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BS277 Biology of Muscle
Fatigue
Dominic Micklewright, PhD.
Lecturer, Centre for Sports & Exercise Science
Department of Biological Sciences
University of Essex
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3
What is the 
cause of 
fatigue 
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Some Key Principles
1. Sports Science is multidisciplinary which
has resulted in different definitions and
explanations of fatigue:
– PHYSIOLOGICAL
– BIOCHEMICAL
– BIOMECHANICAL
– PSYCHOLOGICAL
– NEUROLOGICAL
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Some Key Principles
2. Reductionist approaches:
– Conceptual → Mechanistic (Orange peeling)
– Macro → Micro
– Reductionism limitations due to
misinterpretation of the hierarchy of science
e.g. particle physics, physics, molecular
biology…..psychology, social science
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0
50
100
150
200
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300
350
400
0 1 2 3 4 5 6 7 8 9 10 11
Blood Lactate Concentration (mM)
Po
we
r O
ut
pu
t (
W
)
Some Key Principles
3. Linear Models vs. Complex Systems
Catastrophic 
Failure
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Some Key Principles
Complex Systems & Homeostasis…
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Some Key Principles
4. Task dependency:
– Open vs. Closed Loop Exercise
– Prolonged vs. High Int/Short Duration
– Contraction type (Conc. v Ecc.; Isometric vs.
Isotonic)
– Mode: run vs. cycle vs. row vs. throw etc.
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Some Key Principles
CENTRAL FATIGUE
Upstream of anterior horn cell
CNS
5. Peripheral vs. Central Fatigue:
PERIPHERAL FATIGUE
Downstream of anterior horn cell
PNS & Muscle
10
Some Key Principles
6. The concept of maximal:
– Is maximal really obtainable?
– Max in vivo muscle contraction < max. in vitro
muscle contractions.
– Pacing / teleoanticipation evident in so called
maximal and supramaximal exercise tasks.
– Maximal ‘effort’ is an entirely different concept
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The Models of Fatigue
CV / 
Anaerobic 
Model
Energy 
Supply / 
Depletion 
Model
FATIGUE
Neuromuscula
r Model
Biomechanica
l Model
Thermoregulatory 
Model
Psychological 
Model
Central 
Governor / 
Complex 
Systems 
Model
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Synopsis
CV / Anaerobic Model
Performance limited by:
– Ability of the CV system to supply
oxygenated blood to the muscles.
– Ability of the CV system to remove
metabolites
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CV / 
ANAEROBIC 
FATIGUE
Cardiac Output
CO = HR x SV
↓CO … ↓ muscle blood flow
A-V O2 diff did not reach max at point of 
fatigue therefore CO not the sole cause of 
fatigue (Gonzalez-Alonso & Calbert, 2003)
Red Blood Cells
EPO & Blood doping found to 
↑ RBC count
↑ Cycling performance
…but dangerous
(Hanin & Gore, 2001)
Muscle Blood Flow
-ive linear relationship 
between muscle blood 
flow and power output 
(Saltin et al, 1998)
Oxygen Uptake
Mitochondria size and density (Hoopler & Fluck, 2003)
Capillarisation (Pringle et al., 2003)
Myoglobin capacity (Hoopler & Fluck, 2003)
Aerobic enzyme activity (Hoopler & Fluck, 2003) 
Lac & H+ Removal
AT occurs at a higher % 
of VO2MAX among trained 
(Lucia et al. 2003)
Lac production-removal 
imbalance causes:
↓ intramuscular pH
↓ enzyme activity (PFK)
↓ myoglobin O2 capacity
↑ pain receptor activity
14
Synopsis
Energy Supply / Depletion Model
Fatigue due to :
– Inadequate supply of ATP to the muscle.
– Inadequate depletion of endogenous
substrates.
15
ENERGY 
SUPPLY / 
DEPLETION
McCardle’s Disease
Metabolic myopathy affects 1/100K
↓Capacity to store glycogen
Weakness & pain after exercise
Suggests [glycogen] causes fatigue
ATP Production
Failure to supply ATP via 
various metabolic pathways
Glycolysis & lipolysis 
(Shulman & Rothman, 2001)
But….
Intramuscular ATP never 
below 40% even at fatigue 
(Green, 1997)
Is [ATP] an afferent signal?
Depletion vs. Supply
Depletion assumes 
fatigue is a direct rather 
than indirect result of:
↓Muscle/liver glycogen
↓Blood glucose
↓Phosphocreatine
60% & 86% ↓ in gastroc 
glycogen depletion after 
90-min running among 
rats. (Gigli & Bussman, 
2002)
Not fully depleted so 
cannot be sole cause of 
fatigue
Rate of CH2O Oxidation
Since muscle fatigue not solely due to availability of 
CH2O or ATP some have concluded that rate of 
muscle CH2O oxidation is more important (Noakes et 
al. 2000)
16
Synopsis
Neuromuscular Model
Fatigue due to :
– Inhibition of the neuromuscular pathway.
– Reduction in central neural drive.
– Reduction in responsiveness of the muscle to action
potentials.
– Failure of excitation-contraction coupling mechanisms.
“Functions involved in muscle excitation, recruitment
and contraction are what limit performance.”
(Noakes, 2000)
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NEURO
MUSCULAR
MODEL
Methods (Central vs. Peripheral Determination)
Electromyography (EMG) muscle electrical activity:
Integrated EMG = Filtered & smoothed EMG
Root Mean Squared (RMS) = global EMG signal
M-Wave = compound action potential from brain.
Muscle Twitch Interpolation (MTI) – compare Max Cont. 
between locally twitched vs. voluntary twitched.
Central Activation 
Theory
Lower central activation 
found among young and 
old using MTI during 
isometric induced 
fatigue (Stackhouse et 
al, 2001).
↓Dopamine ↑5HT during prolonged exercise in rats 
(Bailey et al., 1993)
↑Dop/5HT ratio may ↓central activation due to lower 
arousal, motivation & NM coordination. Nutritional CH2O 
may also attenuate changes in ratio (Davis et al., 2000) 
NM Propagation Theory
10%↓ MVC during 
prolonged cycling not 
due to central activation 
(Millet et al., 2003)
Sarcolemma
↓Na+, K+ membrane 
gradient occur during 
prolonged cycling 
resulting in ↓action 
potential i.e. Na+/K+
muscle pump (Fowels et 
al, 2002)
α-Motor Neurone
Muscle receptors less 
responsive when ↑H+, 
↓pH (Lepers et al., 2000)
Time to fatigue ↑ in force 
vs. positioning task. Task 
dependency? (Hunter et 
al., 2004)
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Methods (Central vs. Peripheral Determination)
Electromyography (EMG) muscle electrical activity:
Integrated EMG = Filtered & smoothed EMG
Root Mean Squared (RMS) = global EMG signal
M-Wave = compound action potential from brain.
Muscle Twitch Interpolation (MTI) – compare Max Cont. 
between locally twitched vs. voluntary twitched.
Central Activation 
Theory
Lower central activation 
found among young and 
old using MTI during 
isometric induced 
fatigue (Stackhouse et 
al, 2001).
↓Dopamine ↑5HT during prolonged exercise in rats 
(Bailey et al., 1993)
↑Dop/5HT ratio may ↓central activation due to lower 
arousal, motivation & NM coordination. Nutritional CH2O 
may also attenuate changes in ratio (Davis et al., 2000) 
NM Propagation Theory
10%↓ MVC during 
prolonged cycling not 
due to central activation 
(Millet et al., 2003)
Sarcolemma
↓Na+, K+ membrane 
gradient occur during 
prolonged cycling 
resulting in ↓action 
potential i.e. Na+/K+
muscle pump (Fowels et 
al, 2002)
α-Motor Neurone
Muscle receptors less 
responsive when ↑H+, 
↓pH (Lepers et al., 2000)
Time to fatigue ↑ in force 
vs. positioning task. Task 
dependency? (Hunter et 
al., 2004)
NEURO
MUSCULAR
MODEL
Muscle Power / Peripheral Failure Theory
Fatigue occurs within muscle by alteration of the coupling mechanism between 
the action potential and the contractile proteins. (Hill et al., 2001)
Fatigue of a twitched muscle associated with ↓CA+ from sarcoplasmic reticulum 
which has –ive effect on excitation-contraction coupling process. Reduced CA+ 
return from contractile proteins may also cause ↑muscle relaxation / fatigue 
(McKenna et al, 1996).
After first few minutes low threshold motor units fatigue but are replacedby high 
threshold units (Westgaard & De Luca, 1999). Suggests i) individual motor units 
susceptible to fatigue ii) protective mechanism to prevent catastrophic failure.
Early peripheral fatigue followed by later central fatigue is a safety mechanism to 
prevent catastrophic failure e.g. loss of ATP (St Clair Gibson et al, 2001) 
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Synopsis
Biomechanical Model
Fatigue due to a reduction in mechanical
efficiency and economy which provokes…
– ↑ CV system demand (CV model)
– ↑ Energy consumption (Energy S/D model)
– ↑ Metabolite production (Anaerobic model)
– ↑ Core temperature (Thermoregulatory model)
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BIOMECH. 
MODEL
Efficiency of Motion
↓Efficiency coincides with ↑ VO2 (Passfield & Doust, 
2000) ↓MVC (Lucia et al., 2002).
Better economy/efficiency reported for pro cyclists 
(Lucia et al., 2002) and Kenya runners (Weston et al., 
2000)
EMG vs. MRI Studies
RMS/VO2 ratio declines 
faster in endurances vs. 
non-trained subjects 
(Hug et al., 2004)
EMG studies do not 
reveal diffs. in the 
recruitment of fibre type.
MRI suggests ↑FT recruit 
cycling @ >60% VO2MAX
(Saunders & Evans, 2000)
Synergists & antagonists 
may compensate for 
fatiguing agonsists 
(Hunter et al., 2002)
Stretch/Shortening Cycle
Combined action of muscle to produce efficient movement 
from lengthening (ecc) & shortening (coc.). ↑ Force due to:
↑elastic force in tendons/ligs (Komi, 2000)
↑tx time from stretch to contract (Davis & Bailey, 1997)
Golgi tendon organ/ muscle spindle role as afferent signal?
Mechanisms of Efficiency
Task type x muscle 
property interaction e.g.
Optimal cycling cadence for 
elite 80-90 but for amateur 
70-80 (Takaishi et al., 
1996). Maybe due to… 
↑cardiac output, muscle 
blood flow, muscle O2
uptake, lac removal 
(Gotshall, 1996). 
Faster cadence reduces 
fast twitch fibre recruitment 
which are less efficient than 
slow twitch fibres (Takeshi 
et al., 1998)
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BIOMECH. 
EFFICIENCY 
OF MOTION
Muscle Fibre 
Composition
Intermusc. Coordn. 
(Stretch/Shortening)
Muscle Activation 
Rate (e.g. cadence)
Energy consumption 
/ heat generation
O2 consumption and 
uptake
Accumulation of 
metabolite
% Type I / II 
recruitment pattern
Adapted from Abbiss & Laursen, 2005)
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Synopsis
Thermoregulatory Model
Fatigue due to…
– Reaching a critical core body temperature
– ↑ Core, muscle and skin temp places
demands on other physiological
systems/models…
– CV, anaerobic, energetics, psychological
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Central Thermoregulation
Exhaustion when cycling in 
heat occurred at 39.5°C 
(Nielson et al., 1993) but…
Tucker et al., 2004 saw 
highest power when core 
body temp greatest (39°C).
∴ core temp not sole cause 
of fatigue. Anticipation?
THERMO. 
MODEL
Thermoregulation
• Core body temp = heat production (muscle metabolism) – heat removal 
(convection, conduction, radiation, evapouration).
• Core body temp can ↑ 1°C every 5-7 min but cannot be tolerated @ >40°C for 
prolonged periods. Exercise limited by heat production/dissipation balance.↑
• Environmental temp & hypertherma known to have –ive effect on performance e.g. 
mean PO ↓6.5% when environ. Raised from 23-32°C (Tatterson et al., 2000). 
Peripheral
Central
Hypothalamus
Thermo-
receptors
Sweat, 
Blood Flow
Peripheral
Central
Hypothalamus
Thermo-
receptors
Sweat, 
Blood Flow
Periph. Thermoregulation
Sweating and dissipation of 
heat have ↑CV demand 
due to supplying skin as 
well as muscles with blood 
(Nybo et al., 2001).
Skin flow plateaus but core 
temp continues to rise 
during exercise placing 
extra CV demand (Nielsen 
et al., (1997)
Fatigue related to extra CV 
demand imposed by periph 
theromoregulatory changes
24
Synopsis
Psychological Model
Fatigue due to psychological factors which…
– ↓ Central activation & motivation
– ↑ Perceived exertion & fatigue
25
PSYCHOL. 
MODEL
Rating of Perceived Exertion
The way peripheral sensations associated with 
exercise are perceived.
Borg scale, OMNI scale.
RPE rise with skin temp & HR (Amada-da-
silva, 2004)
Emotion & Drive
Fatigue is an emotion or a 
‘subjective feeling’ state 
dependent upon 
physiological and 
situational environmental 
factors.
Feelings of fatigue may be 
related to motivation, 
anxiety, arousal and 
confidence.
Consciousness
We are not consciously 
aware of specific 
physiological functions 
e.g. muscle blood flow, 
blood pressure, 
glycogen depletion.
RPE is conscious 
awareness based on 
many afferent 
sensations.
Information Processing
Pacing strategies determined by information processing 
between the brain and physiological systems.
Knowledge of distance or time during an event provides 
crucial input to monitor and determine overall pacing 
strategy (St Clair Gibson et al, 2006).
- internal clock - endpoint knowledge - feedback
26
Synopsis
Central Governor / Complex Systems Model
Fatigue due to a central governor maintaining
homeostasis through…
– Integration of peripheral afferent signals and
exogenous reference signals
– Determine efferent muscular control
– Facilitates concepts of teleoanticipation, pacing
and perceived exertion.
– Differentiates between conscious and
subconscious processes.
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Critique of Peripheral Fatigue
– Peripheral fatigue model predicts that exercise
always terminates at an absolute, temporarily
irreversible end point.
– Linear system (power output a direct
consequence of input variable e.g. [Bla]
– Therefore fatigue and the sensation of fatigue)
must coincide with the peripheral physiological
input variable.
– Often they often do not…
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Critique of Peripheral Fatigue
– Complete substrate depletion at fatigue only
found during in vitro studies (Lamb, 1999) but
not during in vivo where there is an intact CNS
(St Clair-Gibson, 2001)
– Not a single study has found a direct
relationship between perceptions of exertion
and physiological variables. Opposite found in
chronic fatigue patients (rest yet feel fatigued).
– Physiological factors do not coincide with
fatigue…
29
Critique of Peripheral Fatigue
– Intramuscular ATP never below 40% even at
fatigue (Green, 1997)
– 60% & 86% ↓ in gastroc glycogen depletion
after 90-min running among rats. (Gigli &
Bussman, 2002)
– A-V O2 diff did not reach max at point of fatigue
therefore CO not the sole cause of fatigue
(Gonzalez-Alonso & Calbert, 2003)
– [Lac] does not peak until up to 15 mins after
exercise.
30
Evidence for Central Governor
– Fatigue not caused by peripheral factors by by
reduced neural command by the brain (Green,
1997)
– Fluctuations in power output (Tucker et al.,
2006) and heart rate during exercise (Palmer et
al., 1994) more representative of a homeostat
system of control rather than a linear model.
– Presense of homeostasis in all organ functions
helps support model.
31
Evidence for Central Governor
– Homeostatic regulation by the CNS could
account for continually changing pattern of
muscle recruitment during exercise.
– Homeostatic control based on a complex black
box calculation (Ulmer, 1996) derived from the
intergration of multiple afferent signals (Lambert
et al., 2005) e.g.
– Rauch et al. (2005) signalling role of muscle
glycogen concentration during prolonged
cycling.
32
Empirical & Theoretical Context
CENTRAL 
GOVERNOR
MUSCLE 
CONTRACTION
PERIPHERAL 
ORGANS
PERIPHERAL 
FATIGUE
CENTRAL 
FATIGUE
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INITIAL PACE DURING FIRST MOMENTS (FEED-FORWARD)
SUBSEQUENTPACING (TELEOANTICIPATION)
1. KNOWLEDGE OF ENDPOINT
2. PREVOIUS EXPERIENCE
CENTRAL 
GOVERNOR
EFFERENT 
CONTROL
St Clair Gibson & Noakes (2006, p.801)
2. PREVOIUS EXPERIENCE
1. KNOWLEDGE OF ENDPOINT (Closed loop or open loop)
Hampson, St Clair Gibson, Lambert, & Noakes (2001, p. 944) 
on Ulmer (1996)
Ansley, Robson, St Clair Gibson, & Noakes (2003, p. 313)St Clair Gibson, Lambert, Rauch, Tucker, Baden, Foster & 
Noakes (2006, p. 708)
3. AFFERENT FEEDBACK
AFFERENT 
FEEDBACK
Rauch, St Clair Gibson, Lambert, & Noakes (2005)
4. PERCEPTIONS OF AND 
BELIEFS ABOUT THE PRESENT 
AND LIKELY FUTURE 
COMPLEX 
ALGORHYTHM
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CENTRAL 
GOVERNOR
EFFERENT 
CONTROL
Previous Experience 
AFFERENT 
FEEDBACK
5. PREVIOUS EXPERIENCE AND MEMORY:
• EXACTNESS / RELEVANCE
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40
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Schema Theory
Bartlett (1932) and Anderson(1977)
Schemata: psychological constructs that allow us to form 
cognitive representations of complex realities.
Korsakov's Syndrome: sufferer’s are unable to form new 
memories, and must approach every situation as if they 
had just seen it for the first time.
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CENTRAL 
GOVERNOR
EFFERENT 
CONTROL
Previous Experience 
AFFERENT 
FEEDBACK
5. PREVIOUS EXPERIENCE AND MEMORY:
• EXACTNESS / RELEVANCE
• DISTORTION / ACCURACY
6. PACING DECISIONS LIKELY TO BE 
INFLUENCED BY MEMORY AS WELL AS 
PERCEPTUAL EXPERIENCE - RPE
6. MEMORY / PREVIOUS EXPERIENCE WILL 
AFFECT THE WAY WE PERCEIVE AND 
INTERPRET AFFERENT SENSATIONS. 
PROVIDE A BASIS FOR ‘EXPECTED 
OUTCOMES’.
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Theoretical Context
CENTRAL 
GOVERNOR
MUSCLE 
CONTRACTION
PERIPHERAL 
ORGANS
EXOGENOUS 
REFERENCE 
SIGNALS
ENDOGENOUS 
REFERENCE 
SIGNALS
PAST
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Fig. 1 Central Governor Model of Fatigue
(Adapted from Lambert, St Clair Gibson & Noakes, 2005)
prior experience
Interpretation
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Fig. 1 Central Governor Model of Fatigue
(Adapted from Lambert, St Clair Gibson & Noakes, 2005)
prior experience
Interpretation
46
“Knowledge of distance or time…during an event 
provides crucial input…to monitor and determine 
overall pacing strategy”
St Clair Gibson, Lambert, Rauch et al., 2006prior experience
Interpretation
“Teleoanticipation…brain…initiates a pacing 
strategy at the start of an event based upon prior 
knowledge of previous similar events”
Ulmer, 1996
“For the brain teleoanticipatory centre to utilise a 
scalar internal clock [it] must be based on 
memories of prior exercise bouts…and repeated 
training [improves its] accuracy”
Ulmer, 1996
“…an internal [scalar] clock is used by the brain to 
generate knowledge of the distance or duration of 
the activity still to be covered, so that power output 
and metabolic rate can be altered appropriately.
St Clair Gibson, Lamber, Rauch et al., 2006
47
PURPOSE OF THE STUDY
To examine how previous experience influences 
cyclists’ perceptions of time, distance and exertion.
HYPOTHESIS
Cyclists who train for time trials without 
performance feedback will develop a more 
accurate perception of time, distance and exertion 
than those who depend on cycle computers.
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Design & Participants
• Two way between & within-subjects 
experimental design used.
• 29 cyclists recruited from Cape Town cycling 
clubs.
• Randomly allocated to conditions.
• Not matched but inclusion / exclusion criteria 
used.
49
Fig 2. Participant Descriptive Data
Note – Comparisons made using a one-way between-subjects ANOVA
0
20
40
60
80
100
120
140
160
180
200
220
Age (yrs) Body Mass (kg) Height (cm) Cycling Exp. (yrs)
Condition
Ag
e (y
rs)
, B
ody
 M a
ss 
(kg
), H
eig
ht (
cm
) Blind Condition (n=10)
Feedback Condition (n=10)
False Feedback Condition (n=9)
NS
NS
NS
NS
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BLIND FEEDBACK FAMILIARISATION CONDITION (UNCERTAIN PERFORMANCE LEARNING)
20 km TIME TRIAL
BLIND TO FEEDBACK
20 km TIME TRIAL
BLIND TO FEEDBACK
20 km TIME TRIAL
BLIND TO FEEDBACK
ACCURATE FEEDBACK FAMILIARISATION CONDITION (REALISTIC PERFORMANCE LEARNING)
20 km TIME TRIAL
ACCURATE FEEDBACK
20 km TIME TRIAL
ACCURATE FEEDBACK
20 km TIME TRIAL
BLIND TO FEEDBACK
FALSE FEEDBACK FAMILIARISATION CONDITION (OPTIMISTIC PERFORMANCE LEARNING)
20 km TIME TRIAL
FALSE FEEDBACK +5%
20 km TIME TRIAL
FALSE FEEDBACK +5%
20 km TIME TRIAL
BLIND TO FEEDBACK
CYCLING TIME TRIALS
(WITHIN-SUBJECTS FACTOR)
T
Y
P
E
 O
F
 F
E
E
D
B
A
C
K
 G
IV
E
N
 D
U
R
IN
G
 
T
H
E
 F
A
M
IL
IA
R
IS
A
T
IO
N
 T
A
S
K
S
(B
E
T
W
E
E
N
-S
U
B
J
E
C
T
S
 F
A
C
T
O
R
)
Fig 3. Experimental Protocol
51
BLIND FEEDBACK FAMILIARISATION CONDITION (UNCERTAIN PERFORMANCE LEARNING)
20 km TIME TRIAL
BLIND TO FEEDBACK
20 km TIME TRIAL
BLIND TO FEEDBACK
20 km TIME TRIAL
BLIND TO FEEDBACK
ACCURATE FEEDBACK FAMILIARISATION CONDITION (REALISTIC PERFORMANCE LEARNING)
20 km TIME TRIAL
ACCURATE FEEDBACK
20 km TIME TRIAL
ACCURATE FEEDBACK
20 km TIME TRIAL
BLIND TO FEEDBACK
FALSE FEEDBACK FAMILIARISATION CONDITION (OPTIMISTIC PERFORMANCE LEARNING)
20 km TIME TRIAL
FALSE FEEDBACK +5%
20 km TIME TRIAL
FALSE FEEDBACK +5%
20 km TIME TRIAL
BLIND TO FEEDBACK
CYCLING TIME TRIALS
(WITHIN-SUBJECTS FACTOR)
T
Y
P
E
 O
F
 F
E
E
D
B
A
C
K
 G
IV
E
N
 D
U
R
IN
G
 
T
H
E
 F
A
M
IL
IA
R
IS
A
T
IO
N
 T
A
S
K
S
(B
E
T
W
E
E
N
-S
U
B
J
E
C
T
S
 F
A
C
T
O
R
)
Fig 3. Experimental Protocol
52
TRIAL 3 - ALL GROUPS PERFORM TIME TRIAL BLIND
20 km MAXIMAL EFFORT SELF-PACED TIME TRIAL
BLIND TO FEEDBACK
WARM UP
10 MIN SP
IN
T
E
R
V
IE
W
E
D
 A
B
O
U
T
 
P
R
E
D
IC
T
IO
N
 S
T
R
A
T
E
G
IE
S
20 km TIME TRIAL
BLIND TO FEEDBACK
4km 8km 12km 16km 20kmt(s)when cyclist actually reaches:
RPE & t(s) when cyclists estimates: 4km 8km 12km 16km
PREDICTION ERROR = ESTIMATED - ACTUAL
(TIME AND DISTANCE)
Fig 4. Blind Time Trial Protocol (All Groups)
53
TRIAL 3 - ALL GROUPS PERFORM TIME TRIAL BLIND
20 km MAXIMAL EFFORT SELF-PACED TIME TRIAL
BLIND TO FEEDBACK
WARM UP
10 MIN SP
IN
T
E
R
V
IE
W
E
D
 A
B
O
U
T
 
P
R
E
D
IC
T
IO
N
 S
T
R
A
T
E
G
IE
S
20 km TIME TRIAL
BLIND TO FEEDBACK
4km 8km 12km 16km 20kmt(s)when cyclist actually reaches:
RPE & t(s) when cyclists estimates: 4km 8km 12km 16km
PREDICTION ERROR = ESTIMATED - ACTUAL
(TIME AND DISTANCE)
Fig 4. Blind Time Trial Protocol (All Groups)
54
Fig 5. Cycling Ergometry Procedures
• Participants own bike and a Computrainer.
• Blind vs. Accurate Feedback vs. False Feedback 
• Time, Speed, Distance, Power, Cadence, RPE
55
Note – A two-way between & within subjects ANOVA (3x4) was used with post hoc 
paired samples t-tests with Bonferonni corrected alpha level of .0167
Fig 6. Distance Prediction Error Trial Main Effects
0
400
800
1200
1600
2000
2400
2800
0 4 8 12 16 20
Distance Cycled Blind (km)
Pre
dic
tio
n E
rro
r fo
r D
ist
an
ce 
(m
) Trial Main Effect: F (3,78)=6.2, p <.001, partial η
2
=.19
t (28)=-2.4
p <.0167
η2=.17
t (28)=-3.6
p <.001
η2=.30
NS
PREDICTS 
EARLY
PREDICTS 
LATE
56
Fig 7. Group Differences in Distance Prediction Errors
0
400
800
1200
16002000
2400
2800
3200
3600
0 4 8 12 16 20
Distance Cycled Blind (km)
Pr
ed
ict
ion
 Er
ror
 fo
r D
ist
an
ce
 (m
)
Blind Familiarisation Group (n=10)
Accurate Feedback Familiarisation Group (n=10)
False Feedback Familiarisation Group (n=9)
PREDICTS 
LATE
PREDICTS 
EARLY
57
Note – A two-way between & within subjects ANOVA (3x4) was used with post hoc 
paired samples t-tests with Bonferonni corrected alpha level of .0167
Fig 8. Time Prediction Error Trial Main Effects
0
30
60
90
120
150
180
210
240
0 4 8 12 16 20
Distance Cycled Blind (km)
Pre
dic
tio
n E
rro
r fo
r T
im
e (
s)
Trial Main Effect: F (3,78)=7.4, p <.0005, partial η
2
=.22
t (28)=-2.7
p <.01
η2=.21
t (28)=-3.7
p <.001
η2=.33
NS
PREDICTS 
LATE
PREDICTS 
EARLY
58
Fig 9. Group Differences in Time Prediction Errors
-20
20
60
100
140
180
220
260
300
340
380
0 4 8 12 16 20
Distance Cycled Blind (km)
Pr
ed
ict
ion
 Er
ror
 fo
r T
im
e (
s)
Blind Familiarisation Group (n=10)
Accurate Feedback Familiarisation Group (n=10)
False Feedback Familiarisation Group (n=9)
PREDICTS 
LATE
PREDICTS 
EARLY
59
Note – Comparisons made using a two-way within subjects ANOVA (3x5) with post 
hoc paired samples t-tests with Bonferonni corrected alpha level of .0083
Fig 10. Perceived Exertion Trial Main Effects
12
13
14
15
16
17
18
19
20
0 4 8 12 16 20
Distance Cycled Blind (km)
Ra
tin
g o
f P
erc
eiv
ed
 Ex
ert
ion
 (6
-20
) RPE Legs Trial Main Effects:
RPE Overall Trial Main Effects:
F (4,68)=24.6, p <.0001, partial η2=.59
t (18)=-7.0
p <.0001
η2=.73
NS
F (4,64)=11.5, p <.0001, partial η2=.42
t (18)=-3.4
p <.005
η2=.40
NS
t (18)=-3.4
p <.005
η2=.40
NS
NS
NS
60
Fig 11. Group Differences in Perceived Exertion
12
13
14
15
16
17
18
19
20
0 4 8 12 16 20
Distance Cycled Blind (km)
Ra
tin
g o
f P
erc
eiv
ed
 Ex
ert
ion
 (6
-20
)
Blind Familiarisation Group (n=10)
Accurate Feedback Familiarisation Group (n=10)
False Feedback Familiarisation Group (n=9)
61
Fig 12. Group Differences in Interpolated Speed Errors
Note – Interpolated average speed was calculated using the time when each 
prediction was made and the respective distance (4,8,12, & 16 km). The error 
is interpolated speed – actual speed.
-7.0
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
0 4 8 12 16 20
Distance Cycled Blind (km)
Pr
ed
ict
ion
 Er
ror
 fo
r S
pe
ed
 (k
m/
h)
Blind Familiarisation Group (n=10)
Accurate Feedback Familiarisation Group (n=10)
False Feedback Familiarisation Group (n=9)
ACTUAL 
SPEED
FASTER THAN 
ACTUAL
SLOWER THAN 
ACTUAL
62
Fig 13. Trial Differences in Actual - Interpolated Speed
31.0
31.5
32.0
32.5
33.0
33.5
34.0
34.5
35.0
35.5
36.0
0 4 8 12 16 20
Distance Cycled Blind (km)
Sp
ee
d (
km
/h)
Actual cycling speed with error bars
representing interpolated speed (n=29)
63
Interviews: Prediction Strategies
• Counting Cadence
• Visualization of a familiar route
• Using warm-up as reference time
• “How I feel”
• “How I feel” + a bit extra
• Music in gym
• The light outside
• Using a shadow as a sundial!
64
Conclusions
• There is a natural tendency to seek out 
reference points. Cycle computers are 
convenient but...
• Over dependence on cycle computers during 
training may lead to understated perceptions of 
time and distance…
• …maybe because attention is partially diverted 
away from natural sensations towards the 
computer…which may affect perceptual 
learning.
65
Conclusions
• Training without a cycle computer may help to 
develop a better natural feel for time and 
distance, perhaps due to attentional focus.
• Potentially this may help them to make better 
judgements when they do use a cycle 
computer…
• …because of an enhanced feel for proximity to 
the endpoint resulting in a less conservative 
pacing strategy.
66
67
68
PERFORMANCE BELIEF UNCERTAINTY (BLIND)
20 KM TT #1
BLIND
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #2
BLIND
PERFORMANCE BELIEF CERTAINTY (TRUE)
20 KM TT #1
TRUE FEEDBACK
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #2
TRUE FEEDBACK
PERFORMANCE BELIEF CERTAINTY (FALSE)
20 KM TT #1
FALSE FEEDBACK +5%
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #2
FALSE FEEDBACK +5%
FAMILIARISATION / CONDITIONING TRIALS BLIND TRIALS PERFORMANCE TRIALS
69
PERFORMANCE BELIEF UNCERTAINTY (BLIND)
20 KM TT #1
BLIND
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #2
BLIND
PERFORMANCE BELIEF CERTAINTY (TRUE)
20 KM TT #1
TRUE FEEDBACK
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #2
TRUE FEEDBACK
PERFORMANCE BELIEF CERTAINTY (FALSE)
20 KM TT #1
FALSE FEEDBACK +5%
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #2
FALSE FEEDBACK +5%
FAMILIARISATION / CONDITIONING TRIALS BLIND TRIALS PERFORMANCE TRIALS
70
Condition-by-Trial Performance Outcomes
Note – Comparisons made using a 2-way between- & within-subjects ANOVA
Cadence
 Trial Main Effect F (3,63) = 2.4, p > 0.05
 Condition Main Effect F (2,21) = 0.9, p > 0.05
 Trial-by-Condition Interaction F (6,63) = 2.8, p < 0.05
Power
 Trial Main Effect F (3,69) = 8.9, p < 0.001
 Condition Main Effect F (2,23) = 6.1, p < 0.01
 Trial-by-Condition Interaction F (6,69) = 2.4, p < 0.05
Speed
 Trial Main Effect F (3,69) = 6.3, p < 0.005
 Condition Main Effect F (2,23) = 4.5, p < 0.05
 Trial-by-Condition Interaction F (6,69) = 2.6, p < 0.05
71
Cadence Condion-by-Trial Interaction
80
85
90
95
100
105
110
115
120
Trial 1
(Fam/Cond)
Trial 2
(Fam/Cond)
Trial 3 (Blind) Trial 4
(Feedback)
Experimental Trial
A v
e r
ag
e 
C
a d
e n
c e
 (r
pm
) Blind Condition (n=10)
Feedback Condition (n=11)
False Feedback Condition (n=10)
Note – Comparisons made using a 2-way between- & within-subjects ANOVA
72
Power Condion-by-Trial Interaction
Note – Comparisons made using a 2-way between- & within-subjects ANOVA
140
155
170
185
200
215
230
245
260
275
290
305
320
335
350
Trial 1
(Fam/Cond)
Trial 2
(Fam/Cond)
Trial 3 (Blind) Trial 4
(Feedback)
Experimental Trial
Av
er
ag
e 
Po
w
er
 (W
)
Blind Condition (n=10)
Feedback Condition (n=11)
False Feedback Condition (n=10)
73
Speed Condion-by-Trial Interaction
Note – Comparisons made using a 2-way between- & within-subjects ANOVA
28
30
32
34
36
38
40
42
Trial 1
(Fam/Cond)
Trial 2
(Fam/Cond)
Trial 3 (Blind) Trial 4
(Feedback)
Experimental Trial
Av
er
ag
e 
Sp
ee
d 
(k
m
/h
) Blind Condition (n=10)
Feedback Condition (n=11)
False Feedback Condition (n=10)
74
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
20% 40% 60% 80% 100%
Time Trial Progression Point
R
at
in
g 
of
 P
er
ce
iv
ed
 E
xe
rt
io
n
Blind Trial (T3)
Feedback Trial (T4)
RPE: Blind Group
75
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
20% 40% 60% 80% 100%
Time Trial Progression Point
Ra
tin
g 
of
 P
er
ce
iv
ed
 E
xe
rti
on
Blind Trial (T3)
Feedback Trial (T4)
RPE: FeedbackGroup
76
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
20% 40% 60% 80% 100%
Time Trial Progression Point
R a
tin
g 
of
 P
er
ce
iv
ed
 E
xe
rti
on
Blind Trial (T3)
Feedback Trial (T4)
RPE: False Feedback Group
77
Conclusions
– Central governor provides and alternative
explanation of fatigue that covers some of the
limitations of peripheral models.
– No single model provides an adequate account
of fatigue.
– Recent work seems to have focused on
interdisciplinary and integrative approaches to
the ‘fatigue’ quagmire.
78
BS277 Biology of Muscle
Fatigue
Dominic Micklewright, PhD.
Lecturer, Centre for Sports & Exercise Science
Department of Biological Sciences
University of Essex