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Deception of cycling distance on pacing strategies, perceptual responses, and neural activity

Abstract and Figures

Pacing during exercise performance is well-established; however, little is known about the neural responses associated with changes in power output and the effect of exercise end-point knowledge. Therefore, the aim of this study was to examine the effect of deception of cycling distance on pacing, cerebral oxy- (O2Hb) and deoxy-haemoglobin concentrations, and alpha (α) wave activity. Ten well-trained male cyclists (23.7 ± 6.6 years) completed three cycling time trials (TT) on a stationary air-braked cycle ergometer and were informed the study was to examine the reliability of 3 × 30-km TT. Participants unknowingly completed three distances (24, 30, and 36 km) in a randomised order. Performance (power output; PO), physiological (heart rate; HR), perceptual (rating of perceived exertion; RPE), and neurological (O2Hb, HHb, and α activity) measures were recorded throughout each TT. Data were converted to a percentage relative to the total distance covered. At 100% completion, HR and PO were lower during the 36 km compared to the 30 km trial (P ≤ 0.01). Compared to the 24 km trial, α waves were reduced at 100% (effect size; ES = 1.01), while O2Hb was greater at 70% of completion in the 36 km trial (ES = 1.39). RPE was also higher for 36 km compared to 30-km trial at 80% and the 24-km trial at 10% and 40–100% of completion (P ≤ 0.02). We conclude that the increase in O2Hb and RPE during the 36-km trial, while a reduction in HR and PO is present, may indicate that the pre-frontal cortex may influence the regulation of exercise performance when deceived of the duration end-point by increasing perception of effort to reduce premature onset of physiological strain.
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INTEGRATIVE PHYSIOLOGY
Deception of cycling distance on pacing strategies, perceptual
responses, and neural activity
Georgia Wingfield
1
&Frank E. Marino
1
&Melissa Skein
1
Received: 1 March 2018 / Revised: 4 August 2018 / Accepted: 5 October 2018
#Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
Pacing during exercise performance is well-established; however, little is known about the neural responses associated with
changes in power output and the effect of exercise end-point knowledge. Therefore, the aim of this study was to examine the
effect of deception of cycling distance on pacing, cerebral oxy- (O
2
Hb) and deoxy-haemoglobin concentrations, and alpha (α)
wave activity. Ten well-trained male cyclists (23.7 ± 6.6 years) completed three cycling time trials (TT) on a stationary air-braked
cycle ergometer and were informed the study was to examine the reliability of 3 × 30-km TT. Participants unknowingly com-
pleted three distances (24, 30, and 36 km) in a randomised order. Performance (power output; PO), physiological (heart rate; HR),
perceptual (rating of perceived exertion; RPE), and neurological (O
2
Hb, HHb, and αactivity) measures were recorded through-
out each TT. Data were converted to a percentage relative to the total distance covered. At 100% completion, HR and PO were
lower during the 36 km compared to the 30 km trial (P0.01). Compared to the 24 km trial, αwaves were reduced at 100%
(effect size; ES = 1.01), while O
2
Hb was greater at 70% of completion in the 36 km trial (ES = 1.39). RPE was also higher for
36 km compared to 30-km trial at 80% and the 24-km trial at 10% and 40100% of completion (P0.02). We conclude that the
increase in O
2
Hb and RPE during the 36-km trial, while a reduction in HR and PO is present, may indicate that the pre-frontal
cortex may influence the regulation of exercise performance when deceived of the duration end-point by increasing perception of
effort to reduce premature onset of physiological strain.
Keywords Anticipation .Central regulation .Cerebral blood flow .Pacing strategies
Introduction
Due to the physiological and metabolic demands of prolonged
exercise efforts, high-intensity exercise (i.e. above anaerobic
threshold) cannot be sustained [4]. As such, athletes initiate
and alter pacing to optimise performance and minimise phys-
iological strain [1]. Previous research suggests that the appli-
cation of pacing strategies may, in part, occur in an anticipa-
tory manner [1,13,27], and the regulation of exercise may be
determined based on metabolic demands and the perception of
effort [13,47]. Athletes will also utilise information about
performance, such as time to completion or remaining dis-
tance, to set and adjust their exercise intensity [19].
Withholding such information about exercise end-point can
alter performance in both prolonged [3] and repeated sprint
efforts [7]. Evidence suggests that these changes in perfor-
mance may be attributed to either peripheral [36]orcentrally
mediated mechanisms [26,30]. Marino [24]proposedthatif
an athlete undertakes a trial with an unknown end-point, there
may be an anticipatory downregulation of neural drive to the
working skeletal muscle via centrally mediated control of ex-
ercise regulation. It is suggested this downregulation may be
the result of a subconscious adjustment to metabolic output
and pacing to complete an exercise boutwithout causing over-
whelming disturbances to a homeostatic environment [1,33].
While there is evidence to suggest that pacing strategies
employed during a cycling trial when blinded to performance
feedback may be based on physiological strain [3], there is
also evidence that points to pacing as being based on percep-
tion of effort [33]. Further, when blinded to performance feed-
back, an athlete may be able to generate a pacing strategy
based on previous experience [27,47]. Swart et al. [43]pro-
posed that strenuous exercise trials may be completed with
some subconscious regulation of anticipatory reserve, where-
by athletes may exercise under their maximal performance
*Georgia Wingfield
gwingfield@csu.edu.au
1
School of Exercise Science, Sport, and Health, Charles Sturt
University, Panorama Avenue, Bathurst, NSW 2795, Australia
Pflügers Archiv - European Journal of Physiology
https://doi.org/10.1007/s00424-018-2218-9
capabilities [41]. When exercise end-point is longer than ex-
pected, perceived exertion may increase in spite of no change
to physiological responses and exercise intensity [15]. This
increase in perceived exertion may be indicative of interrup-
tions to the feedback/feedforward mechanisms of exercise
performance. Additionally, when athletes exercise for an un-
known duration, physiological responses, including heart rate,
will be lower than during exercise trials of a known duration
[15]. Deception of the exercise end-point shows that pacing
strategies adopted during endurance exercise may be regulat-
ed to preserve or maintain homeostasis and minimise percep-
tual strain in anticipation of further physical activity require-
ments or to avoid early termination of the exercise bout and
cellular disruption [5,15,43].
While studies have suggested that centrally-mediated fac-
tors may be associated with the regulation of exercise intensi-
ty, limited research has directly examined the changes in neu-
ral activity while deceiving participants of the exercise end-
point. To measure neural activity during exercise, near infra-
red spectroscopy has been established to be a reliable method
to examine haemoglobin concentrations across the cerebral
cortex [8], while electroencephalogram (EEG) is established
to provide an effective measure of the perceptual, physical,
and emotional responses to exercise [6,32]. Although a link
has been established between motor unit recruitment and pow-
er output (PO) during self-paced exercise [20,27], evidence to
suggest that this may stem from changes across the cerebral
cortex is scant. Studies have reported a relationship between
increasing cortical signals and voluntary exercise efforts [10,
23,35,39], while others have reported a reduction in pre-
frontal oxygenation prior to motor output cessation at exhaus-
tion [40]. In addition to cerebral haemodynamics, Robertson
and Marino [39] have suggested that brain wave activity may
be representative of increased central nervous system (CNS)
processing and cerebral autoregulation to maintain homeosta-
sis. In particular, recent evidence purports contributions from
the pre-frontal cortex (PFC) to the regulation of self-paced
performances by influencing planning and executive func-
tions [14,37]. While these studies may demonstrate changes
in neurological changes and exercise intensity, the neural reg-
ulation of exercise intensity while manipulating exercise end-
point expectations (deception) remains unclear.
Therefore, the primary aim of the current study was to
examine the effect of manipulating cycling distance, without
the participants knowledge, on changes to cerebral
haemoglobin concentrations and EEG activity. A secondary
aim was to determine if unknown changes in cycling distance
may alter pacing strategies, muscle activation, and perceived
exertion. We hypothesise that oxy-haemoglobin (O
2
Hb) con-
centrations and EEG activity will increase when participants
are deceived of exercise end-point and when there is addition-
al distance to be covered. We further hypothesise that changes
in perceived exertion, muscular activation (via
electromyography (EMG)), and pacing strategies will be
based on internal cues including previous experience and es-
calating physiological responses.
Materials and methods
Participants
Ten well-trained, male cyclists volunteered to participate in
the study. They trained > 150 km per week and were at the
state and/or national level of competition (age 23.7 ± 6.6 years,
mass 78.3 ± 5.7 kg, height 183.0 ± 4.6 cm). Ethics approval
was gained from the Human Research Ethics Committee prior
to data collection. Informed consent was obtained from all
individual participants included in the study.
Overview
Participants completed a familiarisation session where they
were informed the purpose of the study was to examine the
reliability of central and physiological responses to three sep-
arate 30-km time trials (TT). In order to ensure all participants
were accustom with the exercise protocol, a familiarisation
session included a 30-km TT on a stationary cycle ergometer
to be used for all succeeding trials (Velotron, RacerMate Inc.,
Seattle, USA). Prior to commencing the 30-km TT, partici-
pants completed 3 × 6-s maximal effort sprints from a station-
ary start for normalisation of the EMG data. During all cycling
efforts of the familiarisation, participants were positioned to
have a feedback of their performance including PO, cadence,
distance covered, and heart rate in real time on a computer
screen. The familiarisation was also completed to minimise
learning effects and increase test-retest reliability [25].
Following familiarisation, participants returned to com-
plete 3 × cycling TT, each separated by 57days(6±1days).
As data collection was completed during the pre-competition
training phase, participants were permitted to complete regular
training; however, they were instructed to arrive on the morn-
ing of testing in a rested state. All trials were completed in
consistent ambient conditions (25.9 ± 2.3 °C). Following a 5-
min warm-up on cycle ergometer, participants completed
maximal sprints and were instructed to remain seated to com-
plete the Ball-out^efforts for 6 s followed by a 24-s recovery.
Following 5-min active recovery from sprints, participants
were instructed to complete a 30-km TT in the quickest time
possible. However, participants were assigned to complete
three trials with three varying distances (24, 30, and 36 km)
in a randomised order. During the TTs, all performance feed-
back was withheld except for heart rate via chest strap and
wrist watch receiver (F1, Polar, Elector-Oy, Kempele,
Finland). Once all participants had completed the study, they
were then made aware of the true nature of the study and the
Pflugers Arch - Eur J Physiol
actual distances covered. Feedback from participants indicates
that the deception was successful as participants were unable
to detect any difference in distance between the cycling trials.
Measures
Peak oxygen uptake
Participants completed a peak oxygen uptake test (VO
2peak
)
using a ramp protocol on a stationary cycle ergometer
(Wattbike Pro, Nottingham, UK) and metabolic cart
(Medgraphics Ultima System, Saint Paul, USA). Following
a 5-min cycling warm-up, the ramp protocol commenced.
For the first minute, PO remained at 120 W and increased by
35 W every minute thereafter until volitional exhaustion.
Performance
Peak PO was recorded as the highest PO during each sprint in
Watts (W) in real-time computer software integrated with the
air-braked cycle ergometer (Velotron, RacerMate Inc., Seattle,
USA). During each TT, PO was continuously recorded using
synchronised computer software (Velotron CS 2008,
RacerMate Inc., Seattle, USA). Thirty-second epochs were
later analysed (Excel2007, Microsoft Corp, Washington,
USA) for mean PO at 10% intervals relative to the total trial
distance during the 24-, 30-, and 36-km trials.
Physiological and perceptual
Prior to cycling trials, a mid-stream urine sample was analysed
to determine urine specific gravity (USG) as a measure of
hydration status via refractometer (PEN-SW, Atago, Tokyo,
Japan) if participants presented USG > 1.020 mmol/L, they
were provided with 500-mL water to consume prior to com-
mencing the TT and USG was retested. Participants wore a
chest strap and wrist-watch receiver (F1, Polar, Elector-Oy,
Kempele, Finland) to determine resting heart rate (HR) while
in a seated, resting position for 5 min. During the cycling
trials, the wrist-watch receiver was placed onto the handlebars
of the bike in clear view for the participant. HR was also
continuously recorded via Bluetooth to Polar Team Sport
computer software (Team Pro 2, Polar, Elector-Oy, Kempele,
Finland). Ratings of perceived exertion (RPE) using the 620-
point Borg RPE scale [9] were recorded every 2 km through-
out the cycling protocol.
Electromyography activity
To examine changes in muscular activity of the right leg ex-
tensors, wireless EMG electrodes (Trigno, Delsys, Boston,
MA, USA) were placed onto the belly of vastus medialis
(VM), rectus femoris (RF), vastus lateralis (VL), and flexor,
biceps femoris (BF). The sites were prepared by marking the
skin with a felt-tip marker, abrading the skin, and cleaning the
area with alcohol wipes. For analyses, the data acquisition
software (EMG Works, Delsys, Boston, MA, USA) was
interfaced with LabChart (LabChart v8.1.6, ADInstruments,
NSW, Aus) and simultaneously recorded throughout the du-
ration of the cycling protocols. The EMG data were sampled
at 1 kHz on the LabChart Software with a bandwidth filter of
20450 Hz applied to remove additional artefact.
The EMG data were later calculated as the root mean
square (RMS) and rectified by applying a median filter and
manually analysed using LabChart Reader (LabChart v8.1.2,
ADInstruments, NSW, Aus). Peak EMG data during the pre-
cycling sprints are described as 100% recruitment, and all
EMG data preceding were normalised to the peak EMG
RMS of the maximal sprints (volts; V) over a 5-s epoch during
each 10% interval of the cycling TTs.
Cerebral blood flow
Near-infrared spectrometry (NIRS) was measured during the
EEG baseline measure with eyes open (EO), at rest and con-
tinuously throughout all cycling trials (NIRO200NX,
Hamamatsu Phototonics, Shizouka, Japan). NIRS was record-
ed by placing light emitter and detector probes (interoptode
distance = 4 cm) between Fp1 and F4 over the pre-frontal lobe
(EEG 1020 system). The site was cleaned with an alcohol
wipe and the probes then fixed via adhesive discs and secured
with black Velcro to deter unnecessary light. NIRS data were
used to examine changes in oxygenated (O
2
Hb), deoxygenat-
ed (HHb) cerebral haemoglobin concentrations, and normal-
ised tissue haemoglobin index (nTHI) at rest and throughout
the cycling trials. The NIRO200NX system emits light
through superficial layers of tissue at 775-, 810-, and 850-
nm wavelengths. O
2
Hb and HHb concentrations were
expressed as μmol/l cm and were calculated during data ac-
quisition by applying a modified Beer-Lamberts Law.
Normalised tissue haemoglobin index was further examined
to support the findings in concentrations of oxygenated
haemoglobin. nTHI is expressed in arbitrary units and is cal-
culated by applying spatially resolved spectroscopy (SRS)
methods to limit interference from superficial layers of tissue
[21]. During resting measures, participants were seated in an
upright position for 5 min prior to commencing data collec-
tion. The NIRS data was sampled at 1/s and a 30-s epoch was
later analysed every 2 km and 10% interval of each trial.
Electroencephalogram
A 20-channel wireless EEG headset was fitted to the partici-
pants head, and the placement of electrodes was based on the
1020 EEG international system (B-Alert, ABM, CA, USA).
EEG baseline data included 2 min EO followed by 2 min eyes
Pflugers Arch - Eur J Physiol
closed (EC), while the participant was seated prior to com-
mencing cycling trials. The participant was instructed to re-
main as still as possible, while baseline resting measurements
were taken while facing a blank wall in a room without noise
or visual distractions. EEG measurements were also recorded
for 1 km (approx. 90 s) at every 3-km distance during each
trial. EEG preparation and recording are consistent with those
outlined in Robertson, Marino [38]. Mean data from channels
C3, C4, P3, and P4, respectively, were used for analyses based
on Brodmanns areas; motor cortex (MC: C3 and C4) due to
the suggested association with motor activity [39]. The parie-
tal lobe (PL; channels P3 and P4) was examined due to asso-
ciation with sensorimotor functions [2]. All EEG data were
processed and analysed using the B-Alert computer software
(B-Alert Lab, ABM, CA, USA) where a decontamination
process removed artefact including EMG and eye-blinks.
Total power in alpha (α) frequency band-waves were
exported into Microsoft Excel and analysed to determine
mean activation across the respective channels. Alpha (α)
waves were examined due to the association with a state
of mental readiness and wakefulness [34]. Recent studies
have demonstrated that alpha wave activity typically in-
creases upon commencement of physical exercise and
may be maintained during submaximal exercise intensities
(~ 50% VO
2
peak) [16,38]. Data were then calculated as
percent (%) change from baseline (EO).
Statistical analyses
A priori sample size calculation was completed using
G*Power (version 3.1.9) for repeated measures, within factors
analysis of variance (ANOVA) using the parameters: ES F=
0.25, αerror probability = 0.05, power = 0.80. Resultsindicat-
ed that a sample size of nine participants would provide the
required statistical power.
Data are presented as mean ± standard deviation (SD). The
data are reported both as a relative value in 10% increments of
the total distance covered and in absolute terms at 2-km inter-
vals for the respective 24, 30, and 36-km trials. Differences
between trials were established using repeated measures (con-
dition × time) ANOVA during the 24-, 30-, and 36-km trials
and protected LSD pairwise comparisons to determine where
significant differences were present. Significance was set at
P0.05. Standardised ES analysis was completed using a
published statistical spreadsheet [18] to examine differences
between the 24-, 30-, and 36-km conditions for all variables.
The following criteria were used for ES interpretation: trivial
(< 0.2), small (0.20.6), moderate (0.61.2), large (1.22), and
very large (> 2.0). Pearson correlation analysis was used to
determine a relationship between RPE and O
2
HB and HHb
and nTHI during each TT. Statistical Package for Social
Sciences (V25, Chicago, IL) was used for statistical analysis.
Results
Performance
Peak and mean values for VO
2peak
is 69.8 and 63.6 ±
5.7 ml kg min
1
, respectively. Further, peak and mean peak
PO achieved during the VO
2peak
criteria were 517 and 457 ±
49 W, respectively.
Mean sprint data demonstrates no differences between the
24- (523 ± 103 W), 30- (538 ± 102), or 36-km (569 ± 122;
P> 0.140) trials for all sprints. As expected, time to comple-
tion was significantly different between the 24- (41:20 ±
2:12 min), 30- (51:22 ± 03:24 min), and 36-km (1:01:49 ±
03:39 min; P< 0.001) trials. ES analysis shows a moderate
effect for PO between the 24- and 30-km trial at 80 and 90%
completion (ES = 0.71 and 0.60, respectively; Fig. 1a) and
between the 24- and 36-km trial at 100% completion (ES =
0.88). Between the 30- and 36-km trial, a large effect was
evident between the 30- and 36-km trial at 100% (ES =
1.37). Absolute PO for the 24-, 30-, and 36-km trials showed
a moderate effect for the 30-km trial compared to the 24-km
trial (ES = 0.72) and the 36-km trial (ES = 0.61) at 22 km
(Fig. 2a). Trivial to small ES were noted for all other time
points during the TT.
Physiological and perceptual
Pre exercise USG was not different between the conditions
(24 km: 1.017 ± 0.004; 30 km: 1.014 ± 0.008; 36 km: 1.016
±0.010;P> 0.07). Figure 1b shows that HR was higher in the
30-km trial at 90 (P= 0.032; ES = 0.90) and 100% (P=0.015;
ES = 1.21) compared to the 36-km trial. In addition, there was
a moderate effect between the 24- and 36-km trials at 100%
completion (ES = 0.78). Absolute HR analysis revealed no
differences between conditions at rest or from 2 to 24 km
(P> 0.08; Fig. 2b), though there was a moderate effect for
the 36-km trial compared to the 24- and 30-km trials at 8,
10, and 22 km (ES = 0.640.85).
Participants reported lower RPE during the 24-km trial at
10% and 70100% compared to the 30-km trial (P<0.022;
ES = 0.921.84; Fig. 1c) and at 10% and 40100% in the 36-
km trial (P< 0.018; ES= 0.642.57). The 36-km trial also had
higher RPE at 80% compared to the 30-km trial (P=0.005;
ES = 1.30). Absolute RPE every 2 km revealed higher RPE
for the 36-km trial compared to the 24-km trial at 16 and
24 km (P< 0.015; ES = 0.680.87).
Electromyography
Relative EMG activity of VM, RF, VL, and BF is presented in
Fig. 3. There was no significant effect between or within con-
ditions for EMG activity at any muscle (P> 0.315); however,
ES analysis of VM and VL activity revealed only small to
Pflugers Arch - Eur J Physiol
moderate differences between conditions (ES = 0.020.47).
There were moderate effects for RF activation for the 24-km
trial compared to the 30-km trial at 50, 70, and 100% comple-
tion (ES = 0.620.92) and large effects at 8090% completion
(ES = 1.551.61). Between the 24- and 36-km trial, there was
a large effect at 50, 60, 80, and 100% completion (ES = 0.85
1.15) and very large at 90% completion (ES = 2.19). A mod-
erate effect was also evident between the 24- and 30-km trial
at 60% completion (ES = 0.78).
Absolute EMG revealed no significant differences be-
tween conditions for all muscles (P> 0.221) and only trivial
and small effects between all conditions for changes in VM
150
180
210
240
270
300
10 20 30 40 50 60 70 80 90 100
Power Output (W)
24 km
30 km
36 km
9
11
13
15
17
19
10 20 30 40 50 60 70 80 90 100
noitrexEdeviecrePfognitaR
Workload Completed
(
%
)
24 km
30 km
36 km
140
150
160
170
180
190
10 20 30 40 50 60 70 80 90 100
-1)
24 km
30 km
36 km
b,d
a,b,c,d *
a,b,c,d,e
*,e
b
a
c
c
ce,d
c
a,d
Fig. 1 Mean ± SD relative workload at each 10% interval for apower
output, bheart rate, and crating of perceived exertion the 24-, 30-, and
36-km trials (n=10);
*
significant difference between the 30- and 36-km
trials (P= 0.005 - 0.015);
a
significant difference between the 24- and 36-
km trials (P=0.0010.022);
b
significant difference between the 24- and
30-km trials (P=0.0010.018);
c
moderate to very large effect between
24- and 30-km trials (ES = 0.711.84);
d
moderate to very large effect
between 24- and 36-km trials (ES = 0.642.57);
e
moderate to very large
effect between 30- and 36-km trials (ES = 0.901.37)
Pflugers Arch - Eur J Physiol
and VL activity from 2 to 30 km (ES = 0.020.49; Fig. 4a).
RF EMG changes are presented in Fig. 4b and revealed
moderate effects for the 24 compared to the 30-km trial at
2 and 16 km (ES = 0.77 and 0.79, respectively). For the 24
compared to the 36-km trial, there was moderate effects at 8,
12, 22, and 24 km (ES = 0.901.18), large effects at 14, 18,
and 20 km (ES = 1.311.49), and very large effects at 16 km
(ES = 2.21). Finally, BF EMG shows a moderate effect for
the 36-km trial compared to the 24- and 30-km trials at 2 km
(ES = 0.66 and 0.67, respectively).
Cerebral oxygenation
There were no significant differences between conditions for
any measure of cerebral oxygenation including O
2
Hb, HHb,
and nTHI (P> 0.101) within the relative or absolute data.
However, ES analysis for changes in O
2
Hb concentration re-
vealed a moderate effect between the 24- and 36-km trial at rest
and 4050% (ES = 0.700.95) and a large effect at 70% com-
pletion (ES = 1.39). Between the 30- and 36-km trials, there was
a moderate effect from 80 to 100% completion (ES = 0.700.82)
170
190
210
230
250
270
290
310
330
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
Power Output (W)
24 km
30 km
36 km
50
70
90
110
130
150
170
190
Rest 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
Heart Rate (beat.min-1)
24 km
30 km
36 km
9
10
11
12
13
14
15
16
17
18
19
20
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
noitrexEdeviecrePfognitaR
Distance
(
km
)
24 km
30 km
36 km
c,e
d,e
d
d,e d,e
d,e
a
b
c
Fig. 2 Mean ± SD at every 2 km interval for apower output, bheart rate,
and crating of perceived exertion the 24-, 30-, and 36-km trials (n=10);
c
moderate effect between 24 and 30 km trials (ES = 0.72);
d
moderate
effect between 24- and 36-km trials (ES = 0.640.77);
e
moderate effect
between 30- and 36-km trials (ES = 0.610.85)
Pflugers Arch - Eur J Physiol
and large effect at 70% completion (ES = 1.35; Fig. 5a). There
was a moderate effect for nTHI concentration between the 24-
and 30-km trials at 20% completion (ES = 0.75). Moderate ef-
fect for nTHI concentration was observed also between the 30-
and 36-km trials at 30% completion (ES = 1.04) and a large
effect at 20% completion (ES = 1.24). ESs in HHb concentration
for the 24-km trial compared to the 30-km trial were moderate
from 50 to 100% completion (ES = 0.650.80) and large at 10
and 20% completion (ES = 1.55 and 1.69, respectively; Fig. 5c).
Between the 30- and 36-km trials, there was a moderate effect
from 50 to 100% (ES = 0.651.08), large effect at 20% (ES =
1.86), and very large effect at 10% completion (ES = 2.16).
Examination of changes in O
2
Hb concentration revealed a
moderate effect between the 24- and 36-km trial at 18 km
(ES = 0.64; Fig. 6a) and between the 30- and 36-km trials at
28 and 30 km (ES = 0.96 and 0.94, respectively). Between the
30- and 36-km trials, there was a moderate effect at 1620 and
22 km (ES = 0.610.73).ModerateESs were evident for nTHI
between the 24- and 30-km trial at 1216, 22, and 24 km
(ES = 0.630.76), between the 24- and 36-km trial at 8, 12,
-6
-2
2
6
10
14
Rest 10 20 30 40 50 60 70 80 90 100
HHb(µ)
24 km
30 km
36 km
-2
0
2
4
6
8
10
12
14
16
Rest 10 20 30 40 50 60 70 80 90 100
O2Hb (µmol L-1
24 km
30 km
36 km
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Rest 10 20 30 40 50 60 70 80 90 100
nTHI (a.u)
Workload Com
p
leted
(
%
)
24 km
30 km
36 km
c,e
d
dd
c,e c,e
d,e
c,e e
)
a
b
c
Fig. 3 Means ± SD prefrontal haemoglobin concentrations at 10%
intervals for aoxygenated haemoglobin, bdeoxygenated haemoglobin,
and cnormalised tissue index in arbitrary units (a.u.) during 24-, 30-, and
36-km trials (n=7);
c
moderate to large effect between 24- and 30-km
trials (ES = 0.651.69);
d
moderate to large effect between 24- and 36-km
trials (ES = 0.701.39);
e
moderate to large effect between 30- and 36-km
trials (ES = 0.652.16)
Pflugers Arch - Eur J Physiol
and 24 km (ES = 0.620.68). Finally between the 30- and 36-
km trial, there was a moderate ES for nTHI at 14, 16, 20, 22,
and 28 km (ES = 0.620.81) and for HHb at 1620 and 24 km
(ES = 0.610.73; Fig. 6c).
-5.0
-2.5
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
Rest2 4 6 8 1012141618202224262830323436
O2Hb (µmol L-1 )
24 km
30 km
36 km
-3.0
-1.5
0.0
1.5
3.0
4.5
6.0
7.5
Rest2 4 6 8 1012141618202224262830323436
HHb (µmol L-1 )
24 km
30 km
36 km
0.50
0.70
0.90
1.10
1.30
1.50
Rest 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
nTHI (a.u.
Distance
(
km
)
24 km
30 km
36 km
d
d
c,d
d,c
c,e c,e
c,e
ee
e
e
e
e
e
e
a
b
c
Fig. 4 Means ± SD prefrontal haemoglobin concentrations at every 2 km
interval for aoxygenated haemoglobin, bdeoxygenated haemoglobin,
and cnormalised tissue index in arbitrary units (a.u.) during 24-, 30-,
and 36-km trials (n=7);
c
moderate effect between 24- and 30-km trials
(ES = 0.630.76);
d
moderate effect between 24- and 36-km trials (ES =
0.620.68);
e
moderate effect between 30- and 36-km trials (ES = 0.61
0.81)
Fig. 5 Mean ± SD relative workload (%) for electromyography activity
across vastus medialis (VM), rectus femoris (RF), vastus lateralis (VL),
and biceps femoris (BF) during the 24-, 30-, and 36-km trials (n=10);
c
large effect between 24- and 30-km trials (ES = 0.621.61);
d
large to very
large effect between 24- and 36-km trials (ES =0.852.19)
Pflugers Arch - Eur J Physiol
10
20
30
40
50
10 20 30 40 50 60 70 80 90 100
VM % Change
24 km
30 km
36 km
5
15
25
35
45
10 20 30 40 50 60 70 80 90 100
RF % Change
24 km
30 km
36 km
20
30
40
50
60
10 20 30 40 50 60 70 80 90 100
Vl % Change
24 km
30 km
36 km
15
25
35
45
55
10 20 30 40 50 60 70 80 90 100
BF % Change
Workload completed
(
%
)
24 km
30 km
36 km
c
c,d
c,d
dc,d
c,d c,d
a
b
c
d
Pflugers Arch - Eur J Physiol
The correlation coefficients between mean RPE and cere-
bral oxygenation during the 24-, 30-, and 36-km trials are
presented in Table 1.O
2
Hb (r= 0.727; P0.02) and HHb
(r= 0.733; P0.01) were strongly associated with mean
RPE for all trials. Further, nTHI and mean RPE were associ-
ated for the 24-km trial (r=0.748;P=0.01).
EEG
Examination of EEG alpha (α) waves revealed no significant
differences between conditions across the MC or PL
(P> 0.114). There was a moderate effect in EEG activity
across the MC for the 24-km trial compared to the 30-km trial
at 25, 75, and 100% completion (ES = 0.770.96; Fig. 7a) and
compared to the 36-km trial at 100% completion (ES = 1.01).
There were also moderate effects for the 30-km trial compared
to 36-km trial at 25, 50, and 100% completion (ES = 0.60
0.94). Examination for activity across the PL showed a mod-
erate effect between the 24- and 30-km trial at 75 and 100%
completion (ES = 0.74 and 1.16, respectively). There was a
large effect between the 24- and 36-km trial at 100% comple-
tion (ES = 1.20) and a moderate effect between the 30- and 36-
km trials at 25% completion (ES = 0.69).
Absolute EEG activity at 2-km intervals revealed moderate
effects across the MC at 5 km between the 24- and 30-km trial
(ES = 0.80; Fig. 7c) and between the 30- and 36-km trials at 8
and 14 km (ES = 0.630.65). Across the PL, between the 24-
and 30-km trials, there was moderate effect a 20 km (ES =
1.10; Fig. 7d)and a large effect at 23 km(ES = 1.26). Between
the 24- and 36-km trial, there was a moderate effect from 17 to
23 km (ES = 0.761.10).
Discussion
While previous research has examined the role of deception
on pacing strategies, the mechanisms responsible remain
somewhat elusive due to the minimal simultaneous use of
performance, perceptual, neurological, and neuromuscular as-
sessments. Therefore, the aim of the present study was to
examine deception of three cycling distances on self-paced
PO, physiological, haemoneurological, and perceptual re-
sponses. A novel aspect of this study was to further elucidate
if changes to cerebral oxygenation, neural activity and surface
EMG altered with changes in pacing strategies, and if these
changes would occur in an anticipatory manner when de-
ceived of trial distance requirements. Our major findings in-
dicate that there may be a relationship between perceived ef-
fort and oxygenation across the pre-frontal region of the brain
as evidenced by strong correlations between such variables for
the 24-, 30-, and 36-km trials. This is may be further evi-
denced by the higher O
2
Hb concentration when exercise dis-
tance was greater than expected in spite of no knowledge of
extended duration and without changes to other physiological
responses. These findings support previous studies [4,7,43,
44], which provide a basis for the anticipatory regulation of
exercise intensity during self-paced exercise.
Our findings support the notion that centrally-mediated
mechanisms may alter performance output during endurance
exercise performances [24,31,33]. Most intriguingly, upon
reaching the final 20% of the 36-km trial, a gradual reduction
in PO manifests up to 14% compared to the 30-km trial at the
end-point. This gradual reduction in PO during the 36-km trial is
translated to 28.8-km absolute distance covered, which is some-
what representative of the information provided to the partici-
pant. As the final 20% of the 36-km trial exceeds the expecta-
tions made by the participant, this suggests that there may have
been a pacing strategy implemented based on previous experi-
ence, and when this expectation was not met and perception of
effort was significantly higher compared to the 30 km trial,
performance reduced accordingly. Jones et al. [19]proposedthat
if the conscious or subconscious recognition of exercise end-
point manipulation occurs, performance will be compromised
as physiological resources will not have been set appropriately
for the pre-determined task. Additionally, across the three trials
in the present study, there appears to be an even pacing profile
employed with a slight reduction in PO in the mid-section of
each trial (see Fig. 1a). This mid-trial downregulation in PO
without differences in physiological responses suggests that par-
ticipants may have reduced exercise intensity following the first
3050% of the trial to account for the unknown distance to be
completed [47]. Others have proposed that when athletes are
blinded to performance feedback, they may be able to attain
satisfactory performance times if there is sufficient experience
gained from similar performances [27]. This suggests that the
familiarisation with the 30-km TT in the present study provided
participants with trial familiarity to generate, and abide by, a
preconceived exercise template [33]. Our findings also show
that when exercise distance exceeds expectation, pacing strate-
gies will change and PO will be reduced.
Not surprisingly, throughout all trials, there was a steady
increase in RPE. The current data show that the participants
reported their subjective perception of effort throughout the
respective trials to be somewhat disconnected to their physio-
logical responses and PO. Paterson and Marino [33]stated
that when deceived of trial duration, athletes may adjust a
subconscious exercise template, including altering physiolog-
ical responses and exercise intensity, to complete an exercise
trial without causing substantial physiological harm. Similar
Fig. 6 Mean ± SD electromyography activity at every 2-km interval
across vastus medialis (VM), rectus femoris (RF), vastus lateralis (VL),
and biceps femoris (BF) during the 24-, 30-, and 36-km trials (n=10);
c
moderate to large effect between 24- and 30-km trials (ES = 0.770.79);
d
moderate to very large effect between 24- and 30-km trials (ES = 0.90
2.21);
e
moderate to large effect between 30- and 36-km trials (ES = 0.68
0.97)
Pflugers Arch - Eur J Physiol
to the current findings, Paterson and Marino [33]reportedthat
HR responses between a deceived trial and known duration
trial showed no difference, while perception of effort was
increased. Unlike the current study, participants were blinded
15
20
25
30
35
40
45
50
2 4 6 8 1012141618202224262830323436
VM % Change
24 km
30 km
36 km
0
10
20
30
40
50
60
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
RF % Change
24 km
30 km
36 km
0
10
20
30
40
50
60
70
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
VL % Change
24 km
30 km
36 km
0
10
20
30
40
50
60
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
BF % Change
Distance
(
km
)
24 km
30 km
36 km
d,e
d,e d,e d,e
d
c,e
c,d,e
e
eee
d,e d,e
d,e
Pflugers Arch - Eur J Physiol
to all feedback, including HR responses. While a similar ex-
ercise protocol has been previously established using decep-
tion of distance and the provision of HR responses [29], it is a
possible limitation of the current study as participants may
have modified pacing based on knowledge of HR responses.
However, additional studies have proposed that the regulation
of exercise performance may be based on perceptions of effort
and metabolic information being centrally interpreted, thereby
adjusting pacing strategies to remain within physiological
constraints [13,47]. The current study demonstrates that a trial
longer than initial expectations (i.e. the 36-km trial) may show
a mutual reduction in exercise intensity and physiological re-
sponses once expectations of performance are exceeded. This
suggests that the false expectations of exercise endpoint may
lead participants to downregulate exercise intensity and sacri-
fice performance to reduce physiological responses in an at-
tempt to maintain homeostasis.
Oxygenated cerebral haemoglobin at the PFC has been in-
directly associated with motor control and is the region of the
brain responsible for movement planning, pacing strategies,
and decision-making [22,42]. Moreover, changes in oxygena-
tion across the PFC may also be associated with executive
function and mental effort which may guide movement behav-
iour in anticipation of exercise end-point [37], or when devia-
tion from an expected exercise end-point has been detected.
Previous literature suggests that a reduction in pre-frontal oxy-
genated haemoglobin (O
2
Hb) may be responsible for changes
in central drive to the active muscle during exhaustive exercise
[35]. During self-paced 5-km running trials with distance feed-
back, Billaut et al. [8] reported cerebral oxygenation was main-
tained until the final km of the trials when participants volun-
tarily increased speed, symbolic of an end-spurt. While Billaut
et al. [8] did not include a deception trial, we can speculate that
an increase in cerebral oxygenation can be associated with an
increase in neural activity, and thus, neural drive to the muscle
for a final end-spurt in PO. Therefore, we propose that the
formation of an exercise template, based on the familiarisation
trial, may have been adjusted in response to interpretation of
physiological responses at the PFC causing neural changes
which in turn manipulate neural drive to active muscle [10,
38]. Interestingly, there appears to be a gradual decline in
O
2
Hb concentration exceeding 70% completion during the
36-km trial occurring almost immediately prior to gradual re-
ductions in performance (PO; Fig. 1a70100%) and physio-
logical responses (HR; Fig. 1b90100%). The higher O
2
Hb
further translates to the difference in concentration between the
30- and 36-km trials at 28 and 30 km (Fig. 4a) and gradual
decline in exercise intensity within the 36-km trial exceeding
the 30-km point up to completion. These data suggest that there
may be some contribution from the pre-frontal region which
has detected the increase in cerebral blood flow and in turn,
downregulated PO to reduce risk of developing ischemia in the
brain and the working musculature [30]. In the present study,
there is a strong relationship between changes in cerebral
haemoglobin concentrations and ratings of perceived effort.
Literature suggests that RPE may reflect the biological de-
mands to maintain a stable internal environment [46]. We pro-
pose that an increase in neural activity across the PFC may in
turn be related to changes in perceived exertion, which may
influence PO. Further, the absolute RPE show no differences
between the 30- and 36-km trials, while changes in O
2
Hb are
evident followed by gradual declines PO when exercise dura-
tion exceeds 30 km. This resembles the strong relationship
between O
2
Hb and RPE, suggesting that contributions from
the PFC may have influenced the change in performance when
expectations of duration were not met. Based on the premise
stated by Tucker et al., [46], these data may further suggest that
mechanisms within the PFC may mediate RPE and the delivery
of efferent command to working musculature to avoid physio-
logical catastrophe and early termination of an exercise bout.
Given the possibility that an exercise template may be set
and regulated in the pre-frontal region of the brain, as deter-
mined by cerebral oxygenation, the generation of efferent com-
mand and delivery to the active musculature could be produced
based on afferent sensory feedback [45]. The MC is a primary
focus in the current study as this region of the brain is associ-
ated with the generation and control of motor tasks [11]. It is
Table 1 Table of correlation coefficients between rating of perceived exertion and oxygenated haemoglobin, rating of perceived exertion and
deoxygenated haemoglobin and rating of perceived exertion and normalised tissue haemoglobin index during the 24-, 30-, and 36-km trials
Mean RPE score correlated with O
2
Hb Mean RPE score correlated with HHb Mean RPE score correlated with nTHI
rPr Pr P
24 km 0.756 0.011 0.755 0.012 0.748 0.013
30 km 0.727 0.017 0.733 0.016 0.314 0.376
36 km 0.839 0.002 0.919 0.000 0.388 0.268
Fig. 7 Mean ± SD for electroencephalogram activity across the scalp at a
motor cortex (MC; channels C3+ C4) and bparietal lobe (PL; channels
P3 + P4) at 25% intervals and cmotor cortex and dparietal lobe at every
2-km interval during the 24-, 30-, and 36-km trials (n=6);
c
moderate to
large effect between 24- and 30-km trials (ES = 0.651.26);
d
moderate to
large effect between 24- and 36-km trials (ES = 0.761.20);
e
moderate to
large effect between 30- and 36-km trials (ES =0.600.69)
Pflugers Arch - Eur J Physiol
-5
5
15
25
35
25 50 75 100
MC % Change
24 km
30 km
36 km
-5
5
15
25
35
25 50 75 100
PL % Change
% Workload completed
24 km
30 km
36 km
0
5
10
15
20
25
30
35
2-3 5-6 8-9 11-12 14-15 17-18 20-21 23-24 26-27 29-30 32-33 35-36
MC % Change
24 km
30 km
36 km
-5
0
5
10
15
20
25
30
35
2-3 5-6 8-9 11-12 14-15 17-18 20-21 23-24 26-27 29-30 32-33 35-36
PL % Change
Distance
(
km
)
24 km
30 km
36 km
c
cd
ce c cde
cc d
c d
d
ee
e
e
a
b
c
d
Pflugers Arch - Eur J Physiol
suggested that activation across the MC is elevated in propor-
tion with increases in exercise intensity [11,12]. Bailey et al.
[6] reported a consistent increase in EEG activity across all sites
(lateral, mid frontal, central, and parietal) and across all fre-
quencies (alpha, beta, and theta) during an exercise trial to
exhaustion. The authors suggested that the changes in the
EEG activity could be the result of increasing physiological
strain with exercise intensity. However, Bailey et al. [6]did
not differentiate between the different EEG sites, nor did the
protocol examine EEG changes during self-paced exercise.
Previous research has also indicated that there may be a
fatigue-related increase in communication across the motor
cortex during exhaustive exercise [17]; however, in the present
study, the 36-km trial shows a moderate effect on MC activity
compared to the shorter 24 and 30-km trials at 100% and a
large effect on PL activity at 100% completion compared to
the 30-km trial. These apparent reductions in alpha activity
across the MC and PL regions of the brain during the longer
duration (36 km) trial appear to be consistent with reductions in
PO. We propose that the reduction in EEG activity during the
longer trial may be indicative of a reduction in neural drive to
the muscle to downregulate exercise intensity once expecta-
tions of trial end-point were not met.
A novel aspect of the current study is the measure of acti-
vation across the cerebral cortex and activation at the muscu-
lature via EMG activity. It has been previously speculated that
changes in surface EMG activity have an association with
changes to pacing strategies and exercise performance [8].
Our data show a 14% reduction in PO for the 36-km trial
compared to the 30-km trial at 100%, but these findings were
not supported by the EMG data. Thus, we further propose that
due to a lack of performance feedback, participants were un-
able to track the activity of the working musculature to PO
during the respective trials. Similarly, Mauger et al. [27]re-
ported a limited association between EMG activity and pacing
strategies when participants were blinded to performance
feedback during successive cycling trials. The current EMG
activity data did not translate to the reduction in EEG activity
occurring across the PL and MC regions of the brain during
the final stages of 36-km trial. These findings suggest that
increases in cerebral oxygenation of the pre-frontal region of
the brain may more likely be linked to interpretation and suc-
cessive planning of motor functioning, including changes to
perceived exertion, and possibly affect efferent command.
In conclusion, the concomitant increase in pre-frontal neural
activity and rating of perceived exertion during a trial where
participants are blinded to performance feedback and are de-
ceived of trial duration may implicate a central mechanism in
the control of performance, including efferent command during
endurance exercise [1]. While pacing strategies can be con-
sciously set and manipulated by athletes [3,28,33,46], we
conclude that there may also be some subconscious aspect
contributing to the regulation of exercise intensity, since
participants downregulated their PO mid trial while there were
no discernible alterations in physiological responses.
Acknowledgments The authors would like to acknowledge the staff at
Cycling NSW for their support with data collection.
Funding information Georgia Wingfield was supported by a post-
graduate scholarship through Charles Sturt University.
Compliance with ethical standards
Conflict of interest The authors declare that there are no conflicts of
interest.
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... Several studies have investigated simulated altitude effects (i.e., hypoxia) on PFC hemodyamics during self-paced endurance performance (Bourdillon et al., 2014;Fan et al., 2013;Ferguson et al., 2018;Hamlin et al., 2010;Nielsen et al., 1999), while others have investigated the potential for ergogenic aids to ameliorate hypoxic effects or increase cerebral perfusion (Decroix et al., 2016(Decroix et al., , 2018Fan et al., 2018;Liao et al., 2019;Shannon et al., 2017;Shaw et al., 2020). In addition, new research has provided insight into the effect of feedback blinding (Wingfield et al., 2018) and distance deception (Wingfield et al., 2019) on PFC oxygenation during cycling TT performance. ...
... The 10-20 electroencephalographic (EEG) system is typically used for optode positioning in sport and exercise research adopting fNIRS (Herold et al., 2018). Ten of the reviewed studies used the 10-20 EEG system for optode placement (Billaut et al., 2010;Bourdillon et al., 2014;Decroix et al., 2016Decroix et al., , 2018Liao et al., 2019;Pires et al., 2016;Santos-Concejero et al., 2015;Wingfield et al., 2018Wingfield et al., , 2019. Seven studies made anatomical references to the PFC (i.e., forehead) but used no recognized system (Fan et al., 2013(Fan et al., , 2018Ferguson et al., 2018;Hamlin et al., 2010;Nielsen et al., 1999;Shannon et al., 2017;Shaw et al., 2020). ...
... Several methods were adopted by the reviewed studies to mitigate the impact of optode movement and to block extraneous light. These included securing optodes in some form of placeholder by black tape or adhesive discs (Billaut et al., 2010;Decroix et al., 2016Decroix et al., , 2018Ferguson et al., 2018;Hamlin et al., 2010;Nielsen et al., 1999;Pires et al., 2016;Santos-Concejero et al., 2015;Shannon et al., 2017;Wingfield et al., 2018Wingfield et al., , 2019, and coverage of optodes with a dark headband or material (Billaut et al., 2010;Decroix et al., 2016Decroix et al., , 2018Santos-Concejero et al., 2015;Shaw et al., 2020). These measures are important for ensuring good optode-skin coupling, limiting ambient light reaching detectors, reducing the prospect of optode movement artifacts and improving the wearer's comfort (Orihuela-Espina et al., 2010). ...
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Recent research has suggested that top-down executive function associated with the prefrontal cortex is key to the decision-making processes and pacing of endurance performance. A small but growing body of literature has investigated the neurological underpinnings of these processes by subjecting the prefrontal cortex to functional near-infrared spectroscopy (fNIRS) measurement during self-paced endurance task performance. Given that fNIRS measurement for these purposes is a relatively recent development, the principal aim of this review was to assess the methodological rigor and findings of this body of research. We performed a systematic literature search to collate research assessing prefrontal cortex oxygenation via fNIRS during self-paced endurance performance. A total of 17 studies met the criteria for inclusion. We then extracted information concerning the methodology and findings from the studies reviewed. Promisingly, most of the reviewed studies reported having adopted commonplace and feasible best practice guidelines. However, a lack of adherence to these guidelines was evident in some areas. For instance, there was little evidence of measures to tackle and remove artifacts from data. Lastly, the reviewed studies provide insight into the significance of cerebral oxygenation to endurance performance and the role of the prefrontal cortex in pacing behavior. Therefore, future research that better follows the guidelines presented will help advance our understanding of the role of the brain in endurance performance and aid in the development of techniques to improve or maintain prefrontal cortex (PFC) oxygenation to help bolster endurance performance.
... Given that a certain level of PFC activation is required for effortful cognition and self-regulation, a drop in PFC activity could result in task failure as seen in animal models (Hosking, Cocker, & Winstanley, 2016). More recently, Wingfield, Marino, and Skein (2019) monitored changes in Cox while manipulating the end-point knowledge of participants performing a cycling trial. Manipulation (or deception) of the end-point caused an increase in Cox and RPE during a 36-km trial, in addition to a reduction in heart rate (HR) and power output (PO). ...
... This did not differ as a function of group (trained vs. untrained). This is partially supportive of previous research, which has shown increases in Cox during exercise (Billaut et al., 2010;Wingfield et al., 2019). Previous research has also shown us that during high exertion, there is a dip in Cox (Rooks et al., 2010) and this is evidenced in, for example, the final 0.5K of a running trial in Billaut et al. (2010). ...
... Whitehead et al. (2018) collected a sample of ten trained and 10 untrained and Massey et al. (2020) collected a sample of six trained and seven untrained. In addition, studies examining Cox collected data from 10 to 11 trained athletes (Billaut et al., 2010;Wingfield et al., 2019). Nonetheless, the effect sizes for the mixed ANOVAs were very large (e.g. ...
Article
Objectives Few studies have directly investigated changes in cortical haemodynamics during a self-paced interval endurance activity, while collecting conscious cognition and physiological performance data. This pilot study used functional Near Infrared Spectroscopy (fNIRS), while capturing conscious cognition using Think Aloud (TA) during an incremental paced cycling exercise. Methods A mixed design was implemented with cycling expertise (untrained vs. trained) as the between groups variable and incremental self-paced stage (5 stages of increasing effort) and site (12 optodes across the PFC) as the within groups variables. Dependent measures were the changes in cortical O2Hb, and physiological indicators (% heart rate max (%HRmax), average power output (APO), peak power output (PPO), rate of perceived exertion (RPE) and blood lactate (Bla)) over time. Participants used TA throughout their second interval trial. Results Trained cyclists had higher APO and maximum power output (MPO) from stages 2 to 5, in addition to a greater increase in PPO over the whole trial. There were significant main effects of stage on %HRmax, Bla and RPE. Differences in cortical haemodynamics were found specifically in areas in the mid left and right PFC. TA data demonstrated that untrained participants verbalised more irrelevant information and feelings of pain and fatigue, in addition to both groups verbalising significantly more motivation-related thoughts during the final stage. Conclusion This pilot is the first to capture changes in Cox, physiological measures and conscious cognition through the use of TA. We demonstrate the potential role of mid- PFC, and how conscious cognition may change over time. This study has implications for coaches and sport psychologists who may want to understand the cognitions of their athlete during an event and support low level athletes in developing a better understanding of the own cognitions.
... Indeed, a high rate of perception exertion (RPE) is often related to the interruption of exercise [28], which can be delayed by adopting cognitive strategies according to the motivational behavior [4] or attentional focus [17]. Conversely, the deception state met by cyclists during exercise could increase the RPE and alter their performance [123]. Moreover, Ekkekakis et al. [22] explained that cognitive state depended on the affective responses athletes meet during exercise, which varied significantly during high-intensity cycling. ...
... Feedback intervention as creating deception about the remaining distance of a cycling exercise impacts cognitive brain areas and cycling performance through the alteration of PFC activity. Indeed, during three TTs of 24, 30, and 36 km performed by well-trained cyclists who were wrongly informed that each TT was 30 km long, the concentration of O 2 Hb in the PFC increased after 70% of TT completion during the 36 km condition, i.e., in the condition where the distance information was lower than the actual distance [123]. This O 2 Hb increase in PFC was related to an increase of RPE and a decrease in HR and PO. ...
Article
Performance in cycling is frequently related to metabolic or biomechanical factors. Overall, the contribution of the neurophysiological system during cycling is often poorly considered in performance optimization. Yet, cycling is a complex whole-body physical exercise that necessitates specific coordination and fine control of motor output to manage the different intensities. The ability to produce different levels of intensity of exercise would require optimizing many functions of the central nervous system from the brain’s treatment of sensory signals to complex motor command execution via the corticospinal pathway. This review proposes an integrative approach to the factors that could influence cycling performance, based on neurophysiological and cognitive markers. First, we report data relying on brain activity signals, to account for the different brain areas and cognitive functions involved. Then, because the motor command is highly dependent upon its regulation along the corticospinal pathway, we expose the modulation of corticospinal and spinal excitabilities during cycling. We present these later by reviewing the literature of studies using transcranial magnetic or percutaneous nerve stimulations. Finally, we describe a model of neural and cognitive adjustments that occur with acute and chronic cycling practices, with several areas of improvement focusing on these factors, including mental and cognitive training.
... These authors stated that participants selected their pacing strategy based on the perceived distance of a trial rather than the actual distance. Using a similar methodology, one study found that the performance is downregulated when the perceived distance does not meet the actual distance [19], while another study demonstrated that the participants selected their pacing strategy according to their perceived effort [17]. Moreover, when the information about activity is not given during the exercise, the performance was reduced with a slower pacing strategy in comparison with the given information [20]. ...
... The system was calibrated prior to every test using a 3-L syringe for volume calibration, as well as a gas cylinder (16% O 2 (oxygen); 4.1% CO 2 (carbon dioxide)) and an ambient air measurement in accordance with the manufacturer's guidelines [29]. The test was considered maximal and stopped when at least three of the following criteria were observed: (a) a VO 2 plateau (increase ≤ 150 mL·min −1 or 2 mL·kg −1 ·min −1 ); (b) a respiratory exchange ratio (RER) ≥ 1.15; (c) 90% of the predicted HRmax; (d) a Rate of Perceived Exertion-RPE (Borg scale) ≥ 19 (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20); (e) the participant was unable to maintain the required pace. The VO 2 was calculated as the average of the five highest values recorded during the final three stages of the test [30]. ...
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It is unclear how athletes regulate their performance prior and during exercise when deceptive methods are applied. Therefore, the aim of this study was to test if time manipulation can influence pacing strategy and running performance. Ten recreationally active subjects were informed they would complete four 60-min time trials only with time feedback. The first session was a familiarization trial (60-min), and in the following three sessions, the time feedback was modified: normal chronometer (NC—60 min.), 10% faster (Faster chronometer—FC—54 min.), and 10% slower (slower chronometer—SC—66 min.). Total distance was different between conditions, while average of total speed, Heart Rate, oxygen consumption, and Rate of Perceived Exertion were similar (p > 0.05). A slow start pacing strategy was adopted in all conditions and did not differ between conditions when averaged across the session; however, when analyzing the first and final 10 min of the session, differences were found between conditions. Finally, the observed time was an important determinant of the regulation of exercise intensity, because, although the pacing strategy adopted in all conditions was regulated according to previous exercise information, adjustments were made in the initial (NC) and final (FC) phases of the trials.
... For example, acute anxiety experienced during exercise has been shown to mitigate declines in inhibitory control under long duration, high intensity exercise (Cantelon et al., 2019). Additionally, research has demonstrated that mental resource allocation, perception of effort and prefrontal cortex activation are differentially affected when exercise end-point is known vs. unknown Wingfield et al., 2019), yet it remains unknown how such anticipation may influence cognitive function during exercise. Given that a motivating factor for much of the research in this field is to characterize performance decrements that could lead to costly performance outcomes (e.g., game-losing play, or life or death decisions), basic work should seek to emulate the emotional and motivational factors that may influence performance in applied settings. ...
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A growing body of work has investigated the effects of acute, or single bouts of, aerobic exercise on cognitive function. However, review of this research has largely focused on changes following exercise, with less focus on cognitive changes during exercise. The purpose of this review is to discuss the critical characteristics of this literature to date, including: (1) what has been done, (2) what has been found, and (3) what is next. Furthermore, previous meta-analytic reviews have demonstrated there is a small positive effect on cognition when measured during exercise, with executive functions showing the largest effects. However, these reviews group executive functions together. Here we explore how inhibition, working memory and cognitive flexibility are individually impacted by factors such as exercise intensity or duration. Searches of electronic databases and reference lists from relevant studies resulted in 73 studies meeting inclusion criteria. Studies were grouped by executive and non-executive cognitive domains, intensity and duration of exercise bouts. Within the executive domain, we found that effects on working memory and cognitive flexibility remain mixed, effects on inhibition are clearer. Moderate intensity exercise improves response time, vigorous intensity impairs accuracy. Moderate to vigorous intensity improves response time across non-executive domains of attention, motor speed and information processing, with no significant effects on accuracy. Memory processes are consistently improved during exercise. Effects of exercise duration on response time and accuracy are nuanced and vary by cognitive domain. Studies typically explore durations of 45 min or less, extended exercise durations remain largely unexplored. We highlight factors to consider when assessing exercise-cognition relationships, as well as current gaps and future directions for work in this field.
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Executive functioning and attention require mental effort. In line with the resource conservation principle, we hypothesized that mental effort would be saved when individuals expected to exercise for a long period. Twenty-two study participants exercised twice on a cycle ergometer for 10 min at 60% of their maximal aerobic power, with the expectation of exercising for either 10 min or 60 min. Changes in activity in the right dorsolateral prefrontal cortex (rdlPFC) and right medial frontal cortex (rmPFC) were investigated by measuring oxyhemoglobin using near-infrared spectroscopy. Attentional focus and ratings of perceived exertion were assessed at three time points (200, 400, and 600 s). The oxyhemoglobin concentration was lower in the rdlPFC and higher in the rmPFC under the 60-min than under the 10-min condition. Also, attention was less focused in the 60-min than in the 10-min condition. We discuss these results as possible evidence of a disengagement of the brain regions associated with mental effort (executive network), in favor of brain regions linked to resting activity (the default network), in order to save mental resources for the maintenance of exercise.
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The extent to which athletic pacing decisions are made consciously or subconsciously is a prevailing issue. In this article we discuss why the one-dimensional conscious-subconscious debate that has reigned in the pacing literature has suppressed our understanding of the multidimensional processes that occur in pacing decisions. How do we make our decisions in real-life competitive situations? What information do we use and how do we respond to opponents? These are questions that need to be explored and better understood, using smartly designed experiments. The paper provides clarity about key conscious, preconscious, subconscious and unconscious concepts, terms that have previously been used in conflicting and confusing ways. The potential of dual process theory in articulating multidimensional aspects of intuitive and deliberative decision-making processes is discussed in the context of athletic pacing along with associated process-tracing research methods. In attempting to refine pacing models and improve training strategies and psychological skills for athletes, the dual-process framework could be used to gain a clearer understanding of (1) the situational conditions for which either intuitive or deliberative decisions are optimal; (2) how intuitive and deliberative decisions are biased by things such as perception, emotion and experience; and (3) the underlying cognitive mechanisms such as memory, attention allocation, problem solving and hypothetical thought.
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This study investigated the effects of a high intensity cycling exercise on changes in spectral and temporal aspects of electroencephalography (EEG) measured from ten experienced cyclists. Cyclists performed a maximum aerobic power test on the first testing day followed by a time to exhaustion trial at 85% of their maximum power output on two subsequent days that were separated by approximately 48 hours. EEG was recorded using a 64 channel system at 500 Hz. Independent component analysis parsed the EEG scalp data into maximally independent components (IC). An equivalent current dipole model was calculated for each IC and results were clustered across subjects. A time-frequency analysis of the identified electrocortical clusters was performed to investigate the magnitude and timing of event-related spectral perturbations. Significant changes (p < 0.05) in electrocortical activity were found in frontal, supplementary motor and parietal areas of the cortex. Overall, there was a significant increase in EEG power as fatigue developed throughout the exercise. The strongest increase was found in the frontal area of the cortex. The timing of event-related desynchronization (ERD) within the supplementary motor area corresponds with the onset of force production and the transition from flexion to extension in the pedalling cycle. The results indicate an involvement of the cerebral cortex during the pedalling task that most likely involves executive control function as well as motor planning and execution.
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The aim of the present study was to investigate the effect of electroacupuncture (EA) with different frequencies on muscle oxygenation in humans. The subjects were 8 healthy male volunteers. Muscle oxygenation was measured using near-infrared spectroscopy (NIRS). Blood pressure (BP) and heart rate (HR) were monitored simultaneously. After baseline recording, EA was given for 15 min and recovery was measured for 20 minutes. The procedure of EA at 1 Hz, at 20 Hz, and at control followed in the same subjects. Tissue oxygenation index (TOI) decreased during EA at 20 Hz (P < 0.05) and increased during the recovery period. Normalized tissue hemoglobin index (nTHI) also decreased during EA at 20 Hz and increased during the recovery period (P < 0.05), whereas TOI and nTHI in the EA at 1 Hz did not change significantly throughout the experiment. The peak TOI and nTHI values at 20 Hz during the recovery period were higher than the values at 1 Hz and in the control (P < 0.05). BP and HR remained constant. These data suggest that the supply of oxygen to muscle decreased during EA at 20 Hz and increased after EA at 20 Hz, without any changes in HR and BP.
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The fundamental tenets of exercise physiology are to describe energy transformations during physical work and make predictions about physical performance during different conditions. Historically, the most popular method to observe such responses during exercise has been the constant load or fixed intensity protocol based largely on the assumption that there is a threshold response of the organism under given conditions. However, constant load exercise does not fully allow for randomness or variability as the biological system is overridden by a predetermined externally imposed load which cannot be altered. Conversely, in self-regulated (paced) exercise there is almost an immediate reduction in power output and muscle recruitment upon commencing exercise. This observation suggests the existence of a neural inhibitory command processes. This difference in regulation demonstrates the inherent importance of variability in the biological system; for in tightly controlled energy expenditure, as is the case during constant load exercise, sensory cues cannot be fully integrated to provide a more appropriate response to the given task. The collective evidence from conventional constant load versus self-regulated exercise studies suggest that energy transformations are indeed different so that the inherent biological variability accounts for the different results achieved by the two experimental paradigms.
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There are a number of mechanisms thought to be responsible for the onset of fatigue during exercise-induced hyperthermia. A greater understanding of the way in which fatigue develops during exercise could be gleaned from the studies which have examined the maintenance of cerebral blood flow through the process of cerebral autoregulation. Given that cerebral blood flow is a measure of the cerebral haemodynamics, and might reflect a level of brain activation, it is useful to understand the implications of this response during exercise and in the development of fatigue. It is known that cerebral blood flow is significantly altered under certain conditions such as altitude and exacerbated during exercise induced – hyperthermia. In this brief review we consider the processes of cerebral autoregulation predominantly through the measurement of cerebral blood flow and contrast these responses between exercise undertaken in normothermic versus heat stress conditions in order to draw some conclusions about the role cerebral blood flow might play in determining fatigue.
Book
Introduction to EEG- and Speech-Based Emotion Recognition Methods examines the background, methods, and utility of using electroencephalograms (EEGs) to detect and recognize different emotions. By incorporating these methods in brain-computer interface (BCI), we can achieve more natural, efficient communication between humans and computers. This book discusses how emotional states can be recognized in EEG images, and how this is useful for BCI applications. EEG and speech processing methods are explored, as are the technological basics of how to operate and record EEGs. Finally, the authors include information on EEG-based emotion recognition, classification, and a proposed EEG/speech fusion method for how to most accurately detect emotional states in EEG recordings. Provides detailed insight on the science of emotion and the brain signals underlying this phenomenon; Examines emotions as a multimodal entity, utilizing a bimodal emotion recognition system of EEG and speech data; Details the implementation of techniques used for acquiring as well as analyzing EEG and speech signals for emotion recognition.
Chapter
Emotion recognition systems using different modalities have become an emerging area of research over the last two decades. This chapter mainly focuses on a review of emotion recognition using modalities like face, speech, gesture, text, and electroencephalogram signals, and gives descriptions of available databases. It provides the basic knowledge to understand emotion recognition systems with different modalities and relevant features.