Transcript fatigue - ORB - University of Essex

BS277 Biology of Muscle
Fatigue
Dominic Micklewright, PhD.
Lecturer, Centre for Sports & Exercise Science
Department of Biological Sciences
University of Essex
1
2
What is the
cause of
fatigue
3
Some Key Principles
1. Sports Science is multidisciplinary which
has resulted in different definitions and
explanations of fatigue:
–
–
–
–
–
PHYSIOLOGICAL
BIOCHEMICAL
BIOMECHANICAL
PSYCHOLOGICAL
NEUROLOGICAL
4
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
5
Some Key Principles
3. Linear Models vs. Complex Systems
400
Catastrophic
Failure
Power Output (W)
350
300
250
200
150
100
50
0
0
1
2
3
4
5
6
7
8
9
10
11
Blood Lactate Concentration (mM)
6
Some Key Principles
Complex Systems & Homeostasis…
7
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.
8
Some Key Principles
5. Peripheral vs. Central Fatigue:
CENTRAL FATIGUE
Upstream of anterior horn cell
CNS
PERIPHERAL FATIGUE
Downstream of anterior horn cell
PNS & Muscle
9
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
10
The Models of Fatigue
CV /
Anaerobic
Model
Central
Governor /
Complex
Systems
Model
Psychological
Model
Energy
Supply /
Depletion
Model
Neuromuscula
r Model
FATIGUE
Thermoregulatory
Model
Biomechanica
l Model
11
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
12
Red Blood Cells
EPO & Blood doping found to
↑ RBC count
↑ Cycling performance
…but dangerous
(Hanin & Gore, 2001)
Lac & H+ Removal
AT occurs at a higher %
of VO2MAX among trained
(Lucia et al. 2003)
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)
CV /
ANAEROBIC
FATIGUE
Muscle Blood Flow
-ive linear relationship
between muscle blood
flow and power output
(Saltin et al, 1998)
Lac production-removal
imbalance causes:
↓ intramuscular pH
↓ enzyme activity (PFK)
↓ myoglobin O2 capacity
↑ pain receptor activity
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)
13
Synopsis
Energy Supply / Depletion Model
Fatigue due to :
– Inadequate supply of ATP to the muscle.
– Inadequate depletion of endogenous
substrates.
14
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)
McCardle’s Disease
Metabolic myopathy affects 1/100K
↓Capacity to store glycogen
Weakness & pain after exercise
Suggests [glycogen] causes fatigue
Is [ATP] an afferent signal?
ENERGY
SUPPLY /
DEPLETION
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)
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
15
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) 16
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)
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.
NEURO
MUSCULAR
MODEL
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)
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NM Propagation Theory
10%↓ MVC during
prolonged cycling not
due to central activation
(Millet et al., 2003)
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.
Sarcolemma
↓Na+, K+ membrane
gradient occur during Muscle Power / Peripheral Failure Theory
prolonged
Fatigue cycling
occurs within muscle by alteration of the coupling mechanism
between
Central Activation
resulting
in ↓action
the action
potential and the contractile
proteins. (Hill et al., 2001) Theory
NEURO
+
+
potential i.e. Na /K
Lower central activation
MUSCULAR
muscle
pump
(Fowels
et
Fatigue of a twitched muscle associated with ↓CA+ from sarcoplasmic
found amongreticulum
young and
al, which
2002) has –ive effect on excitation-contraction
MODEL coupling process.
Reduced
CA+
old using MTI during
returnNeurone
from contractile proteins may also cause ↑muscle relaxation
fatigue
α-Motor
isometric/ induced
(McKenna
et al,less
1996).
Muscle
receptors
fatigue (Stackhouse et
+
responsive when ↑H ,
al, 2001).
After
first few
minutes
↓pH
(Lepers
et al.,
2000) low threshold motor units fatigue but are replaced by high
threshold units (Westgaard ↓Dopamine
& De Luca, ↑5HT
1999).during
Suggests
i) individual
motor
units
prolonged
exercise
in rats
susceptible
fatigue
mechanism
(Bailey et
al., 1993) to prevent catastrophic failure.
Time
to fatigueto
↑ in
forceii) protective
↑Dop/5HT ratio may ↓central activation due to lower
vs. positioning task. Task
Early peripheral
fatigue
by motivation
later central&fatigue
is a safety mechanism
to 2O
arousal,
NM coordination.
Nutritional CH
dependency?
(Hunter
et followed
e.g. also
loss attenuate
of ATP (Stchanges
Clair Gibson
et al,
2001)
in ratio
(Davis
et al., 2000)
al.,prevent
2004) catastrophic failuremay
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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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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)
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)
BIOMECH.
MODEL
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?
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)
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Muscle Fibre
Composition
Muscle Activation
Rate (e.g. cadence)
Intermusc. Coordn.
(Stretch/Shortening)
BIOMECH.
EFFICIENCY
OF MOTION
Energy consumption
/ heat generation
O2 consumption and
uptake
% Type I / II
recruitment pattern
Accumulation of
metabolite
Adapted from Abbiss & Laursen, 2005)
21
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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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).
Central Thermoregulation
Central
Hypothalamus
Sweat,
Blood Flow
Thermoreceptors
Peripheral
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
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
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Synopsis
Psychological Model
Fatigue due to psychological factors which…
– ↓ Central activation & motivation
– ↑ Perceived exertion & fatigue
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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.
Rating of Perceived Exertion
The way peripheral sensations associated with
exercise are perceived.
Borg scale, OMNI scale.
RPE rise with skin temp & HR (Amada-dasilva, 2004)
PSYCHOL.
MODEL
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
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.
- feedback
25
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
subconscious processes.
conscious
and
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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
consequence of input variable e.g. [Bla]
direct
– Therefore fatigue and the sensation of fatigue)
must coincide with the peripheral physiological
input variable.
– Often they often do not…
27
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…
28
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.
29
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.
30
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.
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Empirical & Theoretical Context
CENTRAL
FATIGUE
CENTRAL
GOVERNOR
PERIPHERAL
FATIGUE
MUSCLE
PERIPHERAL
CONTRACTION
ORGANS
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Rauch,
Hampson,
Ansley,
St
Clair St
Robson,
Gibson,
Clair
St Clair
Gibson,
StNoakes
Gibson,
ClairLambert,
Gibson,
Rauch,
Lambert,
&
Tucker,
& Noakes
Noakes
& Noakes
Baden,
(2003,
(2005)
(2001,
Foster
p. 313)
p. &
944)
Gibson
&Lambert,
(2006,
p.801)
on Ulmer(2006,
Noakes
(1996)p. 708)
INITIAL PACE DURING FIRST MOMENTS (FEED-FORWARD)
1. KNOWLEDGE OF ENDPOINT (Closed loop or open loop)
2. PREVOIUS EXPERIENCE
SUBSEQUENT PACING (TELEOANTICIPATION)
1. KNOWLEDGE OF ENDPOINT
2. PREVOIUS EXPERIENCE
3. AFFERENT FEEDBACK
COMPLEX
ALGORHYTHM
CENTRAL
GOVERNOR
4. PERCEPTIONS OF AND
BELIEFS ABOUT THE PRESENT
AND LIKELY FUTURE
AFFERENT
FEEDBACK
EFFERENT
CONTROL
33
Previous Experience
5.
PREVIOUS EXPERIENCE AND MEMORY:
•
EXACTNESS / RELEVANCE
CENTRAL
GOVERNOR
AFFERENT
FEEDBACK
EFFERENT
CONTROL
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35
36
37
38
39
40
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.
41
Previous Experience
5.
6.
6.
PREVIOUS EXPERIENCE AND MEMORY:
•
EXACTNESS / RELEVANCE
•
DISTORTION / ACCURACY
PACING DECISIONS LIKELY TO BE
INFLUENCED BY MEMORY AS WELL AS
PERCEPTUAL EXPERIENCE - RPE
CENTRAL
GOVERNOR
MEMORY / PREVIOUS EXPERIENCE WILL
AFFECT THE WAY WE PERCEIVE AND
INTERPRET AFFERENT SENSATIONS.
PROVIDE A BASIS FOR ‘EXPECTED
OUTCOMES’.
AFFERENT
FEEDBACK
EFFERENT
CONTROL
42
Theoretical Context
EXOGENOUS
REFERENCE
SIGNALS
ENDOGENOUS
REFERENCE
SIGNALS
PAST
CENTRAL
GOVERNOR
MUSCLE
PERIPHERAL
CONTRACTION ORGANS
43
Fig. 1 Central Governor Model of Fatigue
(Adapted from Lambert, St Clair Gibson & Noakes, 2005)
Interpretation
prior experience
44
Fig. 1 Central Governor Model of Fatigue
(Adapted from Lambert, St Clair Gibson & Noakes, 2005)
Interpretation
prior experience
45
“Teleoanticipation…brain…initiates a pacing
strategy at the start of an event based upon prior
knowledge of previous similar events”
Ulmer, 1996
prior experience
“Knowledge of distance or time…during an event
provides crucial input…to monitor and determine
overall pacing strategy” Interpretation
St Clair Gibson, Lambert, Rauch et al., 2006
“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
46
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.
47
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.
48
Fig 2. Participant Descriptive Data
Age (yrs), Body Mass (kg), Height (cm)
220
200
180
160
Blind Condition (n=10)
NS
Feedback Condition (n=10)
False Feedback Condition (n=9)
140
120
NS
100
80
60
NS
40
NS
20
0
Age (yrs)
Body Mass (kg)
Height (cm)
Cycling Exp. (yrs)
Condition
Note – Comparisons made using a one-way between-subjects ANOVA
49
Fig 3. Experimental Protocol
TYPE OF FEEDBACK GIVEN DURING
THE FAMILIARISATION TASKS
(BETWEEN-SUBJECTS FACTOR)
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)
50
Fig 3. Experimental Protocol
TYPE OF FEEDBACK GIVEN DURING
THE FAMILIARISATION TASKS
(BETWEEN-SUBJECTS FACTOR)
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)
51
Fig 4. Blind Time Trial Protocol (All Groups)
20 km TIME TRIAL
BLIND TO FEEDBACK
WARM UP
10 MIN SP
20 km MAXIMAL EFFORT SELF-PACED TIME TRIAL
BLIND TO FEEDBACK
t(s)when cyclist actually reaches: 4km
RPE & t(s) when cyclists estimates:
8km
4km
16km
12km
8km
12km
20km
16km
INTERVIEWED ABOUT
PREDICTION STRATEGIES
TRIAL 3 - ALL GROUPS PERFORM TIME TRIAL BLIND
PREDICTION ERROR = ESTIMATED - ACTUAL
(TIME AND DISTANCE)
52
Fig 4. Blind Time Trial Protocol (All Groups)
20 km TIME TRIAL
BLIND TO FEEDBACK
WARM UP
10 MIN SP
20 km MAXIMAL EFFORT SELF-PACED TIME TRIAL
BLIND TO FEEDBACK
t(s)when cyclist actually reaches: 4km
RPE & t(s) when cyclists estimates:
8km
4km
16km
12km
8km
12km
20km
16km
INTERVIEWED ABOUT
PREDICTION STRATEGIES
TRIAL 3 - ALL GROUPS PERFORM TIME TRIAL BLIND
PREDICTION ERROR = ESTIMATED - ACTUAL
(TIME AND DISTANCE)
53
Fig 5. Cycling Ergometry Procedures
• Participants own bike and a Computrainer.
• Blind vs. Accurate Feedback vs. False Feedback
• Time, Speed, Distance, Power, Cadence, RPE
54
Fig 6. Distance Prediction Error Trial Main Effects
Prediction Error for Distance (m)
2800
Trial Main Effect: F (3,78)=6.2, p <.001, partial η2=.19
2400
2000
t (28)=-3.6
p <.001
η2=.30
1600
1200
t (28)=-2.4
p <.0167
η2=.17
800
400
NS
PREDICTS
LATE
0
0
PREDICTS
EARLY
4
8
12
16
20
Distance Cycled Blind (km)
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
55
Fig 7. Group Differences in Distance Prediction Errors
Prediction Error for Distance (m)
3600
Blind Familiarisation Group (n=10)
Accurate Feedback Familiarisation Group (n=10)
False Feedback Familiarisation Group (n=9)
3200
2800
2400
2000
1600
1200
800
400
PREDICTS
LATE
0
0
PREDICTS
EARLY
4
8
12
16
20
Distance Cycled Blind (km)
56
Fig 8. Time Prediction Error Trial Main Effects
Prediction Error for Time (s)
240
Trial Main Effect: F (3,78)=7.4, p <.0005, partial η2=.22
210
180
t (28)=-3.7
p <.001
η2=.33
150
NS
120
t (28)=-2.7
p <.01
η2=.21
90
60
PREDICTS
LATE
30
0
0
4
PREDICTS
EARLY
8
12
16
20
Distance Cycled Blind (km)
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
57
Fig 9. Group Differences in Time Prediction Errors
Blind Familiarisation Group (n=10)
Accurate Feedback Familiarisation Group (n=10)
False Feedback Familiarisation Group (n=9)
380
Prediction Error for Time (s)
340
300
260
220
180
140
100
PREDICTS
LATE
60
20
-20
0
4
PREDICTS
EARLY
8
12
16
20
Distance Cycled Blind (km)
58
Rating of Perceived Exertion (6-20)
Fig 10. Perceived Exertion Trial Main Effects
20
RPE Legs Trial Main Effects: F (4,68)=24.6, p <.0001, partial η2=.59
19
RPE Overall Trial Main Effects: F (4,64)=11.5, p <.0001, partial η2=.42
18
t (18)=-3.4
p <.005
η2=.40
17
16
t (18)=-7.0
p <.0001
η2=.73
15
14
NS
NS
NS
NS
t (18)=-3.4
p <.005
η2=.40
NS
13
12
0
4
8
12
16
20
Distance Cycled Blind (km)
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
59
Fig 11. Group Differences in Perceived Exertion
Rating of Perceived Exertion (6-20)
20
Blind Familiarisation Group (n=10)
Accurate Feedback Familiarisation Group (n=10)
False Feedback Familiarisation Group (n=9)
19
18
17
16
15
14
13
12
0
4
8
12
16
20
Distance Cycled Blind (km)
60
Prediction Error for Speed (km/h)
Fig 12. Group Differences in Interpolated Speed Errors
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
Blind Familiarisation Group (n=10)
Accurate Feedback Familiarisation Group (n=10)
False Feedback Familiarisation Group (n=9)
FASTER THAN
ACTUAL
ACTUAL
SPEED
SLOWER THAN
ACTUAL
0
4
8
12
16
20
Distance Cycled Blind (km)
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.
61
Fig 13. Trial Differences in Actual - Interpolated Speed
36.0
Actual cycling speed with error bars
representing interpolated speed (n=29)
35.5
35.0
Speed (km/h)
34.5
34.0
33.5
33.0
32.5
32.0
31.5
31.0
0
4
8
12
16
20
Distance Cycled Blind (km)
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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!
63
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.
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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.
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66
67
PERFORMANCE BELIEF UNCERTAINTY (BLIND)
20 KM TT #1
BLIND
20 KM TT #2
BLIND
20 KM TT #3
BLIND
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #4
TRUE FEEDBACK
BLIND TRIALS
PERFORMANCE TRIALS
PERFORMANCE BELIEF CERTAINTY (TRUE)
20 KM TT #1
TRUE FEEDBACK
20 KM TT #2
TRUE FEEDBACK
PERFORMANCE BELIEF CERTAINTY (FALSE)
20 KM TT #1
FALSE FEEDBACK +5%
20 KM TT #2
FALSE FEEDBACK +5%
FAMILIARISATION / CONDITIONING TRIALS
68
PERFORMANCE BELIEF UNCERTAINTY (BLIND)
20 KM TT #1
BLIND
20 KM TT #2
BLIND
20 KM TT #3
BLIND
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #4
TRUE FEEDBACK
20 KM TT #3
BLIND
20 KM TT #4
TRUE FEEDBACK
BLIND TRIALS
PERFORMANCE TRIALS
PERFORMANCE BELIEF CERTAINTY (TRUE)
20 KM TT #1
TRUE FEEDBACK
20 KM TT #2
TRUE FEEDBACK
PERFORMANCE BELIEF CERTAINTY (FALSE)
20 KM TT #1
FALSE FEEDBACK +5%
20 KM TT #2
FALSE FEEDBACK +5%
FAMILIARISATION / CONDITIONING TRIALS
69
Condition-by-Trial Performance Outcomes
Cadence
Trial Main Effect
Condition Main Effect
Trial-by-Condition Interaction
F (3,63) = 2.4, p > 0.05
F (2,21) = 0.9, p > 0.05
F (6,63) = 2.8, p < 0.05
Power
Trial Main Effect
Condition Main Effect
Trial-by-Condition Interaction
F (3,69) = 8.9, p < 0.001
F (2,23) = 6.1, p < 0.01
F (6,69) = 2.4, p < 0.05
Speed
Trial Main Effect
Condition Main Effect
Trial-by-Condition Interaction
F (3,69) = 6.3, p < 0.005
F (2,23) = 4.5, p < 0.05
F (6,69) = 2.6, p < 0.05
Note – Comparisons made using a 2-way between- & within-subjects ANOVA
70
Average Cadence (rpm)
Cadence Condion-by-Trial Interaction
120
Blind Condition (n=10)
Feedback Condition (n=11)
False Feedback Condition (n=10)
115
110
105
100
95
90
85
80
Trial 1
(Fam/Cond)
Trial 2
Trial 3 (Blind)
(Fam/Cond)
Trial 4
(Feedback)
Experimental Trial
Note – Comparisons made using a 2-way between- & within-subjects ANOVA
71
Average Power (W)
Power Condion-by-Trial Interaction
Blind Condition (n=10)
Feedback Condition (n=11)
False Feedback Condition (n=10)
350
335
320
305
290
275
260
245
230
215
200
185
170
155
140
Trial 1
(Fam/Cond)
Trial 2
Trial 3 (Blind)
(Fam/Cond)
Trial 4
(Feedback)
Experimental Trial
Note – Comparisons made using a 2-way between- & within-subjects ANOVA
72
Average Speed (km/h)
Speed Condion-by-Trial Interaction
Blind Condition (n=10)
Feedback Condition (n=11)
False Feedback Condition (n=10)
42
40
38
36
34
32
30
28
Trial 1
(Fam/Cond)
Trial 2
(Fam/Cond)
Trial 3 (Blind)
Trial 4
(Feedback)
Experimental Trial
Note – Comparisons made using a 2-way between- & within-subjects ANOVA
73
Rating of Perceived Exertion
RPE: Blind Group
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
Blind Trial (T3)
Feedback Trial (T4)
20%
40%
60%
80%
100%
Time Trial Progression Point
74
Rating of Perceived Exertion
RPE: Feedback Group
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
Blind Trial (T3)
Feedback Trial (T4)
20%
40%
60%
80%
100%
Time Trial Progression Point
75
Rating of Perceived Exertion
RPE: False Feedback Group
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
Blind Trial (T3)
Feedback Trial (T4)
20%
40%
60%
80%
100%
Time Trial Progression Point
76
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.
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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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