CONTENTS Chapter 1 Introduction Wheelchair propulsion ... · 1 CONTENTS Chapter 1 Introduction 2...

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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)

48

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

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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

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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).


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.


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 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



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.


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).




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:


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.


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).


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.


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).


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.


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



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 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.


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).


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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-80

T1 Subject A T4 Subject A

T1 Subject B T4 Subject B

-250 -200 -150 -100 -50 0 50 100-300

-250

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-50

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-350 -300 -250 -200 -150 -100 -50 0 50 100 150-260

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-350 -300 -250 -200 -150 -100 -50 0 50 100 150-260

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-80

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


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)




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 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



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


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).


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:






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.


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.


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).


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

Chapter 6

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.


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

Chapter 6

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.


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

Chapter 6

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,

Chapter 6

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.

Chapter 7

103

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



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).


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

Chapter 7

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)




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.


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:

Chapter 7

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).


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


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.


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.


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


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

Chapter 7

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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113

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.


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.


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

##


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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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

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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

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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.


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

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141

Summary

Summary

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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 armen 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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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.

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