of robot-aided training. Fifteen items
assessed the interest/enjoyment, perceived competence,
effort/importance, pressure/tension and value/usefulness
subscales; each subscale consisted of three items. In
addition two items were included to assess if patients
experienced pain during treatment with the devices. Each item
rated the statement in a range between 1 (not at all true)
and 7 (very true). In accordance with the
recommendations by the authors of self-determination theory , we
randomly distributed the IMI items in the questionnaire
and formulated them to fit the specific activity of robotic
rehabilitation. The items were translated into Italian by a
professional translator. To our knowledge, the IMI has
never been used to measure motivation in patients after
stroke. For this reason we carried out a preliminary
principal components factor analysis on a sample of subjects
with chronic stroke to explore the validity of the 15-item
motivation questionnaire in this patient category. Four
independent components resulted from the analysis. As
mentioned, two additional items explored the presence/
absence of pain. The pain subscale was obtained by
averaging the scores of the two items. Thus six dependent
variables were obtained from the 17 items. Details about the
validation of the IMI questionnaire in patients after stroke
will be the object of publication elsewhere.
No baseline phase was carried out prior to the study with
the robot. A standard assessment procedure was used at
the start and end of treatment for both groups. This
procedure included the upper limb subsection of the
FuglMeyer scale modified by Lindmak (range: 0115) [28,29]
and the Motor Power Score (range: 020) [30,2] that
measures strength in proximal muscles of the arm,
specifically grading shoulder flexors and abductors and elbow
flexors and extensors on a standard 05 point scale.
In addition we devised a new evaluation metric based on
parameters measured by the robot devices, of use both for
motor deficit evaluation and monitoring of patient
performance during treatment.
Robot score: the line between the starting point and the
target (theoretical path) of a single reaching movement
was divided into ten segments (scoring segments). For
each point of the actual reaching path, the intersection
between the theoretical path and its perpendicular line
passing through that point was found. The score increased
when (with movement executed by voluntary activity) the
point fell in a new scoring segment. If the patient was
unable to complete the motor task the robot would guide the
patient's limb to the target and the score remained
unchanged. When the difficulty level of the task was
changed by extending the range of reaching, the 10
scoring segments were altered accordingly. The single task
score was obtained by summing the scores obtained in
each point to point reaching movement of the task (e.g.
four reaching movements in the case of a square). The
cycle score was obtained by summing the scores obtained
in the tasks executed during each cycle of exercise lasting
5 min. Finally, the Robot score was obtained by averaging
the four cycle scores obtained in the training session.
Performance Index: in the case where a patient obtained a
maximum score, the motor task was changed extending
the range of movement required. The time course of the
patient's performance was then obtained simply as the
product of the Robot score and difficulty level of the task.
Active movement index: in order to quantify the patient's
ability in executing the assigned motor task without robot
assistance, we introduced the Active Movement Index
(AMI) based on the following formula:
AMI = RS/TS * 100
where RS is the Robot score obtained by the patient
during the task by active movement, and TS is the theoretical
score if the patient completed all tasks by means of
Mean Velocity: with both devices it was possible to record
the current position of the handle. In this way the mean
velocity of the handle during the task could be computed.
Several papers have shown that the movement during a
motor task is the combination of a sequence of
sub-movements with a bell-shaped velocity profile . In addition
it has been demonstrated that such components are
clearly distinct at the beginning of treatment (jerky
movements) so resulting in a low mean velocity value, and tend
to merge in the course of treatment so producing a
smoother movement [1,32]. As a consequence, the mean
velocity produced during movement at the end of
treatment has a value higher than that at the beginning of
treatment. Mean velocity can thus be considered as a
measure of smoothness. However two different
smoothness 'scenarios' could theoretically have the same mean
velocity: i.e. a subject moving slowly without a lot of
variation in the speed profile might attain the same mean
velocity as one who starts and stops frequently; but the
resulting smoothness values should be quite different. For
this reason, given the many-faceted aspects represented by
the mean velocity, we decided to consider this metric as a
distinct component of motor performance evaluation.
This parameter in combination with the session score is
very useful for deciding when a change in level of
difficulty of the motor task is required. In fact, if during the
course of training the patient was able to complete the
task with a score close to the maximum (AMI >90%) and
a mean velocity close to 50% maximum velocity of the
exercise, the therapist increased the difficulty level of the
task, extending the path to be covered and/or changing
the reaching point sequence.
Movement accuracy: the accuracy of the movement was
assessed by the following formula:
MD = di / n ( 2 )
where MD (Mean Distance) represents the mean absolute
value of the distance (di) of each point of the path from
the theoretic path. When this parameter approximates
zero movement accuracy will be very high.
Normalized path length: the movement's path length was
calculated with the following formula:
nPL = dPi / PLt ( 3 )
where dPi is the distance between two points of the
patient's path and PLt is the theoretical path length, i.e.
the distance between the starting point and the target. This
parameter is a measure of the efficiency of the movement.
Clinical scales results
The robot-assisted therapy was well accepted and
tolerated by all patients. Group 1 showed a significant
improvement (p < .05) in the Fugl-Meyer scale modified
by Lindmark. Because the Motor Power Score evaluated
only proximal muscles no changes were found in this
group of patients.
Evaluation metric results
Figure 2 shows a typical example in one patient of the
parameters employed for motor performance evaluation
in the application of the wrist robot device.
Panel a) illustrates the Robot score parameter; panel b)
illustrates the performance index obtained by multiplying
the robot score by the difficulty level of the exercise; panel
c) illustrates the active movement index measuring the
mean percentage of the patient's voluntary activity exerted
during a training session.
The AMI parameter shows that at the beginning of
treatment the patient was able to complete only 20% of the
motor task without robot assistance. The score
subsequently increased to reach a maximum half-way through
treatment. At this point the therapist decided to increase
the difficulty level of the task. The score temporarily
declined because the patient once again needed assistance
from the robot device. Then voluntary activity gradually
increased again. After 40 training sessions the patient was
able to complete 90% of the motor task through voluntary
activity. The area under the plot in panel c) represents the
patient's activity during training, the area above the plot,
the robot's activity.
Figure 3 reports an example of the parameters obtained in
a chronic post-stroke patient treated with the
elbowshoulder device. It can be seen that the active movement
index increased up to half-way through treatment at
which point the patient was able to complete the motor
task. The mean speed was constantly increasing,
indicating a continuous improvement of the patient's
performMotor Power Score (020)
Motor Power Score (020)
ance throughout the treatment. The mean distance and
normalized path length decreased, thus showing an
improvement in both accuracy and efficiency of
The figures presented cover a wide spectrum of trends
encountered with patients involved in this study.
Table 1 summarises the mean values standard
deviations of PRE and POST treatment clinical variables and
robot measured parameters, their changes and the p value
of the PRE vs. POST comparison. In Group 1, the robot
score and performance index improved significantly. The
AMI parameter showed a non significant increase
probably due to the small number of subjects. In Group 2, all
robot measured parameters and clinical scale values
showed a statistically significant change. In particular, the
Robot Score, Performance Index, AMI, and Mean Velocity
increased after treatment; Mean Distance and Normalized
Path Length decreased after treatment, so indicating an
improvement in, respectively, accuracy and efficiency of
In addition single subject analysis was carried out for the
AMI parameter and Mean Velocity of the patients treated
with the shoulder-elbow device. Considering that our
patients executed many reaching sequences during a
training session (on average between 10 and 20 depending on
the session number, level of disability, type of task, etc.),
we were able to compare data obtained at the third and at
the last training session for each subject using Student's
ttest for repeated measures. This allowed a single subject
evaluation of the change obtained in the measured
parameters. Figure 4 reports the mean values obtained by
each patient at the third training session (hatched area)
and the change at the end of treatment (dotted area =
significant change, white area = non significant change).
After robot treatment all Group 2 patients showed a
significant increase in the AMI and all patients but one (#6)
a significant increase in mean velocity.
These results confirm the improvement of performance
obtained by our chronic stroke patients after robot-aided
Intrinsic Motivation Inventory results
Due to the fact that it had been just recently introduced to
our institution, the IMI questionnaire was administered
only to a subgroup of Group 2 patients; therefore this
study should be considered as preliminary to a more
extensive clinical study.
Table 2 reports the mean values and standard deviations
of five pre-selected subscales of the IMI questionnaire and
pain subscale, evaluated in 9 of the 12 patients treated
with the elbow-shoulder rehabilitation device. The
interest/enjoyment subscale, i.e. a self-report measure of
intrinsic motivation, obtained a high score and a low
standard deviation. This suggest that our patients found
the robot therapy very interesting.
The perceived competence subscale resulted in a mid
score (subscale value = 4.6). This result is not surprising
because of the different levels of disability of our patients.
In fact, less compromised patients should obtain a better
performance, and therefore consider themselves more
competent in executing the exercise than more
TFimguerceo2urse of the robot measured parameters in a representative patient treated by the wrist rehabilitation device
Time course of the robot measured parameters in a representative patient treated by the wrist rehabilitation device.
Also the effort/importance and value/usefulness subscales
obtained a high score and very low standard deviation so
indicating that patients were highly motivated in the
execution of this type of treatment, and were satisfied with
the results obtained. In particular they perceived that the
learning phenomenon obtained by repeating a movement
could produce positive results in improving their
disability. The pressure/tension and pain subscales obtained a
low score with high standard deviation. This means that
the majority of patients did not experience tension or pain
during training with the robot device.
Only one patient felt tense during the execution of
exercises (she was also under treatment for depression). Two
patients showed discrepancy in the response to the pain
items. This made us suspect that the formulation of the
negative sentence may have been a little confusing so
producing an unreliable response.
Table 3 presents the correlation analysis between the
parameters included in the evaluation metric and the
motivation subscales of the IMI questionnaire. Most of
the robot measured parameters included in the
correlation analysis showed a weak or no correlation with the
interest/enjoyment, perceived competence,
effort/importance, value/usefulness and pressure/tension subscales.
This result is in agreement with other reports in the
literature showing a quite modest correlation between self
reported motivation variables and behavioural indices
. One might expect that an increase of performance
corresponding to an increase of the relative parameter
should be reflected by an improvement of motivation and
TFimguerceo3urse of the robot measured parameters in a representative patient treated by the elbow-shoulder rehabilitation device
Time course of the robot measured parameters in a representative patient treated by the elbow-shoulder rehabilitation device.
hence show a positive correlation. Conversely a negative
indicator of motivation, such as pressure/tension
subscale, should be negatively correlated with increasing
parameters. Equivalent reasoning but with an inverse
correlation should be valid where a decrease of parameter
corresponded to improvement of performance. These
considerations are verified in table 3 only for correlation
values greater than or equal to 0.4, i.e. for moderate
correlation between variables . Mean velocity was the only
parameter showing a moderate correlation both with the
effort/importance and pressure/tension subscales.
The two robots presented fulfill the requirements of our
occupational therapists who need, when administering
robot-aided therapies, to know which motor tasks are
most appropriate for each patient and what difficulty level
of the task is suitable for the patient's residual capacity.
The user interface of the devices we developed allows easy
configuration and adaptation of the tasks. In addition the
feedback scores provided to the patient simulating a
video-game experience may be very useful for
maintaining the patient's interest high throughout the training
session, improving motivation and resulting in a better
Patient motivation can be modified by a number of
processes, such as increasing problem awareness and
information in patients, involving them in the design and
implementation of the treatment program, enhancing
their level of internal control and raising their hope of
recovery. Motivation programs are designed with specific
interventions targeted to modify these factors. We think
that our robot devices and the evaluation metric presented
here can provide a further up-to-date tool to help
therapists promote patient motivation. Of course the visual
SeFitineggrulseresu4bject analysis for the AMI and Mean Velocity
paramSingle subject analysis for the AMI and Mean Velocity
parameters. Each bar reports the mean value obtained by the
patient at the 3rd training session (hatched area) and the
change obtained at the end of treatment (dotted area =
significant change, white area = non significant change).
feedback interface we adopted is very simple; nevertheless
the results of the interest/enjoyment scale for the exercises
proposed are reassuring. And it should be stated that the
easier the gaming interface, the better understood it is by
the patient . On the other hand motivation usually is
not a constant factor but a dynamic process; thus the
willingness of a patient to adhere to a prescribed treatment
may change over time in relationship to many factors, in
particular, the efficacy of the rehabilitation strategies
Group 2 (n = 9 out of 12)
Table 2: Subscale findings of the Intrinsic Motivation Inventory questionnaire evaluated in patients treated with the elbow-shoulder
rehabilitation device (subscale range = 1 7)
Training with robot devices constitutes a different form of
exposure to enriched environments in that the motor
tasks used are specific rather than general. Several reports
in the literature have shown that robot devices may
contribute to improving and accelerating the various stages of
recovery [1-7,36]. In particular the learning process
obtained by movement repetition is not a unitary
phenomenon but can affect many different components of
sensory and motor processing. In normal subjects, the
repetition of a task usually improves motor performance
in terms of accuracy and speed of movement. In
neurological rehabilitation the assessment of motor recovery
should also include the smoothness, efficacy and
efficiency of the movement. Thanks to the quantitative
evaluation metric we developed, the process of post-stroke
motor recovery may be precisely characterized and
quantified in terms of rate of improvement of the patient's
voluntary activity. Moreover, on the basis of the motor
learning model, we can speculate that the mechanisms
underlying this recovery process and resulting in a
voluntary activity increase are likely related to robot induced
improvement in accuracy, velocity, strength and range of
motion of the paretic upper limb. The evaluation metric
presented here makes it possible to precisely plan and,
where necessary, modify the rehabilitation strategies so as
to improve patient adherence to the assigned motor task
and, as a consequence, improve the motor outcome.
Finally, the adherence of our patients to the exercise
program using robot-aided neurorehabilitation could not be
directly measured in this study. In fact, all subjects
included in the study were hospitalized for the robot
treatment period; thus, quantification of missed sessions or
treatment duration, usually considered a measure of
adherence to prescribed home exercise, was not relevant
here. In fact, all patients received the same prescribed
regimen until discharge and the duration of each training
session was established by the device. The fact that robot
therapy was well accepted and tolerated by all patients,
that the robot-measured parameters showed a statistically
significant change, and that the intrinsic motivation scales
showed high scores leads us nevertheless to presume that
also patient's adherence was very high (confirming this is
the fact that there were no drop-outs). In future studies, a
Score (Mean S.D.)
fixed session duration could be suggested by the therapist
at the start of training, but leaving it up to the patient to
decide when to stop therapy. The difference between
suggested and actual durations of each treatment session
could then be considered as a measure of adherence.
A limitation of this study is that no control group was
included. Its inclusion would have allowed comparison of
the robot therapies and subsequent subject motivation
levels with other interventions, thus identifying any
intrinsic motivator as a function of a different extrinsic
sented here permitted the therapist to easily adapt training
to each subject by selecting motor tasks tailored to his/her
disability. The scoring of performance incorporated in the
two rehabilitation robots, and provided to the patient by
visual feedback, allowed us to maintain patients' interest
high during the training. Furthermore, the evaluation
metric proposed allows a precise measure of the patient's
performance so providing the therapist with a tool for
implementing reinforcement techniques (such as giving
positive feedback and commending patients for their
efforts) that can promote patient motivation and enhance
adherence to the training program.
RC conceived the study, participated in its design and
drafted the manuscript. FP participated in the design of
the study, in patient selection and evaluation, and helped
to draft the manuscript. CD and AM
contributions to acquisition, analysis and interpretation
of data. SM, MCC and PD participated in the design of the
robot devices and in the revision of the draft. GM
participated in the design and coordination of the study and
helped to draft the manuscript.
This work was partly funded by the project "Tecniche robotizzate per la
valutazione ed il trattamento riabilitativo delle disabilit motorie dell'arto
superiore", 2001-175, funded by the Italian Ministry of Health.
The sign next to each parameter indicates that improvement in performance is reflected by an increase (+) or decrease (-) in the parameter.
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