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CONTENTS
Chapter 1 Introduction 2
Chapter 2 Wheelchair propulsion technique and mechanical efficiency
after 3 weeks of practice.
(Med Sci Sports Exerc. 34(5): 756-766, 2002)
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Chapter 3 Adaptations in physiology and propulsion techniques during
the initial phase of learning manual wheelchair propulsion.
(Am J of Phys Med. 82(7): 504-510, 2003)
33
Chapter 4 Short-term adaptations in co-ordination during the initial
phase of learning manual wheelchair propulsion.
(J Electromyogr Kinesiol. 13(3): 217-228, 2003)
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Chapter 5 Consequence of feedback-based learning of an effective
force production on mechanical efficiency.
(Clin Biomech. 17(3): 219-226, 2002)
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Chapter 6 Effect of stroke pattern on mechanical efficiency and
propulsion technique in hand rim wheelchair propulsion.
(Med Sci Sports Exerc. Submitted)
83
Chapter 7 Influence of task complexity on the mechanical efficiency
and propulsion technique during learning hand rim
wheelchair propulsion.
(Am J of Phys Med. Submitted)
101
Chapter 8 Epilogue 119
References 130
Summary 141
Samenvatting 147
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Chapter 1
Introduction
Chapter 1
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LEARNING HAND RIM WHEELCHAIR PROPULSION
Although motor learning is implicitly present in daily life, not much is known
about the learning process of gross motor skills. Motor learning has been defined
as “a set of internal processes associated with practice or experience leading to
relatively permanent changes in the capability for skilled behavior” (Schmidt et al.
1999). There are several theories regarding motor skill learning, however, most of
them focus on the way a new task should be presented to the novice subject i.e. in
terms of knowledge of results (information feedback of goal achievement) (Broker
et al. 1993; Salmoni et al. 1984; Schmidt et al. 1991), the learner‟s focus of attention
(internal or external) (Wulf et al. 2001), self-control and practice in dyads
(McNevin et al. 2000).
Furthermore, most motor learning studies have focused on simple motor tasks,
like for example single-joint movements such as elbow flexion/extension (Corcos
et al. 1993; Flament et al. 1999), or have been associated with the development of
motor tasks in children (Ledebt et al. 2000). However, little is known about the
biophysical aspects of gross motor skill learning in the adult.
For studying the biophysical aspects of learning a gross motor task, an activity
should be chosen that is novel for a large group of adults and is relevant to learn.
One interesting area of learning different and new modalities of gross motor skills,
emerges in those persons who become wheelchair dependent. In the context of
rehabilitation, motor skill acquisition is a crucial – but often very implicit -
ingredient in the restoration of motor function and of recovery of mobility.
Therefore, understanding gross motor skill learning is important for an effective
and successful rehabilitation process (Gonzalez et al. 2001). Individuals who - due
to circumstances - are forced to use a wheelchair have to learn this completely new
motor task and many wheelchair-related functional daily activities in adult life.
Learning wheelchair propulsion is important because it enables individuals,
especially those with a lower limb disability, to be as active as the general
population and to maintain employment, to achieve independence in daily life
activities and to pursue recreational activities and social life. Because motor
programs already exist in the adult patient, the major issues in (re)learning a motor
skill include accessing, reorganizing, and utilizing this information.
Despite the fact that the movement pattern of wheelchair propulsion is quite
different to what persons were used to, every individual seems to be able to pick
up this novel task rather quickly. It is quite fascinating how persons are able to do
this because wheelchair propulsion is not an easy task since, among other aspects,
the hands have to couple to a rotating thin rim whereas the motion of the hands
occur predominantly outside the visual field and force production can only
Chapter 1
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effectively take place in 20-50% of the cycle time (Rodgers et al. 2000). Not many
learning studies (Amazeen et al. 2001) have been done with respect to wheelchair
propulsion.
Wheelchair propulsion is a way of locomotion with an overall low gross
mechanical efficiency: it rarely exceeds 10%, meaning that 90% of the internally
produced energy is lost to other processes than propelling the wheelchair (Astrand
et al. 1986). The resultant 10% will be used to overcome, for example, rolling
resistance, internal resistance of the wheelchair system, and air resistance. The
efficiency of wheelchair propulsion is much lower when compared to cycling
(Coyle et al. 1992) but also to other forms of arm exercise such as arm cranking
(Martel et al. 1991; Powers et al. 1984). As a consequence of the low efficiency,
hand rim wheelchair propulsion is associated with a high physical strain in daily life
(Janssen et al. 1994) and leads most likely to a high mechanical load on the upper
extremity (Veeger et al. 2002). That the mechanical load is high or that there is too
much repetitive loading might be shown by the prevalence of wrist and shoulder
pain after long-term wheelchair use, which has been reported to be as high as 73%
in individuals with a spinal cord injury, who rely on manual wheelchairs for
mobility (Subbarao et al. 1995).
LEARNING AND METABOLISM
Improvements in performance result from practice and are a frequently used
measure of learning. However, not every change that occurs as a result of practice
has to imply improvement, therefore, a measure concerning „improvement in
performance‟ should be defined. A general accepted assumption is that subjects
pursue to perform a task with minimal metabolic cost. In the early eighties
Sparrow (Sparrow 1983) linked this assumption to learning since he proposed that
metabolic cost might be a fundamental principle underlying the learning and
control of motor skills. According to his theory organisms select the coordination
and control function that cost the least metabolic energy, and with practice the
selected control parameters are refined to attain the task goal with even less
metabolic energy (Sparrow et al. 1998). Therefore, the present thesis will use gross
mechanical efficiency, and its relationship with technique variables, as central
indicator for improved performance.
To date, there have been very few studies focusing on the relationship between
changes in mechanical efficiency and changes in coordination as a consequence of
practice. In repetitive gross motor tasks, such as crawling (Sparrow et al. 1987) and
ergometer rowing (Sparrow et al. 1999), it was suggested that movements tend to
increase in amplitude and decrease in frequency with practice and that these
Chapter 1
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adaptations led to a higher (mechanical) efficiency. However, the results of the
crawling and rowing studies were not significant, possibly due to the small group
of subjects in these studies. In a more recent study (Lay et al. 2002) the same
research group found a significant increase in economy (in Watts.ml-1) after ten 16-
min. ergometer rowing sessions. According to the authors practice reduced the
metabolic energy cost of performance and practice-related refinements (e.g.
decrease in stroke rate and less variability of peak forces) were associated with
significant reductions in muscle activation (Lay et al. 2002). Almasbakk et al. (2001)
studied the learning process of cyclical, slalom-like, ski movements on a ski
simulator. They found that the change in the coordination pattern was in
congruence with an improvement in gross mechanical efficiency, indicating an
effect of improved technique on the mechanical efficiency.
Since the term mechanical efficiency is quite important in the present thesis, this
term should be clearly defined. Many definitions of mechanical efficiency have
been used in the literature. For an overview of the different concepts of efficiency
of human movement, the reader is referred to Cavanagh and Kram (1985a; 1985b)
or Van Ingen Schenau and Cavanagh (1990). Gross mechanical efficiency (ME) is
the ratio of external power output (Po) over metabolic power (Pmet) (i.e.
100% Pmet Po ME .-1. ). The power output can be calculated exactly when using a
wheelchair ergometer and knowing the torque applied around the wheel axles and
the velocity of the wheels. Metabolic power is derived from food stores, mainly fat
and carbohydrates that is converted into another form of chemical energy, which
in turn is converted into mechanical energy through muscle contractions. In
utilizing food as chemical energy to contract the muscles, oxygen is consumed. The
amount of oxygen consumed during submaximal, steady state exercise can be used
as an indirect method for calculating metabolic power on basis of the type of
foodstuffs being utilized.
Similar to machines, the useful power output will always be less than the metabolic
power due to energy losses in the process. Usually, the performance of activities
that involve large muscles, such as cycling (Coyle et al. 1992), results in a gross
mechanical efficiency of 20-25%. The low gross mechanical efficiency of
wheelchair propulsion may be explained by the small muscle mass involved
compared to leg exercise, the complex functional anatomy of the upper extremity
and shoulder, which requires additional muscle effort to stabilize redundant
degrees of freedom, and the discontinuous movement which needs (de)coupling of
the hands to the rim (Boninger et al. 1997; Woude et al. 2001). Furthermore, gross
mechanical efficiency not only includes the metabolic power consumed to generate
the amount of external mechanical power output but also the metabolic power
Chapter 1
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needed for other processes such as ventilation and trunk stabilization (Stainbsy et
al. 1980). When external mechanical power output increases, e.g. from arm to leg
exercise, the relative contribution of the internal metabolic power (Pint) to the total
metabolic power (Pint plus power output needed to perform the task (Ptask)) will
diminish as it becomes proportionally less, leading to higher gross mechanical
efficiencies with increments in power output ( 100% Ptask)(Pint Po ME .-1. )
(Hintzy et al. 2002; Powers et al. 1984). There are, of course, individual differences
that are influenced by body size, fitness level, and talent in performing a given task.
In theory, the energy cost of hand rim wheelchair propulsion could be influenced
in three distinct components of the wheelchair-user combination: 1) By changing
the mechanical characteristics of the wheelchair itself, e.g. the weight of the chair
(Beekman et al. 1999), since it costs less energy to propel a light wheelchair
compared to a standard wheelchair at the same velocity; 2) By changing the
geometry and fine-tuning of the wheelchair-user interface, e.g. the seat orientation
(Richter 2001; Woude et al. 1989a; Hughes et al. 1992; Masse et al. 1992), camber
(Veeger et al. 1989b), hand rim tube diameter (Linden et al. 1996) and hand rim
shape (Woude et al. In press), the movement of the upper extremity could be
physiologically more optimal e.g. in terms of muscle contractions; 3) And by
training the user him/herself since the mechanical efficiency could increase due to
physiological adaptations, which take place to satisfy the increased demand of the
cardiorespiratory system, and an improvement in propulsion technique.
At this stage, it is important to separate the concepts of training and learning. With
training both physiological adaptations and changes in the propulsion technique or
coordination occur when the intensity, frequency and duration of exercise are
equal to or higher than generally accepted training guidelines, such as those that are
recommended by the American College of Sports Medicine (ACSM 1990).
However, with learning only changes in propulsion technique are meant, without
the simultaneous occurrence of physiological adaptations over time. Thus learning
is implicit to training but not the other way around. Therefore, to study the effect
of learning on the mechanical efficiency only, possible physiological adaptations
should be minimized by using an exercise protocol that is well below the ACSM
guidelines in terms of intensity, frequency and duration of exercise.
PROPULSION TECHNIQUE AND EFFICIENCY
Wheelchair propulsion technique is a very general term and can be split into more
specific terms. When using the term „propulsion technique‟ in the present thesis,
the term comprises timing variables (e.g. cycle frequency, push duration and cycle
time), force application (e.g. the effectiveness of force direction), and inter-cycle
Chapter 1
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variability (i.e. how similar the subsequent pushes are). Experience seems to
influence both energy cost and technique in wheelchair propulsion, as can be
derived from cross-sectional wheelchair studies (Knowlton et al. 1981; Brown et al.
1990; Patterson et al. 1997; Tahamont et al. 1986). Several studies investigated the
difference in, among other variables, efficiency between non-experienced able-
bodied subjects and experienced wheelchair-dependent subjects. Although the
results of these studies are inherently limited due to the cross-sectional design and
different protocols, results suggest that experienced wheelchair users had a
significantly higher efficiency compared to able-bodied subjects (Brown et al. 1990;
Knowlton et al. 1981; Patterson et al. 1997; Tahamont et al. 1986). The question,
which arose, is whether this difference in mechanical efficiency can be explained by
physiological adaptations only or also by an improvement in propulsion technique
or motor control.
That timing variables have an effect upon the efficiency or economy (the rate of
submaximal oxygen uptake for a particular activity and at e.g. a certain speed) has
been shown before by Woude et al. (1989b) and Goosey et al. (2000) with respect
to the cycle frequency. These studies both found that the freely chosen cycle
frequency was most optimal with respect to the mechanical efficiency or economy
and that any other higher or lower cycle frequency showed a lower mechanical
efficiency or economy. Patterson & Draper (1997) found differences in propulsion
time, push angle and work per stroke, with experienced subjects showing higher
values compared to novice able-bodied subjects. These results were more clearly
expressed at higher velocity levels.
Studying the effectiveness of force application during wheelchair propulsion has
been a topic for many years (Veeger 1992; Rozendaal et al. 2000; Dallmeijer et al.
1998; Boninger et al. 1997). The non-tangentially directed propulsion force is
theoretically far less than optimal, and was first assumed to be at least partially
responsible for the low mechanical efficiency (Veeger 1992). However, a model
study showed that an effective force application was accompanied by an increase in
shoulder muscles activity (Veeger 1999). Furthermore, a recent simulation study
concluded that experienced wheelchair users seem to optimize the force pattern by
balancing mechanical effect and musculoskeletal cost of the pushing action
(Rozendaal et al. 2000). Whether completely inexperienced wheelchair users are
able to learn a more effective force application and what the effect on the
mechanical efficiency would be, are yet unclear.
Brown et al. (1990) found a difference in mechanical efficiency between
inexperienced and experienced wheelchair users. Furthermore, wheelchair-
dependent subjects had significantly greater shoulder extension at the point of
Chapter 1
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initial wheel contact as measured by the shoulder angle, while the able-bodied
subjects had significantly greater shoulder range of motion at all work rates in
comparison to wheelchair-dependent subjects. Veeger et al. (1992a) studied the
difference between trained and untrained subjects during a sprint test and only
found differences in kinematics parameters: the able-bodied group extended their
push further, leaned significantly more forward, and started the push with their
arms in a more retroflexed position.
The inter-cycle variability has not been often used in previous wheelchair-related
research. However, it is a common variable in the motor learning research area.
The typical finding is that movement variability reduces as function of practice
(Vereijken et al. 1997). It might be expected that a stable, smooth movement
pattern will lead to less energy expenditure, since fewer corrections are needed, and
thus to a higher mechanical efficiency.
Again, all the above-mentioned studies were cross-sectional. Therefore, it is not
known whether the differences in mechanical efficiency between the groups are
due to physiological adaptations, which could have taken place over time in the
wheelchair-dependent group, or due to differences in propulsion technique
between the experienced and inexperienced groups.
Some training studies have been performed in the past, in which mechanical
efficiency and propulsion technique were evaluated after a period of training
(Dallmeijer et al. 1999b; Rodgers et al. 2001; Woude et al. 1999). A 6-wks training
intervention (including stretching, strengthening, aerobic exercise) of wheelchair
users led to decreased stroke frequency, increased maximum elbow extension
angle, increased trunk and shoulder range of motion, and increased wrist extension
moment (Rodgers et al. 2001). Oxygen uptake values were similar before and after
training although power output increased significantly after training (Rodgers et al.
2001). A 7–wks wheelchair training (30 min, 3.wk-1) had favorable effects on
maximal physical work capacity in able-bodied subjects (Woude et al. 1999). At
submaximal exercise (Dallmeijer et al. 1999b), an increase in stroke angle, push
time and cycle time after 7 weeks of training was found. However, efficiency and
effective force direction did not change in comparison with a control group. Much
to the authors‟ surprise the control group showed a slight improvement in
efficiency and effective force direction as well. Although the low number of
observations for the efficiency may explain the lack of concomitant improvement,
the authors (Dallmeijer et al. 1999b) hypothesized that efficiency and force
application were short-term adaptations. It has been found that the maximal power
output of people with a spinal cord injury during wheelchair propulsion increased
significantly between the start of the rehabilitation process and 3 months later
Chapter 1
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(Dallmeijer et al. 2003). Kemenade et al. (1999), in their study on wheelchair
propulsion under submaximal conditions between the initial stage of rehabilitation
and one year after discharge, found no differences in effective force direction,
mechanical efficiency, and timing variables. However, lack of results could be due
to the very heterogeneous subject group regarding lesion level (ranging from C6 to
L3/4).
So far, no study has been performed that examined changes in efficiency due to
wheelchair skill acquisition only. Since the topic of this thesis is about learning
hand rim wheelchair propulsion, only completely novice wheelchair users could be
included in the different studies. However, this is virtually impossible with novice
wheelchair-dependent subjects. The problem with including novice wheelchair-
dependent subjects is that there are not many subjects at that stage of rehabilitation
who are willing to participate. As a consequence, a subject group will be very
heterogeneous, and it will be virtually impossible to create test conditions that will
be comparable for all subjects. Therefore, it was decided to study able-bodied
subjects without any experience in wheelchair propulsion. This implies that results
hold for individuals with an intact (upper) body and may not be fully transferable
to (novice) wheelchair-dependent individuals since e.g. loss of neuromuscular
functions is likely to influence the learning process of wheelchair propulsion.
AIM OF THIS THESIS
The understanding of motor learning in the context of rehabilitation is still limited
but clearly of theoretical as well as clinical importance. The learning process of
wheelchair propulsion is a good opportunity to study motor learning of a relevant
and novel gross motor task. Furthermore, knowledge about motor skill learning is
important for an effective and successful rehabilitation process (Gonzalez et al.
2001). Since not many studies are yet available on biophysical aspects of learning
gross motor tasks, the first step in this thesis is to investigate what adaptations take
place over time due to systematic practicing a motor task without receiving any
extrinsic (feedback) information. Therefore, the first aim of the present thesis is to
study possible changes in wheelchair propulsion technique/coordination, in
association with gross mechanical efficiency, over time due to a learning process.
The second aim is to define optimal conditions for the learning process such as
instructing them to direct the force mechanically more effectively, to use different
stroke patterns, and performing under different forms of task complexity/
diversity.
Chapter 1
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THESIS OUTLINE
In chapters 2 to 4 the adaptations that take place over a shorter and longer term
are described. In chapter 2 the effect of a 3-week practice period (3 times a week),
with a low intensity and short duration, on propulsion technique (force direction,
timing, inter-cycle variability) and mechanical efficiency was studied. The
hypothesis of this study was that improvements in gross mechanical efficiency and
propulsion technique occur by practicing hand rim wheelchair propulsion over 3
weeks. Chapter 3 focused on changes in propulsion technique and mechanical
efficiency during the initial seconds/minutes of the learning process of completely
novice subjects. It was expected that certain propulsion technique variables, e.g.
the effective force direction, already change during the first seconds/minutes of
practice. This short-term study was extended with electromyography and
kinematics measurements to get an indication of changes in muscle activity
patterns and co-contraction and the movement pattern during the learning process,
which is described in chapter 4. Given the large number of muscles around the
shoulder, movements can be conducted with different sets of active muscles. Early
in the learning process, muscles could be linked into a muscle synergy via muscle
coactivity (Bernstein 1967). The purpose of the experiment of chapter 4 was to
analyze adaptations in kinematics and muscle activity/co-contraction during the
initial phase of learning. The hypothesis was that muscle coactivity is initially high
and will decrease with skill learning when limb stiffness is reduced. A possible
decrease in muscle co-contraction could explain an increase in mechanical
efficiency.
Chapters 5 to 7 concentrate on the optimization of the learning process and thus
on the understanding of effects of some of the boundary conditions. In chapter 5
this is done by letting the subjects learn to direct the force more tangentially with
help of visual feedback on a computer screen. The effect of this more effective
force direction on the mechanical efficiency of wheelchair propulsion was studied.
In the experiment described in chapter 6 subjects learned to propel the wheelchair
with three kinds of stroke patterns, i.e. pumping, semi-circular or single looping
over propulsion. The purpose of the study was to investigate whether one stroke
pattern is more efficient than another in terms of energy expenditure. It was
hypothesized that the semi-circular stroke pattern, in which the hand follows a
path below the hand rim in the recovery phase, was the most efficient pattern as
was suggested in several previous papers. Finally, chapter 7 focused on the effect
of task complexity (i.e. practicing on a stationary wheelchair ergometer, a motor-
driven treadmill or on a wheelchair track) on mechanical efficiency and propulsion
technique during the learning process of wheelchair propulsion. The assumption
Chapter 1
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was that inexperienced able-bodied wheelchair users would achieve a larger
improvement in gross mechanical efficiency and propulsion technique when real-
world conditions are simulated more closely, i.e. when the task is more diverse and
complex.
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Chapter 2
Wheelchair propulsion technique and mechanical efficiency after 3-weeks of practice
Chapter 2
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ABSTRACT
Differences in gross mechanical efficiency between experienced and inexperienced
wheelchair users may be brought about by differences in propulsion technique.
The purpose of this experiment was to study changes in propulsion technique
(defined by force application, left-right symmetry, inter-cycle variability and timing)
and gross mechanical efficiency during a 3-week wheelchair practice period in a
group of novice able-bodied non-wheelchair users. Subjects were randomly divided
over an experimental group (N = 10) and a control group (N = 10). The
experimental group received a 3-week wheelchair practice period (3.wk-1, i.e. 9
practice trials) on a computer-controlled wheelchair ergometer while the control
group only participated in trial 1 and 9. During all 9 practice trials propulsion
technique variables and mechanical efficiency were measured. No significant
differences between the groups were found for force application, left-right
symmetry and inter-cycle variability. The cycle frequency and negative power
deflection at the start of the push phase diminished significantly in the
experimental group in contrast to the control group (p < 0.05). Work per cycle,
push time, cycle time and mechanical efficiency increased. The practice period had
a favorable effect on some technique variables and mechanical efficiency, which
may indicate a positive effect of improved technique on mechanical efficiency.
Although muscle activation and kinematic segment characteristics were not
measured in the present study, they may also impact mechanical efficiency. No
changes occurred over time in most force application parameters, left-right
symmetry and inter-cycle variability during the 3-week practice period, however,
these variables may change on another time scale.
Chapter 2
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INTRODUCTION
Many lower-limb disabled subjects depend upon a wheelchair for their mobility.
Therefore, training and learning of hand rim wheelchair propulsion are essential in
the process of rehabilitation. Novice (recently injured) wheelchair users have to
learn a completely new motor task for the purpose of ambulation. According to
Sparrow (1983) the motor performance of novices is relatively inefficient even
though they may perform at a rate optimal to their stage of learning. With practice,
the movement pattern will be refined to approximate more closely that which is
mechanically and physiologically optimal within the constraints of the task
(Sparrow 1983). Tuller et al. (1982) have shown that a beginner learns a skill by
„freezing out‟ some of the free variation of the body. As skill increases, the
beginner will release the ban on the degrees of freedom.
It can be expected that wheelchair-dependent subjects (WCD), being more
experienced in manual wheelchair propulsion, have a higher gross mechanical
efficiency (i.e. a higher ratio between power output and energy expenditure)
compared to novice able-bodied subjects (ABS). They will probably also differ in
propulsion technique. Studies in this realm are, however, scarce and cross-sectional
in nature. Results on a 30 s hand rim wheelchair sprint test did not indicate
superior results for WCD over ABS concerning power output and force
application, although some differences in kinematics seemed to exist (Veeger et al.
1992a). More important in the light of the present study are studies comparing
WCD with ABS during submaximal tests (Knowlton et al. 1981; Tahamont et al.
1986). They found that WCD had a significantly higher net mechanical efficiency
than ABS. The biomechanical differences between WCD and ABS, like stroke
length, were suggested to be possible influencing factors on mechanical efficiency
(Knowlton et al. 1981). Although the subjects in the above-mentioned studies were
able bodied, they are generally not fully inexperienced. The inclusion criterion for
subjects in the current study was that they had not been using a wheelchair in any
prior instance. Above that, cross-sectional studies do have clear limitations.
However, results may indicate that increasing expertise can lead to shifts in
technique and possibly to a gradual increase of overall mechanical efficiency.
Both physiological adaptations and improved propulsion technique are assumed to
underlie shifts in mechanical efficiency during practice. Identifying the technique
aspects of wheelchair propulsion related to mechanical efficiency is important both
theoretically and practically. Learning of hand rim wheelchair propulsion seems to
provide a valid and interesting model to study motor learning phenomena in adult
individuals (Amazeen et al. 1999). Currently there is little research pertaining to
propulsion technique factors associated with the learning of wheelchair propulsion.
Chapter 2
15
Ultimately, if changes in technique variables improve the mechanical efficiency of
wheelchair propulsion, these findings would enable novice wheelchair-dependent
subjects to optimize wheelchair performance much more effectively from the start
of the rehabilitation (i.e. learning) process onwards. This is particularly important
because hand rim wheelchair propulsion is a way of locomotion with a low gross
mechanical efficiency. Gross mechanical efficiency of wheelchair propulsion rarely
exceeds 11% and is much lower than in arm cranking (16%) (Martel et al. 1991;
Powers et al. 1984) or cycling (18-23%) (Coyle et al. 1992). As a consequence, hand
rim wheelchair propulsion is associated with a high physical strain in daily life
(Janssen et al. 1994) and leads most likely to a high mechanical load on the upper
extremity. The latter may lead to a high prevalence of overuse injuries in shoulder
and wrist (Boninger et al. 1997).
It is important to note the difference between training and learning. As mentioned
before, shifts in mechanical efficiency can take place due to physiological
adaptations or as a consequence of improved propulsion technique. Generally, in
training both physiological adaptations and learning responses (i.e. an improved
propulsion technique) will take place. If one wants to isolate changes in propulsion
technique and ME, physiological adaptations as a consequence of training have to
be excluded. Therefore, the learning protocol has to be at a very low intensity and
duration, and with a limited frequency. Clearly, intensity should be less than the
general training guidelines that are suggested by the ACSM (ACSM 1990).
The process of adaptation during wheelchair training or learning has not been
described in detail. Woude et al. (1999) and Dallmeijer et al. (1999b) performed a
7-week wheelchair training process in 10 ABS on a motor-driven treadmill. They
found substantial effects on performance capacity and timing parameters, but no
changes in characteristics of force application and mechanical efficiency in
comparison to a control group. Kemenade et al. (1999) studied the effects of the
rehabilitation process on mechanical efficiency and technique. They found a
tendency for a larger mechanical efficiency and a smaller outwardly directed force
after approximately one and a half years of rehabilitation. Lack of significant results
for mechanical efficiency in these studies could be due to a too small group size
(Dallmeijer et al. 1999b; Kemenade et al. 1999; Woude et al. 1999) and/or great
range of lesion levels of the subjects (Kemenade et al. 1999).
Wheelchair propulsion is a bilateral, cyclical activity and little is known about the
nature and extent of variability that exists among the movement pattern of a
continuous sequence of push cycles in general (Rao et al. 1996). Variability, or lack
thereof, in a given movement parameter is often used as an index of skilled
performance (Newell et al. 1993). The typical finding is that movement variability
Chapter 2
16
reduces as function of practice and increments of skill (Darling et al. 1987;
Vereijken et al. 1997). Variability in the motor system can be examined at several
levels. The variations may be related to force production, which in turn will be
influenced by variations in the muscle activation and timing, excitability of motor
neurons, and command signals from higher nervous centers (Carlton et al. 1993).
Since regulation of force is a critical function of the motor system, possible
changes in force application parameters due to skill acquisition will be used in the
present study.
The (a)symmetry of the bilateral force production in time and space determines the
direction of coasting. Every small correction that has to be made to keep the
wheelchair in a straight path leads to extra energy loss. It can be hypothesized that
novice individuals produce a less stable coasting line and thus require more
corrections, which is suggested to lead to asymmetric technique parameters.
Bilateral symmetry of the elbow movement pattern was found in WCD by Goosey
and Campbell (1998a) and Jones et al. (1999). However, it is unknown whether
novice subjects display similar stable patterns of bilateral symmetry during steady
state submaximal wheelchair propulsion along a straight line.
In the current study, the following hypothesis was tested: an improvement in
propulsion technique (i.e. a more effective force application and timing, more
bilateral symmetry and less inter-cycle variability) and improved mechanical
efficiency occur as a function of practicing hand rim wheelchair propulsion over a
3-week practice period.
METHODS
Subjects
After having given written informed consent, 20 able-bodied male subjects
participated in the study. Criteria for inclusion were: male, no prior experience in
wheelchair propulsion, absence of any medical contra-indications. Subject
characteristics are listed in Table 1. The protocol of the study was approved by the
Medical Ethical Committee.
Protocol
Subjects were randomly divided over an experimental group (N = 10) and a
control group (N = 10). The experimental group received a 3-week wheelchair
practice period (3.wk-1, 9 practice trials) on a computer-controlled wheelchair
ergometer. Every trial comprised two four-minute exercise blocks at two different
levels of external power output (block 1: 0.15 W.kg-1 and block 2: 0.25 W.kg-1) at a
velocity of 1.11 m.s-1. Two minutes of rest preceded each exercise block. Visual
Chapter 2
17
feedback, on a 15-inch computer screen in front of the subject, was used to give
the subjects feedback on the actual velocity of the left and right side as well as on
the required velocity (1.11 m.s-1). The velocity was made visible by a line which had
to be kept - on average - at certain points indicating a velocity of 1.11 m.s-1 on the
left and right side and had to be kept horizontal (i.e. symmetric for the left and
right side).
Force application, timing parameters, bilateral symmetry, inter-cycle variability and
mechanical efficiency were measured every trial during the 3-week period.
Measuring variables every trial, instead of only during a pre- and post-test, is
necessary to develop a description of the „learning curve‟ and to determine at
which time variables stabilize i.e. do not improve anymore. The control group
participated in the first and last trial only. Although the experimental group and the
control group were asked not to change the normal daily routine during the 3-week
interval, it was not possible to control this aspect completely.
Wheelchair ergometer
All trials were performed on a custom-built wheelchair ergometer. This ergometer
is a stationary, computer-controlled wheelchair simulator that allows for direct
measurement of propulsive torque around the wheel axle, propulsive force applied
on the hand rims and resultant velocity of the wheels (Niesing et al. 1990).
Wheelchair ergometer dimensions were individually adjusted such that when sitting
upright with the hands on the rim top the subject‟s shoulder was directly above the
wheel axle and the elbow angle was approximately 110° with 180° being full
extension. Wheel camber was set at 4º. Seat angle and backrest were set at 5º to the
horizontal and 15º to the vertical axis, respectively.
Ergometer data were collected each exercise block, during the last minute, with a
sample frequency of 100 Hz. Torque, forces and velocity were low-pass filtered
(cut off frequency of 10 Hz, recursive second order Butterworth filter). Because of
resonance in the system the medio-lateral force component was filtered at a lower
cut-off frequency (5 Hz, fourth order Butterworth).
Propulsion technique
Variables were calculated as mean over the whole last minute or as mean and peak
values over each of the pushes of the last minute. The push phase was defined as
the period the hand exerted a positive torque on the hand rim (Figure 1).
From the measured torque (M), wheel velocity (Vw) and wheel radius (rw), the
power output was calculated:
Chapter 2
18
Power output = M . Vw . rw -1 (W) (1)
Mean total power output was the sum of the power output for the left and right
wheel and was calculated over one minute.
The negative deflections or „dips‟ at the start of the push phase and at the end of
the push phase were determined from the power output curve. The negative
deflections or „dips‟ at the start of the push phase and at the end of the push phase
were the most negative power output values respectively at the start and the end of
the push (Figure 2). From the mean power output and the cycle frequency (in Hz)
the work per push cycle was calculated:
Work per cycle = Mean power output . frequency –1 (J) (2)
Force application
Force parameters were calculated as mean and peak values over each of the pushes
over the last minute of an exercise block. The positive forces applied with the hand
on the rim were defined as follows:
Fx: horizontally forward, Fy: horizontally outward, and Fz: vertically downward
(Figure 3). From force components Fx, Fy and Fz, total force applied on the hand
rim (Ftot) was calculated according to:
Ftot = √(Fx2 + Fy2 + Fz2) (N) (3)
The effective force (Fm) was calculated from torque (M) and hand rim radius (rr),
according to:
Fm = M . rr -1 (N) (4)
The fraction of effective force on the hand rims (FEF) was calculated from
equations 3 and 4 for each workload and expressed as a percentage:
FEF = Fm . Ftot -1 . 100 (%) (5)
Because of technical problems with one of the force transducers on the left-hand
side, it was not possible to determine reliable values of Fy on that side. To examine
whether the FEF characteristics of the left-hand side are comparable with the right
hand side over the trials an alternative FEF was calculated for both sides for only
five subjects per group, namely:
FEFalt = (M . rr -1) . (√(Fx2 + Fz2)) -1 . 100 (%) (6)
Chapter 2
19
Finally, the slope of the line between the start of the push and the peak torque in
the push was determined (slope) (Figure 1).
Timing
The cycle frequency was determined from the torque signal and defined as the
number of complete pushes per minute.
The timing parameters cycle time and push time were also determined from the
torque signal of the ergometer (Figure 1). The push time was defined as the
amount of time that the hand exerted a positive torque on the hand rim. The cycle
time was defined as the period of time from the onset of one push phase to the
onset of the next. The push time was also expressed as a percentage of the cycle
time (%push time).
Bilateral symmetry
The difference between the dominant and non-dominant hand for the maximal
value of FEFalt, mean torque, power output, timing variables (frequency, push
time, cycle time and %push time) and the slope was determined as measures of
bilateral symmetry during wheelchair propulsion. The symmetry between the
timing of the right and left hand was also defined from the torque signal as the
right-left difference between the start time of the push (Right-Left push) and as the
right-left difference in time of the peak (Right-Left peak) (Figure 1).
Inter-cycle variability
The inter-cycle variability was determined for each subject for all consecutive push
cycles during the 60-s measurement period for the push time, cycle time, %push
time, power output, FEF, torque, the negative power output dips at the start and
end of the push and the velocity. The mean and standard deviation (SD) of the
variables were calculated over all push cycles in the measurement period. From the
mean and SD the coefficient of variation (CV) was calculated by the formula:
CV = |SD . mean -1| . 100 (%) (7)
Gross mechanical efficiency
Oxygen uptake ( 2OV [l.min-1]) was continuously measured during the whole test
with an Oxycon Champion (Jaeger, Germany). Calibration was performed before
each test with reference gas mixtures. Averaged values of 10 s were sampled. To
obtain an indication of the gross mechanical efficiency (ME) of wheelchair
propulsion, the ratio power output/ energy expenditure was calculated according
to:
Chapter 2
20
ME = Mean power output . Energy expenditure -1 . 100 (%) (8)
where the energy expenditure is calculated from the oxygen uptake and the
respiratory exchange ratio according to Garby and Astrup (1987). The mean power
output was calculated over the last minute of each exercise block. Energy
expenditure was calculated over the last two minutes of each exercise block in
order to minimize errors inherent in the measurement system.
Statistics
To examine possible differences in starting levels between the two subject groups
an Independent t-test was performed.
An ANOVA for repeated measurements, with power output (0.15 and 0.25 W.kg-1)
and trial (1 and 9) as main factors and group (experimental and control) as between
subject factor, was applied to detect significant differences for selected parameters.
The interaction Trial*Group was considered to be the most important effect since
it indicates the differences between the groups over the practice period (trials).
Significance level was set at p < 0.05 for all statistical procedures.
RESULTS
Subjects
All subjects completed all trials. Mean age, body mass and height did not differ
among the groups (Table 1). No significant differences were found in the starting
levels between the experimental group and the control group except in the
difference between the dominant and non-dominant hand for the mean torque at
the external power output of 0.25 W.kg-1 (p=0.046).
Propulsion technique
Figure 4 lists the values of the mean power output and the negative power output
dips at the start and end of the push. The negative power output dip at the start of
the push diminished in both groups over time (for the experimental group from
-5.64 ± 3.52 W at trial 1 to -2.93 ± 1.75 W at trial 9; for the control group from
-4.41 ± 1.08 W at trial 1 to -4.01 ± 1.21 W at trial 9; both at 0.25 W.kg-1) but with a
significantly larger decrease in the experimental group (p = 0.048 for interaction
Trial * Group). The experimental group significantly increased the work per cycle
(0.38 ± 0.06 J at trial 1 to 0.54 ± 0.19 J at trial 9, both at 0.25 W.kg-1) during the
practice period in contrast to the control group (0.39 ± 0.12 J at trial 1 to 0.41 ±
0.14 J at trial 9, both at 0.25 W.kg-1) (p = 0.027 for interaction Trial * Group)
(Table 2). The negative power output dips and the work per cycle were
significantly larger at the higher levels of external power output.
Chapter 2
21
Force application
No effect of practice was found on FEF, FEFalt and slope between the groups
over the trials (Table 2).
The slope was significantly increased at a higher power output.
Timing
Values of push time and cycle time during the 3-week practice period are visualized
in Figure 5. The cycle frequency decreased significantly in the experimental group
(62 ± 12 pushes/minute at trial 1 to 46 ± 12 pushes/minute at trial 9, both at 0.25
W.kg-1) in contrast to the control group (63 ± 17 pushes/minute at trial 1 to 60 ±
17 pushes/minute at trial 9, both at 0.25 W.kg-1) at both external power output
levels (p = 0.006 for interaction Trial * Group)(Table 2). The push time (p = 0.023
for interaction Trial * Group) and cycle time (p = 0.023 for interaction Trial *
Group) increased significantly in the experimental group compared to the control
group. No significant differences were shown for %push time (Table 2).
Cycle frequency, push time and %push time were all significantly larger at the
external power output of 0.25 W.kg-1 compared to 0.15 W.kg-1.
Bilateral symmetry
No effect of practice was found for Right-Left push and Right-Left peak. The
difference of the timing of the start of the push or peak was at the most 0.01 s.
The difference between the dominant and non-dominant hand for the variable
push time showed a significant alteration over the practice period between the
groups (p=0.040 for interaction Trial * Group). The difference in push time
between the dominant and non-dominant hand increased in the experimental
group over the trials, although the largest difference was only 6 ms.
Inter-cycle variability
The variability (SD and CV) of the propulsion technique parameters did not
change significantly over time between the groups. Low coefficients of variation
were found for the velocity, FEF and cycle time (2-11%); moderately low CV‟s
were found for the mean power output, push time and %push time (12- 20%); and
high inter-cycle variability was found for the negative power output dips at the
start (33-50%) and end (47-75%) of the push. Figure 6 shows the CV‟s of push
time, cycle time, FEFmax and mean power output for both groups over the trials.
The push-variability of the torque signal at the right hand side of a subject from
the experimental group at trial 1 and trial 9 is visualized in figure 7. This figure
demonstrates that the variability did not diminish over the trials and also clearly
shows the increase in push time over practice.
Chapter 2
22
Gross mechanical efficiency
Gross mechanical efficiency over time for both groups is plotted in Figure 8. A
significant increase in mechanical efficiency was found for the experimental group
(7.45 ± 0.87% at trial 1 to 8.11 ± 0.56% at trial 9, both at 0.25 W.kg-1) in contrast
to the control group (7.37 ± 0.75% at trial 1 to 7.23 ± 0.90% at trial 9, both at 0.25
W.kg-1)(p = 0.044 for interaction Trial * Group).
Mechanical efficiency was significantly higher at a higher external power output.
Visualization of the results showed that the mechanical efficiency seems to
deteriorate in the control group. This deterioration could lead to significant
differences between the control group and the experimental group and to the
conclusion that the variable improved in the experimental group, while it actually
did not. An analysis on the two power outputs and on all nine trials for the
experimental group only, showed no other results than those mentioned above.
Again a significant improvement of mechanical efficiency over the trials for the
experimental group (p = 0.001) was found, suggesting that the significant
difference found between the experimental group and the control group was due
to an improvement of mechanical efficiency in the experimental group instead of a
deterioration in the control group.
DISCUSSION
During the rehabilitation period persons with (acute) lower-limb disabilities have to
learn a novel gross motor task for mobility, i.e. hand rim wheelchair propulsion. A
few researchers investigated physiological and/or biomechanical changes during a
practice period of a novel gross motor task, such as rowing (Sparrow et al. 1999),
crawling (Sparrow et al. 1987) and skiing (Brinker et al. 1982). However, nothing is
known of the learning process of manual wheelchair propulsion in biophysical
terms. The purpose of this experiment was, therefore, to study the effect of a 3-
week wheelchair-practice program on propulsion technique (defined by force
application, timing, bilateral symmetry and inter-cycle variability) and mechanical
efficiency.
The significant increase in mechanical efficiency in the experimental group during
the learning program in contrast to the control group, was not in accordance with
the results of a 7-week wheelchair training study (Dallmeijer et al. 1999b; Woude et
al. 1999). In a (too) small sample of subjects, the training study was unable to
support a possible effect of training on mechanical efficiency in the experimental
group, despite significant changes in peak oxygen uptake and power output.
Kemenade et al. (1999) found a tendency for a larger mechanical efficiency after
approximately one and a half years of rehabilitation in persons with spinal cord
Chapter 2
23
injury. Besides a training and/or learning effect, this increase in mechanical
efficiency could be due to recovery of functions during the rehabilitation process.
The small but significant increase in gross mechanical efficiency in the present
study (5.54 ± 0.61% at trial 1 to 5.87 ± 0.52% trial 9, both at 0.15 W.kg-1; and 7.45
± 0.87% at trial 1 to 8.11 ± 0.56% at trial 9, both at 0.25 W.kg-1) could theoretically
not be due to an effect of training because the two exercise blocks were at a low
intensity and of a short duration to avoid such an effect (ACSM 1990). The
hypothesis, that the practice period probably led to an improvement in propulsion
technique and subsequently the activity became less strenuous for the subjects, was
thus supported. Therefore, the effect of practice on several propulsion technique
variables has to be observed in more detail. An effect of the practice period was
found in the negative dip in the power output at the beginning of the push phase.
Less negative power was produced over time in the experimental group compared
to the control group at the beginning of the push phase. Negative power
production will reduce overall performance, since it implies the braking of the
wheels. The negative dip is most likely the result of non-optimal coupling
technique in which the hands of the subjects had not attained the required
tangential velocity of the wheels at the moment of first contact (Veeger et al.
1991a). The motion of the hands at the start of the push occurs outside the visual
field, what makes it more difficult to grasp the rims with the same hand velocity
compared to the actual wheel velocity. The results showed that, at a low velocity of
1.11 m.s-1, one learns to diminish the braking torque at the start of the push. Less
negative power was produced over time at the end of the push phase for both
groups, indicating a short-term adaptation.
Like in most tasks, it is necessary to maximize concurrently both the forces
generated and the effectiveness with which these forces are applied in manual
wheelchair propulsion. The effectiveness of the total force vector in association
with the effective force component, indicated by FEFmax and FEFmean,
increased only with a non-significant few percent and in both the experimental
group (80 ± 12% at trial 1 to 84 ± 10% at trial 9) and the control group (81 ± 8%
at trial 1 to 83 ± 11% at trial 9). This was in accordance with a 7-week wheelchair
training study (Dallmeijer et al. 1999b). No differences in FEF were visible
between WCD and ABS in the cross-sectional study on performance and
technique during a 30-s sprint test (Veeger et al. 1992a). These findings suggest
that the force application during hand rim wheelchair propulsion might change on
a short-term, occurring already in the first seconds or minutes of practice.
The cycle frequency of hand rim wheelchair propulsion can be varied to a certain
extent without affecting the mean velocity. This is in contrast to cycling and arm-
Chapter 2
24
cranking, which are more constrained cyclical motions (Woude et al. 1989b). This
can be seen in the results of the present study. Three weeks of practice on the
wheelchair ergometer led to a significant decrease in cycle frequency while the
mean velocity remained the same. Also, significant changes over the trials were
seen between the groups for the push time, cycle time and work per cycle. The
increments in push time and in the work per cycle are well visualized in figure 7 by
respectively the broader peak and the larger surface under the curve. All these
variables increased over time in the experimental group in contrast to the control
group. This indicates a possible adaptation in segment excursions and velocities
and subsequently in muscle contraction characteristics. The 7-week wheelchair
training study showed similar results (Dallmeijer et al. 1999b). That study found,
using video recordings, also a larger stroke angle, which is the angle between the
line from the hand through the wheel axle, relative to the vertical, at the start and
the end of the push phase. Since no kinematics were taken into account in the
present study a larger stroke angle could not be demonstrated. The changes in
stroke angle, cycle frequency and work per cycle appear to be linked (Dallmeijer et
al. 1999b). The increase in work per cycle was confirmed with cross-sectional
results of Veeger et al. (1992a) on the 30 s wheelchair sprint test.
No significant bilateral differences between the groups over time were found
except for the variable push time. The difference in push time between the
dominant and non-dominant hand decreased in the control group, while it
increased in the experimental group. However, our expectations were just the other
way around, more bilateral symmetry after practice. On the other hand, steering is
not a crucial task element on a stationary wheelchair ergometer in contrast to
wheelchair use in real life or on a motor driven treadmill, i.e. bilateral symmetry is
not a „must‟ on a wheelchair ergometer. Despite that, no essential differences were
seen between the dominant and non-dominant side. The apparent symmetry was
underlined by a submaximal wheelchair study (Veeger et al. 1992b), in which
identical mean values of the power output were found and comparable time series
of both power curves. Woude et al. (1998) compared the power production on the
right and left hand during a sprint test on a wheelchair ergometer. They found
some variance but overall good agreement between the left and right hand side.
Goosey and Campbell (1998a) established whether bilateral symmetry exists during
wheelchair propulsion in the elbow movement pattern of trained wheelchair racers.
The main finding from their study was that as a group (N = 7) there were no
significant differences between the left and right arm movement patterns. Jones et
al. (1999) did not find any significant bilateral differences in kinetic parameters in a
group of 11 subjects with paraplegia. Therefore, it can be concluded that -
Chapter 2
25
especially at submaximal exercise levels – bilateral symmetry occurs even at the
start of a learning process. Small differences between left and right may be
explained by hand dominance and the lack of accurate directional information
(Woude et al. 1998).
As was the case for bilateral asymmetry, the expectation was that the variability
would reduce as a function of practice. A high level of push-to-push consistency,
i.e. a low variability, is necessary in the execution of effective movement patterns,
such as in rowing (Smith et al. 1995). However, after three weeks of wheelchair
practice the variability between the pushes was not significantly diminished (figure
6 and 7). The coefficient of variation of different variables varied a lot during the
three weeks. The lack of support for the hypothesis might be due to the fact that
subjects had to propel in a stationary wheelchair ergometer. Subjects did not have
to pay much attention to steering, which normally needs constant attention.
Therefore, the subjects could be distracted more easily and consequently more
variability occurred. The inter-cycle variability of the power output and the velocity
was quite low. This can be partly explained by the fact that the velocity had to be
regulated at a mean constant level on the basis of feedback. The large inter-cycle
variability of some variables may imply the difficulty to improve these variables
and keep them constant during a learning period. One may conclude that bilateral
symmetry is dominantly coordinated from the start on, whereas temporal
consistency in technique shows strong fluctuations over time and no consistent
decrease with practice.
Several studies found low inter-cycle variability for kinematic variables in (racing)
wheelchair propulsion, suggesting that the upper extremity motion pattern was
consistent and repeatable for a single subject (Goosey et al. 1998a; Rao et al. 1996;
Sanderson et al. 1985). However, low inter-cycle variability can be expected in
these studies with WCD subjects or even wheelchair racers because they are
extremely experienced compared to the novice able-bodied subjects in the present
study. In a study on the effect of practice on rowing performance no significant
change in stroke-to-stroke variation was found, although the authors suggested
that there was a trend towards reduced variability in the rowing cycle (Sparrow et
al. 1999). Another study showed that biomechanical and performance variables,
such as stroke-to-stroke consistency, stroke smoothness and propulsive work
consistency, can be used to discriminate accurately between rowers of different
skill levels (Smith et al. 1995).
Lack of significant differences in force application, bilateral symmetry and inter-
cycle variability between the experimental group and the control group after a 3-
week practice period could be due to a too low intensity of the protocol. Under
Chapter 2
26
submaximal conditions, technique may be considered less critical to performance.
This suggests that differences may be (more) expressed at higher intensities.
Effectiveness of force application and gross mechanical efficiency do indeed show
some increase with a higher load (Dallmeijer et al. 1998). However, practicing at
higher intensities will lead to a training effect, which had to be excluded here.
While the gross mechanical efficiency increased in the experimental group
compared to the control group, only significant changes in wheelchair propulsion
technique were visible for the cycle frequency, push time, cycle time, work per
cycle and the negative power dip at the start of the push. Mechanical efficiency can
be influenced by the cycle frequency as was stressed by Woude et al. (1989b) and
Goosey et al. (2000). Goosey et al. (1998b) stated that lower push rates have been
associated with greater pushing economy (defined as oxygen uptake at a given
propulsion speed). A high push rate means that the athlete is experiencing many
shifts in the deceleration and acceleration and inertial moments of the limb
segments, thus influencing muscle activity and co-ordination and subsequently
energy cost and efficiency. A previous study (Goosey et al. 1998b) stated the
hypothesis that a slower push rate may mean that the athlete is able to apply more
force effectively on the hand rim to produce the desired power output with less
muscular effort. The present study found a decrease in cycle frequency over the
trials for the experimental group. But in contrast to an expected increase in work
per cycle and a less negative power dip at the start of the push, this was not
accompanied by an increase in FEF, i.e. a more effective force direction. However,
changes in FEF due to a practice period may be not that self-evident as expected.
Changes in timing parameters, for example cycle frequency, due to learning of a
motor skill are typical in literature. The major practice-related adaptation in walking
on hands and feet was to use longer and slower strides (Sparrow et al. 1987) while
in rowing it was a decreased mean stroke rate over days (Sparrow et al. 1999). It
was assumed that participants in both studies learned to produce a more
economical rate of muscle contraction. The results of the present study are in
agreement with the statement of Sparrow (1999) that the learning of many
repetitive gross-motor tasks might be characterized by a „longer-slower‟ control
mode, i.e. a larger stroke angle/longer push time and cycle time and a decreased
cycle frequency.
Changes in movement patterns and in muscle activity/timing patterns may lead to
alterations in gross mechanical efficiency during a learning process of manual
wheelchair propulsion. Since the shoulder-muscle complex offers a wide range of
movements, this might result in a great variability in repetitive movements of the
upper extremity. In the beginning of skill learning, for example manual wheelchair
Chapter 2
27
propulsion, there will be „freezing out‟ of some of the free variation of the body, so
that it is not allowed into the activity (Tuller et al. 1982). According to this theory,
muscles will not be controlled individually but are functionally linked with other
muscles via muscle co-activity. Acquiring a skill is essentially trying to find ways of
controlling the degrees of freedom and of exploiting the forces made available by
the context (Turvey et al. 1982). Later in learning, the restrictions could be relaxed,
allowing reductions in co-activity in favor of more specific multi-muscle
sequencing. One hypothesis that emerges from this idea is the following: muscle
co-activity should decrease with skill learning as degrees of freedom are freed up
and limb stiffness is reduced (Spencer et al. 1999). Subsequently this may lead to an
improvement in gross ME. The possible reduction in muscle co-activity during the
learning process could be easily measured by EMG. Therefore, EMG and
kinematics measurements will be useful in future learning studies.
CONCLUSION
In this study with novice able-bodied subjects a 3-week practice program on a
wheelchair ergometer resulted in a significant improvement in gross mechanical
efficiency in an experimental group compared to a control group.
Timing variables (push time, cycle time and cycle frequency), work per cycle and
the negative deflection in the power output curve at the start of the push phase
changed also significantly with learning in the experimental group in contrast to a
control group. The wheelchair-practice program had a favorable effect on the
timing parameters and on the mechanical efficiency. This may indicate a positive
effect of the timing parameters on mechanical efficiency.
No changes were seen over the trials in the inter-cycle variability, bilateral
symmetry and force application variables like the direction of the effective force. It
is possible that these variables change in a shorter time span - already in the first
seconds or minutes - or on a longer term than the three weeks used in the present
study.
ACKNOWLEDGEMENT
The experimental assistance of Cécile Boot and Stephanie Valk is greatly
acknowledged.
Chapter 2
28
TABLES
Table 1. Mean and SD of the subject characteristics for the control (C) and experimental (EXP) group. P-
value: results of independent t-test between group means.
C (N = 10) EXP (N = 10) p-value
Mean SD Mean SD
Age (years) 21.3 2.4 21.7 2.2 0.780
Body mass (kg) 76.8 5.5 77.0 12.3 0.951
Height (cm) 183.3 5.9 184.1 9.0 0.262
Dominant arm - Right: N = 9 N = 10
Table 2. Mean and SD for the technique variables at external power outputs 0.15 and 0.25 W .kg-1, at the
beginning (1) and the end (9) of the 3-week practice period for both the experimental (EXP) and control (C)
group. Number of subject is 10 for all variables. See text and figures 1-3 for definition of variables.
*: p < 0.05, indicates the difference between the groups over the practice period.
Trial x
Group
EXP
(0.15 W.kg-1)
C
(0.15 W.kg-1)
EXP
(0.25 W.kg-1)
C
(0.25 W.kg-1)
Mean SD Mean SD Mean SD Mean SD
Work per cycle (J)
1 * 0.23 0.04 0.24 0.07 0.38 0.06 0.39 0.12
9 0.36 0.12 0.26 0.08 0.54 0.19 0.41 0.14
FEFmax (%)
1 79.86 17.54 77.76 8.08 80.03 12.46 80.84 8.38
9 80.11 13.72 78.07 9.91 83.93 10.07 82.64 11.30
FEFmean (%)
1 77.10 13.45 76.47 7.00 75.47 11.64 78.33 7.05
9 79.26 12.62 77.59 8.21 83.04 7.37 79.67 9.11
Slope (Nm/s)
1 92.77 50.90 93.92 43.42 90.38 33.54 115.36 47.30
9 66.85 24.72 87.38 34.47 78.03 22.32 112.68 47.84
Frequency (pushes/min.)
1 * 60.94 12.73 59.27 15.11 62.83 11.80 63.52 16.56
9 41.66 11.83 57.71 16.44 46.35 12.39 60.20 17.23
Push time (s)
1 * 0.35 0.11 0.33 0.09 0.37 0.09 0.35 0.09
9 0.44 0.09 0.34 0.09 0.45 0.08 0.36 0.09
Cycle time (s)
1 * 1.03 0.23 1.08 0.29 0.99 0.22 1.00 0.26
9 1.57 0.51 1.12 0.32 1.40 0.44 1.08 0.35
%Push time (%)
1 33.72 4.62 31.02 3.53 36.75 4.19 34.75 2.68
9 29.21 6.69 30.19 2.50 33.31 7.25 33.42 3.22
Chapter 2
29
FIGURES
Figure 1. Definition of the variables push time, cycle time, slope and the right-left difference of the timing of the
peak (Ri-Le peak) and push (Ri-Le push).
Figure 2. Illustration of the definition of the dips of negative power output at the start (PnegS) and the end
(PnegE) of the push.
0 20 40 60 80 100 120 140 160 180 200-10
-5
0
5
10
15
20
25
Ri-Le peak
Ri-Le pushCycle time
Push
time
Slope
Sample
To
rqu
e (N
m)
Torque signal right (-) and left (.-).
0 20 40 60 80 100 120 140 160 180 200-10
0
10
20
30
40
50
PnegS PnegE
Power output signal
Po
wer
ou
tpu
t (W
)
Sample
Chapter 2
30
M
Fx
Fz Fm
Ftot
M
Fx
Fz Fm
Ftot
Figure 3. Illustration of the torque and force components exerted on the hand rim.
Figure 4. Mean and standard deviation of the power output (POmean) and the dips of negative power output at
the start (PnegS) and end (PnegE) of the push, at trial 1 and 9 for external power output 0.15 W .kg-1 and 0.25
W.kg-1 for the experimental (EXP) and control (C) group. * = significant Trial * Group effect at p < 0.05.
-10
-5
0
5
10
15
20
25
30
Po
wer
ou
tpu
t (W
)
1 - EXP 9 - EXP 1 - C 9 - C
POmean
(0.15 W/kg)
POmean
(0.25 W/kg)
PnegS
(0.15 W/kg)
PnegS
(0.25 W/kg)
PnegE
(0.25 W/kg)
PnegE
(0.15 W/kg)
*
Chapter 2
31
Cycle time and push time
0.0
0.5
1.0
1.5
2.0
2.5
1 C 1 2 3 4 5 6 7 8 9 9 C
Trial
Tim
e (
s)
C - Cycle time 0.15 W/kg
EXP - Cycle time 0.15 W/kg
C - Cycle time 0.25 W/kg
EXP - Cycle time 0.25 W/kg
C - Push time 0.15 W/kg
EXP - Push time 0.15 W/kg
C - Push time 0.25 W/kg
EXP - Push time 0.25 W/kg
*
*
Figure 5. Mean and + or – standard deviations of the push time (PT) and cycle time (CT) for the
experimental (EXP) compared to the control (C) group at external power outputs 0.15 and 0.25 W.kg-1.
* = p < 0.05 for interaction effect Trial * Group.
Figure 6. Impression of the fluctuating mean coefficient of variation (%) for the variables push time, cycle time,
effective force production (FEFmax) and mean power output during three weeks of practice (9 trials) for the
experimental (EXP) group compared to a control (C) group at an external power output of 0.25 W.kg-1.
Coefficient of variation (%)
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
1C 1 2 3 4 5 6 7 8 9 9C
Trial
CV
(%
)
Cycle time (C)
Cycle time (EXP)
Push time (C)
Push time (EXP)
FEFmax (C)
FEFmax (EXP)
Mean Power Output (C)
Mean Power Output(EXP)
Chapter 2
32
Figure 7. Example of the push-variability during the first 15 s of trial 1 (left picture) and the last 15 s of trial
9 (right picture) i.e. after 3 weeks of practice, both at an external power output of 0.25 W.kg-1.
Figure 8. Significant increase in gross mechanical efficiency (mean and standard deviation) over the trials for the
experimental (EXP) group compared to a control (C) group at external power output levels 0.15 and 0.25
W.kg-1. p < 0.05 for interaction effect Trial * Group.
To
rqu
e (N
m)
Sample
To
rqu
e (N
m)
Sample
*
Gross mechanical efficiency (%)
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
1C 1 2 3 4 5 6 7 8 9 9C
Trial
Gro
ss m
ech
an
ica
l eff
icie
ncy
(%
)
C (0.15 W/kg)
EXP (0.15 W/kg)
C (0.25 W/kg)
EXP (0.25 W/kg)
33
Chapter 3
Adaptations in physiology and propulsion techniques during the initial phase of learning manual wheelchair propulsion
Chapter 3
34
ABSTRACT
The purpose of this study was to analyze adaptations in gross mechanical efficiency
and wheelchair propulsion technique in novice able-bodied subjects during the
initial phase of learning hand rim wheelchair propulsion. Nine able-bodied subjects
performed three 4-minute practice blocks on a wheelchair ergometer. The external
power output and velocity of all blocks was respectively 0.25 W.kg-1 and 1.11 m.s-1.
Gross mechanical efficiency, force application, timing, and inter-cycle variability
were measured. No change in gross mechanical efficiency was found. However, a
decrease in cycle frequency was seen, which was accompanied by an increase in
work per cycle and a decrease in percentage push time. The increase in work per
cycle was associated with a higher peak torque. No changes in inter-cycle variability
were visible over time. The timing variables changed already during the initial
phase of learning manual wheelchair propulsion. However, for other variables,
such as force production, gross mechanical efficiency and inter-cycle variability, a
longer practice period, i.e. even months/years, might be necessary to induce a
change. The effective force direction seemed to be optimized from the start of the
learning process onwards.
Chapter 3
35
INTRODUCTION
Training and learning are essential in the process of rehabilitation. Novice (recently
injured) wheelchair users in the process of rehabilitation have to learn a complete
set of new motor patterns of the upper extremities and trunk for the purpose of
propulsion and activities of daily living. Due to the way the task of wheelchair
propulsion has to be executed - in terms of segmental rotations, coupling of the
hand to the rotating rims et cetera - hand rim wheelchair propulsion has a low
gross mechanical efficiency. It has been suggested that a learner has to discover an
appropriate movement pattern and has to find the optimal pattern in terms of
reproducibility and/or efficiency of energy expenditure when confronted for the
first time with such a novel motor task (Almasbakk et al. 2001; Sparrow 1983).
This raised the overall question, which learning processes and adaptations take
place over time as a consequence of practicing a completely novice cyclic gross
motor task like manual wheelchair propulsion?
A previous study with able-bodied subjects showed that a 3-week wheelchair-
practice program (two 4-min. exercise blocks at a low intensity, 3 times a week)
had a favorable effect on timing parameters (cycle frequency, push time and cycle
time) and gross mechanical efficiency (Groot et al. 2002), Chapter 2). However, no
changes in force application and inter-cycle variability occurred during the 3-week
learning program. Dallmeijer et al. (1999b) found similar results in a 7-week
wheelchair-training study (30 min. exercise at 50-70% heart rate reserve, 3 times a
week), changes in timing parameters but no alterations in force application.
Because regulation of force is a critical function of the motor system, possible
changes in force application due to skill acquisition could occur. Based on the
findings of the 3- (Groot et al. 2002), Chapter 2) and 7-week (Dallmeijer et al.
1999b) studies and on cross-sectional wheelchair literature (Veeger et al. 1992a),
possible learning-based changes in force application and inter-cycle variability can
be either long-term adaptations, i.e. the 3-week learning program and even the 7-
week training period were too short for improving these variables, or could be
short-term adaptations, occurring already during the first seconds or minutes of
practice. Therefore, it was suggested in Groot et al. (2002b, Chapter 2) that force
application does adapt partly at a short-term basis as well as in a much more
gradual pattern over the long term. The present study will focus on the suggested
short-term changes to understand which changes in physiology and propulsion
technique take place during the first seconds / minutes of the wheelchair-learning
process. Therefore, the inclusion criterion for subjects in the current study was that
they had not been using a wheelchair in any prior instance. Since nothing is known
about the initial motor learning processes of wheelchair propulsion, it was chosen
Chapter 3
36
to start simple and well controlled with a homogeneous subject group who are able
to propel the wheelchair at a standardized power output and velocity. Therefore,
able-bodied subjects were included since these standardizations and homogeneity
would not be possible with wheelchair-dependent subjects at the early stages of
rehabilitation. Although the results might not be completely transferable to people
with limited functions, especially when these concern the upper trunk and arms, it
will give insight in adaptations in propulsion technique and mechanical efficiency
that take place due to a natural practice period in general. In order to understand
the processes underlying the initial learning of hand rim wheelchair propulsion the
purpose of the present study was to study the short-term adaptations in
wheelchair-propulsion technique (defined by force application, timing and inter-
cycle variability) and gross mechanical efficiency in completely novice able-bodied
subjects in the initial 12 minutes of the learning process on a computer-controlled
wheelchair ergometer.
It was expected that 1) the mechanical efficiency would increase during the practice
period; 2) an increase in the effective force direction would occur already in the
first seconds/minutes of practice (Groot et al. 2002b, Chapter 2); A possible
increase in mechanical efficiency could be due to 3) an improvement in the timing
variables, as was found for the 3-week learning study (Groot et al. 2002b, Chapter
2), or 4) a decrease in inter-cycle variability since the typical finding is that
movement variability reduces as a function of improvement of skill (Darling et al.
1987; Vereijken et al. 1997).
METHODS
Subjects
After having given written informed consent, 9 able-bodied male subjects
participated in the study. Criteria for inclusion were: male, no prior experience in
wheelchair propulsion, absence of any medical contra-indications. The mean age
was 24.0 years (SD = 4.8), mean body mass was 76.4 kg (SD = 8.0) and mean
height was 1.82 m (SD = 10.2). The dominant hand for all subjects was the right
hand. The protocol of the study was approved by the Medical Ethical Committee.
Design
Without prior familiarization, subjects performed three 4-min. submaximal practice
blocks on a computer-controlled wheelchair ergometer. The external power output
of all blocks was 0.25 W.kg-1 and the velocity was 1.11 m.s-1. These submaximal
levels of power output and velocity were chosen to be able to compare the results
of this study with previous studies (Groot et al. 2002, Chapter 2; Groot et al.
Chapter 3
37
2002a, Chapter 5; Veeger et al. 1992c) and to exclude an effect of training or
fatigue. Two minutes of rest preceded each exercise block.
Visual feedback on the actual velocity, presented on a computer screen in front of
the subject, was used by the subject to keep the velocity of the wheels at a constant
level of 1.11 m.s-1 on average in a natural manner (Groot et al. 2002b, Chapter 2).
The consequence of practice on force application, timing and inter-cycle variability
were determined for the right side only.
Wheelchair ergometer
The practice blocks were performed on a custom-built wheelchair ergometer. This
ergometer is a stationary, computer-controlled wheelchair simulator that allows for
direct measurement of propulsive torque around the wheel axle, the 3-D vector of
the propulsive force applied on the hand rims and resultant velocity of the wheels
(Niesing et al. 1990).
Wheelchair ergometer dimensions were individually adjusted according to a
standardized protocol described elsewhere (Groot et al. 2002b, Chapter 2).
Ergometer data were collected with a sample frequency of 100 Hz during the first
practice block from 0.15-0.30 (T1) and from 3.45-4.00 minutes (T2). In the second
and third practice block a 15 s data set was collected from 3.45-4.00 minutes
(respectively T3 and T4). Torque, forces and velocity were low-pass filtered (cut
off frequency of 10 Hz, recursive second order Butterworth filter). Because of
resonance in the system the medio-lateral force component was filtered at a lower
frequency (5 Hz, fourth order).
Gross mechanical efficiency
Oxygen uptake ( 2OV [l.min-1]) was continuously measured during the whole test
with an Oxycon Champion (Jaeger, Germany). Calibration was performed before
each test with reference gas mixtures. Averaged values of 10 s were sampled. The
gross mechanical efficiency (ME) of wheelchair propulsion was calculated
according to:
ME = Mean power output . Energy expenditure -1 . 100 (%) (1)
where the energy expenditure is calculated from the oxygen uptake and the
respiratory exchange ratio according to Garby and Astrup (1987). The mean power
output was calculated over the last 30 s of each exercise block. Energy expenditure
was calculated over the last two minutes of each exercise block.
Chapter 3
38
Force application
Variables were calculated as the averaged mean and / or peak values over the
number of completed pushes of each 15 s period. The push is defined as the
period that the hand exerted a positive torque on the hand rim (Figure 1).
From the measured torque and wheel velocity, the power output was calculated:
Power output = M . Vw . rw-1 (W) (2)
Where: M = torque on the hand rim, Vw = velocity of the wheel, rw = wheel radius.
Mean total power output was the sum of the power output for the left and right
wheel and was calculated over all completed pushes in the 15 s periods.
The positive forces applied with the hand on the rim were defined as follows:
Fx: horizontally forward, Fy: horizontally outward, and Fz: vertically downward.
From force components Fx, Fy and Fz, total force applied on the hand rim (Ftot)
was calculated according to:
Ftot = √(Fx2 + Fy2 + Fz2) (N) (3)
The effective force (Fm) was calculated from torque (M) and hand rim radius (rr),
according to:
Fm = M . rr-1 (N) (4)
The fraction of effective force on the hand rims (FEF), by definition the ratio
between the magnitude of the total force applied and the effective or tangential
component, was calculated from equations 3 and 4 and expressed as a percentage:
FEF = Fm . Ftot-1 . 100 (%) (5)
FEF was expressed as an average (FEFmean) and maximal (FEFmax) value during
the push phase. A low FEF generally indicates a more downward direction of the
total force vector, i.e. a deviation from the effective force (Fm) (Veeger et al.
1992c).
Negative deflections or „dips‟ were calculated from the power output curve.
The negative dips were the most negative power output values respectively before
and after the push phase (Figure 1). From the mean power output and the cycle
frequency (in Hz) the work per push cycle was calculated:
Work per cycle = power output . frequency –1 (J) (6)
Chapter 3
39
Finally, the slope of the line between the start of the push and the peak torque was
determined (Figure 1) to give an indication of how the peak torque is built up over
time.
Timing
The cycle frequency was determined from the 15 s data set of the torque signal and
expressed as the number of complete pushes per minute.
The timing parameters cycle time and push time were also determined from the
torque signal of the ergometer (Figure 1). The push time was defined as the
amount of time that the hand exerted a positive torque on the hand rim. The cycle
time was defined as the period of time from the onset of one push phase to the
onset of the next. The push time was also expressed as a percentage of the cycle
time (%push time).
Inter-cycle variability
To determine the variability of the force application and timing parameters the
coefficient of variation was calculated. The mean and standard deviation (SD) were
calculated over all cycles in the measurement period. From the mean and SD the
coefficient of variation (CV) was calculated by the formula:
CV = |SD . mean-1| . 100 (%) (7)
The CV was determined for each subject for all consecutive push cycles during the
15 s measurement periods for the force application variables: negative power
output dips before and after the push phase, FEFmax, FEFmean, slope, push time,
cycle time, percentage push time.
Statistics
To evaluate a possible learning effect over the 12 minutes practice period, the
changes over the four measurement times, namely T1 (0.15-0.30 minutes of the
first block) and T2, T3 and T4 (respectively 3.45-4.00 minutes of exercise block
one, two and three), were analyzed, with the exception of the gross mechanical
efficiency. The latter was analyzed for T2, T3 and T4 only.
An ANOVA for repeated measurements, with measurement time (T1, T2, T3 and
T4) as main factor, was applied to detect significant differences over time for
selected parameters. A post-hoc Tukey was applied to determine which time
blocks differed significantly from each other. Significance level was set at p < 0.05
for all statistical procedures.
Chapter 3
40
RESULTS
All subjects performed the three submaximal exercise blocks without any problem.
Due to the complete inexperience with this arm task, some subjects felt some weak
muscle fatigue.
Gross mechanical efficiency
The 12 minutes of practice did not lead to a change in the gross mechanical
efficiency over time (p = 0.82) (Table 1).
Force application
No significant differences over time were found for the force application variables,
except for the peak torque signal. The peak torque increased significantly over time
(p=0.02)(Table 1), although the mean torque did not (p=0.07). A significant
difference in the peak torque was found between T1 and T4, indicating a slight
change over time. The mean power output and the negative deflections in the
power output curve before and after the push did not show any significant change
over the 12 minutes period (Table 1). The fraction effective force (FEF) was
calculated for 8 subjects due to problems with Fy of one of the subjects. Since the
FEF did not change over time (Table 1), the FEF values during the first 15 s of
practice (before T1) were visualized push by push as group means. Although, the
first pushes were not yet at the right velocity and, therefore, external power output,
it seems that novice subjects immediately reach a FEF of 70-80% (Figure 2). No
learning effect was found for the variable „slope‟ (Table 1). The work per cycle
increased significantly over time, with the largest increase between T2 and T3
(Table 1).
Timing
By definition the cycle frequency lowers, given the shift in work per cycle and the
constant power output over time (T1: 61 ± 12 pushes/minute → T2: 57 ± 12
pushes/minute → T3: 53 ± 15 pushes/minute → T4: 51 ± 13 pushes/minute)(p
= 0.00). The push time did not change significantly over time (Figure 3), while the
cycle time (Figure 3) and the percentage push time did (T1: 36 ± 7 %push time →
T2: 34 ± 8 % push time → T3: 33 ± 8 % push time → T4: 32 ± 8 % push time)(p
= 0.05).
Inter-cycle variability
FEFmax, FEFmean, cycle time, push time and percentage push time showed a
relatively low coefficient of variation (<10%). Slope showed a moderate inter-cycle
variability (ranged between 15-21%). A high inter-cycle variability was found for
Chapter 3
41
the negative power output dips before and after the push phase (respectively 26-
45% and 45-59%).
A significant increase in inter-cycle variability over the practice period was found
for the push time (T1: 5.92 ± 2.10 % → T2: 6.50 ± 1.94 % → T3: 5.79 ± 1.56 %
→ T4: 8.63 ± 4.82 %)(p = 0.03) and the negative power output dip before the
push phase (T1: 25.07 ± 7.53 % → T2: 38.78 ± 16.22 % → T3: 44.86 ± 11.78 %
→ T4: 42.12 ± 11.91 %)(p = 0.00).
DISCUSSION
Although the results of the present study with able-bodied subjects are not
completely transferable to novice wheelchair-dependent subjects with disabilities of
trunk and/or upper extremity, this well controlled study gives a good indication
about which adaptations do take place during the first seconds/minutes of the
learning process of manual wheelchair propulsion. The few complaints of muscle
fatigue at the end of testing could probably not be avoided due to the complete
inexperience of the subjects regarding wheelchair propulsion and cyclic arm
exercise in general.
Mechanical efficiency
The most important change in learning a cyclic gross motor skill is an
improvement in gross mechanical efficiency, i.e. a reduction of energy cost, since
the mechanical efficiency is generally suggested to be an indicator for a more
refined and optimized movement pattern (Sparrow 1983). Previous studies
demonstrated a higher mechanical efficiency for experienced wheelchair-dependent
subjects compared to less experienced able-bodied subjects (Knowlton et al. 1981;
Tahamont et al. 1986). In contrast to what was hypothesized, in the present study
no improvement of the gross mechanical efficiency was found over time. A
practice period of 12 minutes seems to be too short to show an effect of practice
on the mechanical efficiency. At the end of the practice period the task was still
fairly new for the novice wheelchair users and they were probably still exploring
this new way of ambulation, trying different strategies. The increase in the inter-
cycle variability during the practice period, for the push time and the negative dip
before the push, could be an indication for this exploration phase. The mechanical
efficiency is dependent upon physiological and technique factors. The lack of
change in the mechanical efficiency was expected from a physiological viewpoint.
On the other hand, although no difference was found in the mechanical efficiency
over time, adaptations in force application, timing and/or variability in the
execution of the task could still have taken place during the 12 minutes of practice.
Chapter 3
42
Force application
The fraction effective force did not change during either a 3-week learning study
(Groot et al. 2002), Chapter 2) or a 7-week training study (Dallmeijer et al. 1999b).
Since there were also no significant differences in FEF between experienced and
less experienced subjects during wheelchair sprinting (Veeger et al. 1992a), it could
be expected that the fraction effective force initially changes on a short-term,
within seconds or minutes. The results of the present study showed that subjects
apply the force in a consistent way from the start of the novel task onwards and
that the fraction effective force did not change during the 12 minutes of practice.
Previous studies stated that force direction is based on optimization of cost and
effect (Groot et al. 2002a, Chapter 5; Rozendaal et al. 2000; Veeger 1992). It seems
that novice wheelchair users were able to find this optimum right from the start of
this novel gross motor task. Since a feedback-based learned fraction effective force
of around 100% does not improve the mechanical efficiency (Groot et al. 2002a,
Chapter 5), this might not be the most important variable to pay attention to
during the learning process.
The negative dips in the power output curve before and after the push phase did
not diminish during the practice period. Negative power production will reduce
overall performance, since it implies braking. The dip before the push is possibly
the result of coupling of the hands of the subject to the rim, in which the hands
had not attained the required tangential velocity of the wheels at the moment of
first contact (Veeger et al. 1992c). Novice able-bodied subjects do not seem to be
able to incorporate this new movement in their motor system within 12 minutes. A
longer practice period appeared to lead to significant reduction of negative work in
the dip before the push as was shown by the results of the 3-week practice study
(Groot et al. 2002b, Chapter 2). An even longer practice period, i.e. more than
three weeks, might be necessary to induce a significant improvement in the
negative dip after the push.
Timing variables
The timing variables changed remarkably during the short practice period. The
cycle frequency decreased significantly with 10 pushes/minute during the 12
minutes of practice. When the practice period is longer, the effect on the cycle
frequency is even larger. This was shown in previous studies with decrements in
cycle frequency of 16-19 and 22 pushes/minutes after respectively three weeks of
practice and seven weeks of training (Groot et al. 2002b, Chapter 2; Dallmeijer et
al. 1999b). An even longer period of practice may not lead to much further
Chapter 3
43
reduction of the cycle frequency since it will basically be dictated by the mechanical
constraints of the task and the physical characteristics of the musculoskeletal
system. A cycle frequency of 40-46 pushes/minute was found after three and seven
weeks of practice which compared well with a cycle frequency of 40 and 55
pushes/minute of experienced wheelchair-dependent subjects, although the latter
group was wheeling on a treadmill at a different velocity or power output of
respectively 0.55 and 1.11 m.s-1 and 20.4 and 39.8 W (Woude et al. 1989b).
Although the cycle frequency decreased, and subsequently the cycle time increased,
the push time remained constant. This means that the duration of the recovery
phase increased over the practice period. An increase in the recovery time would
enable the subjects to choose a different (e.g. longer) hand trajectory. Several
recovery styles have been described in the past (Boninger et al. 2002; Sanderson et
al. 1985; Shimada et al. 1998). It has been suggested that the recovery style could
influence the mechanical efficiency, which is surely not the case in this short-term
learning study. By definition, with the constant power output and reduction in
cycle frequency, the work per cycle had to increase over time (23.9 to 29.6 J). This
result was similar to the 3-week learning study, which showed even a larger
increase (22.6 to 32.7 J), when the pre- and post-tests were compared. This
increase in work was most probably generated through an increase in peak torque
since the push time did not change. It could be suggested that a lower cycle
frequency, and therefore a reduction of the number of de/accelerations of the
upper extremity per time unit as well as a reduction of the overall negative power
in the dips, relates to the mechanical efficiency. However, the present results do
not support this hypothesis since the cycle frequency diminished significantly
without a subsequent increase in the mechanical efficiency.
Inter-cycle variability
A typical finding is that movement variability reduces as a function of
improvement of skill (Darling et al. 1987; Vereijken et al. 1997). For example, in
rowing a high level of stroke-to-stroke consistency is necessary in the execution of
an effective movement pattern, there being a high degree of dependence between
successive movements (Smith et al. 1995). More variability in the movement
pattern could lead to the necessity for more corrections to maintain, for example,
the desired velocity and a good left-right symmetry. However, the need for
corrections would subsequently lead to more energy loss. The inter-cycle variability
gives an indication of how stable the movement pattern was. The inter-cycle
variability of the force application variables was comparable with which was found
in the 3-week learning study (Groot et al. 2002b, Chapter 2) except that the inter-
Chapter 3
44
cycle variability values of the push time and percentage push time were lower in
the present study. Although a decrease in movement variability was expected as a
function of practice and increments of skill (Darling et al. 1987; Vereijken et al.
1997), no decrease in inter-cycle variability was found during the first minutes or
over three weeks of wheelchair practice (Groot et al. 2002b, Chapter 2). On the
other hand, Bernstein (1967) proposed that, early in learning, redundancy might be
constrained by freezing out degrees of freedom via muscle coactivity. Later in
learning, these restrictions could be relaxed, which could lead to more variability in
the movement pattern. The inter-cycle variability of the push time and the negative
dip before the push increased in the present study, which might be an indication of
„unfreezing‟. One hypothesis that emerges from this idea is the following: muscle
coactivity is initially high and will decrease with skill learning as degrees of freedom
are freed up and limb stiffness is reduced. Subsequently, this may lead to an
improvement in gross mechanical efficiency. Since muscle activity and kinematics
could be easily measured, this will be useful in future learning studies.
CONCLUSION
Twelve minutes of manual wheelchair practice in novice able-bodied subjects
induced already a significant decrease in the cycle frequency, which was
accompanied by an increase in work per cycle and cycle time and a decrease in
percentage push time. Since the push time remained the same, the increase in work
per cycle was found to be due to an increase in the peak torque. For changes in
other variables a longer practice period might be necessary, for example for the
gross mechanical efficiency and to find a decrease in inter-cycle variability. On the
other hand, the results of the present study combined with those from previous
studies indicate that some variables are optimized from the start onwards. An
example is the fraction effective force, since no difference was found after 3
(Groot et al. 2002b, Chapter 2) or 7 (Dallmeijer et al. 1999b) weeks of practice or
compared to experienced subjects (Veeger et al. 1992a).
ACKNOWLEDGEMENT
The experimental assistance of Stefan van Drongelen is greatly acknowledged.
Chapter 3
45
TABLES
Table 1. Mean values and SD of experimental variables over time and statistical results. For definition of
variables, see text. * Results of a post-hoc test Tukey revealed that: the peak torque differed significantly between
T1 & T4 and Work per cycle differed significantly between T1 & T3 and T1 & T4.
T1 = Block 1
0.15-0.30 min.
T2 = Block 1
3.45-4.00 min.
T3 = Block 2
3.45-4.00 min.
T4 = Block 3
3.45-4.00 min.
Time
Effect
P N Mean SD Mean SD Mean SD Mean SD
Mean power output (W) 9 22.63 2.94 22.29 2.78 22.68 2.80 22.54 2.54 0.61
Negative dip before push (W) 9 -3.86 1.49 -3.34 1.73 -2.86 1.28 -3.04 1.13 0.20
Negative dip after push (W) 9 -1.73 0.81 -1.45 0.60 -1.62 1.12 -1.59 0.42 0.84
Peak torque (Nm) 9 17.56 5.32 18.37 6.41 19.74 7.27 20.73 7.50 0.02*
Mean torque (Nm) 9 9.89 2.66 10.28 3.13 10.70 3.59 11.11 3.83 0.07
Slope (Nm.s-1) 9 96 46 95 41 87 32 94 44 0.77
Work per cycle (J) 9 23.38 7.50 24.77 7.98 28.92 12.83 28.78 11.39 0.00*
FEFmax (%) 8 82.01 3.76 82.75 6.94 81.09 5.97 79.60 7.34 0.61
FEFmean (%) 8 75.25 5.78 75.89 6.24 74.14 4.03 73.34 6.89 0.74
Mechanical efficiency (%) 9 7.43 0.71 7.51 0.64 7.51 0.76 0.82
Chapter 3
46
FIGURES
0 20 40 60 80 100 120 140 160 180 200-10
-5
0
5
10
15
20
25
Cycle time
Push
time
Slope
Sample
Torq
ue (
Nm
)Torque signal right
Power loss
= Work per cycle
0 20 40 60 80 100 120 140 160 180 200-10
-5
0
5
10
15
20
25
Cycle time
Push
time
Slope
Sample
Torq
ue (
Nm
)Torque signal right
Power loss
= Work per cycle= Work per cycle
Figure 1. Illustration of the definition of push time, cycle time, slope, work per cycle and power loss before and
after the push time.
Figure 2. Group mean and standard deviations (N = 8) of FEFmax and FEFmean for the first 10 pushes of
the practice period.
First FEF values
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10
Push
FE
F (
%)
FEFmax
FEFmean
Chapter 3
47
Figure 3. Change in cycle and push time over the practice period. T1 = 0.15-0.30 min. of block 1; T2-T3-T4:
3.45-4.00 min. of respectively block 1, 2 and 3. * = significant main effect for cycle time.
Push time and Cycle time
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
T1 T2 T3 T4
Tim
e (
s)
Push time
Cycle time
*
48
Chapter 4
Short-term adaptations in co-ordination during the initial phase of learning manual wheelchair propulsion
Chapter 4
49
ABSTRACT
The purpose of this study was to analyze adaptations in kinematics and muscle
activity/co-contraction in novice able-bodied subjects during the initial phase of
learning hand rim wheelchair propulsion. Nine able-bodied subjects performed
three 4-minute practice blocks on a wheelchair ergometer. The external power
output and velocity were constant for all blocks, respectively 0.25 W.kg-1 and 1.11
m.s-1. Electromyography of 16 arm, shoulder, back and chest muscles and
kinematics were measured. Some small changes in the segmental movement
pattern were seen during the practice period. Moreover, an increase in muscle
activity and co-contraction of several muscles was found over time. The hypothesis
that subjects instinctively search for an optimum frequency, in which the recovery
phase is related to the eigenfrequency of the arms and, therefore, the least muscle
activity, could not be supported. Since co-contraction of antagonist pairs remained
the same or even increased, the hypothesis that there would be a decrease in
muscle co-contraction as a result of practice, was not confirmed. This study was
probably too short for the novice subjects to explore this new task of wheelchair
propulsion completely and reach an optimum in terms of cycle frequency and
muscle activity / co-contraction.
Chapter 4
50
INTRODUCTION
Energy efficiency is one of the characteristics attributed to skilled movements.
Sparrow et al. (1998) stated that adaptive movement patterns emerge as a function
of the subject‟s innate tendency to minimize metabolic energy expenditure with
respect to task and environment. In manual wheelchair propulsion a significant
increase in gross mechanical efficiency was found after 3-weeks of practice (Groot
et al. 2002b, Chapter 2) in a novice wheelchair-user group. Simultaneously, a
decrease in cycle frequency was seen, which was accompanied by an increase in
work per cycle (Groot et al. 2002b, Chapter 2). It was suggested that the
improvement in timing variables had a positive effect on the mechanical efficiency.
However, in a study focusing on the short-term adaptations in physiology and
propulsion technique (Groot et al. 2003a, Chapter 3), it was found that the timing
variables already changed within the initial 12 min. of practice while the mechanical
efficiency remained the same in a group of completely novice able-bodied
wheelchair users. A decrease in cycle frequency and an increase in work per cycle
were found during the initial 12 min. of practice. The latter seemed to be due to an
increase in peak torque over the practice period since no change in push time was
found. Woude et al. (1989b) stated that shifts in timing will affect the kinematics of
motion and thus influencing muscle activity and coordination. A wheelchair user
can maintain the same external power output by varying the cycle frequency in
combination with the work per cycle. If the cycle frequency decreases, the work
per cycle has to increase to maintain the external power output and, therefore, the
force application has to be enhanced or has to occur during a longer trajectory.
The latter will demand increased segment excursions and an increased stroke angle.
An increase in muscle activity during the push phase, and subsequently during the
whole cycle, could be expected to accomplish this enhanced force application.
Since the mean external power output remains the same, the averaged level of
muscle activity over a certain time period could be expected to remain the same
too, regardless of the cycle frequency - work per cycle combination. The work per
cycle is accomplished during the push phase while the recovery phase has been
called a „passive‟ period (Veeger et al. 1992c). However, it is suggested that
ac/decelerating the arms during the recovery phase costs some amount of energy
although this does not contribute directly to propelling the wheelchair. If the
recovery phase indeed costs energy, then a high cycle frequency leads to more
ac/decelerations of the arms and subsequently a higher energy loss. Previous
studies found that the physiologically most efficient cycle frequency is the freely
chosen frequency in comparison to paced frequencies below and above, both in
experienced and less experienced wheelchair users (Goosey et al. 2000; Woude et
Chapter 4
51
al. 1989b). The relationship between cycle frequency and energy cost in hand rim
wheelchair propulsion appeared to be hyperbolic where the freely chosen
frequency is close to minimum energy cost. This is in contrast to cycling, where the
freely chosen pedal rate is unrelated to gross mechanical efficiency and where the
mechanical efficiency is highest at the lowest pre-set pedal rate (Hansen et al.
2002). In walking, Holt et al. (1991) found that preferred stride frequency produces
a minimal metabolic cost as a result of the leg oscillating at resonance. Preferred
behaviors seem to follow laws generated by the relationship between body-scaled
(e.g. segment length) and environmental (gravity) parameters, i.e. in the study of
walking (Holt et al. 1991). The idea of the leg swinging at resonant frequency is
supported by electromyography (EMG) studies that have reported very little
muscle activity during walking at a self-selected walking speed in the swing leg
muscles of healthy subjects (Selles et al. 1999). These studies lead to the hypotheses
that novice wheelchair users instinctively search for a cycle frequency with a
recovery phase closest to the resonance frequency of their arms, and when they
find this optimum frequency it will correspond to the least EMG activity.
Several studies have been conducted to examine muscle activity patterns during
wheelchair propulsion (Harburn et al. 1986; Mulroy et al. 1996; Rodgers et al. 1994;
Schantz et al. 1999; Veeger et al. 1991a) but, as far as is known, alterations in
muscle activity patterns over time due to natural training or learning in hand rim
wheelchair propulsion have not been studied before. Given the large number of
muscles around the shoulder, movements can be conducted with different sets of
active muscles. One way to constrain this redundancy is to link muscles together
into a muscle synergy. Bernstein (1967; Newell et al. 2001) proposed that, early in
learning, redundancy might be constrained by reducing (“freezing out”) the
number of degrees of freedom via muscle coactivity. Later in learning, these
restrictions could be relaxed. If this is true, a corollary would be the following:
muscle coactivity is initially high and will decrease with skill learning as degrees of
freedom are freed up and limb stiffness is reduced. The push phase of wheelchair
propulsion is a guided movement, with not many degrees of freedom, in contrast
to the recovery phase, in which the hand can choose many paths to return to the
initial push position. Therefore, it is expected that possible changes in co-
contraction are particularly, if not only, visible during the recovery phase, especially
at the end of the recovery phase when the moving hands have to be coupled to the
rotating rim outside the visual field. Furthermore, a possible decrease in muscle co-
contraction could explain the change in mechanical efficiency as was seen in the 3-
week practice study (Groot et al. 2002b, Chapter 2).
Chapter 4
52
In order to test the hypotheses mentioned above, the present study focused on the
short-term adaptations in kinematics and muscle activation patterns in completely
novice able-bodied subjects in the initial 12 min. of the learning process on a
computer-controlled wheelchair ergometer.
METHODS
Subjects
After having given written informed consent, 9 able-bodied male subjects
participated in the study. Criteria for inclusion were: male, no prior experience in
wheelchair propulsion, absence of any medical contra-indications. The mean age
was 24.0 years (SD = 4.8), mean body mass was 76.4 kg (SD = 8.0) and mean
height was 1.82 m (SD = 10.2). All subjects were right-handed. The study was
approved by the Medical Ethical Committee.
Design
Without prior familiarization, subjects performed three 4-min. submaximal practice
blocks. The practice blocks were performed on a stationary, computer-controlled
wheelchair ergometer (Niesing et al. 1990). The external power output of all blocks
was 0.25 W.kg-1 and the velocity was 1.11 m.s-1. Two minutes of rest preceded each
exercise block. The protocol was described in detail in Groot et al. (2003a, Chapter
3).
Wheelchair ergometer
Wheelchair ergometer dimensions were individually adjusted according to a
standardized protocol described elsewhere (Groot et al. 2002b, Chapter 2). The
ergometer allows for a direct measurement of propulsive torque around the wheel
axle and the resultant velocity of the wheels (Niesing et al. 1990). From the torque
signal the push and recovery phases were determined (Groot et al. 2003), Chapter
3).
Kinematics
Movement analysis was performed with a three-camera Optotrak system. The
three-dimensional positions of markers were recorded at 100 Hz during the first
practice block from 0.15-0.30 (T1) and from 3.45-4.00 minutes (T2). In the second
and third practice block a data set was collected from 3.45-4.00 minutes
(respectively T3 and T4). The Optotrak computer was synchronized with the
ergometer computer. Markers were positioned on the right side, on the hand (fifth
metacarpal), wrist (caput ulna), elbow (epicondylus lateralis), and shoulder (angulus
Chapter 4
53
acromialis). From measurements with Optotrak the following parameters were
determined: begin angle (°), end angle (°) and stroke angle (°) (Figure 1). Begin and
end angle were defined as the angle between the line from the hand marker (on
fifth metacarpal) through the wheel axle, relative to the vertical, at the start and the
end of the push phase. Stroke angle was defined as the sum of the begin and end
angle. For every cycle the relative 3-D locations (in m, anterior/posterior,
medial/lateral, cranial/caudal) of adjacent upper extremity points (shoulder –
elbow, elbow – wrist, wrist – hand) at time of the start and end of the push phase
were determined according to Chow et al. (2000). To determine whether the
position of the trunk in the chair changed over time, the position of the acromion
with regard to the wheel axle was analyzed in the three directions at the time of the
start and end of the push phase.
The mean and peak velocity and ac/deceleration of the hand in the
anterior/posterior (x) direction were calculated for the push and recovery time
separately.
Muscle activity
The electromyography (EMG) of muscles of the forearm, upper arm, shoulder,
back and chest were measured to obtain an indication of the level of activity and
the tendency for co-contractions between certain sets of muscles over time. The
following 16 muscles were determined: m. extensor carpi ulnaris, m. extensor carpi
radialis, m. brachioradialis, m. biceps brachii, m. triceps brachii caput lateral and
longum, m. pectoralis major pars sternocostalis and clavicularis, m. trapezius pars
descendens, transversa and ascendens, m. deltoideus pars anterior, medialis and
posterior, m. serratus anterior and m. latissimus dorsi.
The bipolar EMG data were captured by Ag/AgCl, circular electrodes (Medicotest,
Blue Sensor, type N-00-S) of about 11 mm diameter. Prior to the experiment, after
shaving, gentle abrasion, and cleaning by alcohol of the skin, surface EMG-
electrodes were positioned at the approximate geometrical center of each muscle
on the right side (Hermens et al. 1999). The center-to-center electrode distance was
2 cm. The EMG signals were amplified, band-pass filtered (10-200 Hz) and stored
on a disk at a sample frequency of 1000 Hz. The EMG was synchronized with the
Optotrak computer by means of a pulse. For each muscle, a static maximal
voluntary contraction (MVC) was recorded and used for reference. For the MVC
measurements subjects were asked to push as hard as they could against the tester‟s
resistance in several positions of the upper extremity and trunk (Hermens et al.
1999) when sitting in the wheelchair ergometer. This was performed once for each
muscle tested to exclude fatigue effects prior to the actual exercise blocks. Linear
envelopes were constructed by re-sampling EMG signals with a frequency of 100
Chapter 4
54
Hz, preceded by rectifying and low-pass filtering (8th order Butterworth filter, Fc
= 6 Hz (Winter 1979)) of the signal. Thereafter the EMG values were normalized
to the highest muscle activity obtained in the MVC test. An electromechanical
delay of 100 msec was used (from results of unpublished data) for synchronization
of EMG and ergometer and kinematic data. The EMG data were normalized to
percentage cycle time and ensemble-averaged for all complete cycles during the
measurement period for each subject, leading to one average cycle (in percentage
cycle time) for each subject.
To get an indication of possible changes in the level of muscle activity the
ensemble-averaged cycle for each subject was integrated over 1% steps. The level
of muscle activity for each muscle, as well as the co-contraction, was determined
for respectively the push, recovery and cycle time, all calculated from the
ensemble-averaged cycle. Also, the level of muscle activity and co-contraction
during all complete cycles, i.e. from the start of the first push until the start of the
last push in the 15 s period, were determined by integrating the rectified, filtered,
and normalized (to MVC) EMG signal over 1 s steps. It was expected that the
possible effect of a lower cycle frequency, and thus less ac/decelerations of the
arms, on the muscle activity was better visible in this 15 s analysis than in the
ensemble-averaged cycle.
By overlaying the linear envelopes of an agonist – antagonist pair and calculating
the area of overlap the amount of co-contraction was assessed (Figure 2). This
created a co-contraction index for each pair at T1, T2, T3 and T4. The level of co-
contraction was established for the following muscle combinations: m. extensor
carpi radialis - m. extensor carpi ulnaris, m. biceps brachii - m. triceps brachii caput
longum, m. biceps brachii - m. triceps brachii caput lateral, m. trapezius transversa
- m. serratus anterior, and m. deltoideus anterior - m. deltoideus posterior.
As said before, all complete cycles were used to compute within-subject ensemble
averages. These ensemble averages were in turn averaged across all subjects to yield
a grand ensemble normalized average for each of the four measurement periods.
Inter-cycle variability
The coefficient of variability (CV) was determined for every muscle of each subject
from the mean and SD values of the integrated EMG signals of all pushes,
recoveries and cycles in the measurement period according to:
CV = |SD . mean-1| . 100 (%) (1)
Chapter 4
55
Statistics
To evaluate a possible learning effect over the 12 minutes practice period, the
changes over the four measurement times, namely T1 (0.15-0.30 minutes of the
first block) and T2, T3 and T4 (respectively 3.45-4.00 minutes of exercise block
one, two and three), were analyzed. An ANOVA for repeated measurements, with
measurement time (T1, T2, T3 and T4) as main factor, was applied to detect
significant differences over time for selected parameters. Significance level was set
at p < 0.05 for all statistical procedures.
RESULTS
Kinematics
Descriptive statistics for stroke angle, hand velocity and de/acceleration are
presented in Table 1, and relative joint locations at time of hand contact and
release for each testing time appear in Table 2. No difference in velocity was visible
regarding the four measurement times, i.e. subjects were already at the desired
velocity (and power output) at T1. Despite a significant reduction in cycle
frequency over the 12 min. period (T1: 61 ± 12 pushes/minute → T2: 57 ± 12
pushes/minute → T3: 53 ± 15 pushes/minute → T4: 51 ± 13 pushes/minute)
(Groot et al. 2003a, Chapter 3), stroke angle, begin angle and end angle did not
change significantly over time (Table 1).
The position of the trunk in the chair – as derived from the shoulder-wheel axis
distance - at the start and end of the push phase did not alter during the practice
period. In contrast, the medio-lateral distance, at both the start and end of the
push phase, between the shoulder and the elbow diminished significantly over
time, while the medio-lateral distance between the elbow and wrist increased
significantly over time (Table 2). Since the position of the trunk did not change
over time and the hand is fixed in the medio-lateral direction onto the rim, this
indicates an inward movement of the elbow (adduction of the upper arm). The
medio-lateral distance between the wrist and hand increased significantly and the
cranial-caudal distance between the wrist and hand decreased significantly at the
end of the push phase (Table 2). This indicates more palmar flexion and possibly
ulnar deviation of the hand.
In the anterior-posterior direction, the mean velocity and mean and peak
acceleration of the hand during the push phase and the peak acceleration of the
hand during the recovery phase changed significantly over time (Table 1).
Chapter 4
56
Muscle activity
The activity pattern of each muscle at T1, T2, T3 and T4 is visualized in Figure 3.
Muscle activity during push time: M. pectoralis major pars sternocostalis (p=0.04,
T1 vs. T3) and m. deltoideus posterior (p=0.02, T1 vs. T2) showed an increase in
muscle activity during the push phase over time. No change in co-contraction was
found during the push time (Figure 4).
Muscle activity during recovery time: M. trapezius descendens (p=0.00, T1 vs. T2,
T3 and T4) and ascendens (p=0.00, T1 vs. T3 and T4), m. deltoideus posterior
(p=0.02, T1 vs. T2 and T4), m. serratus anterior (p=0.03, T1 vs. T4) and m.
lattisimus dorsi (p=0.05) showed an increase in muscle activity during the recovery
phase over time. Significantly more co-contraction was found over the practice
period for m. biceps brachii and m. triceps brachii caput longum (p=0.02, T1 vs.
T2) and for m. trapezius transversa and m. serratus anterior (p=0.00, T1 vs. T4)
during the recovery phase (Figure 4).
Muscle activity during cycle time: A significant increase in muscle activity over the
practice period during the (normalized) cycle time was visible for m. trapezius
descendens (p=0.00) and ascendens (p=0.00), and m. deltoideus posterior
(p=0.02). All three muscles showed most of the change already between T1 and
T2, i.e. within four minutes of practice. A significant increase in co-contraction of
m. trapezius transversa and m. serratus anterior (p=0.02) was found during the
cycle time (Figure 4).
When the integrated EMG of all complete cycles in the 15 s measurement period
was calculated, i.e. not normalized to percentage cycle time and ensemble averaged,
a significant increase in muscle activity was shown in m. biceps brachii (p=0.03),
m. trapezius descendens (p=0.00) and ascendens (p=0.00), m. deltoideus medialis
(p=0.02) and posterior (p=0.00) and m. serratus anterior (p=0.05), with most of
the change between T1 and T2. The level of co-contraction of all complete cycles
during the 15 s measurement period increased significantly between T1 and T2 for
the antagonists m. biceps brachii – m. triceps brachii caput lateral (p=0.02), and m.
biceps brachii – m. triceps brachii caput longum (p=0.01).
Inter-cycle variability
An increase in inter-cycle variability over time was found during the cycle time for
all muscles except for m. triceps brachii caput longum, m. trapezius descendens
and ascendens. The coefficient of variation of the activity of the muscles varied
between 27-51% during the push phase and 23-62% during the recovery phase
(with a high variation for m. serratus anterior of 75-97%). No change in inter-cycle
variability was found during the push phase while a decrease was found for m.
serratus anterior (p=0.04) during the recovery phase.
Chapter 4
57
DISCUSSION
Learning of a motor task is associated with a number of changes in limb kinematics
and muscle activity that produces the movement (Flament et al. 1999). Previous
research (Groot et al. 2003a, Chapter 3) on short-term adaptations in propulsion
technique found changes in timing parameters, indicating changes in kinematics
and muscle activity. Although it is often suggested that changes in timing relate to
changes in the mechanical efficiency (Groot et al. 2002b, Chapter 2; Woude et al.
1989b) this was not supported by a previous report by the authors that focused on
the short-term changes in propulsion technique (Groot et al. 2003a, Chapter 3).
However, changes in the movement pattern and muscle activity still could have
occurred in association with changes in timing. The decrease in cycle frequency
and subsequently increase in work per cycle during the first 12 min. of wheelchair
practice (Groot et al. 2003a, Chapter 3) was clearly associated with an increase in
peak torque while the push time remained the same. The increase in peak torque
was obviously accomplished by the increase in acceleration of the hand during the
push phase, which was found in the present study. Even though the push time
remained the same, there could be an increase in the stroke angle due to the
increased acceleration of the hands. The stroke angle in the current study showed a
non-significant tendency to increase over time. It has been shown before that a
longer training period (7-weeks) induces a significantly higher end and stroke angle
in non-wheelchair users (Dallmeijer et al. 1999b). Some small changes in the
relative joint locations were found in the current study, which could relate to the
small but non-significant changes in begin and end angle.
When more work per cycle occurs, the muscles, which have to produce this extra
work, have to be more active during the push phase. So, the first question is, “How
do the muscle activity patterns of the present study relate to the increase in work
per cycle?” Secondly, “Are the results of these novice wheelchair users regarding
muscle activity patterns similar to the results of experienced wheelchair users of
other studies?” Of the muscles, which could be anticipated to propel the
wheelchair forward and from which an increase in activity was expected as a
consequence of the increase in work per cycle, i.e. m. biceps brachii, m. triceps
brachii, m. deltoideus anterior, m. pectoralis major pars clavicularis and
sternocostalis, only an increase of m. pectoralis major pars sternocostalis was
found during the push phase. The increase in activity of m. deltoideus posterior
over time, which was found in the present study during the push phase, would not
be useful for increasing the work per cycle. Schantz et al. (1999) also found high
activity of m. deltoideus posterior during the push phase in their study with
paraplegic and tetraplegic subjects. They hypothesized that the possible function of
Chapter 4
58
m. deltoideus posterior activity during the push phase is stabilization of the
shoulders. With an increase in work per cycle due to an increase in muscle activity
of m. pectoralis major there probably should also be a corresponding increase in
m. deltoideus posterior to keep the shoulder stabilized. The increase in muscle
activity over time during the recovery phase (m. deltoideus posterior, m. trapezius
ascendens and descendens, m. serratus anterior and m. latissimus dorsi), indicated
by the integrated EMG, might be due to the increase in recovery time in absolute
terms. Furthermore, the longer recovery time seems to lead to more variability in
the movement pattern, which might lead to more muscle activity.
The biceps brachii and the brachioradialis were hardly active during the whole
cycle. This result was not similar to that reported by other investigators (Masse et
al. 1992; Rodgers et al. 1994; Veeger et al. 1989a). They found m. biceps brachii
activity during the initial part of the push phase and during the latter part of the
recovery phase. Thus m. biceps brachii served as a forearm flexor to pull during
the initial phase and again at the end of recovery as the arm returned to starting
position (Rodgers et al. 1994). Since the begin angle was very small in this novice
able-bodied subject group, the pull phase was probably very short too and,
therefore, not much m. biceps brachii activity was required during the push phase
in the present study. To flex the elbow during the recovery phase does not seem to
cost a lot of biceps or brachioradialis activity, when expressed with reference to
their MVCs. That these results are not similar to previous studies (Rodgers et al.
1994; Masse et al. 1992; Veeger et al. 1989a) might be due to the subjects‟ use of a
pumping recovery style (Sanderson et al. 1985; Schantz et al. 1999; Veeger et al.
1989a) in which the hands are brought back over the top of the wheel which could
be in contrast to styles used by more experienced wheelchair users. M. trapezius
transversa, a scapular retractor, functioned antagonistically to m. serratus anterior
as has been shown before (Mulroy et al. 1996). M. lattisimus dorsi did not show a
consistent pattern of activity during the propulsion cycle and was low in intensity,
as was also shown by Mulroy et al. (1996).
Changes in the amount of muscle activity were expected to result from changes in
the cycle frequency, and these were expected to be most clearly visible in the
analysis of all complete cycles in the 15 s period. The effects of arm
ac/decelerations in the recovery phase is less clear when looking at one averaged
and normalized cycle in contrast to analyzing a certain time block with more/less
cycles involved dependent upon the cycle frequency. Previous research suggested
that a lower cycle frequency leads to fewer de/accelerations of the arms and
subsequently less muscle activity (Woude et al. 1989b). However, the present
results do not support that theory, i.e. muscle activity did not decrease (and even
Chapter 4
59
increased in a number of muscles) with a lower number of pushes. It has also been
reported before that the most efficient cycle frequency is the freely chosen
frequency in experienced as well as less experienced wheelchair users (Goosey et al.
2000; Woude et al. 1989b), which is not the lowest frequency possible. The first
hypothesis of the present study was that novice wheelchair users instinctively
search for an optimum cycle frequency and when they find this optimum
frequency the least EMG activity will be found. It was suggested that this optimum
cycle frequency could relate to the eigenfrequency of the arms during the recovery
phase as was previously found regarding the legs in walking (Holt et al. 1991).
Novice wheelchair users do not seem to find this optimum cycle frequency within
12 min. of practice because the cycle frequency of the novice subjects was still
higher than that found in previous studies after 3 (Groot et al. 2002b, Chapter 2)
or 7 (Dallmeijer et al. 1999b) weeks of practice, and, furthermore no change in the
mechanical efficiency in novice subjects has been reported (Groot et al. 2003a,
Chapter 3). Moreover, an increase instead of a decrease in activity was found for
several muscles. The theory of a segment oscillating at resonance during the
recovery phase might not completely be applicable to novice wheelchair users since
the arms do not really swing passively during a good deal of the recovery phase as
the leg does, especially when using the pumping propulsion pattern (Sanderson et
al. 1985; Schantz et al. 1999; Veeger et al. 1989a). The theory would be more
applicable when the (semi-)circular propulsion pattern (Boninger et al. 2002;
Sanderson et al. 1985; Schantz et al. 1999; Shimada et al. 1998; Veeger et al. 1989a)
is used by the subjects. In this pattern, the hands show more swing motion from
the end to the start point of the push phase below propulsion. Also, one needs to
realize that walking is a fully automatic movement pattern in the adult, while hand
rim wheelchair propulsion in the current study was a completely novel task.
Since the task was completely new for the novice able-bodied subjects, Bernstein‟s
theory suggests that they might begin by reducing („freezing‟) the number of
degrees of freedom by muscle co-contractions (Bernstein 1967), then as they
become more used to the task, they might start to „unfreeze‟, resulting in less co-
contraction and thus lower energy costs. However, in this study, no change in co-
contraction was visible during the push phase. The lack of change was expected
since the push phase is a guided movement with not many degrees of freedom.
However, an unexpected increase in co-contraction was found for m. biceps
brachii – m. triceps brachii longum and m. trapezius transversa – m. serratus
anterior during the recovery phase. The high co-contraction of the anterior and
posterior part of m. deltoideus might be necessary for stabilization of the shoulder.
This result was also found in experienced subjects by Schantz et al. (1999), but this
Chapter 4
60
finding was in contrast with results of Mulroy et al. (1996) and Rodgers et al.
(1994). Although it was suggested that more co-contraction would lead to a lower
mechanical efficiency, this was not supported since the latter remained the same
over the practice period (Groot et al. 2003a, Chapter 3). Moreover the time scale of
practice (and learning) in the current study may not fully fit the
„freezing/unfreezing‟ theory and needs to be studied on a larger time scale in the
future.
The inter-cycle variability gives an indication about the stability of the movement
pattern. Typically movement variability reduces as function of practice and
increments of skill (Darling et al. 1987; Vereijken et al. 1997). In previous studies,
no decrease in inter-cycle variability of force application has been found during the
first minutes of practice (Groot et al. 2003a, Chapter 3) or over three weeks of
wheelchair practice (Groot et al. 2002b, Chapter 2). More variability in the
movement pattern could lead to the necessity for more corrections to maintain e.g.
the desired velocity and a good left-right symmetry, leading to more energy loss.
Remarkable was that the variability of the movement pattern during the recovery
phase increased over the practice period. Figure 5 shows the movement pattern
that was visible for most subjects (pattern of subject B) in contrast to the less
variable movement pattern of subject A. This increase in variability over time
could be due to the increase of the recovery time. Therefore, the subjects had more
time to get back to the initial push position. Another explanation for this increase
in variability could be that the subjects were less concentrated at the end of the
test. In the beginning, the task is completely new and they have to focus on what
they are doing while at the end, the task will be performed more automatically and
less concentration is needed to complete the task. Furthermore, the wheelchair task
in the present study was fairly easy and submaximal, giving the subjects the
opportunity to explore the task, using different propulsion styles, leading to more
variability. Or, as found by Tuller et al. (1982), a beginner learns a skill by reducing
some of the free variation of the body. As skill increases, the beginner will release
the ban on the degrees of freedom and subsequently this will lead to more
variability. The latter was not supported by the co-contraction findings, i.e. there
was an increase in co-contraction of some muscle pairs instead of a decrease.
Harburn and Spaulding (1986) found a high inter-subject variability but a low intra-
subject variability (i.e. less than 10% as stated by them) from cycle to cycle. The
low intra-subject variability of muscle recruitment patterns suggests that their
subjects, in both wheelchair-dependent and able-bodied group, had a stable
movement pattern and that the wheelchair task seemed to be a learned skill.
Indeed, the able-bodied subjects in the study of Harburn and Spaulding (1986)
Chapter 4
61
were familiar with wheelchair mobility before the start of the test, i.e. not
completely novice as the able-bodied subjects in the present study. The inter-cycle
variability in the novice subjects was almost always higher than 10%, up to 51%
and 62% for respectively the push and recovery phase. The shoulder muscle
complex offers a wide range of movements, which could subsequently lead to a
high variability between push cycles, especially in inexperienced subjects. As was
the case with the negative power output dip at the start of the push phase, the push
time (Groot et al. 2003a, Chapter 3) and the movement pattern, the inter-cycle
variability of some muscles increased during the practice period.
CONCLUSION
Some small changes in the segmental movement pattern and an increase in muscle
activity and co-contraction of several muscles were found during the 12 min. of
practice in association with the changes in timing parameters. However, the
subjects had probably not found their optimum cycle frequency yet because the
cycle frequency of the novice subjects was still higher than found in previous
studies after 3 (Groot et al. 2002b, Chapter 2) or 7 (Dallmeijer et al. 1999b) weeks
of practice. Since the subjects did not reach their optimum cycle frequency within
12 min., the hypothesis that the optimum cycle frequency relates to the
eigenfrequency of the arms and will lead to a higher mechanical efficiency and
lower level of muscle activity, could not be supported. Furthermore, the suggested
releasing or „unfreezing‟ of degrees of freedom by a decrease in muscle co-
contraction as a result of practice, was not confirmed since co-contraction of
antagonist pairs remained the same or even increased. This study was probably too
short for the novice subjects to explore this new task of wheelchair propulsion
completely and reach an optimum in terms of cycle frequency and mechanical
efficiency.
ACKNOWLEDGEMENT
The experimental assistance of Stefan van Drongelen is greatly acknowledged.
Chapter 4
62
TABLES
Table 1. Changes in mean and peak velocity and de/acceleration of the hand during the push and recovery phase
over time in the sagital plane, and stroke angles (degrees). T1 = Block 1
0.15-0.30 min.
T2 = Block 1
3.45-4.00 min.
T3 = Block 2
3.45-4.00 min.
T4 = Block 3
3.45-4.00 min.
Time
Effect
(p) N Mean SD Mean SD Mean SD Mean SD
Begin angle (°) 9 10.65 8.33 12.97 8.94 15.47 10.01 14.64 6.70 0.25
End angle (°) 9 57.94 6.09 59.30 7.10 58.71 8.32 57.36 7.36 0.56
Stroke angle (°) 9 68.59 9.69 72.27 9.59 74.18 11.84 71.95 9.02 0.18
PUSH Mean velocity 8 0.76 0.08 0.75 0.08 0.80 0.07 0.81 0.07 0.01
Peak velocity 8 1.13 0.09 1.15 0.15 1.14 0.14 1.17 0.05 0.86
Mean de/acceleration 8 -1.15 0.75 -1.11 0.62 -0.39 0.68 -1.21 1.10 0.04
Peak de/acceleration 8 10.11 3.82 15.58 8.21 19.09 6.46 11.25 6.31 0.04
RECOVERY Mean velocity 8 -0.40 0.16 -0.40 0.13 -0.37 0.12 -0.39 0.13 0.76
Peak velocity 8 1.00 0.13 1.11 0.15 1.02 0.21 1.09 0.14 0.48
Mean de/acceleration 8 0.68 0.50 0.86 0.38 0.50 0.32 0.68 0.46 0.06
Peak de/acceleration 8 19.10 5.70 29.77 7.95 21.75 7.47 25.24 7.68 0.01
Table 2. Relative locations (m) of shoulder, elbow, wrist and hand at the time of hand contact and hand release.
X +: First joint in front of second joint; Y +: First joint medial of second joint; Z +: First joint above second
joint. T1 = Block 1
0.15-0.30 min.
T2 = Block 1
3.45-4.00 min.
T3 = Block 2
3.45-4.00 min.
T4 = Block 3
3.45-4.00 min.
Time
Effect
(p) N Mean SD Mean SD Mean SD Mean SD
HAND CONTACT Shoulder – Elbow X 8 16.98 2.97 16.83 3.75 18.58 2.69 18.13 2.66 0.10
Shoulder – Elbow Y 8 18.25 2.43 18.39 2.78 17.28 1.78 17.11 1.92 0.03
Shoulder – Elbow Z 8 15.59 6.04 15.29 5.02 14.48 5.32 14.93 5.97 0.21
Elbow – Wrist X 8 -9.56 6.07 -8.03 5.09 -7.65 4.21 -8.74 4.17 0.45
Elbow – Wrist Y 8 4.04 2.50 3.93 2.78 5.20 1.87 5.24 1.48 0.02
Elbow – Wrist Z 8 23.83 1.70 24.54 1.78 24.83 1.33 24.36 1.20 0.18
Wrist – Hand X 8 -3.75 0.76 -3.55 0.71 -3.51 0.74 -3.70 0.70 0.48
Wrist – Hand Y 8 2.48 0.61 2.61 0.61 2.59 0.62 2.65 0.64 0.65
Wrist – Hand Z 8 6.58 1.34 6.55 1.25 6.65 1.33 6.48 1.40 0.74
HAND RELEASE
Shoulder – Elbow X 8 -7.80 2.54 -8.21 2.95 -8.18 2.99 -8.28 2.79 0.86
Shoulder – Elbow Y 8 16.96 1.52 15.90 1.17 15.85 1.01 16.03 1.07 0.01
Shoulder – Elbow Z 8 27.75 4.95 28.30 4.40 28.06 3.98 27.49 4.11 0.30
Elbow – Wrist X 8 -13.21 2.02 -13.15 1.69 -13.36 1.73 -14.06 1.49 0.19
Elbow – Wrist Y 8 5.15 0.74 5.61 0.49 5.71 0.58 5.55 0.44 0.05
Elbow – Wrist Z 8 22.03 1.66 21.91 1.72 21.75 1.70 21.35 1.69 0.13
Wrist – Hand X 8 -0.57 1.16 -0.74 1.00 -0.51 1.11 -0.84 1.10 0.57
Wrist – Hand Y 8 3.17 1.01 3.57 1.01 3.58 0.85 3.56 0.63 0.01
Wrist – Hand Z 8 7.05 0.93 6.85 0.73 6.75 0.84 6.67 0.86 0.04
Chapter 4
63
FIGURES
Figure 1. Definition of the variables begin angle (BA), end angle (EA), stroke angle (SA) and top dead center
(TDC).
Figure 2. Definition of co-contraction. The area of overlap (gray) is the amount of co-contraction.
Wheeling direction
BA EA
SA
Start push
phase
TDC
Wheel axle
End push
phase
Cocontraction of m. trapezius transversa ( - ) and m. serratus anterior (.)
Sample
%
M
VC
Co-contraction of m. trapezius transversa ( - ) and m. serratus anterior (.)
Sample
%MVC
Chapter 4
64
Figure 3. Mean muscle activity patterns for T1 (0.15-0.30 min. of the first block) and T2, T3 and T4 (3.45-
4.00 min. of the each block), normalized by MVC and cycle time. The vertical lines indicate the end of the push
phase. (T1 = __, T2 = - -, T3 = …, T4 = _ . _).
Chapter 4
65
Co-contraction
0
0.2
0.4
0.6
0.8
1
1.2
1.4
ECU-E
CR T
1
ECU-E
CR T
2
ECU-E
CR T
3
ECU-E
CR T
4
BB-T
BLA
T1
BB-T
BLA
T2
BB-T
BLA
T3
BB-T
BLA
T4
BB-T
BLO
T1
BB-T
BLO
T2
BB-T
BLO
T3
BB-T
BLO
T4
DA-D
P T
1
DA-D
P T
2
DA-D
P T
3
DA-D
P T
4
TT-SA T
1
TT-SA T
2
TT-SA T
3
TT-SA T
4
Inte
gra
ted
EM
GRecovery time
Push time
Figure 4. Change in co-contraction during the push, recovery and cycle time over the practice period. T1 = 0.15-
0.30 min. of block 1; T2-T3-T4: 3.45-4.00 min. of respectively block 1, 2 and 3. ECU-ECR: m. extensor
carpi ulnaris and radialis; BB-TBLA/TBLO: m. biceps brachii and m. triceps caput lateral / longum; DA-
DP: m. deltoideus anterior and posterior; TT-SA: m. trapezius transversa and m. serratus anterior.
# = significant for cycle time. * = significant for recovery time
Figure 5. Two typical examples (subjects A and B) of different changes in hand movement patterns in the
sagital plane over time (T1: 0.15-0.30 min. of the first block and T4: 3.45-4.00 min. of the last block).
-250 -200 -150 -100 -50 0 50 100-300
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T1 Subject A T4 Subject A
T1 Subject B T4 Subject B
* * #
66
Chapter 5
Consequence of feedback-based learning of an effective hand rim wheelchair force production on mechanical efficiency
Chapter 5
67
ABSTRACT
The purpose of this study was to investigate the effect of visual feedback on
effective hand rim wheelchair force production and the subsequent effect on gross
mechanical efficiency. In mechanical terms, the low gross mechanical efficiency of
hand rim wheelchair propulsion may be the result of ineffective force production.
Ten subjects in an experimental group and ten subjects in a control group
practiced three weeks (3.wk-1, i.e. a pre-test and 8 trials) on a computer-controlled
wheelchair ergometer. Every trial consisted of two blocks of 4 minutes at 0.15 and
0.25 W.kg-1 and 1.11 m.s-1. On three trials an additional block at 0.40 W.kg-1 was
performed. The experimental group practiced with and the control group practiced
without visual feedback on the effectiveness of force production. During all trials
oxygen uptake, power output, forces and torque on the hand rims were measured.
In comparison with the control group, the experimental group at trial 8 had a
significantly more effective force production compared to the control group
(respectively 90-97% vs. 79-83%), but showed a significantly lower mechanical
efficiency (respectively 5.5-8.5% vs. 5.9-9.9%). Findings indicate that the most
effective force production from a mechanical point of view is not necessarily the
most efficient way - in terms of energy cost - from a biological point of view and
that force direction is based on an optimization of cost and effect.
Chapter 5
68
INTRODUCTION
Hand rim wheelchair propulsion is a way of locomotion with a low gross
mechanical efficiency (ME). Gross ME of wheelchair propulsion rarely exceeds
11% and is much lower than in arm cranking (16%) (Martel et al. 1991; Powers et
al. 1984) or cycling (18-23%) (Coyle et al. 1992). As a consequence, hand rim
wheelchair propulsion is associated with a high physical strain in daily life (Janssen
et al. 1994) and leads most likely to a high mechanical load on the upper extremity.
The latter may lead to a high prevalence of overuse injuries in shoulder and wrist
(Boninger et al. 1997). It was suggested that propulsion technique plays a role in
the low ME (Veeger et al. 1992c). Therefore, it is important to study which aspects
of propulsion technique are associated with ME and how hand rim wheelchair
propulsion technique can be improved in terms of efficiency and mechanical
strain.
What is known about the gross ME of hand rim wheelchair propulsion is that, at
least partially, it is the result of non-optimal tuning of the wheelchair to the
physical characteristics of the user (Woude et al. 1989c). The low ME can also be
due to the occurrence of, so called, ineffective propulsion technique characteristics,
such as braking torques at the start and end of the push phase (Veeger et al. 1991a;
Veeger et al. 1992c), and/or a propulsion force whose direction is – at least from a
mechanical viewpoint - not fully optimal, as it would be when tangential to the
hand rims (Veeger et al. 1992c).
From a purely mechanical standpoint, the greater the portion of force directed
tangentially to the hand rim and the more positive the torque around the hand, the
greater the moment developed around the wheel hub. Individuals who apply large
non-tangential forces will require larger total forces to produce the same effective
torque (Boninger et al. 1997). Veeger and colleagues (Dallmeijer et al. 1994; Veeger
et al. 1991a; Veeger et al. 1992b; Veeger et al. 1992c) have described the tangential
versus total force produced and developed the fraction of effective force (FEF).
This measure is defined as the ratio of effective (tangential) force and total force,
expressed as a percentage, and was used to describe how effective an individual
was in applying forces to the hand rim. The FEF is dependent on the direction of
the propulsion force that is applied and on the direction and magnitude of torque
around the hand (Veeger et al. 1992a). The FEF was found to be low (between 57
and 81%) in able-bodied and low-level spinal cord injured subjects (Dallmeijer et
al. 1994; Dallmeijer et al. 1998; Veeger et al. 1992a; Veeger et al. 1992b; Veeger et
al. 1992c; Veeger et al. 1991b), as well as in wheelchair athletes (Woude et al. 1998).
A low FEF generally indicates a more downward direction of the total force
vector. Boninger et al. (1997) using a comparable but not identical measure
Chapter 5
69
(squared tangential force / squared resultant force of the force components in
three directions, expressed as a percentage) found equally low values (52-54%) in
experienced wheelchair users on a wheelchair dynamometer. Since able-bodied as
well as experienced wheelchair-dependent subjects appear to direct the force
always more downward, this may indicate that the force is directed to the best of
abilities - regarding joint mechanics and muscle coordination - when directed non-
tangentially (Roeleveld et al. 1994).
Besides a simulation experiment of force direction (Rozendaal et al. 2000), which
suggested that experienced users optimize the force pattern by balancing the
mechanical effect as well as the musculoskeletal cost, no literature is yet available
concerning the consequences of a high FEF on gross ME in hand rim wheelchair
propulsion. Therefore, a visual feedback computer program on FEF was
developed. This was implemented in a practice period to obtain a group, who
could apply a high FEF, for studying the effect on ME. Although feedback on
force application was found to be effective in changing pedal force patterns in
cycling (Broker et al. 1993; Sanderson et al. 1990), it was not certain whether
subjects could indeed improve FEF with help of visual feedback. Therefore, the
first purpose of the study was to investigate the effect of visual feedback on FEF.
The second and main purpose of the present study was to investigate the
consequences of a – learned – high FEF on gross ME, compared to a freely
chosen FEF in two otherwise comparable novice, able-bodied subject groups.
Novice able-bodied wheelchair users were included in this study since experienced
wheelchair users already found a balance between mechanical effect and
musculoskeletal cost.
METHODS
Subjects
Twenty able-bodied male subjects participated in this study. Criteria for inclusion
were: male, no prior experience in wheelchair propulsion, absence of any medical
contra-indications like, among others, complaints of the musculoskeletal system.
Subject characteristics are listed in Table 1. All subjects completed a medical
history questionnaire and were informed of the nature and possible risks involved
in the study before giving their informed consent to participate. Subjects were not
informed about the precise purpose of the study. They only were told that they
participated in a wheelchair training study. The protocol of the study was approved
by the Medical Ethical Committee.
Chapter 5
70
Design
Subjects were randomly divided into an experimental group (EXP; N=10) and a
control group (C; N=10). Both groups practiced for three weeks, three times per
week, on a computer-controlled wheelchair ergometer. All subjects performed a
pre-test at the beginning of the three weeks, followed by eight practice trials. The
pre-test and the eight practice trials comprised two four-minute exercise blocks at
two different levels of external power output (first block: 0.15 W.kg-1 and second
block: 0.25 W.kg-1) at a velocity of 1.11 m.s-1. The intensity of 0.25 W.kg-1 and the
velocity (1.11 m.s-1) were chosen for comparison with previous studies (Linden et
al. 1996; Veeger et al. 1992c). Since it was expected from results of a previous
study (Dallmeijer et al. 1998) that the experimental effects would be more
markedly present at higher levels of power output, an extra practice block was
added on trial one, four, and eight with an external power output of 0.40 W.kg-1
and a velocity of 1.11 m.s-1. To exclude an effect of training on gross ME, the
groups received this extra block on three trials only. The second trial was chosen to
perform this extra block for the first time, so the subjects first could familiarize
with the wheelchair ergometer before propelling at this relatively high intensity.
Each exercise block was preceded by two minutes of rest.
Subjects were asked to propel the wheelchair as naturally as possible. All subjects
were given visual feedback on the velocity, and were able to keep the mean velocity
at a constant level (1.11 m.s-1). Feedback on velocity was presented on a screen in
front of the subjects (Figure 1). In order for EXP to reach a more effective
propulsion technique, subjects were asked to propel on the basis of visual feedback
of a graphical representation of FEF. The feedback of the velocity and FEF were
both nearly instantaneous. The successfulness of such a feedback on force
application was shown before in cycling (Sanderson et al. 1990). The FEF
feedback, given on the same screen as the feedback on the velocity (Figure 1), was
a graphic that showed a single vertical line, ranging from 50-100%, representing
the actual FEF for the right side only. The presented FEF was low-pass filtered
(cut off frequency of 1.5 Hz) in order to show a gradually increasing/decreasing
FEF line per push. EXP were instructed to try to increase the computer-generated
representation of FEF as high as possible. Subjects were not aware that the line
represented the magnitude of the FEF and was meant to assist them in directing
the force tangentially and/or to increase the torque around the hand. C were
propelling with a freely chosen natural technique i.e. without visual feedback on
FEF on any of the trials.
Chapter 5
71
Wheelchair ergometer
The practice trials were all performed on a custom-built wheelchair ergometer.
This ergometer was a stationary, computer-controlled wheelchair simulator that
allows for direct measurement of propulsive torque around the wheel axle,
propulsive force applied on the hand rims, and resultant velocity of the wheels
(Niesing et al. 1990). Wheelchair ergometer dimensions were individually adjusted
such that when sitting upright with the hands on the rim top the subject‟s shoulder
was directly above the wheel axle and the elbow angle was approximately 110° with
180° being full extension. Camber of the wheels was set at 4º. Seat angle and
backrest were set at 5º to the horizontal and 15º to the vertical axis, respectively.
Ergometer data were collected within each exercise block, during the last minute,
with a sample frequency of 100 Hz.
Torque, forces and velocity were low-pass filtered (cut off frequency of 10 Hz,
recursive second order Butterworth filter). Because of resonance in the system the
medio-lateral force component was filtered at a lower frequency (5 Hz, fourth
order).
Force effectiveness
Variables were calculated as mean over the whole last minute of each exercise
block or as mean values for each of the pushes of this last minute. The push is
defined as the amount of time that the hand exerted a positive torque on the hand
rim (Figure 2).
From the measured torque and wheel velocity, the power output (PO) on each
wheel was calculated as:
PO = M . Vw . rw-1 (W) (1)
Where: M = torque around the axle, Vw = velocity of the wheel, rw = wheel radius.
Mean total power output (POmean) was the sum of the power output for the left
and right wheel and was calculated as an average value over one minute.
The global coordinate system in which forces were analyzed, was defined as
follows:
Fx: horizontally forward, Fy: horizontally outward, and Fz: vertically downward.
From force components Fx, Fy and Fz, total force applied on the hand rim (Ftot)
was calculated, according to:
Ftot = √(Fx2 + Fy2 + Fz2) (N) (2)
Chapter 5
72
The force component tangential to the hand rims, called here effective force (Fm)
was calculated from torque (M) and hand rim radius (rr) according to:
Fm = M . rr-1 (N) (3)
The fraction effective force on the hand rims (FEF) was calculated from equations
2 and 3 for each workload and expressed as a percentage:
FEF = Fm . Ftot-1 . 100 (%) (4)
Gross mechanical efficiency
Oxygen uptake ( 2OV [l.min-1]) was continuously measured during the whole test
with an Oxycon Champion (Jaeger, Germany). Calibration was performed before
each test with reference gas mixtures. The gross ME of wheelchair propulsion was
calculated, according to:
ME = POmean . En-1 . 100 (%) (5)
The mean power output (POmean) was calculated from the ergometer data over
the last minute of each exercise block. The energy expenditure (En) was calculated
from the oxygen uptake and the respiratory exchange ratio according to Garby and
Astrup (1987). En was calculated over the last two minutes of each exercise block
in order to minimize errors inherent in the measurement system.
Statistics
An independent t-test was applied on the subject characteristics to detect
significant differences between the groups. An ANOVA for repeated
measurements was applied, with external power output (0.15 and 0.25 W.kg-1) as
within-subject factor and group (EXP and C) as between-subject factor, on the
pre-test values of FEF and gross ME to test for possible differences between the
two groups.
Since the objective is to evaluate the consequence of learned differences in FEF,
EXP and C were studied at the end of the practicing period (trial 8) only for the
three levels of power output combined. An ANOVA for repeated measurements,
with external power output (0.15, 0.25 and 0.40 W.kg-1) as within-subject factor and
group (EXP and C) as between-subject factor, was applied to test the hypothesis,
i.e. to detect significant differences for the force variables and gross ME between
the groups on trial eight.
Chapter 5
73
To investigate the possible relationship between FEFmean and gross ME on trial
eight, Pearson‟s correlation coefficients were calculated for each of the external
power output levels and for 20 subjects.
Significance level was set at p < 0.05 for all statistical procedures. RESULTS
Subjects
All subjects completed all the trials. Mean age, body mass and height did not differ
significantly between the groups (Table 1). No significant differences were found
in pre-test levels of FEF and gross ME between the two groups (Table 1).
Force effectiveness
Mean forces and FEFmean, averaged over the push phase, at trial eight are listed in
Table 2. FEFmean at trial eight (Figure 4) differed significantly between groups. A
larger FEFmean was observed for EXP at all external power outputs (respectively
90%, 97% and 97%) in comparison with C (respectively 79%, 83% and 83%). The
pattern of change in FEFmean across PO levels was about the same for both
groups. This was indicated by the absence of a significant interaction effect PO *
group.
Fx showed no significant difference between the two groups, whereas Fy and Fz
did. Fy was directed inwards in EXP and outwards in C at trial eight. Fz was
significantly lower for EXP in contrast to C at the last trial. Although FEFmean
differed significantly between the groups, Fm and Ftot did not show any
significant difference.
Gross mechanical efficiency
Gross ME at trial eight (Figure 5) differed significantly between the groups (Table
2) with EXP showing a systematically lower gross ME (respectively 5.5%, 7.0%
and 8.5%) compared to C (respectively 5.9%, 8.1% and 9.9%). Gross ME
increased significantly with a higher load in both groups. The difference in gross
ME between EXP and C increased also with a higher power output level, as is seen
in Figure 5.
No significant correlation was found between FEFmean and gross ME on trial
eight at any of the levels of power output (r = 0.14 at 0.15 W.kg-1, r = -0.30 at 0.25
W.kg-1, and r = -0.38 at 0.40 W.kg-1 (N=20 for all calculations)).
Chapter 5
74
DISCUSSION
Previous research suggested that an ineffective force production, that is a low FEF,
may at least in part be responsible for a low gross mechanical efficiency (Veeger et
al. 1992a; Linden et al. 1996). The present study was designed to investigate the
effect of a learned high FEF on gross mechanical efficiency in hand rim wheelchair
propulsion.
Although feedback on force application was found to be effective in changing
pedal force patterns in cycling (Broker et al. 1993; Sanderson et al. 1990), it was
unknown whether it was possible for the subjects to attain a more effective
propulsion technique in manual wheelchair propulsion with help of visual
feedback. The results of the FEF values – even more than 100% in some of the
EXP subjects - showed that a mechanically effective propulsion technique is
possible through feedback-based learning. The high values of FEF reflected the
strength of the visual feedback, which was in accordance with previous studies
using visual feedback on a biomechanical variable to acquire new skills (Gauthier
1985) or to modify skills (Broker et al. 1993; Sanderson et al. 1990) in cyclical
motions such as rowing and cycling. Values of FEF and ME and the differences in
these variables between EXP and C increased with a higher load, as was also
shown in a previous study (Dallmeijer et al. 1998). This stresses the influences of
more strenuous boundary conditions of the task on technique related parameters.
Under low submaximal conditions, technique may be considered less critical to
performance and, therefore, differences may be (more) expressed at higher
intensities.
The FEF is a complex phenomenon and, by definition, dependent on the direction
of the propulsion force that is applied and on the direction and magnitude of the
torque around the hand (Veeger et al. 1992a). Equation 3 shows that in our
experimental set-up (Niesing et al. 1990) the effective force on the hand rim (Fm)
is directly calculated from the torque around the axle (M), which is again
dependent on the torque around the hand (Mh) and the tangential (i.e. effective)
part (Feff) of the total force applied (equations 6 & 7; Figure 3).
M = Mh + Feff . rr (Nm) (6)
Equations 3. and 6. lead to:
Fm = (Mh + Feff . rr) . rr
-1 (N) (7)
The absence of a torque around the hand would lead to a FEF value of 100%
when simultaneously Ftot is directed perfectly tangential (Roeleveld et al. 1994;
Chapter 5
75
Veeger et al. 1992a). In some subjects FEF exceeded 100% which means that a
positive torque around the hand was present. Since kinematics was not included in
the measurements, it was not possible to calculate Feff and to subsequently obtain
Mh. Previous work (Linden et al. 1996) showed that the direction of the torque
around the hand was opposite to the propulsion torque for most of the push
phase. This is possibly due to the need to keep sufficient contact with the rims in
order to be able to apply force on those rims. However, that a positive Mh is
possible was previously shown by a study of Veeger et al. (1992a), in which a top
basketball player had a FEF of 94% and was the only subject who produced a
positive Mh.
There were no significant differences in Fm and Ftot between the groups, possibly
due to the high standard deviation of these variables in EXP. Since both Fm and
Ftot showed a tendency to be respectively higher and lower in EXP, this probably
contributed to the significantly higher FEF in EXP compared to C. A tendency to
a decrease in Ftot in EXP is associated with significant changes in the force
components, Fy and Fz.
Since a higher fraction effective force could be attained, this raised the question
why the „naturally learning‟ subjects in the control group and (experienced) subjects
in previous research (Dallmeijer et al. 1994; Dallmeijer et al. 1998; Veeger et al.
1992a; Veeger et al. 1992b; Veeger et al. 1992c; Veeger et al. 1991b; Woude et al.
1998) did not acquire – in mechanical terms – a high FEF. Possibly, the relatively
low effectiveness of force production, which was shown in C, may be the direct
result of the fact that wheelchair propulsion is a guided movement. Since during
the push phase the hands have to hold the rims in order to be able to apply force,
the wheelchair user has the option not to apply a mechanically effective force. In
fact, any direction of force will be possible as long as this force will have a certain
tangential component. The „choice‟ made by subjects for a less effectively directed
force, might thus be based on the innate capacity of biological systems to adapt to
movement tasks in a biologically optimal way, thus preventing other „biologically‟
detrimental effects under the given boundary conditions (Roeleveld et al. 1994;
Veeger 1993).
Indeed, EXP showed a higher FEF but a lower gross ME compared to C, which
was in contrast to what was expected from a mechanical viewpoint. There are
several theories that could explain the present findings. First, the lack of
improvement in gross ME in EXP can be due to the previously described conflict
around the elbow that arises with the application of a tangential force direction
(Roeleveld et al. 1994; Veeger et al. 1992c). This conflict between the torque
production requirements and the movement-related requirement of the active
Chapter 5
76
muscles (Roeleveld et al. 1994; Veeger et al. 1992c) is illustrated by Figure 6. When
Ftot is directed tangentially the elbow joint is extending in order to follow the hand
rim, while at the same time a flexing moment ought to be generated for directing
the force tangentially (Veeger et al. 1994). This situation would lead to the
production of negative power, and hence, be ineffective regarding co-ordination
and physiology (Linden et al. 1996). Also, it is not possible to maximize elbow
extension torque (i.e. triceps contribution) when the force has to be directed
tangentially. Since the present study was partly an explorative one, no EMG
measurements were done in the present study and no answer can be given to the
question whether and how EXP, who achieved a high FEF, resolved the possible
arising conflict in the elbow in terms of muscle activity and timing.
According to a second theory, the dissipation of energy, that can occur when the
force is directed tangential to the hand rim, could be easily remedied by
incorporating bi-articular muscles (Gielen et al. 1990). The required flexion torque
in the elbow joint for directing the force can be obtained by activation of m.
biceps, which is preferential above lengthening of mono-articular muscles resulting
in negative work done. On the other hand, lengthening of biceps, which would
arise due to the elbow extension necessary to follow the hand on the rim, is
compensated for by anteflexion of the shoulder. This theory states that it is
possible to direct the force tangentially without dissipating mechanical energy.
However, an isometric contraction of the biceps also costs energy and a significant
difference in gross ME was found in the present study between EXP and C,
indicating a relative loss in efficiency in EXP compared to C.
A third theory states that when applying a tangential propulsion force, there will be
an increased power production around the shoulder (Veeger et al. 1992c) due to an
increased moment arm of the propulsion force. This implies that the shoulder
muscles have to be used more heavily and the compression force in the
glenohumeral joint is subsequently expected to increase (Veeger 1999). Modelling
results showed that the use of the effective force direction indeed led to higher
muscle forces and a higher compressive load on the glenohumeral joint (Veeger
1999; Veeger et al. 1999). One of the main reasons for this high compression force
in the shoulder was the additional muscle force that was needed to stabilize the
glenohumeral joint to obtain the desired force direction. Since this extra muscle
activity would not all contribute to propulsion, this might induce a lower
mechanical efficiency.
A simulation experiment was performed by Rozendaal and Veeger (2000) to
evaluate the relationship between mechanical effect and musculoskeletal cost (i.e.
costs at the joint level) in wheelchair propulsion. The results of the simulation
Chapter 5
77
study indicate that the actual direction of force generation is a compromise
between the mechanically most effective direction and the force direction that can
be sustained by the arm at minimum metabolic cost.
Previous cross-sectional work (Dallmeijer et al. 1998) showed that persons with
tetraplegia (TP) had a considerably lower effectiveness of propulsion technique and
gross ME compared with persons with paraplegia (PP) (FEFmax for TP was 55-
60% vs. 78-83% for PP). However, as a consequence of loss of arm muscle
function in TP – in particular lack of hand grip function and elbow extensor
function - TP found probably also the most effective force direction within the
constraints of their biological system, similar to PP and C of the present study.
Therefore, it will not be useful for PP and TP to learn a more effective propulsion
technique with help of visual feedback on FEF.
Based on the current comparison between EXP and C, it may be concluded that a
high FEF does not lead to an improved performance in terms of gross ME and
that push performance is based on a „minimization of energy losses criterion‟. The
experimental results in the current study do not imply that FEF is of a fixed
magnitude and may not react to long-term practice and training or as a
consequence of functionality (Dallmeijer et al. 1998; Woude et al. 1998). Apart
from the probable effect of talent and level of disability, cross-sectional results of
Woude et al. (1998) suggest that small increments in FEF may be reached as a
consequence of training.
Other technique related parameters are probably responsible for the increase in
gross ME as a consequence of natural learning in C. Future research should focus
on whether the high FEF is achieved by changes in Mh or Feff and should
investigate the muscle activity patterns, especially in the framework of improved
ME with learning/practice. Also important is that researchers should look at the
whole wheelchair and user system from a combined mechanical and biological
perspective instead of drawing conclusions without taking all perspectives into
account.
CONCLUSION
Visual feedback on the force effectiveness appeared to be a useful learning tool in
hand rim wheelchair propulsion. The experimental group showed a higher
effective force production than the natural learning control group. Conversely,
however, the experimental group showed a lower gross mechanical efficiency
compared to a control group. This indicates that the most effective propulsion
technique from a mechanical point of view is not necessarily the most efficient way
of propulsion from a biological point of view. Other technique parameters than an
Chapter 5
78
improved effective force direction are responsible for the improvement in gross
mechanical efficiency in the control group as a consequence of natural learning and
training.
ACKNOWLEDGEMENT
The experimental assistance of Cécile Boot and Stephanie Valk is greatly
acknowledged.
Chapter 5
79
TABLES
Table 1. Mean and SD of the subject characteristics and pre-test levels of FEFmean and gross mechanical
efficiency (ME) for the experimental (EXP) and control (C) groups and results of the statistical tests for
determining differences between the groups. External PO
(W.kg-1)
EXP (N=10) C (N=10) P-value
Mean SD Mean SD
Age (years) 21.6 2.4 21.7 2.2 0.923
Body mass (kg) 77.0 8.5 77.0 12.3 1.000
Height (cm) 186.5 6.4 184.1 9.0 0.501
FEFmean 0.15 71.1 7.6 77.1 13.4 0.089
0.25 76.7 9.3 75.5 11.6
Gross ME 0.15 5.3 0.5 5.5 0.6 0.284
0.25 7.1 0.6 7.5 0.9
Table 2. Mean and SD of the force variables and gross mechanical efficiency (ME) at trial 8 for the two groups
and results of the ANOVA for repeated measurements. External PO
(W.kg-1)
EXP C PO Group PO *
Group Mean SD Mean SD
POmean (W) 0.15 14.23 2.07 13.90 1.68
0.25 23.15 3.40 23.39 3.05 0.00 0.79 0.34
0.40 36.88 5.11 38.19 5.27
V (m.s-1) 0.15 1.09 0.09 1.06 0.04
0.25 1.07 0.10 1.06 0.05 0.21 0.91 0.00
0.40 1.06 0.08 1.08 0.07
Fm (N) 0.15 35.54 23.35 28.60 5.89
0.25 44.73 20.93 40.58 7.08 0.00 0.55 0.17
0.40 57.96 17.51 56.64 8.12
Fx (N) 0.15 31.55 21.43 21.13 3.81
0.25 38.04 18.48 30.53 6.28 0.00 0.38 0.19
0.40 46.99 13.28 40.73 9.39
Fy (N) 0.15 -2.58 5.18 1.08 3.15
0.25 -3.73 5.85 0.47 4.11 0.14 0.05 0.16
0.40 -3.33 7.40 3.05 5.42
Fz (N) 0.15 13.03 8.27 28.24 6.52
0.25 16.91 9.38 35.88 5.07 0.00 0.00 0.03
0.40 27.13 10.80 51.23 7.75
Ftot (N) 0.15 37.18 22.62 36.53 6.36
0.25 45.47 19.85 48.89 6.31 0.00 0.59 0.01
0.40 60.16 18.20 68.49 10.02
FEF (%) 0.15 90.22 17.44 79.26 12.62
0.25 97.47 5.19 83.04 7.37 0.08 0.00 0.79
0.40 96.56 4.24 83.14 4.82
ME (%) 0.15 5.52 0.59 5.87 0.52
0.25 6.96 0.66 8.11 0.56 0.00 0.00 0.00
0.40 8.46 1.11 9.88 0.67
Chapter 5
80
FIGURES
Figure 1. Screen showing the velocity (left) and FEF (right) feedback given to the subjects.
Figure 2. Illustration of the definition of the push.
100% FEF
Gradually
fluctuating
FEF line
50% FEF
1.11 m.s
-1on
the right side
Fluctuating
velocity line
1.11 m.s
-1 on
the left side
0 m.s
-1
Push
Torq
ue
aroun
d w
hee
l ax
le (
Nm
)
Samples
Torque on the right side
Chapter 5
81
Figure 3. Illustration of the torques and forces applied to the hand rim. Mh = torque around the hand; Fx =
force direction horizontally forward; Fz = force direction vertically downward; Fm = effective force on the hand
rim; Ftot = total propulsion force applied; M = torque around the wheel axle.
Figure 4. FEFmean (mean and SD) at trial eight for EXP and C at external power output levels 0.15, 0.25
and 0.40 W.kg-1.
Mh
M
Ftot
Fx
Fz Fm
0.00
20.00
40.00
60.00
80.00
100.00
120.00
0.15 0.25 0.40
FE
Fm
ean
(%
)
EXP
C
External power output (W.kg
-1)
Chapter 5
82
Figure 5. Gross ME (mean and SD) at trial eight for EXP and C at external power output levels 0.15, 0.25
and 0.40 W.kg-1.
Figure 6. Illustration of the effect of a nearly tangential applied hand force versus the normal force application.
0.00
2.00
4.00
6.00
8.00
10.00
12.00
0.15 0.25 0.40
Gro
ss M
E (
%)
EXP
C
External power output (W.kg
-1)
Torque-related requirements.
Movement-related requirements.
Shoulder
Wrist
Elbow
Effective force
direction
Shoulder
Wrist
Elbow
Actual force
direction
Negative
power
83
Chapter 6
Effect of stroke pattern on mechanical efficiency and propulsion technique in hand rim wheelchair propulsion
Chapter 6
84
ABSTRACT
The purpose of this study was to investigate the effect of different wheelchair
stroke patterns on efficiency, propulsion technique and load on the upper
extremity joints. Subjects were randomly divided over two velocity groups (1.11
m.s-1 (N = 14) and 1.39 m.s-1 (N = 11)). External load for both groups was set at
0.23 N.kg-1. All subjects performed four 4-min. exercise blocks. During the first
block the subjects propelled the wheelchair in their own preferred way. In block 2-
4 subjects had to propel the wheelchair with the pumping, semi-circular or single
looping over propulsion stroke pattern. During all blocks gross mechanical
efficiency and propulsion technique variables were measured. For four subjects
input data were collected for a musculoskeletal model of the upper extremity. A
significant difference was found for the mechanical efficiency with pumping
showing the highest mechanical efficiency and semi-circular the lowest regardless
of velocity. Timing variables and negative power deflections before and after the
push phase showed significant differences between the stroke patterns. Stroke
patterns showed no significant differences concerning peak joint moments and
glenohumeral contact forces. Pumping is the energetically most efficient stroke
pattern in contrast to the semi-circular pattern in this inexperienced, able-bodied
subject group. Propulsion technique and modeling results could not explain the
difference in efficiency. It was suggested that the muscle contraction velocity might
be more optimal in pumping compared to the semi-circular pattern.
Chapter 6
85
INTRODUCTION
In hand rim wheelchair propulsion different stroke patterns are described
(Boninger et al. 2002; Sanderson et al. 1985; Schantz et al. 1999; Shimada et al.
1998; Veeger et al. 1989a). Although the hands have to follow the path of the rim
during the push phase, subjects can vary the stroke length and the hands are free to
choose a path during the recovery phase. Five types of movements during hand
rim wheelchair propulsion were found in the literature, which can be primarily
characterized by the trajectory of the hand during the recovery phase (see Figure
1): (1) a movement with the hand returning along a path similar to that in the push
phase but in the opposite direction, a so-called „pumping‟ movement (Sanderson et
al. 1985; Schantz et al. 1999; Veeger et al. 1989a); (2) a more „semi-circular‟
movement with close to a straight line below the hand rim from the end to the
start point of the push phase (Boninger et al. 2002; Schantz et al. 1999); (3) a
movement creating a more or less „circular or elliptic‟ motion below the hand rim
(Sanderson et al. 1985; Schantz et al. 1999; Shimada et al. 1998; Veeger et al.
1989a); (4) a movement with a „single looping over propulsion‟, in which the hands
rise above the hand rim during the recovery phase (Boninger et al. 2002; Dallmeijer
et al. 1994; Shimada et al. 1998); and finally the (5) „double looping over
propulsion‟, in which the hands rise initially above the hand rim and then cross
over and drop under the hand rim during the recovery phase (Boninger et al. 2002;
Dallmeijer et al. 1994; Shimada et al. 1998). The pumping technique is assumed to
be inefficient (Sanderson et al. 1985; Veeger et al. 1989a) due to the suggested
abrupt braking and acceleration of the shoulder / arm complex at the switch from
push phase to recovery phase and reverse. The same applies for the single looping
over propulsion technique regarding the switch from recovery to push phase. The
assumedly less efficient pumping technique was seen in the more inexperienced
subjects (Sanderson et al. 1985; Veeger et al. 1989a). Several studies have suggested
that the circular stroke pattern was used by the more experienced and efficient
wheelchair users (Shimada et al. 1998; Veeger et al. 1989a). Sanderson and Sommer
(1985) hypothesized that the circular pattern would be more advantageous because
of its prolonged push phase. Although it has thus been suggested that the (semi-)
circular pattern would be more efficient in terms of energy cost compared to the
pumping pattern, this has never been confirmed by experimental data. Even
though merely suggested in scientific journals, the (semi-) circular pattern is also
generally seen as the most advantageous. For example Croteau, a manual
wheelchair user with a spinal cord injury, wrote: “We are looking for a smooth
circular motion of your arms, which should look like an old steam locomotive.
This technique requires the least amount of effort to accomplish a complete stroke.
Chapter 6
86
Bringing your hands back over the top of the wheel is by far the worst and most
common mistake people in wheelchairs make. Avoid this bad habit at all costs”
(Croteau 1998). The main hypothesis of the current study is, therefore, that the
(semi-) circular stroke pattern will be the most efficient pattern as was suggested in
previous studies described above.
If there is indeed a difference in efficiency between the stroke patterns, the second
question is: what causes this difference? Because the push phase is a guided
movement in which the hand is fixed to the rotating rim, no differences apart from
stroke length and push time between the patterns are expected regarding force
application, which was already found by Boninger et al. (2002). Furthermore, the
amount of power loss before and after the push phase, most likely the result of
non-optimal (un)coupling of the hands to the rim, might differ between the stroke
patterns because the path of the hands differ between the patterns before they
attach to or after they let go of the rim. It could be hypothesized that when the
hands move in the same direction as the wheel, such as in the (semi-)circular
pattern, this would lead to less energy losses before and after the push phase
compared to the pumping pattern in which the hands have to immediately switch
the movement direction to the opposite direction.
However, if there is a difference in mechanical efficiency between the stroke
patterns, most of the explanation might be found in the execution of the recovery
phase. It has been previously suggested (Groot et al. 2003b, Chapter 4) that the
optimum cycle frequency, i.e. with the highest mechanical efficiency, during
wheelchair propulsion might be related to the eigenfrequency of the arm as was
previously found regarding the legs in walking (Holt et al. 1991). The theory of a
segment oscillating at resonance frequency during the recovery phase is only
applicable when the arms swing really passively, which seems to be approached
most closely during the (semi-)circular pattern. Thus, this theory also suggests that
the (semi-)circular pattern might be the most efficient pattern.
The actually chosen stroke pattern might form the best balance between
mechanical requirements and biomechanical possibilities of the human body
(Veeger 1999). Many considerations influence the selection of the movement
pattern. An important aspect is minimization of the effort required, e.g. metabolic
cost. Other relevant factors may be the avoidance of discomfort, pain or long-term
damage, e.g. overstretching of joints or the prevention of high impact forces
(Rozendaal 1999). There appears to be a balance between effect and cost regarding
the force direction in manual wheelchair propulsion, which was demonstrated by a
simulation study of Rozendaal and Veeger (2000). The same „cost-effect‟ strategy
might be expected regarding the choice for a stroke pattern. Thus, the choice for a
Chapter 6
87
certain stroke pattern might not only be based on optimization of mechanical
efficiency but also on mechanical load on the shoulder, i.e. an optimal balance
between efficiency and load. To investigate the possible difference in load on the
upper extremity between the stroke patterns, joint moments and contact forces in
the glenohumeral joint were calculated with the Delft Shoulder and Elbow Model
(Helm 1994).
It has been found previously that the choice of a stroke pattern is dependent on
speed. As speed increased, fewer experienced wheelchair users (38 subject with a
spinal cord injury at T2 or below) used the semi-circular pattern and more subjects
used the single looping over propulsion technique (Boninger et al. 2002).
According to Vanlandewijck et al. (1994) experienced wheelchair users adapt their
propulsion technique, not by changing their propulsion style, but by increasing the
amplitude of the movement pattern during the recovery phase. Trunk flexion as
well as flexion of the upper arms are shown to be strongly related to changes of
speed (Veeger et al. 1989a). Due to an increase in hand rim velocity, the wheelchair
user has to make contact with the hand rims with higher hand speed. Furthermore,
an increased backward arm-swing induces a supplementary acceleration of the
wheelchair-user system in the beginning of the recovery phase. These segmental
accelerations at the beginning and end of the recovery phase, necessary to adapt to
the increased speed, may influence the choice of stroke pattern and subsequently
the mechanical efficiency.
Therefore, the purpose of the present study was to analyze 1) which of the four
stroke patterns (freely chosen, pumping, semi-circular or single looping over
propulsion) was the most efficient pattern in terms of energy cost; 2) when a
difference exists between the stroke patterns regarding the mechanical efficiency
whether this could be explained by differences in biomechanical terms, e.g
propulsion technique, joint moments, glenohumeral contact forces; and 3) whether
an effect of velocity exists on the most efficient stroke pattern.
METHODS
Subjects
After having given written informed consent, 24 able-bodied male subjects
participated in the study. Criteria for inclusion were: male, no prior experience in
wheelchair propulsion, absence of any medical contra-indications. Subjects were
asked to adapt to the prescribed stroke patterns at a fixed velocity. To evaluate a
possible effect of velocity subjects were divided into two groups, the 1.11 m.s-1
velocity group (N = 13) and the 1.39 m.s-1 velocity group (N = 11). Group
characteristics are described in Table 1. One of the subjects of the low velocity
Chapter 6
88
group and two subjects of the high velocity group were left-handed. The protocol
of the study was approved by the Medical Ethical Committee.
Design
To evaluate four different stroke patterns, subjects performed four 4-min.
submaximal exercise blocks on a computer-controlled wheelchair ergometer. The
external power output of all blocks was 0.23 N.kg-1 and the velocity was 1.11 m.s-1
or 1.39 m.s-1 for the lower and higher velocity group respectively. Two minutes of
rest preceded each exercise block.
The protocol started with a 2 min. familiarization period. After 2 min. of rest the
first exercise block started in which the subjects were asked to propel the
wheelchair ergometer in their own preferred way (FREE). In block 2-4 the subjects
had to propel the wheelchair with the pumping (PUMP), semi-circular (SEMI) or
the single looping over propulsion (SLOP) technique (Figure 1). These techniques
were chosen as the extremes of the five described techniques and could properly
be performed on the wheelchair ergometer. The order of the three techniques was
counter-balanced for all subjects so that a learning or fatigue effect could be
excluded from the experiment. Subjects had a 4 min. rest period after each exercise
block during which the stroke pattern was explained that they had to perform in
the subsequent exercise block. This explanation was given on paper in text and by
means of figures, such as shown in Figure 1. Subjects were then allowed to practice
this new technique for 1 min., before the 2 min. of rest and the new exercise block
started.
A mirror was placed at an angle of 45º in front of the subject, giving the subject
visual feedback of the path of his hand. Visual feedback on the actual velocity,
presented on a computer screen in front of the subject, was used by the subject to
keep the velocity of the wheels at a constant mean level of 1.11 or 1.39 m.s-1 and in
a natural manner (Groot et al. 2002b, Chapter 2).
Movement analysis was performed with a digital video camera, which was placed at
the right side of the subject to record the hand movement in the sagital plane. A
marker was attached to the third metacarpal and to the wheel axle. The path of the
hand was plotted to categorize their own preferred technique in FREE and to
check whether the subjects really performed the stroke pattern that they were
supposed to use.
Chapter 6
89
Wheelchair ergometer
The practice blocks were performed on a stationary, computer-controlled
wheelchair ergometer that allows for direct measurement of propulsive torque
around the wheel axle, the 3-D vector of the propulsive force applied on the hand
rims and resultant velocity of the wheels (Niesing et al. 1990). Wheelchair
ergometer dimensions were individually adjusted according to a standardized
protocol described elsewhere (Groot et al. 2002b, Chapter 2).
Ergometer data were collected with a sample frequency of 100 Hz during the last
15 s of an exercise block. Torque, forces and velocity were low-pass filtered (Fc =
10 Hz, recursive second order Butterworth filter). Because of resonance in the
system the medio-lateral force component was filtered at a lower frequency (Fc =
5 Hz, fourth order).
Gross mechanical efficiency
Oxygen uptake was continuously measured during the whole test with an Oxycon
Alpha (Jaeger, Germany). Calibration was performed before each test with
reference gas mixtures. Averaged values of 10 s were sampled. The gross
mechanical efficiency (ME) of wheelchair propulsion was calculated according to:
ME = Mean power output . Energy expenditure -1 . 100 (%) (1)
From the measured torque and wheel velocity, the power output was calculated:
Power output = M . Vw . rw-1 (W) (2)
Where: M = torque on the hand rim, Vw = velocity of the wheel, rw = wheel radius.
Mean total power output was the sum of the power output for the left and right
wheel and was calculated over all completed pushes in the 15 s measurement
periods.
The energy expenditure was calculated from the oxygen uptake and the respiratory
exchange ratio according to Garby and Astrup (1987). Energy expenditure was
calculated over the last two minutes of each exercise block.
Propulsion technique
Variables were calculated as the averaged mean values over the number of
completed pushes of each 15 s period. The push is defined as the period that the
hand exerted a positive torque on the hand rim (Groot et al. 2002b, Chapter 2).
The negative deflections or „dips‟ just before and after the push phase, i.e. the
positive torque signal, were calculated from the power output curve. The negative
Chapter 6
90
dips were defined as the most negative power output values respectively prior to
and just after the push (Groot et al. 2002b, Chapter 2).
The cycle frequency was determined from the 15 s data set of the torque signal and
expressed as the number of complete pushes per minute. The push time was
defined as the amount of time that the hand exerted a positive torque on the hand
rim (Groot et al. 2002b, Chapter 2). The cycle time was defined as the period of
time from the onset of one push phase to the onset of the next. The push time was
also expressed as a percentage of the cycle time (%push time).
Begin and end angle were defined as the angle between the line from the hand
marker through the wheel axle, relative to the vertical, at the start and the end of
the push phase. Stroke angle was defined as the sum of begin and end angle.
Musculoskeletal model
For 4 subjects from the low velocity group position recordings were performed
with a 3-camera 3-D opto-electronic system (Optotrak, Northern Digital,
Waterloo, Canada). Characteristics of this group are described in Table 1. The
Optotrak computer was synchronized with the ergometer computer. Only the right
side was measured with markers on bony landmarks of the hand, forearm, upper
arm, and thorax (Helm 1997). The three-dimensional positions of markers were
recorded at 100 Hz during the last 15 s of each exercise block. Calibration
measurements were done prior to the actual experiment in which the position of
the scapula was determined with a locator with the subjects‟ hands on the rims at
15 degrees behind top dead center. From the scapula calibration measurements
prior to the experiment and the actual marker position of the humerus during the
experiment, the positions of anatomical landmarks on the scapula during the actual
experiment were reconstructed. From the bony landmarks the local coordinate
systems of trunk, upper arm, forearm and hand were reconstructed according to
the proposal of the International Shoulder Group (Helm 1997). The orientation of
the scapula and clavicula were calculated using a regression model of Pascoal
(Pascoal 2001). Position and force data were used as input for the Delft model of
the shoulder and elbow (Helm 1994). Output variables of the model are, among
others, joint moments and contact forces in the glenohumeral joint. Net moments
around the shoulder were expressed as three separate components relative to the
thorax, as well as the vector sum of these components. The components roughly
correspond to ante-/retroflexion, endo-/exorotation and ab-/adduction. Peak and
mean glenohumeral contact forces were calculated for push and recovery phase
separately. More details regarding the model are described elsewhere (Veeger et al.
2002).
Chapter 6
91
Statistics
An independent t-test was used to determine differences between the two velocity
groups regarding age, body mass and height. To evaluate possible differences
between the stroke patterns regarding mechanical efficiency, propulsion technique
and modeling data an ANOVA for repeated measures was applied with „stroke
pattern‟ as main within-subject factor and „velocity group‟ as between-subject
factor (except for the modeling data). Significance level was set at p < 0.05 for all
statistical procedures.
RESULTS
The high velocity group was significantly younger compared to the low velocity
group (Table 1). Body mass and length were not significantly different between the
groups.
The mean velocity during the exercise blocks was slightly lower (1.04-1.06 m.s-1 and
1.27-1.32 m.s-1) than the required velocity of 1.11 m.s-1 and 1.39 m.s-1. A significant
difference was found in the velocity between the four stroke patterns, with FREE
(1.04 ± 0.06 m.s-1 and 1.27 ± 0.05 m.s-1) and SLOP (1.06 ± 0.05 m.s-1 and 1.32 ±
0.05 m.s-1) showing respectively the lowest and highest velocity. However, the
mean power output during all blocks was respectively 22-23 W for the low velocity
group and 31.3-33.5 W for the high velocity group and was not significantly
different between the stroke patterns. The freely chosen stroke pattern of the 1.11
m.s-1 group was categorized as PUMP for 10 subjects and as SLOP for 3 subjects.
For the higher velocity group this result was just the other way around, 1 subject
used PUMP and 10 subjects SLOP.
Gross mechanical efficiency
A significant difference was found in mechanical efficiency between the four
stroke patterns, with PUMP being the most efficient (7.10 ± 1.03 % and 7.62 ±
0.94%) and SEMI (6.69 ± 1.17% and 6.96 ± 0.75%) being the least efficient,
independent of velocity (Figure 2).
Propulsion technique
A significant difference in the negative power output dip before the start of the
push was shown, the dip of SEMI was smaller compared to the other patterns
(Table 2). The negative dip after the push phase also showed a significant
difference between the patterns, with SLOP showing the smaller dip (Table 2).
The cycle frequency showed a significant difference between the patterns, with
SEMI and SLOP showing a lower cycle frequency (Table 2), and subsequently a
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92
higher cycle time (Figure 3), compared to FREE and PUMP. SEMI had a
significantly longer push time (Figure 3). The percentage push time was the lowest
for SLOP (Figure 3). Also, stroke angle values were significantly different between
the four stroke patterns (Table 2), with SEMI showing the highest stroke angles
(Figure 4).
Modeling data
Table 3 shows values of the mechanical efficiency and propulsion technique
variables for the 4 subjects of whom also modeling data is available. No significant
differences were found in mechanical efficiency and propulsion technique between
the stroke patterns in this small group.
Peak torques in the shoulder, elbow and wrist, during the push phase averaged
over 4 subjects are shown in Table 3. Peak and net moments in the shoulder, and
peak moments in the elbow and wrist were comparable in magnitude for the four
stroke patterns.
The peak glenohumeral contact forces during the push phase varied between 524
N (FREE) and 694 N (SLOP)(Figure 5) but were not significantly different
between the groups (p = 0.20). Also, no significant difference was found between
the stroke patterns regarding peak glenohumeral contact force during the recovery
phase (ranging between 276 N (PUMP) and 380 N (SLOP), p = 0.19) (Figure 5).
Mean glenohumeral contact forces for the push phase (307 N (FREE) - 412 N
(SLOP), p = 0.16)) and recovery phase (196 N (PUMP) - 265 N (SLOP)) were not
significantly different between the stroke patterns (Figure 5). However, mean
glenohumeral contact force for the recovery phase tended to be lower (p = 0.065)
in PUMP compared to the other stroke patterns.
DISCUSSION
The purpose of this study was to investigate which stroke pattern was the most
efficient and subsequently to find an explanation for this possible difference in
terms of propulsion technique and mechanical strain. Because experienced
wheelchair users already have a preferred stroke pattern, the present study included
novice able-bodied male subjects only.
Gross mechanical efficiency
The own preferred stroke pattern of these inexperienced wheelchair users was
PUMP (mainly in the low velocity group) and SLOP (mainly in the high velocity
group). Use of mainly PUMP by inexperienced subjects was found in previous
studies (Sanderson et al. 1985; Veeger et al. 1989a). However, the suggestion that
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93
inexperienced wheelchair users use the least efficient (i.e. PUMP) pattern could not
be supported by this study. The experimentally imposed PUMP appeared to be the
most efficient pattern in contrast to SEMI and regardless of the velocity (1.11 and
1.39 m.s-1) in these novice subjects under the current testing conditions. This result
is in contrast to the hypothesis mentioned in the introduction.
Propulsion technique
The negative power output dips before and after the push phase showed a
significant difference between the groups. The negative dips are assumed to be the
result of non-optimal (un)coupling technique of the hand to the rim in which the
hands of the subjects had not attained the required tangential velocity of the
wheels at the moment of first contact (Sanderson et al. 1985; Veeger et al. 1991a).
Although SEMI showed the expected significantly smaller negative dip before the
push phase compared to the other patterns, the mechanical efficiency was
unexpectedly lower. These results indicate that there is no inverse relationship
between the negative dip before the push phase and mechanical efficiency, within
the context of the current stroke patterns studied here, as was hypothesized.
Sanderson and Sommer (1985) suggested that SEMI would be more advantageous
because of its prolonged push phase. Although SEMI indeed showed a
significantly higher push time and stroke angle compared to the other stroke
patterns this was not accompanied by a higher mechanical efficiency. Furthermore,
SEMI and SLOP showed a lower cycle frequency, and subsequently a higher cycle
time, compared to FREE and PUMP. The most efficient stroke pattern (PUMP)
had a cycle frequency that was closest to the freely chosen cycle frequency in
FREE. The execution of the recovery phase of both SEMI and SLOP leads to an
„imposed‟ lower cycle frequency because of the longer trajectory of the hand. This
„forced‟ lower cycle frequency might lead to the lower mechanical efficiency found.
In the same context, it was remarkable that FREE was not more efficient
compared to the imposed other patterns. Normally, trained wheelchair users as
well non-wheelchair users seem to be able to choose a certain way of propulsion
that is the most efficient. For example, Van der Woude et al. (1989b) and Goosey
et al. (2000) found that the optimum cycle frequency was close to the freely chosen
cycle frequency at any given velocity. The same kind of result was found in
experiments, which studied the effect of a more effective force direction on the
mechanical efficiency (Groot et al. 2002a, Chapter 5; Rozendaal et al. 2000).
Subjects seemed to choose a direction of force generation that is a compromise
between the mechanically most effective force direction and the force direction
that can be sustained by the arm at minimum metabolic cost (Rozendaal et al.
2000). Novice subjects seemed to be able to find this optimum force direction
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94
right from the start of the learning process of wheelchair propulsion (Groot et al.
2003a, Chapter 3). Since subjects seem to pick up the most efficient way of
wheelchair propulsion instinctively, it was not expected that the imposed patterns
showed a comparable or even higher mechanical efficiency relative to the FREE
condition. The fact that FREE was not the most efficient pattern might be due to
a learning effect, i.e. FREE was always the first exercise block followed by the
other three stroke patterns in a counter-balanced way. On the other hand, previous
research did not find an improvement in mechanical efficiency within the first 12-
min. of practice in completely novice wheelchair users although timing variables
already changed on this short term (Groot et al. 2003a, Chapter 3). A different
explanation may be the instructional context of the three experimental stroke
patterns. After instruction subjects might be more focused on appropriate
performance of the stroke pattern compared to FREE.
The differences in timing variables and mechanical efficiency between the stroke
patterns might be associated to the contraction velocity of the active muscles
(Helm et al. 1999). The efficiency for individual muscles may decrease if the active
muscles are not operating at their optimum contraction velocity. The joint angular
velocities and the muscle moment arms determine the contraction velocity of the
muscles (Helm et al. 1999). The joint angular velocities are to a large extent
determined by the constrained motion trajectory of the hand following the rim in
the push phase but in the present study also by the imposed movement pattern
during the recovery phase of the different stroke patterns. According to the force-
velocity relationship of muscles, the maximal external power output of muscles is
around 0.3 vmax. One might assume that it would be profitable to use at least the
major muscles at their optimum power output velocity, i.e. around 0.3 vmax. Van
der Helm and Veeger (1999) showed that this is not the case in normal wheelchair
propulsion, i.e. with a freely chosen pattern. The difference in mechanical
efficiency between the stroke patterns might, therefore, be explained by sub-
optimal muscle contracting velocities, caused by the constrained - and partly
imposed – hand trajectory.
In contrast to the other patterns, the arm movement during the recovery phase of
SEMI seems quite passive, i.e. the arm seems to be allowed to swing back to the
initial position on the rim like a pendulum. It has been found in walking that the
preferred stride frequency produces a minimal metabolic cost as a result of the leg
oscillating at resonance (Holt et al. 1991). By using SEMI the cycle frequency could
relate to the eigenfrequency of the arms during the recovery phase and, therefore,
would cost less energy. In contrast, in PUMP the arms have to be flexed actively to
return the hand to the initial position on the hand rim. This theory could, however,
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95
also not be supported by the mechanical efficiency results of the present study.
Above that, none of the novice subjects showed SEMI as the preferred, i.e. freely
chosen, stroke pattern.
Modeling data
The four subjects, of whom also modeling data are available, did not show
significant differences in gross mechanical efficiency or propulsion technique. This
might explain, together with the small sample size, the lack of significant
differences between the stroke patterns regarding peak joint moments during the
push phase. The peak joint moments were comparable with a study by Veeger et
al. (1991b), using the same protocol and also able-bodied subjects, although the
shoulder anteflexion torque is lower and the elbow extension torque is somewhat
higher in the present study. Also, the peak and mean glenohumeral contact forces
during push and recovery time were not significantly different between the stroke
patterns. The mean glenohumeral contact force during the recovery time was
comparable to previous research, investigating the load on the shoulder with four
different conditions (10 and 20 Watt, 0.89 and 1.39 m.s-1) in three experienced
wheelchair users (Veeger et al. 2002). However, in the current results mean
glenohumeral contact force during the recovery phase tended to be lower for
PUMP. When the shoulder movers have to be used more heavily, this would most
likely lead to higher compression forces (Veeger 1999). Furthermore, when the
shoulder muscles have to be more active, this might induce a lower mechanical
efficiency if the muscles are active in a less optimal trajectory of their force-velocity
curve (Helm et al. 1999; Veeger 1999). The lower glenohumeral contact force
found in PUMP during the recovery phase compared to the other stroke patterns
might, therefore, relate to the highest mechanical efficiency found in PUMP.
However, when calculating Pearson‟s correlation coefficients between efficiency
and glenohumeral contact forces low values were found (r < 0.32 for N = 16 i.e.
four stroke patterns and four subjects).
Velocity effect
Furthermore, it was surprising that the results regarding the most efficient imposed
stroke patterns were consistent over the two testing velocities and thus subject
groups. In a study of Boninger et al. (2002), investigating the propulsion pattern of
38 experienced spinal cord injured (SCI) wheelchair users, when the speed
increased (0.9 to 1.8 m.s-1) fewer subjects used SEMI. The most common
propulsion pattern in their study was SLOP (39% of the subjects at 0.9 m.s-1 and
53% at 1.8 m.s-1). Also, only 1 of 7 experienced wheelchair users with SCI changed
his stroke pattern over 2 different speeds (1.3-2.2 m.s-1) in a study by Shimada et al.
Chapter 6
96
(1998). It appears warranted to conclude that the self-chosen stroke pattern can
change dependent upon the velocity, however, PUMP seems the most efficient
pattern in terms of energy expenditure as was found in the two velocity groups in
the present study.
Since the results of the present study cannot be generalized to all groups of
wheelchair users, future studies should focus on the effect of stroke pattern on the
mechanical efficiency in for example experienced wheelchair users with a SCI,
especially those with high lesions who do not have intact arm function. Due to the
loss of effective finger flexion, people with a high SCI do not really grip the rims
but „hit‟ the rims with their palms. According to Dallmeijer et al. (1994), subjects
with a complete cervical lesion are not able to make an (active) extension in the
elbow as a result of reduced triceps function. They tend to make a pull movement
with the arms on the rims, which is initiated in the shoulders. This pull movement
is in contrast to the push movement as was shown by subjects with a thoracic or
lumbar lesion. The expectation was, therefore, that high spinal cord injured
subjects have higher begin angles (Dallmeijer et al. 1994). When they really start the
push phase with a higher begin angle, then SEMI could be more advantageous
since they can passively swing their arms backwards and do not need to actively
extend their arms, which is the case in PUMP when using a high begin angle.
Which stroke pattern is most favorable in wheelchair users with for example a
spinal cord injury needs more study.
CONCLUSION
It can be concluded that PUMP is the energetically most efficient stroke pattern in
contrast to SEMI regardless of the velocity in this novice non-wheelchair user
group under the current testing conditions. Propulsion technique and modeling
data could not give a clear explanation why PUMP is more efficient compared to
the other stroke patterns. It was suggested that muscle contraction velocity might
be more optimal in PUMP compared to SEMI. Differences between the stroke
patterns in muscle contraction velocity but also in peak and mean relative muscle
forces will be investigated in a future study.
Chapter 6
97
TABLES
Table 1. Mean and SD of group characteristics.
Significant is p < 0.05. V = 1.11 m.s-1
(N+13)
V = 1.39 m.s-1
(N=11)
Model group
(N=4)
Mean SD Mean SD p-value Mean SD
Body mass (kg) 74.5 6.8 83.6 10.4 0.07 74.0 2.7
Length (m) 1.85 0.07 1.87 0.07 0.77 1.79 0.03
Age (years) 21.7 3.4 20.7 1.5 0.01 25.8 1.7
Table 2. Mean and SD of propulsion technique variables of the 4 stroke patterns.
N = number of subjects; v = velocity (1=1.11 m.s-1 and 2 = 1.39 m.s-1);
Significant is p < 0.05. N V FREE
Mean ±
SD
SEMI
Mean
± SD
PUMP
Mean
± SD
SLOP
Mean
± SD
Stroke
p-value
V
p-value
Stroke
x v
p-value
Dip before
push (W)
13 1 -3.8 ± 1.7 -1.9 ± 0.9 -3.8 ± 1.8 -3.8 ± 2.4 0.00 0.00 0.90
11 2 -6.1 ± 2.4 -4.2 ± 1.8 -6.6 ± 3.2 -6.6 ± 2.5
Dip after
push (W)
13 1 -1.9 ± 1.0 -2.1 ± 1.1 -1.7 ± 0.5 -0.9 ± 0.4 0.00 0.00 0.07
11 2 -3.9 ± 2.7 -2.5 ± 1.5 -4.4 ± 3.2 -2.1 ± 0.8
Frequency
(push./min)
13 1 70 ± 18 56 ± 10 66 ± 17 53 ± 9 0.00 0.66 0.15
11 2 66 ± 10 56 ± 7 61 ± 10 56 ± 6
Stroke angle
(º)
13 1 62.7 ± 7.3 71.1 ± 8.2 61.7 ± 5.8 63.0 ± 4.9 0.00 0.00 0.10
9 2 84.4 ± 5.6 84.3 ± 3.5 80.5 ± 6.2 80.9 ± 4.7
Table 3. Mean and SD of the peak joint moments during the push phase of the four different stroke patterns.
N FREE
Mean ± SD
SEMI
Mean ± SD
PUMP
Mean ± SD
SLOP
Mean ± SD
Stroke
p-value
Shoulder anteflexion 2 12.5 ± 4.6 9.5 ± 0.5 12.4 ± 1.2 13.4 ± 3.1 0.45
Shoulder retroflexion 4 -8.0 ± 5.3 -8.7 ± 5.3 -6.7 ± 8.3 -12.3 ± 7.6 0.42
Shoulder endorotation 4 5.8 ± 2.3 6.4 ± 0.7 6.2 ± 2.8 5.5 ± 4.1 0.92
Shoulder adduction 4 11.5 ± 5.4 16.4 ± 7.1 12.6 ± 7.6 14.7 ± 4.0 0.27
Net moment 4 18.8 ± 6.4 18.7 ± 6.7 19.8 ± 7.1 21.5 ± 4.5 0.75
Elbow flexion 4 2.0 ± 1.7 2.0 ± 1.3 1.7 ± 2.2 2.3 ± 2.8 0.97
Elbow extension 4 -13.1 ± 7.4 -14.5 ± 7.0 -13.9 ± 9.7 -16.0 ± 8.1 0.77
Wrist flexion 4 5.2 ± 3.3 6.5 ± 3.5 5.1 ± 3.7 5.9 ± 3.0 0.26
ME (%) 4 8.3 ± 0.8 8.0 ± 0.4 7.9 ± 0.6 8.0 ± 0.7 0.41
Dip before push (W) 4 -2.66 ± 0.85 -1.84 ± 0.59 -2.68 ± 1.70 -2.51 ± 1.32 0.67
Dip after push (W) 4 -1.63 ± 0.90 -1.59 ± 0.31 -1.96 ± 0.62 -0.68 ± 0.55 0.05
Frequency 4 51.8 ± 9.8 47.8 ± 9.7 49.6 ± 13.1 44.6 ± 9.6 0.15
Push time (s) 4 0.43 ± 0.05 0.45 ± 0.04 0.41 ± 0.08 0.42 ± 0.11 0.70
Cycle time (s) 4 1.19 ± 0.26 1.30 ± 0.25 1.28 ± 0.36 1.40 ± 0.32 0.20
Chapter 6
98
FIGURES
Figure 1. Five types of recovery movements found in the literature. 1) pumping (PUMP); 2) semi-circular
(SEMI); 3) circular; 4) single looping over propulsion (SLOP); 5) double-looping over propulsion.
Figure 2. Mechanical efficiency (mean and SD) of the 4 stroke patterns for the 2 different velocity groups.
* = Significant difference between stroke patterns.
1 2 3
4 5
11 22 33
44 55
Mechanical efficiency
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
FREE SEMI PUMP SLOP
mechanic
al effic
iency (
%)
1.11 m/s
1.39 m/s
*
Chapter 6
99
Figure 3. Differences in cycle time (mean + SD), divided in push (PT) and recovery time (RT), for the four
stroke patterns. * = Significant difference between stroke patterns.
Figure 4. Differences in stroke angle (mean + SD), divided in begin angle (BA) and end angle (EA), for the
four stroke patterns. * = Significant difference between stroke patterns.
Timing
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.11 m/s 1.39 m/s 1.11 m/s 1.39 m/s 1.11 m/s 1.39 m/s 1.11 m/s 1.39 m/s
FREE SEMI PUMP SLOP
seco
nd
s
RT
PT
Stroke angle
0
10
20
30
40
50
60
70
80
90
100
1.11 m/s 1.39 m/s 1.11 m/s 1.39 m/s 1.11 m/s 1.39 m/s 1.11 m/s 1.39 m/s
FREE SEM I PUM P SLOP
An
gle
(D
egre
es)
EA
BA
*
*
Chapter 6
100
Figure 5. Differences in maximal and mean glenohumeral (GH) contact force between the styles during the push
and recovery time. The dotted line indicates 100% body weight. Velocity = 1.11 m .s-1 and external power output
= 0.23 N.kg-1
GH contact force
0
100
200
300
400
500
600
700
800
Max. Push time Mean Push time Max. Recovery time Mean Recovery time
Co
nta
ct
forc
e (
N)
FREE
SEMI
PUMP
SLOP
100%BW (N = 4)
101
Chapter 7
Influence of task complexity on mechanical efficiency and propulsion technique during learning hand rim wheelchair propulsion
Chapter 7
102
ABSTRACT
The purpose of this study was to investigate the consequence of task complexity
during the learning process of hand rim wheelchair propulsion on gross
mechanical efficiency and propulsion technique. Three groups of able-bodied
subjects (N=10 each) received a 3-week wheelchair practice period (3.wk-1, i.e. 9
practice trials) on a computer-controlled wheelchair ergometer, a motor-driven
treadmill and a circular wheelchair track. During practice trials 1 and 9, propulsion
technique variables and gross mechanical efficiency were measured. After
conclusion of all trials a transfer test was performed, in which the treadmill group
was tested on the track and the track group was tested on the treadmill. No
differences in the changes over time in gross mechanical efficiency and propulsion
technique could be discerned between the three groups. A time effect was shown
for cycle frequency, push time and cycle time, stroke angle, work per cycle, and a
reduction in inter-cycle variability was found over time. No differences were found
between the track and treadmill groups on the transfer tests, with the exception of
a significantly lower inter-cycle variability for some variables for the treadmill
group. Under the current experimental conditions, task complexity does not have
an influence on the biophysical consequences of the learning process of hand rim
wheelchair propulsion when focusing on the outcome measures gross mechanical
efficiency and propulsion technique. The 3-wk practice period had a favorable
effect on some technique parameters regardless of the complexity of practice.
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INTRODUCTION
Simple laboratory motor tasks are studied extensively in motor learning studies
(Flament et al. 1999; Darling et al. 1987; Spencer et al. 1999), however, research on
complex cyclic motor skills is sparse. The usefulness of simple laboratory tasks for
understanding the processes underlying everyday motor learning seems to be
limited, and generalizations to the training of complex motor skills on the basis of
such findings appear to be problematic.
Manual wheelchair propulsion is a motor task that needs to be learned from
scratch in recently injured adults. This gives researchers the opportunity not only
to learn about the process of adaptation and learning of manual wheelchair
propulsion, but also studying the learning of a functional new motor task in adult
life within the context of current learning theories.
The understanding of the learning process of hand rim wheelchair propulsion is
important for novice wheelchair users since lower limb disabled subjects are
dependent upon a wheelchair for their mobility. The gross mechanical efficiency,
i.e. the ratio between external power output and metabolic power, of hand rim
wheelchair propulsion is low, leading to a high physical strain in daily life (Janssen
et al. 1994) and to a considerable mechanical load on the upper extremities (Veeger
et al. 2002). A 3-wk practice program for novice able-bodied subjects on a
computer-controlled wheelchair ergometer showed an improvement in mechanical
efficiency as well as in propulsion technique variables such as cycle time, push time
and work per cycle (Groot et al. 2002b, Chapter 2). A decrease in inter-cycle
variability was expected since that variable is often used as an indicator of skill
(Darling et al. 1987; Vereijken et al. 1997). However, the inter-cycle variability as
well as the direction of force application did not change during the mentioned 3
weeks of practice on a wheelchair ergometer. The lack of results regarding those
variables might have been due to the fact that all practice trials were performed on
a stationary wheelchair. This implied that: feedback was limited to the hands and
some trunk movements whereas the physical environment was fixed the body did
not experience inertia effects and the information of speed was derived from a
computer screen (Woude et al. 2001). Also, no interaction existed between balance
control and translational inertia, nor was steering a crucial task element. Therefore,
the discovery content of learning under these task conditions seems to be limited.
In contrast, during wheelchair exercise on a motor-driven treadmill more visual
cues are available (movement of the wheelchair-user combination over the belt:
for-aft & left-right), steering is a requirement of task performance and the
sensation of inertia is realistic (Ingen Schenau 1980). The task, however, is still
simple repetitious and monotonous controlled wheeling when compared to real-
Chapter 7
104
world wheelchair use. Wheeling in a free environment includes inertia, visual
information flow as well as the additional requirement of braking and negotiating
corners. The realistic tasks in a free environment appear more critical for proper
everyday performance compared to wheeling on a treadmill or ergometer. It was,
therefore, hypothesized that the more complex and diverse task conditions in a
free environment might enhance task proficiency to a higher level compared to
exercise under treadmill or even more so compared to ergometer conditions, when
frequency, intensity and duration of exercise are equal. Sparrow (1983) proposed
that the fundamental principle underlying the learning and control of motor skills
is minimizing metabolic energy expenditure. When performing a motor task at a
constant power output level, measurements of oxygen uptake or heart rate can give
an indication whether there is a decrease in metabolic energy expenditure due to
practice. The present study used gross mechanical efficiency (ME; i.e. the ratio
between external power output and energy expenditure) as an indicator for
improved performance.
There are several theories about the effect of task complexity on the learning
process. However, not all hypotheses point to the same direction. The hypothesis
of one of the existing theories (Wulf et al. 2002) is that complex skill learning
might be enhanced by providing the learner with practice conditions that start with
a task with not many task demands, such as on the ergometer. After a few practice
trials skill could be more refined by challenging the learner with increased task
demands such as wheeling on the treadmill or track. However, another theory
states that directing the learners‟ attention to the effects of their movement is more
beneficial for learning than focusing the learners‟ attention on their own
movements (Wulf et al. 2001). It is suggested that on a stationary wheelchair
ergometer subjects do not have to pay much attention to their environment and
thus to the effect of their movements, which could be detrimental for performance
in contrast to practicing on a treadmill or track, where subjects have less time to
focus on the execution of their movements. According to theory and practice in
occupational therapy, adding a purpose or functional relevance to a task, like
wheeling on a track, is generally found to enhance the acquisition of motor skills as
compared with simulated activity, like practicing on a wheelchair ergometer (Wulf
et al. 2001).
The purpose of this study was to investigate the effect of task complexity, i.e.
wheeling on a stationary computer-controlled wheelchair ergometer, a motor-
driven treadmill, or a track, on the learning process. The first hypothesis is that
inexperienced able-bodied wheelchair users will achieve a larger improvement in
ME and change in propulsion technique during the same practice period when
Chapter 7
105
real-world conditions are simulated more closely, i.e. when the task is more diverse
and complex .
The second hypothesis is that there will be a positive transfer of learning from the
task, which is most complex and diverse (track) to tasks that are less complex
(treadmill) but not the other way around. Since the least complex task does not
comprise all task elements necessary to perform the most complex task, it is
expected that a positive effect of practice will not be as clearly visible as when a
more complex task has to be performed. This transfer effect could be easily tested
by including an extra post-test in which subjects have to perform the same task on
a different system.
Furthermore, it is expected that ME is higher when the task is less complex/
diverse (third hypothesis), i.e. initially smaller energy losses will occur on the
ergometer since fewer corrections are necessary because less disturbances will
occur during this task.
METHODS
Subjects
After having given written informed consent, 30 able-bodied male subjects
participated in the study. Criteria for inclusion were: male, no prior experience in
wheelchair propulsion, absence of any medical contra-indications. The protocol of
the study was approved by the Ethical Committee.
Protocol
Subjects were randomly divided over an ergometer group (ERGO, N = 10), a
treadmill group (TREAD, N = 10), and a wheelchair track group (TRACK, N =
10). Group characteristics are listed in Table 1. All groups received a three-week
wheelchair practice period (3.wk-1, 9 practice trials). Every trial comprised two 4-
minute exercise blocks, preceded by two minutes of rest. The first and ninth trial
were the pre- and post-test, with the same protocol as trials 2-8 but with
measurements of forces, moments and metabolic cost. All groups practiced at a
velocity of on average 1.11 m.s-1. The two 4-minutes exercise blocks of ERGO and
TREAD, consisted of two different levels of external power output (block 1: 0.15
W.kg-1 and block 2: 0.25 W.kg-1). For practical reasons no standardized extra
resistance could be applied when propelling on the wheelchair track in block 2.
The testing track was circular and consisted of a sequence of 6 straight hallway
sections, 52 meters long in total, circa 1.80 meters wide and without any obstacles.
The floor surface consisted of linoleum. The first practice block of TRACK was
driven clockwise and the second practice block counter-clockwise. To test the
Chapter 7
106
effect of transfer of learning, TRACK and TREAD participated in a second post-
test. During the second post-test TRACK was tested on the treadmill and TREAD
was tested on the track. The details on ERGO were identical to what is described
in Groot et al. (2002b, Chapter 2). Since ERGO practiced wheelchair propulsion
2.5 years ago, it was not possible to also include them in the transfer tests.
Equipment
The pre- and post-test of TREAD and TRACK were performed in a wheelchair
(total weight of 19 kg) with an instrumented hand rim and wheel on the right side.
A 3D force/torque transducer (AMTI M3-1000) and a potentiometer are built in
between the wheelchair wheel and the hand rim. A bicycle speedometer with a
digital display was attached to the left wheel of the chair and placed in view of the
participant to provide visual feedback of propulsion velocity.
The practice trials, in between the pre- and post-test, on the treadmill and hallway
were performed in a modified basketball wheelchair (Morrien Tornado, total mass
of 20.1 kg). The configuration of the instrumented wheelchair and the practice
wheelchair were set up to be as equal as possible in terms of seat height, camber
(4°), hand rim configuration, tires and tire pressure. Rolling resistance of both
wheelchair-user systems on the treadmill was determined with the use of a drag test
(Woude et al. 1986). From the measured drag force, the imposed treadmill belt
velocity (1.11 m.s-1), and the desired external power output levels (0.15 and 0.25
W.kg-1) the additional weight that had to be added to the back of the wheelchair via
a pulley system was calculated.
The pre- and post-test and the practice period of ERGO were performed on a
custom-built wheelchair ergometer. This ergometer is a stationary, computer-
controlled wheelchair simulator that allows for direct measurement of propulsive
torque around the wheel axle, propulsive force applied on the hand rims and
resultant velocity of the wheels (Niesing et al. 1990). Wheelchair ergometer
dimensions were individually adjusted according to a standardized protocol
described elsewhere (Groot et al. 2002b, Chapter 2). Visual feedback on the actual
velocity, presented on a computer screen in front of the subject, was used by the
subject to keep the velocity of the wheels at a constant mean level of 1.11 m.s-1 in a
natural manner (Groot et al. 2002b, Chapter 2).
Gross mechanical efficiency
Metabolic cost was continuously measured during trial 1 and 9 for each 4-minute
exercise block with an Oxycon Champion (Jaeger, Germany) for ERGO and with
the portable K4 b2 (Cosmed, Italy) for TREAD and TRACK. Calibration was
performed before each test with reference gas mixtures. Averaged values of 10 s
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107
were sampled. To obtain an indication of the gross mechanical efficiency (ME) of
wheelchair propulsion, the ratio power output/ energy expenditure (PO/En) was
calculated according to:
ME = POmean . En-1 . 100 (%) (1)
From the measured torque and wheel velocity, the power output was calculated:
Power output = M . Vw . rw-1 (W) (2)
Where: M = torque on the hand rim, Vw = velocity of the wheel, rw = wheel radius.
Mean total power output (POmean) was the sum of the power output for the left
and right wheel of the ergometer and was calculated over all full cycles of one
minute. To acquire the total power output of the treadmill test, the power output
of the right wheel was multiplied by two because the power output of both wheels
was assumed to be the same. The power output during the exercise blocks on the
track differed between the blocks due to the braking torque of the wheel on the
inside of the circular track, which was needed to negotiate corners (Figure 1).
Therefore, to get an indication of the total power output on the track, the power
output values of the right wheel during the first and second block were combined.
The energy expenditure (En) was calculated from the oxygen uptake and the
respiratory exchange ratio according to Garby and Astrup (1987). Energy
expenditure was calculated over the last two minutes of each exercise block.
Propulsion technique
During all tests, propulsion technique data were collected for each exercise block
during the last minute, with a sample frequency of 100 Hz. Torque, forces and
velocity data of the ergometer and instrumented wheel were low-pass filtered (Fc =
5 Hz, recursive second order Butterworth filter).
Variables were calculated as the mean over the whole last minute or as mean and
peak values over each of the pushes of the last minute. During block 1 the
(instrumented) wheel, which was located at the inside of the round, showed
irregular torque data due to the turns that had to be made on the track (Figure 1).
Therefore, a selection of cycles was made in block 1 during which the subject was
going straight. Data analysis was done on these selected cycles only. The push
phase was defined as the period in which the hand exerted a positive propulsion
torque around the wheel axle.
From the POmean and the cycle frequency in Hz (f) the work per cycle (Wcycle)
was calculated:
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108
Wcycle = POmean . f –1 (J) (3)
The stroke angle was calculated from potentiometer data of the instrumented
wheelchair and was defined as the angle between hand contact and hand release,
i.e. the start and end of the exertion of the positive propulsion torque.
Force application
Force parameters were calculated as mean and peak values over each of the pushes
over the last minute of an exercise block. For the right hand side only, from force
components Fx, Fy and Fz, the torque around the wheel axle (M) and rim radius
(r), the fraction effective force on the hand rims (FEF) was calculated:
1F
r
MFEF (4)
Timing
The cycle frequency (f) was determined from the torque around the wheel axle and
defined as the number of complete pushes per minute. The push time was defined
as the amount of time that the hand exerted a positive torque on the hand rim. The
cycle time was defined as the period of time from the onset of one push phase to
the onset of the next. The push time was also expressed as a percentage of cycle
time (%PT).
Inter-cycle variability
The inter-cycle variability was determined for each subject for all consecutive push
cycles, during the 60-s measurement period for ERGO and TREAD and for the
selected cycles of TRACK, for the timing variables (push time, cycle time and
%PT) and for PO, FEF, M, and the velocity. The mean and standard deviation
(SD) of the variables were calculated over all push cycles in the measurement
period. From the mean and SD the coefficient of variation (CV) was calculated by
the formula:
CV = |SD . mean-1| . 100 (%) (5)
Statistics
An ANOVA for repeated measures was used to evaluate the main effects (Time,
Power output, Group) and interaction effects over the trials between the three
groups for the mean and coefficient of variation of the variables. The within-
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109
subject factors were Time (trial 1 & 9) and Power output (block 1: 0.15 W.kg-1 and
block 2: 0.25 W.kg-1). The interaction Time*Group was considered to be the most
important since it indicates the learning differences between the groups over the
practice period (to test hypothesis 1). When there was no Time*Group effect, the
Time effect (whether there was an increase/decrease over time of a certain variable
for all groups) and Group effect (whether there was a difference between the
group values, i.e. to test hypothesis 3) were analyzed.
For studying the effect of transfer of learning (i.e. to test hypothesis 2) an ANOVA
for repeated measures was performed with group (TREAD and TRACK) as
between-subject factor, and power output as within-subject factor. This was done
for the treadmill and track test separately, to exclude a possible effect of the
external power output level, which did not differ between block 1 and 2 during the
track test in contrast to the treadmill test.
Significance level was set at p < 0.05 for all statistical procedures.
RESULTS
Gross mechanical efficiency
No Time*Group effect was found regarding ME, which means that the type of
practice had no significant influence on possible changes in mechanical efficiency
(Table 2). Also no Time effect was found, i.e. there was no increase in ME after
the 3-wk practice period when the three groups were considered together (Figure
2). However, this could be due to the difference in power output between the
groups. Although not wanted, the power output showed a significant Time effect
(Table 2), with TREAD and TRACK showing a slight decrease in power output
over time. The observed difference in power output between groups was expected
since, in contrast to ERGO and TREAD, no extra external power output was
imposed on TRACK during the second block.
A significant Group effect was found, with ERGO, i.e. the group with the easiest
task, always showing a higher ME compared to TREAD and TRACK (Table 2,
Figure 2).
There was no significant difference in ME between TRACK and TREAD on the
transfer tests.
Propulsion technique
A significant Time*Group effect was found for the work per cycle. ERGO showed
a significantly larger increase in work per cycle compared to TREAD and TRACK
(Table 2).
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110
No differences between TRACK and TREAD were found on the transfer tests
concerning work per cycle and stroke angle.
Force application
No Time*Group effect was found regarding direction of the effective force and
stroke angle (only TREAD vs. TRACK) (Table 2), i.e. the practice groups showed
a constant force direction and an increase in stroke angle. A significant Group
effect was found for the variables FEFmean and FEFmax, with ERGO showing
higher FEF values compared to TREAD and TRACK (Table 2). The stroke angle,
which was not determined in ERGO, increased significantly over the practice
period for both TREAD and TRACK (significant Time effect).
FEFmax showed a significant difference between TREAD and TRACK on the
transfer test. TREAD showed a higher FEFmax (78.0 ± 14.6% and 84.3 ± 12.8%)
on the track test compared to TRACK (65.8 ± 9.9% and 76.0 ± 9.0%), but no
difference between the groups was shown when the test was performed on the
treadmill.
Timing
No Time*Group effect was found for the timing variables (cycle frequency, push
time, cycle time) (Table 2). However, there was a significant Time effect regarding
the cycle frequency, push and cycle time, i.e. all groups showed a decrease in cycle
frequency and subsequently an increase in push time and cycle time over the
practice period (Figure 3).
No differences in the timing variables were found between TREAD and TRACK
during the transfer tests.
Inter-cycle variability
Although there were significant Time effects and Group effects regarding inter-
cycle variability, no differences over the practice period were found between the
groups (no Time*Group effect). A Time effect was shown for the variables: mean
power output (PO) during the push phase (Figure 4), mean torque (M), mean
velocity (v), which all showed a lower inter-cycle variability at the post-test
compared to the pre-test. There was a significant Group effect regarding inter-
cycle variability for all variables, except for the push time, with ERGO always
showing the lowest inter-cycle variability and TRACK almost always the highest
inter-cycle variability (Figure 4).
When analyzing the transfer tests, a significant difference was found in the
variability between TRACK and TREAD on the treadmill test regarding the mean
power output and torque. FEFmax also showed a significant difference between
Chapter 7
111
the two groups on the track test. TREAD showed a lower variability concerning
these variables compared to TRACK.
DISCUSSION
Gross mechanical efficiency
Since the fundamental principle underlying the learning and control of motor skills,
used in the present study, is assumed to be the minimization of metabolic energy
expenditure (Sparrow 1983), gross mechanical efficiency was used as an indicator
of improved performance. During practice a beginner pursues a more efficient
movement, which might be accomplished by improvements in segment timing,
tuning and coordination of muscle activation (Rosenbaum 1991). It was
hypothesized that inexperienced wheelchair users would achieve a larger
improvement in ME when real-world conditions are simulated more closely, i.e. in
this experiment practicing on the wheelchair track. This hypothesis was not
supported by the results of the present study. The increase in gross ME over the
trials was not significantly different for the three groups. The transfer tests also did
not reveal a difference between TREAD and TRACK regarding gross mechanical
efficiency.
A decrease in power output in TREAD and TRACK was shown over time, which
was an unexpected and undesirable effect. This decrease in power output, although
test conditions were exactly the same, has a direct effect on ME and might explain
the lack of results regarding the comparison between the practice groups. To give
an indication of the effect of the decrease in power output on the mechanical
efficiency, a relationship can be formulated from the data of the two exercise
blocks of TREAD at trial 9. When the power output of TREAD would have been
exactly equal for trials 9 and 1, thus 1.3 W (block 1) and 1.6 W (block 2) higher at
trial 9, the associated mechanical efficiency would also be higher, i.e. 4.5% and
7.3% compared to the measured 4.3% and 6.9%. These results indicate that there
would have been even a larger increase in mechanical efficiency for TREAD when
the power output would have remained the same. Although the increase in
mechanical efficiency over time is larger for TREAD compared to ERGO,
mechanical efficiency values for ERGO are higher at any point in time. For
TRACK this relationship between the mechanical efficiency and power output is
more difficult to estimate since only one power output level over the two blocks is
available.
Since the testing track and the wheelchair were exactly the same and the velocity
only slightly decreased during block 2 (1.19 to 1.17 m.s-1), the power output could
be expected to remain the same. The decrease in power output in TREAD was
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112
unexpected since both the velocity and resistance were imposed. So, it could be
that these subjects had a different sitting posture (e.g. more forward/backward)
and more trunk movement, which is expected to increase the power output, during
the pre-test compared to the post-test, which initially would lead to a higher rolling
resistance and subsequently a higher power output. The wheelchair subjects in the
present study were completely novice at the start of the experiment. Therefore,
some of the TREAD subjects found it difficult to keep the right speed and to
propel the wheelchair rectilinear on the treadmill during the pre-test. They did not
receive any time to get acquainted with the new task before the first test.
Furthermore, because some of the TREAD subjects drove irregularly over the belt
during the pre-test, this also could lead to extra resistance since the wheeling
direction of the wheelchair was not always fully aligned to the turning direction of
the belt. Therefore, the decrease in power output in TREAD (and TRACK) could
possibly be seen as a learning effect, i.e. after practice subjects were able to propel
the wheelchair more smoothly forward and thus at a lower mechanical cost.
The overall lower ME in TREAD (block 1: 4.2-4.3%) and TRACK (block 1: 4.4-
4.4%) compared to ERGO (block 1: 5.6-5.9%) might also be due to more trunk
movement in TREAD and TRACK. In contrast to ERGO, the latter mentioned
groups have to balance their body/trunk in the chair to prevent tipping of the
wheelchair. As a consequence of the need to balance the body, more muscle
activity will occur, which would lead to extra internal energy cost since these
muscles would not contribute to the delivered power output. Veeger et al. (1992b)
found that the range of motion of the trunk was small but, however, significantly
larger on a treadmill compared to the ergometer at the same power output
conditions.
When two similar tasks of different complexity are comprised of physically
identical elements, the simpler task will share all of its elements with the more
complex; the latter will contain additional elements. If the more complex task is
acquired initially, the acquisition of the simpler task should be facilitated since its
elements are included in the first task (Kleinman 1983). Therefore, it might be
expected that TRACK would show a better performance compared to TREAD on
the transfer tests. Although practicing on a track is more diverse, i.e. less
monotonous, compared to treadmill practice, the treadmill task might be the more
difficult task. Speed and wheeling direction are much stronger imposed in TREAD
compared to TRACK. TRACK subjects, who can easily vary their speed, have
more time to correct possible mistakes, which makes the task easier. Nevertheless,
no differences between TREAD and TRACK were found for mechanical
efficiency and propulsion technique on the transfer tests, which indicated that
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under the current experimental conditions one learning condition was not in favor
of the other.
Force application and timing
The work per cycle increased significantly more in ERGO compared to the other
groups. This was probably due to the larger decrease in cycle frequency in ERGO,
although this was not significantly larger than the decrease in cycle frequency in the
other groups. The smaller decrease in cycle frequency and small decrease in power
output in TREAD and TRACK might together lead to a difference in work per
cycle compared to ERGO. The timing variables always change as a consequence
of practice. This decrease in cycle frequency and, subsequently, increase in push
time and cycle time was also found in a 7-wk training study (Dallmeijer et al.
1999b) and even when practicing on a very short term (within minutes) (Groot et
al. 2003a, Chapter 3). From the results of the present study it could be concluded
that the effect of practice on timing variables is not strictly dependent upon the
type of practice, i.e. in a stationary wheelchair or wheeling with more task diversity
on a treadmill or track.
A significant increase in stroke angle over time was found for both TREAD and
TRACK. This result was similar to results by Dallmeijer et al. (1999b) after 7-
weeks of training. A more remarkable result was that TREAD showed a higher
FEFmax compared to TRACK at the transfer test on the track. However, no
difference in FEFmax was visible when both groups were tested on the treadmill.
Inter-cycle variability
A previous study showed that the inter-cycle variability did not change on a short
term, i.e. within the first minutes of the learning process (Groot et al. 2003b,
Chapter 4). Furthermore, three weeks of practice on a wheelchair ergometer
(Groot et al. 2002b, Chapter 2) also did not show a decrease in inter-cycle
variability. Since the two previously mentioned studies were both performed on a
stationary wheelchair and, therefore, subjects did not have to pay much attention
to for example steering, it was suggested that the inter-cycle variability would
decrease when real-world wheelchair propulsion is practiced more closely.
However, no differences in inter-cycle variability over the practice period were
found between the groups. When the groups were considered together, a
significant effect of time was found for the inter-cycle variability of three variables,
i.e. mean power output, torque and velocity. This result was expected since several
studies, focusing on skill acquisition of other motor tasks, found a decrease in
inter-cycle variability as a result of practice (Darling et al. 1987; Vereijken et al.
1997). A high level of push-to-push consistency, i.e. a low variability, is necessary
Chapter 7
114
in the execution of effective movement patterns (Smith et al. 1995). ERGO,
showing a lower inter-cycle variability compared to TREAD and TRACK, directed
the force significantly more effective than the other two groups. A similar result
was found for the transfer test on the track, i.e. TREAD showed a lower variability
in FEFmax but a higher mean FEFmax compared to TRACK. From these results
it could be suggested that less variability indeed leads to a more effective force
application.
ERGO always showed a lower coefficient of variability compared to the other two
groups. This could be explained by the fact that the task was less complex. Subjects
in the ergometer group did not have to steer or balance in the chair and, therefore,
had fewer disturbances and/or conditions to control for while propelling the
wheelchair. From the results of the present study it could be suggested that inter-
cycle variability could be used as an indicator of task complexity. TRACK showed
the highest level of variability, which was expected since they had to brake,
especially the wheel at the inside of the track, to make a turn and after the turn
they had to accelerate to get up to the right speed and direction again. This de- and
accelerating of the wheelchair induced, of course, more variability in the signals, as
was also shown in Figure 1. When TREAD and TRACK were tested in the
transfer tests, TREAD showed significantly less variability for certain variables
compared to TRACK. This could be due to the more monotonous practice period
on the treadmill, leading to a more consistent and rhythmic movement pattern.
Unfortunately, no results are available of the performance of ERGO subjects
tested on the treadmill or track.
In conclusion, only few differences in propulsion technique and ME between the
groups over the practice period were found in the present study. The lack of
results of some variables might be influenced by the decrease in power output over
time in TREAD and TRACK, which could be a learning effect itself. However, the
hypotheses from the different learning theories, mentioned in the introduction,
could not be supported in the current experimental context. Firstly, for these
novice able-bodied wheelchair users within the evaluated experimental conditions
task complexity had no influence on the learning effect. Secondly, it was suggested
that focusing on the execution of the movements could be detrimental for
performance in contrast to focusing on the effect of the movements. On a
stationary wheelchair ergometer subjects do not have to pay much attention to
their environment and thus to the effect of their movements in contrast to
practicing on a treadmill or track where subjects have less time to focus on the
execution of their movements. Since almost no differences between the groups
were found in ME and propulsion technique over the practice period the
Chapter 7
115
hypothesis regarding focusing on the effect or execution of the movements could
not be supported. Finally, subjects did not achieve a better performance when the
real world was simulated more.
Although a few differences existed between the learning conditions, it seems
warranted to conclude that a wheelchair ergometer is a valid system for learning
wheelchair propulsion experiments. This might be particularly important for
recently injured individuals in a rehabilitation center, who have to learn wheelchair
propulsion. Persons with, for example, a spinal cord injury and who do not have a
good balance, are probably better off by starting the learning process in a stable
wheelchair ergometer since they also can change propulsion technique and ME in
such a stationary device. Furthermore, by using an ergometer they will be able to
practice at extremely low, or even negative, external loads if necessary. It should be
kept in mind, however, that practicing wheelchair propulsion on a stationary
ergometer leads to successful performance in that setting but might do little to
prepare the patient for mobility at home or in the community since ME and
propulsion technique are not the only important factors in wheelchair mobility. As
soon as performance becomes consistent, patients are able to sit in a (less stable)
wheelchair and have sufficient work capacity, they can also learn specific
wheelchair tasks like maneuvering a wheelchair in small spaces, over a doorstep or
a kerb and making a wheelie. Also of practical relevance is investigating whether
positive transfer effects occur in terms of task specificity. For example, is only
practicing wheelchair propulsion helpful for improving wheelchair performance or
does arm crank exercise also have a positive influence on wheelchair performance?
Since arm crank exercise is less strenuous compared to wheelchair propulsion, a
positive transfer effect might be an important finding. Further research needs to be
conducted to evaluate the practical notions as well as the underlying theory
regarding task complexity and specificity.
ACKNOWLEDGEMENT
The experimental assistance of Sandra Silvis and Judith van Velzen is greatly
acknowledged.
Chapter 7
116
TABLES
Table 1. Mean ± SD of the group characteristics. * = Significant difference between the groups regarding age,
with the TRACK being significantly younger than TREAD. Group Age (years) Body mass (kg) Height (m)
Ergometer 21.7 ± 2.2 77.0 ± 12.3 1.84 ± 0.9
Treadmill 22.1 ± 1.2 74.7 ± 7.3 1.84 ± 0.7
Track 19.8 ± 1.5 76.3 ± 6.7 1.86 ± 0.8
Table 2. Mean ± SD of propulsion technique and mechanical efficiency during the first (1) and last (9) trial at
block 1 and 2. In the right column the significant main and interaction effects are shown of PO = Power Output;
G = Group; and T = Time. Variable Ergometer Treadmill Track p-value
1 9 1 9 1 9
PO (W)
Block 1 13.8 ± 2.1 13.9 ± 1.8 11.6 ± 2.8 10.3 ± 3.3 13.1 ± 2.5 11.5 ± 2.2 T=0.02; G=0.00;
PO=0.00;
PO*G=0.00;
Block 2 23.3 ± 3.5 23.5 ± 3. 3 24.8 ± 4.2 23.2 ± 3.9 13.1 ± 2.5 11.5 ± 2.2
Work per Cycle (J)
Block 1 13.9 ± 2.1 21.7 ± 7.3 14.5 ± 5.4 14.1 ± 4.0 12.8 ± 3.4 14.9 ± 4.6 T*G=0.03; T=0.00;
G=0.00; PO=0.00;
PO*G=0.00;
Block 2 22.7 ± 3.9 32.7 ± 11.0 26.3 ± 7.8 29.6 ± 8.4 13.7 ± 4.5 14.3 ± 4.1
FEFmean (%)
Block 1 77.1 ± 14.6 79.4 ± 13.1 64.9 ± 8.0 63.6 ± 13.5 64.2 ± 9.2 61.4 ± 10.9 G=0.00; PO=0.00;
PO*T=0.01;
PO*G=0.04;
Block 2 75.3 ± 12.0 83.3 ± 7.8 69.0 ± 7.3 75.8 ± 11.8 67.5 ± 6.7 68.6 ± 8.9
Frequency (pushes.min-1)
Block 1 61.0 ± 12.8 41.7 ± 11.9 54.6 ± 22.0 44.7 ± 13.4 64.1 ± 16.8 48.4 ± 10.6 T=0.00; PO=0.00;
PO*G=0.02;
Block 2 62.8 ± 11.8 46.4 ± 12.4 60.9 ± 19.3 49.2 ± 12.5 61.1 ± 16.7 50.3 ± 11.4
Stroke angle (°)
Block 1 68.0 ± 12.5 80.3 ± 12.2 59.7 ± 12.0 70.9 ± 13.0 T=0.00; PO=0.00;
PO*G=0.01;
Block 2 75.4 ± 17.8 89.7 ± 13.0 77.2 ± 13.4 88.7 ± 10.9
Gross ME (%)
Block 1 5.6 ± 0.7 5.9 ± 0.5 4.2 ± 1.0 4.3 ± 1.2 4.4 ± 1.3 4.4 ± 0.8 G=0.00; PO=0.00;
PO*G=0.00;
Block 2 7.5 ± 1.0 8.1 ± 0.6 6.2 ± 0.9 6.9 ± 1.0 4.7 ± 0.9 4.2 ± 0.7
Chapter 7
117
FIGURES
Figure 1. Typical example of the torque signal during the track test. At block 1, the instrumented wheel is on the inside
of the hallway round, and a negative torque due to braking / making a turn is visible, while during
block 2 the instrumented wheel is on the outside of the hallway round.
Figure 2. Mean and standard deviations of the mechanical efficiency of all groups over time. Pre = pre-test, Post =
post-test and extensions 1 and 2 refer to the exercise blocks. * = Significant Group effect.
Gross mechanical efficiency
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
pre 1 post 1 pre 2 post 2 pre 1 post 1 pre 2 post 2 pre 1 post 1 pre 2 post 2
Gro
ss
me
ch
an
ica
l eff
icie
nc
y (
%)
ERGO TREAD TRACK
*
Chapter 7
118
Figure 3. Changes in push time (white bar), recovery time (black bar) and cycle time (whole bar) between the groups
over time and standard deviation of the cycle time. # = Significant Time effect.
Figure 4. Difference in coefficient of variation between the practice groups for the variables mean power output and cycle
time for trial 1 and 9 at block 1. # = Significant Time effect; * = Significant Group effect
Push time & Cycle time
0.0
0.5
1.0
1.5
2.0
2.5
Pre 1 Post 1 Pre 2 Post 2 Pre 1 Post 1 Pre 2 Post 2 Pre 1 Post 1 Pre 2 Post 2
Tim
e (
s)
RT
PTERGO TREAD TRACK
##
Inter-cycle variability
0
5
10
15
20
25
30
35
40
45
ERGO TREAD TRACK
Co
eff
icie
nt
of
Va
ria
tio
n (
%)
Mean PO 1
Mean PO 9
Cycle Time 1
Cycle Time 9
#*
*
119
Chapter 8
Epilogue
Chapter 8
120
LEARNING MANUAL WHEELCHAIR PROPULSION
Studying the learning process of manual wheelchair propulsion is of theoretical and
clinical importance. Every person, also those in the experiments of this thesis,
seems to be able to pick up this novel gross motor task rather quickly although it is
not as easy as it might appear. Among other things, during the task the hands have
to couple to a rotating thin rim, and the movement of the arms occur partly
outside the visual field. Therefore, it is fascinating that novice wheelchair users are
able to perform this novel gross motor task almost immediately from the start
onwards as experienced wheelchair users do, e.g. showing unexpected, comparable
force directions. However, previous literature found differences between
experienced wheelchair users and novice (able-bodied) wheelchair users in for
example efficiency measures and timing (Brown et al. 1990; Knowlton et al. 1981;
Patterson et al. 1997; Tahamont et al. 1986).
In this thesis the starting hypothesis was that novice wheelchair users are able to
optimize their performance by practicing wheelchair propulsion without receiving
any extrinsic (feedback) information on performance. The improvement in their
wheelchair performance was suggested to be shown by a higher gross mechanical
efficiency after practice, which might be related to changes in propulsion technique
(e.g. timing, force application, inter-cycle variability) that have taken place over
time. Besides the positive effect of a learning period, it was assumed that the
performance of novice wheelchair users might be optimized even more when
defining optimal conditions, such as instructing them to direct the force
mechanically more effectively, to use different stroke patterns, and performing
under different forms of task complexity / diversity.
GROSS MECHANICAL EFFICIENCY AND MOTOR SKILL
Learning a motor skill is a complex process that requires spatial, temporal, and
hierarchical organization in the central nervous system. Changes in the central
nervous system are not directly observable but are inferred from changes in motor
behavior (O'Sullivan et al. 2000). Improvements in task performance result from
practice or experience and are a frequently used measure of learning. For example,
with practice an individual is able to develop appropriate sequencing of movement
components with improved timing and reduced effort (O'Sullivan et al. 2000). A
good indicator of (reduced) submaximal steady state effort after a practice period is
the (increased) gross mechanical efficiency. In the present thesis the variable gross
mechanical efficiency was used as an indicator of skill since it was assumed that
reduction of metabolic cost is associated with the learning and control of gross
motor skills. Achieving a high mechanical efficiency is important because it
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121
indicates that a certain task, with a fixed power output and velocity, can be
performed with less energy expenditure. When the external power output is held
constant, energy savings might be achieved during the motor learning process by
reducing the internal mechanical work required to coordinate and control the limbs
(Sparrow et al. 1998). So, improvements in propulsion technique due to learning or
after instructions/extrinsic feedback, are suggested to lead to an increase in
mechanical efficiency. The results presented in this thesis indeed showed that a 3-
wk practice period had a favorable effect on mechanical efficiency (Chapter 2).
Furthermore, changing the propulsion technique, i.e. letting the subjects propel the
wheelchair with a mechanically more effective force direction (Chapter 5) or
another stroke pattern (Chapter 6), also affects the mechanical efficiency. These
direct effects on metabolic cost agree with previous studies focusing on the
learning of a gross motor task in relation with energy expenditure, like crawling
(Sparrow et al. 1987), ski movements on a ski apparatus (Almasbakk et al. 2001),
and rowing (Lay et al. 2002; Sparrow et al. 1999). Those studies also reported a
(tendency of) decreased metabolic energy expenditure after practice at the same
workload. From the results of all these studies it can be concluded that mechanical
efficiency seems to be a useful measure of motor skill. However, it should be taken
into account that the variable mechanical efficiency can be used only during
submaximal, steady state exercise. For studies focusing on improvement in
propulsion technique (learning) without a simultaneous occurrence of physiological
adaptations (training) this may not be a problem because exercise should be of low
intensity and short duration. Learning phenomena of gross cyclic motor activity at
higher exercise levels require different outcome measures in terms of metabolic
cost. Oxygen uptake or heart rate might then be valid indicators of improvement
of motor skill when the task can be controlled precisely.
Which variables related to propulsion technique (i.e. timing, force application,
inter-cycle variability) changed and, therefore, might have influenced the
mechanical efficiency, will be discussed in the next paragraph.
PROPULSION TECHNIQUE AND EFFICIENCY
Timing
The propulsion technique variables that were always subject to change, on a short
and longer practice term (Chapter 2-3) and also between stroke patterns (Chapter
6), were the timing variables. It has been found previously, in both experienced
and non-wheelchair users (Goosey et al. 2000; Woude et al. 1989b), but also in
walking (Minetti et al. 1995), that the cycle frequency with the lowest energy cost is
Chapter 8
122
the freely chosen frequency when compared to paced frequencies above and below
the freely chose frequency. This indicates that even novice wheelchair users
employ an innate setting of movement frequency that is efficient almost from the
start onwards. Due to a learning process a drop in preferred cycle frequency was
seen (Chapter 2 and 3). Other research projects studying the effect of practice of
novel gross motor tasks found similar results, i.e. a lower cycle frequency and a
lower push or stroke time after practice (Lay et al. 2002; Sparrow et al. 1987). The
reduction in cycle frequency during learning wheelchair propulsion was associated
with adaptations in other variables, like an increase in push time, work per cycle,
and stroke angle. It could be hypothesized that this decrease in cycle frequency
might be associated with a decrease in the number of de/accelerations of the arms.
This might lead to a reduction in the number of muscle contractions and
subsequently to a higher mechanical efficiency. However, that this relationship may
be too simple was shown by the results of the optimum, i.e. freely chosen, cycle
frequency that is far from the lowest frequency possible (Goosey et al. 2000;
Woude et al. 1989b). Also, the cycle frequency of the most efficient – but imposed
– stroke pattern (i.e. pumping) was comparable to the cycle frequency found in the
freely chosen stroke pattern (Chapter 6). It could be suggested that the imposed
semi-circular stroke pattern and the imposed single looping over propulsion
pattern, which both had a lower cycle frequency because of the longer trajectory of
the hand in the recovery phase, were less efficient because of this „forced‟ lower
cycle frequency. At the freely chosen frequency there probably is a combination of
an optimum of speed and force of individual muscles, as well as tuning of agonist
and antagonist muscles (Woude et al. 1989b). But why does the freely chosen
frequency then decrease as a function of practice? It could be that the freely
chosen frequency at each stage of learning is the most efficient frequency at that
particular moment of learning given the particular underlying biological state the
human system evolves in due to learning, e.g. fine tuning of the neuromuscular
system. If that is the case, then the next step will be to investigate why there is a
change in number of cycles of the most efficient frequency during the learning
process.
Amazeen et al. (2001) studied the locomotor-respiratory coupling during
wheelchair propulsion in novice and experienced wheelchair users. One of their
experiments showed that the coupling of movement frequency and respiration
frequency was markedly different in that more experienced, but able-bodied,
wheelchair users tended to maintain a 2:1 ratio whereas novices tended to alternate
between 2:1 and at least one other frequency ratio (Amazeen et al. 2001) during the
different trials in which velocity and load varied. The lack of experience with the
Chapter 8
123
task possibly prevented novices from being able to vary both their rate of
propulsion and respiration simultaneously to hold the ratio between propulsion
and respiration constant. It can be hypothesized that novice wheelchair users are
able to perform at an imposed lower movement frequency but that they are not yet
able to couple the physiological subsystems to this lower movement frequency
with the lowest energy cost, which might need practice.
In the study focusing on the short-term adaptations (12 min) in propulsion
technique (Chapter 3) subjects were able to lower their cycle frequency by around
10 pushes/minute but not to cycle frequency values found after three or more
weeks of practice (decrements of around 20 pushes/minute) (Chapter 2,
(Dallmeijer et al. 1999b)). Probably the subjects in the short-term study were still in
an exploration phase regarding neuromuscular and mechanical fine-tuning as well
as with respect to the coordination of the different systems i.e. locomotor-
respiratory coupling since the mechanical efficiency did not change significantly.
How this decrease in cycle frequency relates to locomotor-respiratory coupling and
whether this has an effect on the mechanical efficiency must be subject of
continued study.
Force application
The second category of variables related to propulsion technique that might
influence the gross mechanical efficiency are the force application variables. It was
hypothesized that the low effectiveness of force application at least partly could
explain the low mechanical efficiency of wheelchair propulsion (Veeger et al.
1991a; Veeger et al. 1992a). However, these authors also assumed that this pattern
of force application might be, from a physiological point of view, the optimal
(mechanical) solution of force application, given the constraints of the wheelchair-
user system. A model study showed that to generate a purely effective propulsion
force the moment balance for the elbow must shift from triceps to biceps, which
would lead to high levels of co-contraction around the elbow and is also
accompanied by an increase in shoulder muscle activity (Veeger 1999). Also, the
simulated glenohumeral compression forces were slightly higher for the effective
force direction, which might indicate increased shoulder muscle activity (Veeger
1999). That the freely chosen force direction was the most optimal solution, was
also supported after simulation of the force direction (Rozendaal et al. 2000) based
on data of experienced wheelchair users, and using a criterion defined as the ratio
of mechanical effect and musculoskeletal cost. Because the force direction does
not seem to change due to practice within minutes or over 3 to 7 weeks (Chapter 2
and 3 and (Dallmeijer et al. 1999b)), it appears comparable among a wide range of
Chapter 8
124
(experienced) wheelchair users and subjects (Dallmeijer et al. 1998; Veeger et al.
1992a), and a feedback-based learned effective force direction had a negative effect
on the mechanical efficiency (Chapter 5), it seems that the force direction applied
by the subjects is indeed the most optimal within the constraints of the task.
According to Rozendaal et al. (2003) a reduction of the radial force component can
be achieved only by a differently designed propulsion system of the wheelchair
and/or change in the wheelchair-user interface (Veeger 1999). This warrants,
however, further experimental work.
Including electromyography and kinematics measurements in a study, such as
described in Chapter 5, would be helpful to investigate how the subjects adapt
their movement pattern and muscle activation patterns to obtain the more
tangentially directed force and substantiate the notions of Veeger (1999) and
Rozendaal (2003).
From the results of Chapter 5 it can be concluded that wheelchair propulsion
should be studied from a combined mechanical and physiological perspective since
optimization from a mechanical viewpoint not automatically implies that it is the
best way for the physiological system.
Inter-cycle variability
Two theories have been stated in the present thesis regarding movement variability
with respect to learning. According to the first theory (Tuller et al. 1982), a
beginner learns a motor skill by reducing some of the free variation of the body.
This could be accomplished by an increase in limb stiffness due to muscle
coactivity. As skill increases, the beginner will release the ban on the degrees of
freedom and subsequently this will lead to more variability. However, the latter was
not supported by the muscle co-contraction findings of chapter 4, i.e. there was an
increase in co-contraction of some muscle pairs instead of a decrease.
On the other hand, it is a typical finding that movement variability reduces due to
practice and increments of skill (Darling et al. 1987; Newell et al. 1993; Vereijken et
al. 1997). In this second theory, motor learning has been seen as a transition from
variable and inconsistent actions to patterned, consistent ones (Manoel et al. 1995).
A lower inter-cycle variability has been associated with a more effective movement
pattern (Smith et al. 1995). However, within the current study no significant
decrease in inter-cycle variability was found on a short-term basis (Chapter 3) or
after 3-weeks of practice (Chapter 2). This indeed is in contrast to what was
expected, based on other studies focusing on the effects of practice on variability
(Lay et al. 2002; Smith et al. 1995).
However, when three wheelchair practice groups were considered together, a
significant decrease was found in the inter-cycle variability of the mean power
Chapter 8
125
output, torque and velocity after three weeks of practice (Chapter 7). Whether a
longer practice period has more effect on the inter-cycle variability, i.e. a larger
decrease or more variables showing a decrease, and/or if there is a combination of
the two theories (i.e. initially a decrease due to limb stiffness and muscle coactivity
and later in skill learning an increase in variability), needs further study.
MUSCLE ACTIVITY
It might be expected that with practice there is a change in magnitude of muscle
activity and that there is also a refinement of timing of the muscle activity patterns
with practice (Sparrow 1983). In contrast to the results of Chapter 4 a recent study
(Lay et al. 2002), in which the effect of practice on the rowing performance was
studied in a group of six inexperienced rowers, found a significant decrease in
muscle activity (analyzed with integrated EMG) of the biceps brachii after a much
longer practice period, i.e. ten 16-min. practice sessions. However, when looking at
the analyzed data of the first practice day (20 s collected EMG at 0.20, 2.20 and
15.20 min.) no such differences in integrated EMG values could be seen for the
biceps brachii or vastus lateralis. This could indicate that a decrease in integrated
EMG needs a practice period of more sessions / weeks. Therefore, a possible
decrease in muscle activity after several weeks of practice could still be an
explanation for the increase in mechanical efficiency in the 3-wks wheelchair study
(Chapter 2). Including EMG measurements in a future study focusing on long-
term wheelchair practice effects (several weeks to months) would be useful for that
reason.
The precise timing, i.e. on- and offset, of muscle activity during the whole cycle
and also the inter-muscular timing before and after a certain amount of practice
needs attention. When looking at the mean muscle activity patterns in Chapter 4
(Figure 3) no clear changes in activity patterns of the different muscles were visible
during the 12-min. of practice. However, a more objective measure of the on- and
offset of voluntary muscle contractions (Staude et al. 1999) should be incorporated
into the analysis in future studies.
According to Bernstein (1967) early in learning redundancy might be constrained
by reducing („freezing out‟) the number of degrees of freedom via muscle
coactivity. Later in learning these restrictions could be relaxed, leading to fewer
muscle co-contractions. However, in contrast to Bernstein‟s theory, there was no
decrease in co-contraction of antagonist pairs in the 12 min. practice study
described in chapter 4. Co-contraction remained the same or even increased during
the 12 min. Co-contraction will always be necessary to optimize the force direction
in such a way that there is a balance between mechanical effect and
Chapter 8
126
musculoskeletal cost. Since the inter-cycle variability of muscle activity for almost
all muscles increased over the 12 min. of practice, it was suggested that the novice
wheelchair users were still in an exploration phase. To investigate this unfreezing /
muscle co-contraction theory in more detail, a longer practice period needs to be
studied.
PRACTICAL IMPLICATIONS
First of all, it is important to know that a learning process induces changes in
propulsion technique and, more essential, in gross mechanical efficiency. An
important finding of the present thesis is that practicing with, respectively, a
wheelchair ergometer, a wheelchair on a treadmill or on a testing track, did not lead
to other adaptations over time when focusing on mechanical efficiency and
propulsion technique under the described conditions (Chapter 7). The measured
variables of the three practice groups changed in a similar direction. Subjects were
able to perform the motor task in an altered environmental situation, i.e. were
resistant to contextual change (O'Sullivan et al. 2000), which was shown by the
additional transfer tests in which the treadmill group was tested on the track and
vice versa. This positive transfer effect is important since the way in which training
effects transfer either to a new task or to a new environment is assumed to be a
critical issue in rehabilitation (Shumway-Cook et al. 2001). The practical value of
this finding is that neurologically impaired individuals in a rehabilitation process,
who for instance have a poor balance, could start practicing in a stable wheelchair
ergometer. The advantage of using a wheelchair ergometer is that the external
resistance can be adjusted to a level that can be handled by the patient. Using a
wheelchair on a normal floor surface has initially a too high resistance for some of
the users, as for instance individuals with a high spinal cord injury early in the
rehabilitation process (Dallmeijer et al. 1999a). Of course, they should transfer to a
normal wheelchair as soon as possible, in order to learn specific wheelchair tasks
necessary for daily life wheeling (e.g. maneuvering, balancing, negotiating slopes
and obstacles). Since variable practice increases the ability to adapt and generalize
learning (Shumway-Cook et al. 2001), this variety of wheelchair tasks might allow a
person to perform better on novel variations of the task. Chapter 7 indicated that
task complexity during practice does not influence the final performance under
those experimental conditions. Besides task complexity also task specificity needs
more attention in the context of rehabilitation. For example arm crank exercise
could be used to enhance cardiovascular and endurance capacity of the upper
extremity and trunk muscles. However, according to the principle of specificity,
Chapter 8
127
exercise training should simulate, as closely as possible, the conditions of a specific
sport to elicit the greatest (physiological) adaptations (Tanaka 1994). Therefore, it
is hypothesized that arm crank exercise will not sufficiently improve the
performance of wheelchair propulsion. Furthermore, Kauzlarich and Thacker
(1987) showed that balancing a rod in the palm of the hand is a mathematical
problem similar to the wheelie balance. After limited testing with able-bodied
subjects they suggested that a simple test of determining the shortest length of rod
one can balance in the palm of the hand, together with measurement of hand rim
force capability and reaction time, might indicate whether a wheelchair user will be
able to learn quickly a wheelie balance. The question that arises is whether practice
of balancing a rod is helpful for improving wheelie performance. If practice does
not have to be wheelchair specific to improve wheelchair performance, this would
be an important finding because arm crank exercise or balancing a rod induces less
mechanical load on the upper extremity compared to wheelchair exercise.
In general, it seems that novice wheelchair users are able to intuitively pick up the
most efficient propulsion technique at their stage of learning. Studies focusing on
the freely chosen cycle frequency (Woude et al. 1989b; Goosey et al. 2000) and the
effective force direction (Chapter 5; Rozendaal et al. 2000) demonstrated that
subjects automatically use the most efficient way of propulsion. There seems no
reason to advise against using the pumping stroke pattern, which is frequently used
by inexperienced wheelchair users. In contrast to the advice given in literature that
is written for wheelchair users, the pumping patterns showed positive mechanical
efficiency results compared to the semi-circular stroke pattern and no difference in
mechanical load on the upper extremity in an able-bodied subject group and under
the experimental conditions (Chapter 6).
LIMITATIONS AND FUTURE RESEARCH
Although the gross mechanical efficiency improves with practice and/or by
changing the propulsion technique, the gross mechanical efficiency of wheelchair
propulsion remains low. To decrease the metabolic cost to the lowest level
possible, wheelchair propulsion should of course be studied from a broader
perspective than the learning issue only. This means that the wheelchair itself
might be optimized more e.g. in terms of material, and internal friction. Secondly,
more research should be done regarding optimization of the wheelchair–user
interface, i.e. what is the best adjustment of the wheelchair with respect to the
antropometrics and functionality of the wheelchair user. The third aspect focuses
on the wheelchair user, where besides the propulsion technique (learning) also the
physical capacity (training) needs more attention. Because the focus of this thesis
Chapter 8
128
was on the level of the wheelchair user only, suggestions for future research will be
done at the level of the wheelchair user only.
Because little was known about the initial motor learning processes of wheelchair
propulsion, it was chosen to start simple and well controlled with homogeneous
subject groups who are able to propel the wheelchair at a standardized power
output and velocity. Therefore, able-bodied subjects were included in highly
controlled lab experiments. Although the results might not be completely
transferable to people with limited functions, especially when these concern the
upper trunk and arms, the experiments gave insight in adaptations in propulsion
technique and mechanical efficiency that takes place due to a practice period in
able-bodied subjects. However, studies should also include novice wheelchair-
dependent subjects to investigate the possibly different process of adaptation,
compensation and learning because of disability, e.g. lack of trunk stability or hand
function for gripping the rims.
To study only the effect of learning on the mechanical efficiency, the exercise
protocol was chosen to be of low intensity, short duration and limited frequency.
By choosing this protocol, the assumption was that no training effect would occur.
It is, however, not possible to apply this „learning protocol‟ in a rehabilitation
setting since rehabilitation clearly involves peak level exercise at relatively high
frequency and duration. Effects of training and implicit learning during
rehabilitation must be studied.
Motor learning is a result of practice and is highly dependent on sensory
information and feedback processes. Knowledge of results (i.e. extrinsic feedback)
is an important learning variable (Shumway-Cook et al. 2001). However, for certain
types of tasks intrinsic (i.e. visual and kinesthetic) feedback is sufficient to provide
most error information. Verbal feedback, video replays and biofeedback are
examples of augmented feedback, which can be used to enhance motor learning
during wheelchair practice (O'Sullivan et al. 2000). Results of a study focusing on
teaching wheelchair skills indicate that a systematic method incorporating specific
instructions for learning a new wheelchair skill leads to a faster learning process
than learning by trial and error (Bullard et al. 2001). Chapter 5 indicated that
subjects are able to learn a different propulsion technique, i.e. a mechanically more
effective force direction, with help of visual feedback on a computer screen
although they were not aware of the meaning of the signal on the screen. The
effect of this more effective force direction on the gross mechanical efficiency was
studied. It would be fascinating to study whether subjects are able to increase the
gross mechanical efficiency by giving direct feedback of the gross mechanical
efficiency on a screen. If subjects are able to do so, the question is whether they
Chapter 8
129
achieve this by changing the propulsion technique and/or loco-motor coupling or
employ (unexpected) other strategies.
130
References
References
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141
Summary
Summary
142
BIOPHYSICAL ASPECTS OF LEARNING HAND RIM WHEELCHAIR PROPULSION
Learning and training are essential in the process of rehabilitation. Novice (recently
injured) hand rim wheelchair users in the process of rehabilitation have to learn a
complete set of new motor patterns of the upper extremities and trunk to perform
activities of daily living and for the purpose of ambulation: i.e. propelling a
wheelchair with their arms. Adaptations in the human system and in the
organization of movements will take place as elements of learning and training.
Learning of motor skills may be viewed as the process of skill acquisition, which
leads to improved task performance and proficiency. To date motor learning of
manual wheelchair propulsion has received only little research attention (Amazeen
et al. 2001; Dallmeijer et al. 1999b; Rodgers et al. 2001; Woude et al. 1999),
however, it is of theoretical as well as clinical importance. The process of learning
wheelchair propulsion is a good example to study motor learning of a relevant and
novel gross motor task. Furthermore, knowledge about motor skill learning is
important for an effective and successful rehabilitation process. The current thesis
was intended to fill in the first gaps in this respect. The thesis has addressed the
biophysical aspects of the learning process of hand rim wheelchair propulsion in
able-bodied, novice wheelchair users. Important in this respect is that learning is
defined as changes in propulsion technique without the simultaneous occurrence
of physiological adaptations over time, which is the case in training. The first aim
of this thesis was to investigate what adaptations in mechanical efficiency – an
indicator of how much of the internally liberated energy is used to deliver a certain
external power output - and propulsion technique take place over time in novice,
able-bodied wheelchair users due to a learning process of hand rim wheelchair
propulsion. After gaining some general knowledge about the learning process of
wheelchair propulsion, the second aim was to further define a few optimal
conditions for the learning process.
ADAPTATIONS IN EFFICIENCY AND PROPULSION TECHNIQUE
Firstly, the effect of 3-wks hand rim wheelchair practice on mechanical efficiency
and propulsion technique was studied in completely novice able-bodied wheelchair
users (Chapter 2). Results showed that a practice period of three weeks (3.wk-1, 2
four-min. exercise blocks each trial at an external power output of 0.15 and 0.25
W.kg-1 and a velocity of 1.11 m.s-1) on a wheelchair ergometer, without giving any
instruction or feedback, had a positive effect on the mechanical efficiency. The
Summary
143
mechanical efficiency increased in the practice group in contrast to a control group
who did not receive any practice but only came in for a pre- and post-test.
The timing variables (cycle frequency, cycle time, push time) and subsequently the
work per cycle also changed significantly over time, with a decrease in cycle
frequency and consequently an increase in the other variables. So, the practice
period had favorable effects on some technique variables and on mechanical
efficiency, which in turn may indicate a positive effect of improved technique on
mechanical efficiency. Since no changes occurred over time in most force
application parameters (among which the effective force direction), left-right
symmetry, and inter-cycle variability during the 3-wk practice period, it was
hypothesized that these variables may change in view of the innate optimization of
propulsion technique either on a shorter (within seconds/minutes) or longer
(months/years) time scale.
To examine the changes in propulsion technique and mechanical efficiency on a
short-term basis, a study was performed to analyze adaptations in novice able-
bodied wheelchair users during the first 12 min. (external power output of 0.25
W.kg-1 and a velocity of 1.11 m.s-1) of learning hand rim wheelchair propulsion
(Chapter 3). In contrast to other studies, propulsion technique measurements were
already started after 15 s instead of starting to collect data only in the last minute of
the four-minutes exercise block. Again, the timing variables significantly changed
during the initial phase of the learning process. However, mechanical efficiency did
not change significantly within the 12 minutes of practice. This study also indicated
that the effective force direction seems to be optimized from the start of the
learning process onwards since the values during the first pushes were comparable
to results after 12 min. or 3-weeks of practice. Because some other technique
variables, such as the inter-cycle variability, did not change on this short-term basis
as well as after 3 weeks, it was suggested that a longer practice period, i.e. even
months or years, might be necessary to induce this change.
The previous short-term study was extended with measurements of movement
patterns and muscle activity (Chapter 4) to investigate possible changes in
segmental movement patterns and muscle activation / co-contraction, which are
suggested to occur together with the changes in timing. Given the large number of
muscles around the shoulder, movements can be conducted with different sets of
muscles. According to Bernstein‟s theory (1967), a beginner learns a motor skill by
reducing some of the free variation of the body, which might be possible via
muscle co-contractions and increased limb stiffness. As skill increases, these
restrictions could be relaxed, meaning that coactivity might be initially high and
subsequently decreases with progress in skill learning. The hypothesis that muscle
Summary
144
co-activity would decrease as a consequence of practice could not be supported by
the results. Co-contraction of antagonist pairs remained the same or even
increased. Also, the hypothesis that subjects instinctively search for an optimum
cycle frequency, in which the recovery phase is related to the eigenfrequency of the
arms and, therefore, the least muscle activity, could not be supported. The 12 min.
of practice probably were too short for the novice subjects to explore this new task
of wheelchair propulsion sufficiently and reach a more optimum technique in
terms of cycle frequency and muscle activation.
EFFECTS OF INTERVENTIONS ON EFFICIENCY AND PROPULSION TECHNIQUE
Results of chapter 2-4 indicate that just practicing hand rim wheelchair propulsion,
without receiving instructions or feedback, already has an effect on the timing
variables and, when practicing long enough, on mechanical efficiency. It is,
however, suggested that certain interventions (i.e. feedback of force control, stroke
pattern, or task complexity) may improve the performance at a higher pace or to a
higher level. Therefore, in Chapter 5, one group of subjects was instructed to
direct the force more tangentially to the rim by means of feedback on a computer
screen. Previous studies suggested that the non-tangential force direction, which is
normally seen in wheelchair propulsion, was the most optimal from a physiological
point of view in contrast to the mechanical viewpoint. It was tested in chapter 5
whether a more effective force direction indeed leads to a lower gross mechanical
efficiency. After 3-weeks of practice the subjects of the feedback group were able
to direct the force more effectively compared to a control practice group, who did
not receive any feedback or other information. However, gross mechanical
efficiency was significantly lower in the feedback group compared to the control
group. The findings of this experimental study were similar to a simulation study
(Rozendaal et al. 2000) that the most effective force production from a mechanical
viewpoint is not necessarily the most efficient way – in terms of energy cost – from
a biological point of view. From Chapter 5, it can be concluded that learning a
more effective force direction by visual feedback is not useful for increasing the
mechanical efficiency of wheelchair propulsion.
Another intervention for trying to optimize the mechanical efficiency, was teaching
the novice subjects different stroke patterns (Chapter 6). Previous literature
suggested that a specific stroke pattern, i.e. the semi-circular stroke pattern in
which the hands follow a path below the hand rim during the recovery phase,
would be the most efficient stroke pattern. However, when comparing three
Summary
145
different imposed stroke patterns and the freely chosen stroke pattern, the findings
were just the other way around. The semi-circular stroke pattern was the least
efficient stroke pattern. The pumping stroke pattern, in which the hands follow the
same path during the push and recovery phase, was the most efficient stroke
pattern in this novice able-bodied subject groups regardless of the velocity.
Modeling data did not show results that advocate against using the pumping
technique with respect to strain in the glenohumeral joint or joint moments. The
exact reason why pumping is more efficient compared to the other stroke pattern,
is not clear yet. It was suggested that it could be due to the cycle frequency, which
was closest to the freely chosen cycle frequency, in contrast to the longer and,
therefore, more „forced‟ push frequencies of the semi-circular and single looping
over propulsion technique. These more optimal push frequencies might be
associated to the optimum contraction velocity of the muscles, i.e. the force-
velocity relationship of the different muscles.
To investigate whether task complexity and/or boundary conditions are important
for the learning process of hand rim wheelchair propulsion regarding mechanical
efficiency and propulsion technique, three groups of novice able-bodied subjects
practiced hand rim wheelchair propulsion with more/less diverse wheelchair tasks,
i.e. on an ergometer, treadmill or track. It was expected that the group that
practiced on a testing track (most complex task with steering, balancing etc.) would
learn more compared to the groups that practiced with less diverse tasks like
propelling on a treadmill or - even more monotonous - on a stationary wheelchair
ergometer. However, no differences in changes over time in gross mechanical
efficiency and propulsion technique could be discerned among the three practice
groups. An effect over time was shown for these three groups in the timing
variables, i.e. a decrease in cycle frequency and subsequently an increase in push
time, cycle time, stroke angle and work per cycle was found for all groups. A
reduction in inter-cycle variability was also found over time. Since no differences
were found between the groups, this indicates that task complexity does not have
an influence on the learning process of hand rim wheelchair propulsion under the
experimental conditions described in chapter 7.
In chapter 8 general conclusions were formulated and suggestions for further
research were proposed. A natural learning process of 3 weeks induces significant
changes in propulsion technique and energy expenditure. Mainly the timing
variables are subject to change as a consequence of practice. These variables
already change during the initial phase, i.e. first minutes, of practice. Novice
wheelchair users seem to be able to optimize performance by using the pumping
stroke pattern. Changing the force direction to a mechanically more effective
Summary
146
direction or starting directly to learn wheelchair propulsion with a normal
wheelchair on a track, compared to treadmill and ergometer practice, seems not to
be helpful for improving the mechanical efficiency. Although many questions
remain to be answered, this thesis is the start of knowledge development on the
optimization of the learning process of hand rim wheelchair propulsion. Studying
the effects of a longer wheelchair practice period (i.e. months or years) and
including novice wheelchair-dependent subjects were, among other things,
proposed as topics that need further attention in future research.
147
Samenvatting
Samenvatting
148
BIOFYSISCHE ASPECTEN VAN HET LEREN VAN ROLSTOELRIJDEN
Leren en trainen zijn essentiële onderdelen van het revalidatieproces. Nieuwe
rolstoelgebruikers moeten andere arm- en rompbewegingen aanleren met, onder
andere, als doel zichzelf te kunnen voortbewegen. Met andere woorden, ze moeten
leren een rolstoel aan te drijven met hun armen. Aanpassingen in het menselijke
lichaam en in de organisatie van bewegingen zullen plaatsvinden door leren en
trainen.
Het leren van motorische vaardigheden kan worden gezien als een proces van het
verwerven van vaardigheden, dat uiteindelijk zal leiden tot een verbeterde en
vakkundige uitvoering van de taak. Er is nog niet veel onderzoek gedaan naar het
leren van hoepel aangedreven rolstoelrijden (Amazeen et al. 2001; Dallmeijer et al.
1999b; Rodgers et al. 2001; Woude et al. 1999). Onderzoek hiernaar is zowel van
theoretisch en klinisch belang. Het leren rolstoelrijden is een goede gelegenheid om
meer te begrijpen omtrent het aanleren van nieuwe en relevante, motorische taken.
Bovendien kan kennis over het aanleren van een motorische vaardigheid belangrijk
zijn voor een effectief en succesvol revalidatieproces. Het onderzoek waarover in
dit proefschrift wordt gerapporteerd, is bedoeld om de eerste kennis hieromtrent te
genereren.
Het onderzoek heeft zich gericht op de biofysische aspecten van het leren
rolstoelrijden in gezonde, onervaren rolstoelgebruikers. Belangrijk hierbij is dat
leren wordt gedefinieerd als veranderingen in de aandrijftechniek zonder dat
fysiologische aanpassingen optreden. Het laatste is het geval bij training. Het eerste
doel van dit proefschrift was te onderzoeken welke aanpassingen er optreden in
mechanische efficiëntie - een maat die aangeeft hoeveel van de vrijgemaakte
energie wordt gebruikt om een bepaald uitwendig vermogen te leveren - en in de
aandrijftechniek van onervaren, gezonde rolstoelgebruikers door een oefenproces
van rolstoelrijden. Naast het verwerven van algemene kennis over het effect van
een oefenproces van rolstoelrijden, was het tweede doel om enkele optimale
leercondities te definiëren.
AANPASSINGEN IN EFFICIËNTIE EN AANDRIJFTECHNIEK
Allereerst is het effect van 3 weken oefenen in een rolstoel op de (bruto)
mechanische efficiëntie en aandrijftechniek bestudeerd in een groep volledig
onervaren, gezonde rolstoelgebruikers (Hoofdstuk 2). De resultaten toonden aan
dat een oefenperiode van drie weken (3 keer per week, elke keer 2
Samenvatting
149
inspanningsblokken van vier minuten op een extern vermogen van respectievelijk
0.15 en 0.25 W.kg-1 en een snelheid van 1.11 m.s-1) in een rolstoelergometer, zonder
enige instructie of extrinsieke feedback, een positief effect had op de mechanische
efficiëntie. De mechanische efficiëntie nam toe in de oefengroep in tegenstelling
tot een controlegroep, die niet oefende maar alleen reed in een rolstoel tijdens een
voor- en nameting. De tijdsafhankelijke techniekvariabelen (duwfrequentie,
cyclustijd, duwtijd) en de hoeveelheid arbeid per cyclus veranderden ook significant
in de tijd. De duwfrequentie werd lager en er was een toename in de andere
genoemde variabelen. De oefenperiode had dus een positief effect op sommige
techniekvariabelen en op de mechanische efficiëntie. Aangezien er geen
veranderingen in de tijd optraden in de meeste variabelen betreffende de
krachtoverbrenging (waaronder de effectieve krachtrichting), links-rechts
symmetrie en de variabiliteit tussen de cycli tijdens de oefenperiode van 3 weken,
werd er verondersteld dat deze variabelen veranderen op een kortere (binnen
seconden/minuten) of langere (maanden/jaren) termijn.
Om de kortetermijn veranderingen in aandrijftechniek en mechanische efficiëntie
te onderzoeken, is vervolgens een studie uitgevoerd om de adaptaties te analyseren
in onervaren, gezonde rolstoelgebruikers tijdens de eerste 12 minuten van het
leerproces van rolstoelrijden (Hoofdstuk 3). In tegenstelling tot ander onderzoek,
werd al gestart met het meten van de aandrijftechniek na 15 seconden in plaats van
alleen tijdens de laatste minuut van een vier minutendurend inspanningsblok. De
tijdsafhankelijke techniekvariabelen veranderden weer significant tijdens de initiële
fase van het leerproces. De mechanische efficiëntie veranderde echter niet
significant binnen de 12 oefenminuten. Dit onderzoek bewees ook dat de
effectieve krachtrichting optimaal lijkt te zijn vanaf de start van het leerproces,
aangezien de waarden gemeten tijdens de allereerste duwfasen vergelijkbaar waren
met de waarden na 12 minuten of 3 weken oefenen. Omdat een aantal andere
techniekvariabelen, zoals b.v. de variabiliteit tussen de cycli, niet veranderden op
deze kortetermijn en ook niet na 3 weken oefenen, werd verondersteld dat een
langere oefenperiode, dat wil zeggen maanden of jaren, nodig kan zijn om een
verandering hierin te bewerkstelligen.
De hiervoor genoemde kortetermijnstudie werd uitgebreid met metingen van het
bewegingspatroon en de spieractiviteit (Hoofdstuk 4). Verondersteld werd dat
veranderingen in het bewegingspatroon en de spieractiviteit samen op zouden
treden met de veranderingen in tijdsafhankelijke techniekvariabelen. Gezien het
grote aantal spieren rond de schouder, kunnen bewegingen tot stand komen door
activiteit van verschillende spieren. Volgens een theorie van Bernstein (1967) leert
een beginner een motorische vaardigheid door de vrije variatie van het lichaam te
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verminderen. Dit zou volgens Bernstein mogelijk zijn door het gelijktijdig
aanspannen van bepaalde spieren met tegengestelde functies (co-contractie).
Waardoor een toegenomen stijfheid van de ledematen tot stand komt. Als de
vaardigheid toeneemt, kunnen deze beperkingen weer langzaam worden
opgeheven. Dit zou betekenen dat co-contracties in het begin veel voorkomen,
maar vervolgens afnemen als men vaardiger wordt. De hypothese dat co-
contracties afnemen als gevolg van oefenen, kan niet worden ondersteund door de
resultaten van hoofdstuk 4. De co-contractie van bepaalde spieren bleef gelijk of
nam zelfs toe tijdens de oefenperiode van 12 min. Ook de hypothese dat personen
instinctief op zoek gaan naar een optimale duwfrequentie, waarin de herstelfase
gerelateerd is aan de eigenfrequentie van de armen en daardoor aan de laagste
spieractiviteit, kon niet worden ondersteund. De 12 oefenminuten waren
waarschijnlijk niet lang genoeg om deze nieuwe taak behoorlijk onder de knie te
krijgen en een optimaler duwfrequentie en spieractivitiepatroon te bereiken.
EFFECT VAN INTERVENTIES OP DE EFFICIËNTIE EN DE AANDRIJFTECHNIEK
De resultaten van hoofstukken 2 tot en met 4 toonden aan dat alleen het oefenen
van rolstoelrijden, zonder het krijgen van instructies of extrinsieke feedback, al een
effect heeft op de tijdsafhankelijke techniekvariabelen en, als er lang genoeg wordt
geoefend, de mechanische efficiëntie. Op grond van de literatuur werd echter
verondersteld dat bepaalde interventies (d.w.z. feedback van de krachtrichting,
baan van de hand, taakcomplexiteit) de prestatie sneller zouden verbeteren of tot
een hoger niveau zouden brengen.
In hoofdstuk 5 werd daarom een groep proefpersonen geleerd om met behulp van
feedback op een computerscherm de kracht meer aan de raaklijn langs de hoepel
te richten. In eerder onderzoek werd verondersteld dat de meer naar beneden
gerichtte krachtvector, die normaal gezien wordt tijdens rolstoelrijden, het meest
optimaal is vanuit een fysiologisch perspectief in tegenstelling tot de gewenste
krachtrichting vanuit mechanisch perspectief. In hoofdstuk 5 werd getest of een
mechanisch effectievere krachtrichting inderdaad leidt tot een lagere mechanische
efficiëntie. Na 3 weken oefenen waren de proefpersonen uit de feedback groep,
vergeleken met een controlegroep die geen feedback of andere informatie ontving,
in staat om de kracht effectiever te richten. Vergeleken met de controlegroep was
de mechanische efficiëntie significant lager in de feedback groep. De bevindingen
van dit experimentele onderzoek waren gelijk aan een simulatiestudie (Rozendaal et
al. 2000). Immers, de meest effectieve krachtrichting vanuit een mechanisch
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perspectief, dus gericht langs de raaklijn van de hoepel, bleek niet
noodzakelijkerwijs de meest efficiënte manier vanuit een fysiologisch standpunt,
met name in termen van energieverbruik. Uit de resultaten van hoofdstuk 5 kan
worden geconcludeerd dat het leren van een effectievere krachtrichting door
middel van visuele feedback niet bruikbaar is om de mechanische efficiëntie van
rolstoelrijden te verhogen.
Een andere interventie om te proberen de mechanische efficiëntie te optimaliseren,
was het leren van verschillende bewegingsbanen van de hand in de herstelfase, bij
nieuwe, gezonde rolstoelgebruikers (hoofdstuk 6). In eerder onderzoek werd
gesuggereerd dat één specifieke bewegingsbaan van de hand, namelijk de semi-
circulaire techniek waarin de hand tijdens de herstelfase een halve cirkel maakt
onder de hoepel, de meest efficiënte techniek zou zijn. Na het vergelijken van drie
verschillende bewegingsbanen van de hand en een vrijgekozen bewegingsbaan,
waren de bevindingen precies tegengesteld. De semi-circulaire techniek was de
minst efficiënte techniek. De techniek waarin de hand een pompende beweging
maakt, waarbij de hand tijdens de duw- en herstelfase dezelfde baan volgt, bleek de
meest efficiëntie techniek, ongeacht de snelheid van rijden. Data uit een
schoudermodel (van een subgroep) lieten geen resultaten zien die tegen het gebruik
van de pompende beweging pleiten wat de mechanische belasting betreft op de
gewrichten. De precieze reden waarom de pompende bewegingsbaan van de hand
efficiënter is vergeleken met de andere bewegingsbanen is nog niet duidelijk.
Verondersteld wordt dat dit door de duwfrequentie wordt veroorzaakt. De
duwfrequentie was tijdens de pompende beweging het meest gelijk aan de
vrijgekozen frequentie, in tegenstelling tot de langere, opgelegde duwfrequenties
tijdens onder andere de semi-circulaire techniek. De meer optimale duwfrequentie
zou geassocieerd kunnen zijn met optimale contractiesnelheden van de spieren.
Om inzicht te krijgen in de invloed van taakcomplexiteit tijdens het leren van
rolstoelrijden op de mechanische efficiëntie en aandrijftechniek, oefenden 3
groepen nieuwe, gezonde rolstoelgebruikers het rolstoelrijden met meer of minder
complexe rolstoeltaken (hoofdstuk 7). De rolstoeltaken bestonden uit oefenen op
een rolstoelergometer, een lopende band of een parcours. De verwachting was dat
de groep die op het parcours oefende (meest complexe taak met sturen, balanceren
etc.) meer zou leren in vergelijking met de groepen die oefenden met de minder
diverse taken zoals rolstoelrijden op een lopende band of – nog monotoner – op
een stationaire rolstoelergometer. Het oefenprotocol was vergelijkbaar met het
protocol gebruikt in hoofdstuk 2. Geen onderscheid werd echter gevonden tussen
de drie oefengroepen in veranderingen in de tijd van de mechanische efficiëntie en
aandrijftechniek. Een tijdseffect werd gevonden voor de tijdsafhankelijke
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152
techniekvariabelen; dat wil zeggen een afname in duwfrequentie en vervolgens een
toename in duwtijd, duwhoek en arbeid per cyclus werd gevonden bij alle drie de
groepen. Een afname in de variabiliteit tussen de cycli in de tijd werd ook voor alle
proefpersonen tezamen gevonden. Aangezien geen verschillen konden worden
onderscheiden tussen de groepen, werd geconcludeerd dat taakcomplexiteit geen
invloed heeft op het leerproces van rolstoelrijden onder de experimentele condities
beschreven in hoofdstuk 7.
In hoofdstuk 8 werden algemene conclusies geformuleerd in het licht van de
algemene discussie en werden suggesties gedaan voor toekomstig onderzoek. Een
leerproces van 3 weken leidt tot veranderingen in aandrijftechniek en mechanische
efficiëntie. De tijdsafhankelijke techniekvariabelen veranderen hoofdzakelijk als
gevolg van oefening. Deze variabelen veranderen al tijdens de initiële leerfase, en
wel in de eerste minuten van oefenen. Nieuwe rolstoelgebruikers lijken in staat te
zijn om de prestatie te optimaliseren door gebruik te maken van de pompende
bewegingsbaan van de hand. Het veranderen van de krachtrichting naar een, vanuit
een mechanisch standpunt, effectievere richting of het direct starten van het
leerproces van rolstoelrijden op een parcours, vergeleken met oefenen op een
lopende band of ergometer, lijkt niet zinvol te zijn voor het verbeteren van de
mechanische efficiëntie.
Alhoewel veel vragen onbeantwoord blijven, is dit proefschrift het begin van
kennisontwikkeling omtrent het leerproces tijdens hoepel aangedreven
rolstoelrijden. Het bestuderen van de effecten van een langere oefenperiode met
rolstoelrijden (maanden/jaren) en het includeren van nieuwe, rolstoelafhankelijke
proefpersonen worden ondere andere voorgesteld als onderwerpen voor
toekomstig onderzoek.