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R E S E A R C H N O T E

The role of cocontraction in the impairment of movementaccuracy with fatigue

Olivier Missenard

Denis Mottet

Stephane Perrey

Received: 4 October 2007 / Accepted: 19 December 2007

Springer-Verlag 2008

Abstract The present experiment was designed to test the

hypothesis that fatigue-induced impairment in movementaccuracy is caused by a decrease in muscle cocontraction

rather than a reduced ability to produce muscular force.

Seven participants performed fast and accurate elbow

extensions aimed at a target, before and after a fatigue

protocol. The inertia of the manipulandum was decreased

after the fatigue protocol so that the ratio of required to

available force during movements was identical pre- and

post-fatigue. After the fatigue protocol, movement end-

point accuracy decreased and movement endpoint

variability increased. These alterations were associated

with a decrease in cocontraction. We concluded that the

impairment of movement accuracy during fatigue could not

be explained by the lack of available force, but was likely

to be due to a fatigue-induced decrease in muscular co-

contraction. We then speculate that fatigue influences the

relative weights of accuracy and energy economy in the

optimisation of sensorimotor control.

Keywords Fatigue Movement accuracyMotor control Cocontraction EMG

Introduction

Muscular fatigue is experienced in many situations where

movement control is crucial, from the use of manmachine

interfaces to taking a final shot in a professional basketball

game. Thus, it is of particular interest to understand the

functional consequences of muscular fatigue. Fatigue is

classically defined as a loss of maximal available force

(e.g. Edwards1981). It is likely that this loss of available

force affects motor control, especially for movements

requiring high forces, but fatigue has also been shown to

impair movement accuracy for movements requiring rela-

tively small forces (Hoffman et al.1992; Jaric et al.1999).

This effect on accuracy, when the level of available force

does not seem to be a limiting factor for motor control,

suggests that other factors besides the lack of available

force may play an important role in the impairment of

movement accuracy with fatigue.

A likely candidate to explain the impairment of move-

ment accuracy with fatigue is muscular cocontraction,

defined as the simultaneous activation of agonist and

antagonist muscles around a joint. Indeed, cocontraction

has been shown to increase movement endpoint accuracy

(e.g. Gribble et al. 2003). Moreover, when participants are

requested to use cocontraction to point at a target, endpoint

accuracy is improved (Osu et al.2004). This improvement

is mainly attributed to the fact that cocontraction increases

limb impedance (Osu and Gomi1999), and thus limits the

variability induced by neuromuscular noise (Selen et al.

2005). Hence, if fatigue decreases cocontraction, we can

predict a decrease in movement endpoint accuracy and an

increase in endpoint variability.

To our knowledge, the effect of fatigue on cocontraction

during aimed arm movements has never been studied.

However, it makes sense that fatigue could decrease

O. Missenard D. Mottet (&) S. PerreyEA 2991, University Montpellier 1, 700 av. du pic Saint Loup,

34090 Montpellier, France

e-mail: [email protected]

O. Missenard


S. Perrey


1 3

Exp Brain Res

DOI 10.1007/s00221-007-1264-x


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cocontraction levels. The rationale for this hypothesis is,

first, that a decrease in limb impedance during movement

has been already observed during fatigue (Selen et al.

2007). Second, cocontraction is metabolically costly since

it requires additional muscular activation, and thus it could

be decreased in order to minimize energy expenditure

when energy reserve is decreased.

This study was designed to test the hypothesis thatfatigue-induced impairment in movement accuracy is

caused by a decrease in muscle cocontraction rather than a

reduced ability to produce muscular force.

Methods

Seven right-handed participants (three females and four

males) between the ages of 24 and 34 took part in the

study. They had to perform pointing movements before and

after a fatigue protocol. Maximal voluntary contraction

(MVC) was measured at the beginning of the experiment,after the fatigue protocol, and at the end of the experiment,

in order to evaluate the effect of fatigue on force generation

capabilities. All study procedures complied with the Hel-

sinki declaration for human experimentation and were

approved by the local ethics committee.

Figure1 shows a schematic representation of the

experimental setup and the experimental protocol. Partici-

pants sat in a chair during the whole experiment. The chair

was positioned in front of a 2 9 3 m screen with the elbow

and forearm of their right arm resting on a manipulandum

that consisted of an aluminium bar. The elbow was aligned

on the vertical axis of rotation of the manipulandum, and

the arm was abducted 90. Participants grasped a vertical

handle so that they could rotate the manipulandum in the

horizontal plane. When flexing or extending their elbow,

participants moved a laser dot on the screen, indicating the

actual position of the joint.

During MVC measurements and fatigue protocol, the

manipulandum was locked at 90 (180 corresponding to

full elbow extension). Extension and flexion forces were

measured with a strain gauge (accuracy 0.5 N, FN3030,

FGP Sensors, Les Clayes Sous Bois, France) placed in the

plane of rotation of the manipulandum. During pointingmovements, the elbow angle was measured by a potenti-

ometer fixed on the axis of rotation of the manipulandum.

Pairs of Ag/AgCl electrodes (Controle Graphique Medical,

Brie-Comte-Robert, France) were used to record surface

electromyography (EMG) of the biceps brachii, the bra-

chioradialis, and the long and lateral heads of triceps all

along the experimental protocol. Electrode location was set

according to SENIAM recommendations (Hermens et al.

2000). Inter electrode distance was 10 mm. EMG signal

was amplified (91000, Biovison, Wehrheim, Germany).

All signals were sampled at 1,000 Hz with an A/D USB

DAQ 6009 National Instrument card (National Instru-ments, Austin, TX, USA), and stored on a computer for

subsequent analysis.

For the MVC measurement sessions, participants had to

perform alternately 2 maximal isometric flexions and 2

maximal isometric extensions. Contraction duration was

5 s, and contractions were separated by 45 s of passive rest.

Maximal torque was computed as the maximal torque value

observed during a 500 ms window. We retained the MVC

value corresponding to the mean of the 2 MVCs performed.

The maximal EMG value (EMGmax) was the mean rectified

and filtered EMG recorded during the 500 ms corre-

sponding to the maximal torque of the highest trial.

The fatigue protocol consisted of the repetition of 20-s

isometric contractions. The workload was fixed at 60% of

the MVC measured at the beginning of the experiment.

Strain gauge

0% 60% 100%

90

Start line Target

Laser

A B

C

Fatigue protocol MVC2Pre-fatiguemovement session

Post-fatiguemovement session

MVC3MVC1

Fig. 1 Experimental setup during the fatigue protocol (a) and pointing movements (b), and experimental protocol (c)

Exp Brain Res

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Contractions were elbow flexions and extensions per-

formed alternately, separated by periods of 15 s of passive

rest. A visual feedback was projected on the screen to

allow participants to control their force level. Participants

had to continue the task until exhaustion, when they were

unable to maintain the workload for at least 5 s.

During the pointing movement sessions, participants

could not see their arms. For each trial, participants wereasked to move the laser dot from the starting position (70)

to the target (110). Participants were asked to point as

accurately as possible without correcting their movement

online. To avoid eventual online corrections, the laser dot

disappeared 100 ms after movement onset. Since move-

ment accuracy is related to movement time and kinematics

(Woodworth 1899), participants were asked to perform

300 ms movements with a tolerance of 30 ms. Partici-

pants were informed of their movement endpoint position

and movement time 1 s after movement end. If movement

time was not in the acceptable range, the trial was repeated.

The percentage of trials that did not satisfy the movementtime constraint was 33 15%. A Student t test revealed

that this percentage was unaffected by fatigue (t= 0.49,

P = 0.64). Movement sessions ended once 15 acceptable

trials were performed.

An inertial load of 1.5 kg was added on the manipu-

landum in order to impose the peak torque required during

movement. To obtain a peak torque corresponding to 40%

of participants MVC both pre- and post-fatigue, we

adapted the distance between the load and the axis of

rotation of the manipulandum. This distance was computed

by taking into account the anthropometric properties of

participants limbs based on Winters tables (Winter2005),

and the fact that the mean value of peak acceleration was

about 2,800 s-2. The value of 2,800 s-2 was estimated

from a pre-test experiment. The distance D (m) was

obtained with the following equation:

D

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiT

Apeak Iforearm Ihand Im

L

s 1

where T was the required peak torque during movement

(40% of MVC), Apeakwas the estimated peak acceleration

(2,800 s-2), Iforearm was the inertial moment of the fore-

arm (between 0.010 and 0.024 Nm), Ihand was the inertialmoment of the hand (between 0.024 and 0.066 Nm), Imwas the inertial moment of the manipulandum (between

0.023 and 0.029 Nm, depending on the handle position),

and Lwas the load added on the manipulandum (1.5 kg).

In order to keep the ratio of required to available force

constant between the pre- and post-fatigue conditions, the

distance between the load and the axis of rotation was

computed with respect to the current extension MVCs of

each participant. Consequently, for each participant, the

inertia of the manipulandum was smaller in the post-fatigue

movement session, to compensate for the fatigue-induced

decrease in MVC.

Figure2shows an example of data recorded during the

movement session. For the 15 acceptable trials in each

condition that were retained for analyses, joint angle signal

was filtered with a second order Butterworth low-pass filter

with a 10 Hz cut-off frequency. Filtered signal was differ-entiated to obtain angular velocity and acceleration. We

distinguished two measures of movement time (Selen et al.

2006). Movement time used to constrain movement duration

(MT1) was defined as the time interval between the first

moment when velocity exceeds 10 s-1 and the following

time when the velocity falls to 10 s-1. Since a terminal

backward submovement could occur, we defined the

movement time used for all the subsequent analysis (MT2) as

the time interval between the first moment when velocity

exceeds 10 s-1 and the last time when the velocity falls to

10 s-1. We measured movement endpoint accuracy with

constant error, defined as the mean distance between theposition at movement end and the target location. Movement

endpoint variability was assessed by variable error, defined

as the mean distance between the endpoint of each trial and

the overall average endpoint position within the session.

EMG signal obtained during movement was full-wave

rectified and filtered with zero lag (second order Butter-

worth low-pass filter with a cut-off frequency of 6 Hz) to

determine the linear envelope. The EMG linear envelope

was normalized relative to EMGmax. To avoid possible

effects of fatigue-induced changes in elbow muscular

synergies, we computed mean EMG of each synergist pairs

(triceps lateral head-triceps long head vs. biceps brachii-

brachioradialis). Agonist and antagonist activations were

defined as the integral of the agonist and antagonist bursts,

respectively. Since burst duration varied across trials, we

divided these values by the respective burst duration. Based

on EMG signals, we estimated cocontraction with an index

of cocontraction (CI) adapted from Kellis et al. (2003). CI

was defined as follow:

CI

Rtft0

EMGmin dt

Rtf

t0EMGago + EMGant

dt

100 2

where t0 is movement onset, tfis movement end, EMGminis at each sampling point in time the EMG signal of the

synergist pair which has the lower normalized activity,

EMGago the EMG of the agonist pair, and EMGant the

EMG of the antagonist pair.

The effects of fatigue on the variables computed from

the MVC procedures and the movement sessions were

assessed with one-way repeated-measures ANOVA. All

values are expressed as mean inter-participants standard

deviation (SD). Statistical significance was set at a = 0.05.

Exp Brain Res

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Results and discussion

We first have to assess the validity of our experimental

paradigm. Our fatigue protocol was designed to induce a

substantial loss of maximal available force. The decline in

MVC torque after the fatigue protocol was 33.9 9.8%

for the extensor muscles [MVC1 = 46.9 16.6 Nm vs.

MVC2 = 30.3 12.2 Nm, F(2,12) = 20.4, P\ 0.01] and

25.7 10.7% for the flexor muscles [MVC1 = 67.4

19.4 Nm vs. MVC2 = 49.0 14.3 Nm, F(2,12) = 19.5,

P\ 0.01]. MVC values recorded during the terminal

MVC session were significantly lower than during the

pre-fatigue session, in extensors [MVC3 = 36.9 14.3

Nm, F(2,12) = 20.4, P\ 0.01] and in flexors [MVC3 =

56.5 18.3 Nm, F(2,12) = 19.5, P\ 0.01]. Taken toge-

ther, these results indicate that our protocol was

successful in inducing a significant fatigue that lasted

during the whole second pointing movement session, in a

similar fashion in flexor and extensor muscular groups.

We also have to asses the validity of our pointingmovement protocol. First, we wanted to avoid any effects

of movement kinematics on endpoint accuracy. This was

done by imposing MT1 with a tolerance of 30 ms.

Unexpectedly, MT1 values showed a small but significant

decrease in the post-fatigue condition [310 4 ms vs.

297 7 ms, F(1,6) = 14.0, P\ 0.01], and peak velocity

increased [246.8 9.9 s-1, vs. 261.2 10.3 s-1,

F(1,6) = 13.6, P\ 0.05]. This could have been due to

fatigue recovery during the second movement session.

However, peak acceleration values were not significantly

different pre- versus post-fatigue [2,400 371 s-2 vs.

2,786 459 s-2, F(1,6) = 3.07, P = 0.13], and, moreimportantly, we found no significant difference between

pre- (489 65 ms) and post-fatigue conditions (528

94 ms) for MT2 values [F(1,6) = 1.0, P = 0.35]. Thus we

concluded that, despite small differences for some vari-

ables, the movement kinematics was globally similar

between the two movement sessions, and could not be the

main cause of the decrease in endpoint accuracy with

fatigue. Second, we wanted that participants peak torques

during each movement remained close to 40% of their

current MVC. This was done by adapting the inertial load

on the manipulandum. The lack of significant difference in

agonist EMG activity in the pre- versus post-fatigue session

[34.2 10.3% vs. 37.6 8.1%, F(1,6) = 0.94, P = 0.37]

indicated that movements required a similar percentage of

MVC in the two conditions. This indicated that the ratio of

required to available force during movement was not dif-

ferent pre- and post-fatigue, and thus that the load was

correctly adapted in the post-fatigue movement session.

Once verified that our experimental paradigm success-

fully induced fatigue and successfully normalised the force

required during movements to the current participants

capabilities, we can now address the questions that are

central to the present experiment. The first goal was to

demonstrate that fatigue can impair movement accuracy

even when the ratio of required to available force is

unchanged. The lack of available force can reduce the

ability to accelerate and decelerate the limb. Especially, if

antagonist muscles are unable to slow the movement down,

the accuracy can be decreased since antagonist muscles play

a major role in the control of the final position (Wierzbicka

and Wiegner1996). However, this would be a critical factor

only in the case of movements requiring forces that are

similar to or higher than the available force. This was

70

110

100 ms10%E

MGmax

1mV

50/sec

A

B

C

D

movement movementonset end

Fig. 2 Example of movement data analysis. a filtered position

profile. The dashed line represents the target, b velocity profile.

Dashed lines represent the values of 10 and -10 s-1 used for the

detection of movement onset and end, c from top to bottom, rectified

electromyographic (EMG) traces of triceps long head, triceps lateral

head, biceps brachii, brachioradialis. d EMG linear envelop of

agonists (grey line) and antagonists (black line). The grey area

represents EMGminused for the computation of cocontraction (Eq. 2)

Exp Brain Res

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obviously not the case in the present experiment, where the

maximal force requirement was maintained constant at 40%

of the available force. We observed that variable error and

constant error increased significantly post-fatigue, as shown

in Fig.3 [1.7 0.5 vs. 2.0 0.5, F(1,6) = 12.9,

P\ 0.05 and 1.8 0.4 vs. 2.6 0.7, F(1,6) = 12.2,

P\ 0.05, respectively]. There was no tendency either for

undershoot or overshoot of the target after fatigue sincemean movement endpoint position was unchanged (pre-

fatigue: 110.4 1.0 vs. post-fatigue: 111.0 1.5,

F(1,6) = 0.87, P = 0.39). This is direct evidence that fati-

gue can affect movement accuracy even if the ratio of

required to available force is unchanged. Consequently, the

impairment of endpoint accuracy could not be attributed to

the lack of available force. This raises the question of the

part played by other factors in the control of movement

accuracy during fatigue.

What other factors than the lack of available force could

be responsible for movement accuracy impairment with

fatigue? We argue that fatigue is a more complex phe-

nomenon than a simple decrease in force generation

capabilities. In particular, fatigue could affect the way the

central nervous system (CNS) deals with accuracy. The

CNS has mainly two ways to deal with movement accuracy.

The first way is to adapt movement time and kinematics to

the accuracy requirement. For instance, movements with

low accuracy constraints are rapid and have a bell shapedvelocity profile, whereas movements with high accuracy

requirements are characterised by longer movement times

and earlier peak velocity (Woodworth1899). This duration

scaling with accuracy is known as the speed accuracy trade-

off. In our experiment, the CNS could not use this strategy

since we imposed a constant movement time. In such cases,

when movement time is imposed, it has been shown that the

CNS can adapt to the accuracy constraint with an alternative

strategy, namely by increasing muscular cocontraction (e.g.

Gribble et al. 2003; Osu et al. 2004). This increase in co-

contraction increases limb impedance and joint stability,

and minimizes the perturbing effects of forces arising fromlimb dynamics (Osu and Gomi 1999). Cocontraction has

been shown to increase when the accuracy requirements

increase (Gribble et al.2003), and it has been demonstrated

experimentally (Osu et al. 2004) and numerically (Selen

et al. 2005) that an increase in cocontraction improves

movement endpoint accuracy.

In order to get an insight into limb impedance, cocon-

traction can be inferred from EMG signals (Osu and Gomi

1999). When studying cocontraction on the basis of EMG

signals during fatigue, caution must be taken because the

relation between EMG and force is modified: a given force

is obtained with a higher muscular activation (e.g. Hunter

et al. 2003). In our study, the fact that EMG-force rela-

tionship changes with fatigue was not an obstacle to the

validity of CI. Indeed, CI was computed with the simul-

taneous agonists and antagonists activation, and not only

with the activity of a single muscular group. Thus, CI was

not sensitive to changes in the absolute values of activa-

tion. Nevertheless, a condition was needed for this CI to be

valid during fatigue: the level of fatigue must be similar in

agonist and antagonist groups. This condition was verified

in our experiment. We observed, as shown in Fig. 3, that

cocontraction decreased significantly during the post-fati-

gue movement session [29.8 5.9% vs. 20.4 3.3%,

F(1,6) = 8.8, P\ 0.05]. Given the role of cocontraction in

movement accuracy, the observed decrease in cocontrac-

tion could be the main factor responsible for the

impairment of endpoint accuracy during fatigue. This

finding is in line with a recent study that showed with direct

measurement that elbow impedance was decreased during

fatigue in a target tracking task (Selen et al.2007). It is also

possible that joint stiffness was reduced because intrinsic

stiffness and reflex contributions decreased with fatigue

1

2

3

4

0

1

2

3

4

Pre-fatigue Post-fatigue



Cocontractionindex(%)

Variableerror()

Constanterror()

B

A

C

0

1

2

3

*

*

*

0

10

20

30

40

Fig. 3 Mean values of constant error (a), variable error (b), and

cocontraction index (c) in the two movement sessions. Each line

corresponds to participants individual evolution. Vertical bars

represent the inter-participants standard deviation. * Significant

difference (P\ 0.05)

Exp Brain Res

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(Zhang and Rymer 2001). This may have amplified the

effect of the decrease in cocontraction on joint impedance

and movement accuracy.

Based on the present findings, we have proposed that

fatigue modifies the way the CNS deals with the control of

accuracy by decreasing cocontraction. Our protocol was

designed so that the lack of force was not a limiting factor

for movement control: participants had enough reserve offorce in the fatigue condition and could have increased

cocontraction. If participants were able to improve move-

ment accuracy with cocontraction, why wasnt it used? It

could be that the CNS changes the respective importance

assigned to accuracy control and energy expenditure during

fatigue. It is now well established that the motor system

operates according to optimality principles that represent

constraints as cost functions to minimize (for a review see

Todorov 2004). Since energy minimization alone fails to

account for many behaviour and especially arm movements

(Nelson1983), it has been proposed that the performance

criterion mostly involves a mix of cost terms. For instance,Todorov (2004) proposed that motor behaviour arises from

the simultaneous minimization of an error cost (task per-

formance) and an effort cost (energy expenditure). This

idea is supported by the fact that non-fatigued humans

usually manage to satisfy task requirements while mini-

mizing energy expenditure. For instance, it has been shown

that cocontraction represents a compromise between

energy consumption and control of movement accuracy

(Hogan 1984), and that the CNS is able to adapt to per-

turbation by selecting coactivation levels that minimize the

metabolic cost (Franklin et al. 2004). During fatigue

however, energy reserve is decreased so that the nervous

system has obvious reasons to care more about energetic

efficiency. Thus, during fatigue, the CNS could plan and

execute movements by according more importance to

energy expenditure minimization, with the immediate

consequence of a decrease in accuracy. In other words, the

CNS could have chosen to dedicate more importance to

energy economy than to task performance. Future investi-

gations to test this hypothesis, and more generally to study

the optimisation principles used by the CNS during fatigue,

should improve our understanding of sensorimotor control

when facing perturbations.

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