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REGULATION OF ENDURANCE
PERFORMANCE: NEW
FRONTIERS
EDITED BY:
Alexis R. Mauger, Florentina J. Hettinga, Dominic P. Micklewright,
Andrew Renfree, Benjamin Pageaux, Hollie S. Jones and Jo Corbett
PUBLISHED IN: Frontiers in Physiology

1 November 2017 | Regulation of Endurance Performance: New Frontiers
Frontiers in Physiology
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ISSN 1664-8714
ISBN 978-2-88945-329-0
DOI 10.3389/978-2-88945-329-0
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REGULATION OF ENDURANCE
PERFORMANCE: NEW FRONTIERS
Regulation of endurance performance: new frontiers.
Image licensed under CC0.
Topic Editors:
Alexis R. Mauger, University of Kent, United Kingdom
Florentina J. Hettinga, University of Essex, United Kingdom
Dominic P. Micklewright, University of Essex, United Kingdom
Andrew Renfree, University of Worcester, United Kingdom
Benjamin Pageaux, Université de Bourgogne Franche-Comté, France
Hollie S. Jones, University of Central Lancashire, United Kingdom
Jo Corbett, University of Portsmouth, United Kingdom
Successful endurance performance requires the integration of multiple physiological and psy-
chological systems, working together to regulate exercise intensity in a way that will reduce time
taken or increase work done.The systems that ultimately limit performance of the task are hotly
contested, and may depend on a variety of factors including the type of task, the environment,
external influences, training status of the individual and a host of psychological constructs.

These factors can be studied in isolation, or inclusively as a whole-body or integrative system.
A reductionist approach has traditionally been favoured, leading to a greater understanding
and emphasis on muscle and cardiovascular physiology, but the role of the brain and how this
integrates multiple systems is gaining momentum. However, these differing approaches may
have led to false dichotomy, and now with better understanding of both fields, there is a need
to bring these perspectives together.
The divergent viewpoints of the limitations to human performance may have partly arisen
because of the different exercise models studied. These can broadly be defined as open loop
(where a fixed intensity is maintained until task disengagement), or closed loop (where a fixed
distance is completed in the fastest time),which may involve whole-body or single-limb exercise.
Closed loop exercise allows an analysis of how exercise intensity is self-regulated (i.e. pacing),
and thus may better reflect the demands of competitive endurance performance.However,whilst
this model can monitor changes in pacing, this is often at the expense of detecting subtle differ-
ences in the measured physiological or psychological variables of interest. Open loop exercise
solves this issue, but is limited by its more restrictive exercise model. Nonetheless, much can be
learnt from both experimental approaches when these constraints are recognised. Indeed, both
models appear equally effective in examining changes in performance, and so the researcher
should select the exercise model which can most appropriately test the study hypothesis. Given
that a multitude of both internal (e.g. muscle fatigue, perception of effort, dietary intervention,
pain etc.) and external (e.g. opponents, crowd presence, course topography, extrinsic reward
etc.) factors likely contribute to exercise regulation and endurance performance, it may be that
both models are required to gain a comprehensive understanding.
Consequently,this research topic seeks to bring together papers on endurance performance from
a variety of paradigms and exercise models, with the overarching aim of comparing, examining
and integrating their findings to better understand how exercise is regulated and how this may
(or may not) limit performance.
Citation: Mauger, A. R., Hettinga, F. J., Micklewright, D. P., Renfree, A., Pageaux, B., Jones, H. S.,
Corbett,J.,eds.(2017).Regulation of Endurance Performance: New Frontiers. Lausanne: Frontiers
Media. doi: 10.3389/978-2-88945-329-0

Table of Contents
Editorial
07 Editorial: Regulation of Endurance Performance: New Frontiers
Florentina J. Hettinga, Andrew Renfree, Benjamin Pageaux, Hollie S. Jones, Jo Corbett,
Dominic Micklewright and Alexis R. Mauger
Section 1: Fatigue and Recovery
11 Endurance Performance during Severe-Intensity Intermittent Cycling: Effect of
Exercise Duration and RecoveryType
Luis F
. Barbosa, Benedito S. Denadai and Camila C. Greco
20 No Critical Peripheral FatigueThreshold during Intermittent IsometricTime to
Task FailureTest with the Knee Extensors
Christian Froyd, Fernando G. Beltrami, Guillaume Y. Millet and Timothy D. Noakes
30 AreThere Critical FatigueThresholds? Aggregated vs. Individual Data
Daria Neyroud, Bengt Kayser and Nicolas Place
36 Differences in Muscle Oxygenation, Perceived Fatigue and Recovery between
Long-Track and Short-Track Speed Skating
Florentina J. Hettinga, Marco J. Konings and Chris E. Cooper
50 Fatigue Induced by Physical and Mental Exertion Increases Perception of Effort
and Impairs Subsequent Endurance Performance
Benjamin Pageaux and Romuald Lepers
Section 2:The Role of the Brain
59 No Influence ofTranscutaneous Electrical Nerve Stimulation on Exercise-
Induced Pain and 5-Km CyclingTime-Trial Performance
Andrew W. Hibbert, François Billaut, Matthew C. Varley and Remco C. J. Polman
72 Cerebral Regulation in Different Maximal Aerobic Exercise Modes
Flávio O. Pires, Carlos A. S. dos Anjos, Roberto J. M. Covolan, Fabiano A. Pinheiro,
Alan St Clair Gibson, Timothy D. Noakes, Fernando H. Magalhães and
Carlos Ugrinowitsch
83 The Ergogenic Effects ofTranscranial Direct Current Stimulation on Exercise
Performance
Luca Angius, James Hopker and Alexis R. Mauger
Section 3:Training Physiology of Endurance Performance
90 High Intensity IntervalTraining in Handcycling:The Effects of a 7 WeekTraining
Intervention in Able-bodied Men
Patrick Schoenmakers, Kate Reed, Luc Van Der Woude and Florentina J. Hettinga

99 Short and LongTerm Effects of High-Intensity IntervalTraining on Hormones,
Metabolites, Antioxidant System, Glycogen Concentration, and Aerobic
Performance Adaptations in Rats
Gustavo G. de Araujo, Marcelo Papoti, Ivan Gustavo Masselli dos Reis,
Maria A. R. de Mello and Claudio A. Gobatto
109 AcclimationTraining Improves Endurance Cycling Performance in the Heat
without Inducing Endotoxemia
Joshua H. Guy, David B. Pyne, Glen B. Deakin, Catherine M. Miller and
Andrew M. Edwards
118 Effects of Neuromuscular Electrical StimulationTraining on Endurance
Performance
Menno P
. Veldman, Julien Gondin, Nicolas Place and Nicola A. Maffiuletti
Section 4: Limits of Human Endurance Performance: Physiology
and PersonalityTrait
123 Master Athletes Are Extending the Limits of Human Endurance
Romuald Lepers and Paul J. Stapley
131 Passion and Pacing in Endurance Performance
Lieke Schiphof-Godart and Florentina J. Hettinga
Section 5:The Psychological Perspective: Deception Studies and Importance of
the Environment
137 The Influence of Mid-Event Deception on Psychophysiological Status and
Pacing Can Persist across Consecutive Disciplines and Enhance Self-paced
Multi-modal Endurance Performance
Daniel Taylor and Mark F
. Smith
154 Deceptive Manipulation of Competitive Starting Strategies Influences
Subsequent Pacing, Physiological Status, and Perceptual Responses during
CyclingTimeTrials
Emily L. Williams, Hollie S. Jones, S. Andy Sparks, David C. Marchant, Adrian W.
Midgley, Craig A. Bridge and Lars R. McNaughton
163 Improvements in CyclingTimeTrial Performance Are Not Sustained Following
the Acute Provision of Challenging and Deceptive Feedback
Hollie S. Jones, Emily L. Williams, David Marchant, S. Andy Sparks, Craig A. Bridge,
Adrian W. Midgley and Lars R. Mc Naughton
172 The Science of Racing against Opponents: Affordance Competition and the
Regulation of Exercise Intensity in Head-to-Head Competition
Florentina J. Hettinga, Marco J. Konings and Gert-Jan Pepping
179 The Manipulation of Pace within Endurance Sport
Sabrina Skorski and Chris R. Abbiss
187 Cycling in the Absence ofTask-Related Feedback: Effects on Pacing and
Performance
Benjamin L. M. Smits, Remco C. J. Polman, Bert Otten, Gert-Jan Pepping and
Florentina J. Hettinga
196 Effect of Environmental and Feedback Interventions on Pacing Profiles in
Cycling: A Meta-Analysis
Michael J. Davies, Bradley Clark, Marijke Welvaert, Sabrina Skorski,
Laura A. Garvican-Lewis, Philo Saunders and Kevin G. Thompson

Section 6:The Role of Cognition in Pacing
220 Pacing Profiles in CompetitiveTrack Races: Regulation of Exercise Intensity Is
Related to Cognitive Ability
Debbie Van Biesen, Florentina J. Hettinga, Katina McCulloch and Yves Vanlandewijck
230 Thinking and Action: A Cognitive Perspective on Self-Regulation during
Endurance Performance
Noel E. Brick, Tadhg E. MacIntyre and Mark J. Campbell
237 Cognitive Fatigue InfluencesTime-On-Task during Bodyweight Resistance
Training Exercise
James R. Head, Matthew S. Tenan, Andrew J. Tweedell, Thomas F
. Price,
Michael E. LaFiandra and William S. Helton

EDITORIAL
published: 21 September 2017
doi: 10.3389/fphys.2017.00727
Frontiers in Physiology | www.frontiersin.org September 2017 | Volume 8 | Article 727 |
Edited and reviewed by:
Gregoire P. Millet,
University of Lausanne, Switzerland
*Correspondence:
Florentina J. Hettinga
fjhett@essex.ac.uk
Specialty section:
This article was submitted to
Exercise Physiology,
a section of the journal
Received: 07 August 2017
Accepted: 07 September 2017
Published: 21 September 2017
Citation:
Hettinga FJ, Renfree A, Pageaux B,
Jones HS, Corbett J, Micklewright D
and Mauger AR (2017) Editorial:
Regulation of Endurance
Performance: New Frontiers.
Front. Physiol. 8:727.
doi: 10.3389/fphys.2017.00727
Editorial: Regulation of Endurance
Performance: New Frontiers
Florentina J. Hettinga1
*, Andrew Renfree2
, Benjamin Pageaux3
, Hollie S. Jones4
,
Jo Corbett5
, Dominic Micklewright1
and Alexis R. Mauger6
1
School of Sport, Rehabilitation and Exercise Science, University of Essex, Colchester, United Kingdom, 2
Institute of Sport
and Exercise Science, University of Worcester, Worcester, United Kingdom, 3
CAPS UMR1093, Institut National de la Santé
et de la Recherche Médicale (INSERM), Université de Bourgogne-Franche Comté, Dijon, France, 4
School of Psychology,
University of Central Lancashire, Preston, United Kingdom, 5
Department of Sport and Exercise Science, University of
Portsmouth, Portsmouth, United Kingdom, 6
School of Sport and Exercise Sciences, University of Kent, Canterbury,
United Kingdom
Keywords: pacing, endurance, sport performance, training, fatigue, brain, recovery
Editorial on the Research Topic
Regulation of Endurance Performance: New Frontiers
INTRODUCTION
Successful endurance performance requires the integration of multiple physiological and
psychological systems, working together to regulate exercise intensity in a way that will reduce
time taken or increase work done. The systems that ultimately limit performance of the task
are hotly contested, and may depend on a variety of factors including the type of task, the
environment, external influences, training status of the individual and a host of psychological
constructs. These factors can be studied in isolation, or inclusively as a whole-body or integrative
system. A reductionist approach has traditionally been favored, leading to a greater understanding
and emphasis on muscle and cardiovascular physiology, but the role of the brain and how this
integrates multiple systems is gaining momentum. However, these differing approaches may have
led to false dichotomy, and now with better understanding of both fields, there is a need to bring
these perspectives together.
The divergent viewpoints of the limitations to human performance may have partly arisen
because of the different exercise models studied. These can broadly be defined as open loop (where
a fixed intensity is maintained until task disengagement), or closed loop (where a fixed distance
is completed in the fastest time), which may involve whole-body or single-limb exercise. Closed
loop exercise allows an analysis of how exercise intensity is self-regulated (i.e., pacing), and thus
may better reflect the demands of competitive endurance performance. However, whilst this model
can monitor changes in pacing, this is often at the expense of detecting subtle differences in
the measured physiological or psychological variables of interest. Open loop exercise solves this
issue, but is limited by its more restrictive exercise model. Nonetheless, much can be learnt from
both experimental approaches when these constraints are recognized. Indeed, both models appear
equally effective in examining changes in performance, and so the researcher should select the
exercise model which can most appropriately test the study hypothesis. Given that a multitude of
both internal (e.g., muscle fatigue, perception of effort, dietary intervention, pain etc.) and external
(e.g., opponents, crowd presence, course topography, extrinsic reward etc.) factors likely contribute
to exercise regulation and endurance performance, it may be that both models are required to gain
a comprehensive understanding.
Consequently, this research topic seeks to bring together papers on endurance
performance from a variety of paradigms and exercise models, with the overarching
aim of comparing, examining and integrating their findings to better understand how
7

Hettinga et al. Regulation of Endurance Performance
exercise is regulated and how this may (or may not) limit
performance. To explore new frontiers, we welcomed the
submission of original research, review and perspective articles
on endurance performance, which specifically consider the scope
and impact of their findings in the broader context of exercise
regulation.
TOPIC CONTENT
This resulted in the acceptance of 24 papers (14 original
research papers, 4 perspectives, 4 mini-reviews, a review,
and an opinion) written by in total 84 contributing authors.
Overall, the topic combines physiological with psychological
viewpoints and papers explore closed-loop as well as open-loop
exercise. Research papers from a predominantly physiological
perspective were all directed toward a better understanding
of endurance performance and its limitations, and/or directed
toward optimizing endurance performance, and incorporated a
wide range of methods.
FATIGUE AND RECOVERY
Fatigue and recovery were covered by several papers. VO2
kinetics and recovery in intermittent exercise was explored by
Barbosa et al. They found that endurance performance was
negatively influenced by active recovery only during shorter
high-intensity intermittent exercise, though probably unrelated
to differences in VO2 kinetics. Froyd et al. explored the critical
fatigue threshold that has been proposed to limit endurance
performance via inhibitory feedback from the group III and IV
muscle afferents. They found that subjects did not terminate
knee-extensor exercise at task failure because they had reached
a critical threshold in peripheral fatigue and the existence of a
critical peripheral fatigue threshold during intermittent isometric
exercise to task failure with the knee extensors can thus be
questioned. Also Neyroud et al. explored the critical fatigue
threshold in their perspective article, highlighting the importance
of considering interpretation of individual data and not only
of group means. Muscle oxygenation, perceived fatigue and
recovery were explored in speed skating by Hettinga et al.
Patterns of reoxygenation and deoxygenation in the working
muscles during a race are different for long-track and short-track
speed skating, providing with more insights into the mechanistic
physiological principles relevant for performance and recovery
in elite athletes in different sports, and on how technical factors
are impacting on those. Finally, Pageaux and Lepers explored
mental and physical fatigue in their mini-review, and identified
perception of effort as the variable altered by both prior physical
exertion and mental exertion, that should be included in future
studies.
THE ROLE OF THE BRAIN
Two experimental studies focused on the role of the brain
in the regulation of exercise intensity. Hibbert et al. explored
transcutaneous electrical nerve stimulation (TENS) effects on
exercise-induced muscle pain, pacing strategy, and performance
during a 5-km cycling time trial. Effects were found to be
non-significant, and effectiveness of TENS could be questioned.
There were indications that there was a possible effect at the
start of the trials. Pires et al. explored cerebral regulation
in different maximal aerobic exercise modes. Primary motor
cortex activation was preserved throughout exercises, suggesting
that central factors are at least partly centrally–coordinated.
Angius et al. mini-reviewed the ergogenic effect of transcranial
direct current stimulation on exercise performance, showing
promising opportunities. However, also here it came forward
that given the uncertain mechanisms and the inconsistency of
outcomes of tDCS prior to exercise, the use of tDCS in exercise
should be treated with some caution and future research is
needed.
TRAINING PHYSIOLOGY OF ENDURANCE
PERFORMANCE
Four experimental papers focused on training physiology of
endurance performance. Schoenmakers et al. demonstrated
that high intensity upper body interval training (HIIT)
resulted in larger training effects compared to continuous
training, and recommended to incorporate HIIT sessions
in training regimes of recreationally active and trained
handcyclists. De Araujo et al. discussed effects of HIIT
they had found on hormones, metabolites, the anti-oxidant
system, glycogen concentration and aerobic performance
adaptations in rats into the training context of endurance
runners. Guy et al. focused on effects of heat training on
both endurance performance and biomarkers associated with
inflammatory and immune system responses. Heat training
enhanced performance and did not pose a substantial challenge
to the immune system. Veldman et al. explored effects of
neuromuscular electrical stimulation training on endurance
performance, potentially particularly relevant for individuals
with muscle weakness or patients who cannot perform voluntary
contractions.
LIMITS OF HUMAN ENDURANCE
PERFORMANCE: PHYSIOLOGY AND
PERSONALITY TRAITS
Limits of human performance were addressed in a mini-review
assessing the impact of age on physiological parameters,
overviewing research on master athletes from Lepers and Stapley
This paper strongly focused on physiological characteristics,
where Schiphof-Godart et al. also included a psychological
perspective to explore training behavior. They outline the
possible influence of an athlete’s passion in sport related to
their exercise behavior and decision-making related to the
regulation of exercise intensity. They conclude that taking
into account athletes’ passion could therefore be a useful
tool for adequate coaching and monitoring of athlete well-
being.
Frontiers in Physiology | www.frontiersin.org September 2017 | Volume 8 | Article 727 | 8

THE PSYCHOLOGICAL PERSPECTIVE:
DECEPTION STUDIES AND IMPORTANCE
OF THE ENVIRONMENT
From a psychological perspective, deception was a popular topic.
Taylor and Smith demonstrated that mid-event pace deception
can have a practically meaningful effect on multi-modal
endurance performance, though the relative importance of
different psychophysiological and emotional responses remains
unclear. Williams et al. explored deceptive manipulation of
competitive starting on several psychological and physiological
parameters. Results demonstrated that with no detriment to
performance time, but less physiological strain and more positive
psychological perceptions, a pacing strategy adopting a slower
start could be considered more beneficial during a stimulated
16.1 km cycling time trial. Jones et al. showed that time trial
improvements were not sustained following acute provision of
challenging and deceptive feedback. The presence of the pacer
rather than the manipulation of performance beliefs acutely
facilitated time trial performance and perceptual responses. This
is in line with suggestions in the perspective of Hettinga et al.,
in which the science behind head-to-head competition was
explored. They conclude that athlete–environment interactions
are crucial factors in understanding the regulation of exercise
intensity when racing against other competitors or pacers. Also
Skorski et al. mention that environmental factors as important.
In their mini-review, they conclude that pacing manipulations
should be explored to further understand the complexity of how
humans regulate pace.
When environmental factors are crucial, also the availability
of feedback needs to be considered. Smits et al. examined
the influence of the absence of commonly available task-
related feedback on effort distribution and performance in
experienced endurance athletes. They demonstrated that prior
knowledge of task demands together with reliance on bodily
and environmental information can be sufficient for experienced
athletes to come to comparable time trial performances. In their
meta-analysis, Davies et al. explored the effects of environmental
feedback interventions on pacing. In line with the above
studies, 26 cycling studies demonstrated environmental effects
of hypoxia, thermal aspects and feedback on pacing and
performance.
THE ROLE OF COGNITION IN PACING
Also, cognitive aspects in pacing were covered. Van Biesen et al.
explored in their original research study if the regulation of
exercise intensity during competitive track races was different
between runners with and without intellectual impairment.
Runners with intellectual impairment have difficulties to
efficiently self-regulate their exercise intensity. Their limited
cognitive resources may constrain the successful integration
of appropriate pacing strategies during competitive races, and
establishes the role of cognitive factors in pacing and the
regulation of exercise intensity. Brick et al. provided a cognitive
perspective on self-regulation and endurance performance in
their perspective article. They highlighted the roles of attentional
focus, cognitive control, and metacognition in self-regulated
endurance performance and mental fatigue. Mental fatigue was
further explored in the study of Head et al. focusing on exploring
cognitive fatigue in an experimental study. The authors found
a decreased Time-on-Task in bodyweight resistance training
exercise tasks.
CONCLUSION
Recently, many researchers have focused on proposing
frameworks to better understand the regulation of exercise
intensity (Noakes, 1997; Marcora, 2008; Foster et al., 2009;
Millet, 2011; Renfree et al., 2014; Smits et al., 2014; Hettinga
et al.; Micklewright et al., 2017; St Clair Gibson et al., 2017;
Venhorst et al., 2017). This research topic supports the notion
that both internal and external variables need to be incorporated
in frameworks exploring the regulation of exercise intensity.
Both physiology and psychology are crucial for endurance
performance, and aspects such as competitive environment,
cognition and fatigue seem to be requisite to understand the
regulation of exercise intensity. As yet, the way in which these
factors interact in determining endurance performance is not
fully understood.
The 24 papers comprising this research topic all explored
the mechanisms involved in the regulation of exercise intensity.
This issue was addressed within the context of a single bout
of exercise, and across longer periods of time as is the case
with long term changes in performance in masters athletes. As
a whole, the papers have contributed to further understanding of
fatigue recovery, the role of the brain in regulatory processes,
the relationship between physiological training responses and
endurance performance, the limits of human performance and
the influence of personality traits on endurance performance, and
lastly the influence of environment, deception, and cognition on
pacing. The number and range of issues considered within the
broad subject of the regulation of exercise performance illustrates
the complexity of the topic. Indeed, it is clear that (as stated
in the introduction) a reductionist approach to understanding
the regulatory process is unlikely to be sufficient. Although the
papers comprising this research topic have greatly contributed
to furthering our understanding of the key issues, it is still not
clear how factors are “weighted” in terms of the extent to which
they inform exercise regulation. Papers in this research topic
alone have suggested that physiological status, psychological
traits, and interactions with other competitors are all important.
Researchers are encouraged to address the relative importance
of these individual contributory factors in informing acute and
chronic whole body behavior and performance during endurance
exercise.
AUTHOR CONTRIBUTIONS
FH drafted and finalized the manuscript. ARM, AR, BP, and HJ
significantly contributed to the drafts toward the final product
and critically reviewed the manuscript. All authors (FH, ARM,

AR, BP, HJ, JC, and DM) provided valuable comments, thoughts
and insights throughout the entire process of this topic that
contributed to the final draft of this editorial, and approved the
final version.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2017 Hettinga, Renfree, Pageaux, Jones, Corbett, Micklewright and
Mauger. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (CC BY). The use, distribution or reproduction in
other forums is permitted, provided the original author(s) or licensor are credited
and that the original publication in this journal is cited, in accordance with accepted
academic practice. No use, distribution or reproduction is permitted which does not
comply with these terms.

ORIGINAL RESEARCH
published: 02 December 2016
doi: 10.3389/fphys.2016.00602
Frontiers in Physiology | www.frontiersin.org December 2016 | Volume 7 | Article 602 |
Edited by:
Jo Corbett,
University of Portsmouth, UK
Reviewed by:
José González-Alonso,
Brunel University London, UK
Ana Sousa,
Universidade de Trás-os-Montes e
Alto Douro, Portugal
*Correspondence:
Camila C. Greco
grecocc@rc.unesp.br
Specialty section:
Accepted: 18 November 2016
Published: 02 December 2016
Citation:
Barbosa LF, Denadai BS and
Greco CC (2016) Endurance
Performance during Severe-Intensity
Intermittent Cycling: Effect of Exercise
Duration and Recovery Type.
doi: 10.3389/fphys.2016.00602
Endurance Performance during
Severe-Intensity Intermittent Cycling:
Effect of Exercise Duration and
Recovery Type
Luis F. Barbosa, Benedito S. Denadai and Camila C. Greco*
Human Performance Laboratory, Biosciences Institute, São Paulo State University, Rio Claro, Brazil
Slow component of oxygen uptake (VO2SC) kinetics and maximal oxygen uptake
(VO2max) attainment seem to influence endurance performance during constant-work
rate exercise (CWR) performed within the severe intensity domain. In this study, it was
hypothesized that delaying the attainment of VO2max by reducing the rates at which VO2
increases with time (VO2SC kinetics) would improve the endurance performance during
severe-intensity intermittent exercise performed with different work:recovery duration and
recovery type in active individuals. After the estimation of the parameters of the VO2SC
kinetics during CWR exercise, 18 males were divided into two groups (Passive and
Active recovery) and performed at different days, two intermittent exercises to exhaustion
(at 95% IVO2max, with work: recovery ratio of 2:1) with the duration of the repetitions
calculated from the onset of the exercise to the beginning of the VO2SC (Short) or to the
half duration of the VO2SC (Long). The active recovery was performed at 50% IVO2max.
The endurance performance during intermittent exercises for the Passive (Short = 1523
± 411; Long = 984 ± 260 s) and Active (Short = 902 ± 239; Long = 886 ± 254 s)
groups was improved compared with CWR condition (Passive = 540 ± 116; Active =
489 ± 84 s). For Passive group, the endurance performance was significantly higher
for Short than Long condition. However, no significant difference between Short and
Long conditions was found for Active group. Additionally, the endurance performance
during Short condition was higher for Passive than Active group. The VO2SC kinetics was
significantly increased for CWR (Passive = 0.16 ± 0.04; Active = 0.16 ± 0.04 L.min−2)
compared with Short (Passive = 0.01 ± 0.01; Active = 0.03 ± 0.04 L.min−2) and Long
(Passive = 0.02 ± 0.01; Active = 0.01 ± 0.01 L.min−2) intermittent exercise conditions.
No significant difference was found among the intermittent exercises. It can be concluded
that the endurance performance is negatively influenced by active recovery only during
shorter high-intensity intermittent exercise. Moreover, the improvement in endurance
performance seems not be explained by differences in the VO2SC kinetics, since its
values were similar among all intermittent exercise conditions.
Keywords: aerobic, oxygen uptake, passive, active, exercise tolerance
11

Barbosa et al. Endurance Performance during Intermittent Cycling
INTRODUCTION
The parameters of the power-time relationship, termed critical
power (CP) and the curvature constant (W’), have been used to
analyze the physiological responses and endurance performance
during high-intensity exercise (Poole et al., 1988). CP has been
considered the lower boundary of the severe-intensity domain
and the W’ determines the amount of external work that can be
performed above CP, irrespective of the rate of its expenditure
(Jones et al., 2010). By definition, all severe-intensity work
rates (i.e., CP) performed until voluntary exhaustion drive
pulmonary oxygen uptake (VO2) to a maximal value (i.e.,
maximal oxygen uptake—VO2max) (Jones et al., 2010). However,
during exhaustive exercise performed above the upper bound of
the severe intensity domain, exercise duration would be too short
to permit attainment of VO2max Caputo and Denadai (2008).
Several studies have demonstrated that endurance exercise
performance within severe-intensity domain was coincident with
the depletion of the W’, accumulation of metabolites associated
with fatigue (i.e., PCr, Pi, and H+), and attainment of VO2max
due to VO2 slow component (VO2SC) development (Fukuba
et al., 2003; Chidnok et al., 2013). Indeed, VO2SC has been
associated with loss in muscular efficiency (Jones et al., 2011) and
has been negatively related with endurance performance (Zoladz
et al., 1995; Murgatroyd et al., 2011; Barbosa et al., 2014a).
VO2 kinetics and muscle [PCr] responses to high-intensity
exercise have been reported to present both fundamental and
slow component phases (Rossiter et al., 2002) being intrinsically
linked. Indeed, Rossiter et al. (2002) have reported similar
values of the time constant (τ) of the fundamental component
([PCr] = 38 s; VO2 = 39 s), as well as the relative amplitude
of the slow component ([PCr] = 13.9%; VO2SC = 15.3%) of
muscle [PCr] and VO2 during high-intensity exercise. It has been
proposed that progressive intramuscular depletion [PCr] during
exhaustive exercise performed within severe intensity domain
provides the appropriate stimulus to oxidative phosphorylation,
determining the development of VO2SC and, consequently, the
attainment of VO2max (Rossiter et al., 2002). Thus, both creatine
phosphate depletion and development of the VO2SC seem to
be intimately associated with endurance performance during
constant-work rate exercise (CWR) performed within the severe
intensity domain.
While this scenario is well established during CWR exercise,
very little information is available during intermittent exercise,
which has been considered an important tool in training
programs aiming to improve aerobic fitness in health and in
disease (Laursen and Jenkins, 2002; Hwang et al., 2011). Indeed,
intermittent exercise can improve performance comparing to
CWR during high-intensity exercise (Millet et al., 2003; Chidnok
et al., 2012), since the former allows resynthesis of intramuscular
substrates ([PCr]) and/or clearance of fatigue-related metabolites
(i.e., reconstitution of W’) (Chidnok et al., 2013). However,
several aspects seem to influence endurance performance during
high-intensity intermittent exercises. For instance, endurance
performance is progressively shorter when the work-recovery
“duty-cycle” (e.g., 10:20 s, 30:60 s, 60:120 s, and 90:180 s) (Turner
et al., 2006) and/or exercise intensity performed during active
recovery is increased (i.e., light, moderate, heavy and severe)
(Chidnok et al., 2012). These aspects influence PCr kinetics
(Chidnok et al., 2013) and hypothetically, the changes of the
rates at which VO2 increases during high-intensity intermittent
exercises (i.e., VO2SC). Indeed, Chidnok et al. (2012) have
demonstrated that enhanced endurance performance during
severe-intensity intermittent exercise could be explained by
the reconstitution of W’ during recovery intervals performed
at lower-intensity domains (i.e., light and moderate). At this
condition, the reconstitution of W’ was associated with a
blunted increase in both VO2 and integrated EMG with
time, supporting the hypothesis that VO2SC kinetics influences
endurance performance during intermittent exercise. However,
as discussed above, endurance performance during severe
intermittent exercise is markedly modulated by both work-
recovery duration and exercise intensity performed during active
recovery. Thus, the possible relationship between VO2SC and
endurance performance during intermittent exercise performed
with different durations (e.g., short vs. long) and recovery type
(i.e., passive vs. active) remains elusive, and further studies are
warranted.
However, an important issue must be considered when the
possible influence of VO2SC on endurance performance is
investigated. Knowing that work-recovery duration influences
endurance performance during severe intermittent exercise
(Turner et al., 2006), it appears appropriate to compare exercise
duration before (short condition) and after (long condition)
the emergence of VO2SC. However, many studies have verified
that both the emergence and the amplitude of VO2SC (and
possibly the [PCr]) present a large intra-individual variation
(Murgatroyd et al., 2011; Barbosa et al., 2014b). Thus, it would
be interesting to analyze the responses of VO2 kinetics and
endurance performance during severe intermittent exercise,
with both the duration of exercise and recovery periods being
determined based on the individual VO2SC kinetics response.
Thus, the current study was undertaken to compare the
endurance performance and VO2SC kinetics during high-
intensity intermittent exercise performed with different
work:recovery duration (short vs. long) and recovery types
(passive vs. active) in active individuals. It was hypothesized
that: (a) endurance performance would be improved during the
exercise with passive recovery, regardless of the duration of the
repetition, and; (b) endurance performance would be improved
during the intermittent exercise with short duration, regardless
of the recovery type. We also hypothesized that the possible
interaction between exercise duration and recovery type during
intermittent high intensity exercise would influence the changes
to the rates at which VO2 increases with time (VO2SC kinetics)
and consequently, endurance performance.
MATERIALS AND METHODS
Subjects
Eighteen male students (24.7 ± 4.1 years; 80.5 ± 12.5 kg; 178.1
± 7.6 cm) that were physically active but did not participate in
any regular physical exercise or sport program volunteered for
Frontiers in Physiology | www.frontiersin.org December 2016 | Volume 7 | Article 602 | 12

the study. All participants were healthy and free of cardiovascular,
respiratory, and neuromuscular disease. All risks associated with
the experimental procedures were explained prior to involvement
in the study and each participant signed an informed consent
form. The study was performed according to the Declaration of
Helsinki and the protocol was approved by the University’s Ethics
Committee.
Experimental Design
The participants were instructed to report to the laboratory at
the same time of the day (±2 h) on four separate occasions
within a period of 2–3 week. Firstly, each volunteer performed
an incremental test until exhaustion to determine the lactate
threshold (LT), VO2max and the intensity associated with
VO2max (IVO2max). Thereafter, the volunteers were divided
into two groups: passive recovery (PR) and active recovery (AR)
with similar IVO2max values. They performed the following
protocols, on different days: (1) a total of two repetitions of
square-wave transitions from rest to a power corresponding
to 95% of the IVO2max to determine the parameters of VO2
kinetics. Each bout was separated by 60 min of passive rest. The
VO2 responses to the two severe exercise bouts were averaged
before the analysis to reduce the breath-to-breath noise and
enhance confidence in the parameters derived from the modeling
process (Lamarra et al., 1987) and; (2) two intermittent exercises,
with the duration of the repetitions calculated from the onset
of the exercise to the beginning of the VO2SC (Short) or to
the half duration of the VO2SC (Long). The interval between
the experimental sessions was 48–72 h. The participants were
instructed to arrive at the laboratory in a rested and fully hydrated
state at least 3 h post-prandial. They were also asked not to
perform any strenuous activity during the day before each test.
Procedures
Incremental Test
Each participant performed an incremental exercise test to obtain
volitional fatigue on an electronically braked cycle ergometer
(Excalibur sport, Groningen, Netherlands) to determine the
participant’s LT, VO2max, and IVO2max. The incremental
protocol started at a power output of 35 W, with increasing
increments of 35 W every 3 min. Previous studies have
demonstrated no differences in VO2max between incremental
tests involving 1- or 3-min stage durations (Bentley and
McNaughton, 2003; Roffey et al., 2007; Adami et al., 2013). The
pedal cadence was kept constant (70 rpm) (Marsh and Martin,
1997). Throughout the tests, the respiratory and pulmonary gas-
exchange variables were measured using a breath-by-breath gas
analyzer (Quark PFTergo, Cosmed, Italy). The VO2max was
defined as the highest average 15-s VO2 value recorded during
the incremental test. IVO2max was defined as the power output at
which the VO2max occurred. At the end of each stage, an earlobe
capillary blood sample (25 µL) was collected into an eppendorf
tube and analyzed for its lactate concentration ([La]) using an
automated analyzer (YSI 2300 STAT, Yellow Spring, Ohio, USA).
Plots of the blood [La] against the power output and VO2 were
given to two independent reviewers, who determined LT as the
first sudden and sustained increase in the blood lactate level
above the resting concentrations.
Constant-Workload Exercise
The participants performed two exercise transitions at 95%
IVO2max, separated by 60 min of rest. The first transition lasted
6 min and was conducted to determine the VO2 kinetics. The
second transition was conducted until voluntary exhaustion to
determine the VO2 kinetics (first 6 min) and the tlim (time
to exhaustion). The protocol began with a 5 min warm-up at
50% IVO2max and was followed by a 7 min of passive rest.
Then, the participants performed 3 min of unloaded cycling
at 20 W, followed by a step change in the power output to
95% IVO2max. The pedal cadence was kept constant at 70 rpm.
The second transition was terminated when the participant
could not maintain a cadence of 65 rpm for 5 s despite
verbal encouragement. The end-exercise VO2 was defined as
the mean VO2 measured during the final 15 s of exercise.
For the determination of [La] peak, capillary blood samples
were collected 1, 3, and 5 min after the exercise, as previously
described.
Intermittent Exercises
The intermittent exercises were performed at 95% IVO2max,
with the duration of the repetitions calculated from the onset
of the exercise to the beginning of the VO2SC (i.e., time delay
before the onset of the development of the VO2SC—Short) or
the half duration of the VO2SC (i.e., 50% of the difference
between the Short work interval duration and the time to achieve
VO2max—Long) (Figure 1). The recovery was passive (PR) or
active (AR) (50% IVO2max), with duration corresponding to the
half of the repetition (effort:recovery ratio of 2:1). The exercises
were performed until voluntary exhaustion. The criterion of
exhaustion used was the same used for the constant-workload
exercise. The end-exercise VO2 was defined as the mean VO2
measured during the final 15 s of exercise. If the duration of
the last repetition was shorter than 90 s, the highest value of the
previous bout was considered, to avoid underestimating the VO2
value.
FIGURE 1 | Definition of the work intervals of the Short (beginning of
the slow component) and Long (half duration of the slow component)
intermittent protocols.

Modeling of VO2 during Constant-Workload Exercise
The breath-by-breath data from each exercise were manually
filtered to remove outlying breaths, which were defined as breaths
±3 SD from the adjacent five breaths. The breath-by-breath data
were interpolated to give second-by-second values. For CWR, the
two transitions were then time aligned to the start of the exercise
and averaged to enhance the underlying response characteristics.
The first 20 s of data after the onset of exercise (i.e., the phase
I response) (Whipp and Rossiter, 2005) were deleted, and the
biexponential model was used to analyze the VO2 response to
severe exercise, as described by the following equation:
VO2(t) = VO2baseline + Ap [1 − e−(t−TDp)/
τp
]
+ As [1 − e−(t−TDs)/
τs
] (1)
where: VO2(t) is the absolute VO2 at a given time t; VO2baseline
is the mean VO2 in the baseline period; Ap, TDp, and τp
are the amplitude, time delay, and time constant, respectively,
describing the phase II increase in VO2 above baseline; and As,
TDs, and τs are the amplitude of, time delay before the onset
of, and time constant describing the development of the VO2SC,
respectively. An iterative process was used to minimize the sum
of the squared errors between the fitted function and the observed
values. VO2baseline was defined as the mean VO2 measured
over the final 60 s of exercise preceding the step transition to
severe exercise. The amplitude of the VO2SC was determined as
the increase in VO2 from TDs to the end of the modeled data
(defined as As’). The end-exercise VO2 was defined as the mean
VO2 measured over the final 15 s of exercise. The TD identified
from Equation 1 was utilized to individualize the duration
of the repetitions performed during short and long protocols
(please see Section Intermittent exercises) and to estimate the
VO2SC kinetics [i.e., the slow component trajectory (L.min−2)],
as described below.
In addition, a single-exponential model without time delay,
with a fitting window commencing at t = 0 s (equivalent to the
mean response time), was used to characterize the kinetics of
the overall VO2 response to exercise. The following equation
describes this model:
VO2(t) = VO2baseline + A [1 − e−(t/
τ)
] (2)
where: VO2(t) represents the absolute VO2at a given time t,
VO2baseline represents the mean VO2 measured over the final
60 s of baseline pedaling, and A and τ represent the amplitude
and time constant, respectively, which describe the overall
increase in VO2 above the baseline. The VO2 was assumed to
have essentially reached its maximal value when the value of
[1–e−(t/τ)] from Equation 2 was 0.99 (i.e., when t = 4.6 × τ);
it was assumed at this time that VO2 was at its maximal
value. Therefore, for each exercise, the time to achieve VO2max
(TAVO2max) was defined as 4.6 × τ. VO2SC kinetics [i.e., the
slow component trajectory (L.min−2)] was also estimated by
calculating the slope of the VO2 response using linear regression
analysis (Chidnok et al., 2012). The data obtained before TDs
(determined from Equation 1) were deleted to remove the
influence of the fundamental response phase, and thereafter,
VO2 values at 60-s intervals were determined until reaching the
TAVO2max value and were fitted using the following equation:
VO2 = ax + b (3)
where: x represents the time, a represents the slope, and b
represents the y-intercept.
Modeling of VO2 during Intermittent Exercise
VO2SC kinetics [i.e., the slow component trajectory (L.min−2)]
was estimated by calculating the slope of VO2 response using
linear regression analysis (Chidnok et al., 2012). Final VO2 values
(i.e., the average VO2 during 15 s) of each work cycle during
intermittent exercise were determined up to the last completed
cycle and fit using the Equation 3.
Statistical Analysis
The data are presented as means ± SD. The normality of data
was checked by the Shapiro-Wilk test. A 2 × 3 two-way factorial
analysis of variance (group vs. exercise condition), with repeated
measures for the exercise condition factor (CWR vs. Short vs.
Long) was used to analyze the VO2, tlim, slope VO2, [La] and
HR data. When a significant interaction was found, follow-up
analyses were performed using Tukey HSD test. The significance
level was set at p 0.05, and effect sizes were calculated using
partial eta-squared (η2). All analyses were completed using the
Statistical Package for the Social Sciences (SPSS v.20.0, SPSS Inc.,
Chicago, IL, USA).
RESULTS
Table 1 presents the mean ± SD values of the variables obtained
during the incremental test for both PR and AR groups. No
significant difference was found between the groups (p 0.05).
The VO2 response profiles of a representative subject obtained
during the different exercise conditions for both PR and AR
groups are depicted in Figure 2. Based on the VO2 kinetics
parameters obtained during CWR, the repetition duration for the
Short (PR = 105 ± 29 s; AR = 132 ± 39 s) and Long (PR = 252
± 50 s; AR = 253 ± 56 s) tests were not significantly different
between the groups (p 0.05).
Figure 3 presents the mean ± SD values of end-exercise VO2
measured during the different exercise conditions for both PR
and AR groups. There was a significant main effect for the
TABLE 1 | Mean ± SD values of the variables obtained during the
incremental test for both passive (PR) and active (AR) recovery groups.
PR (N = 9) AR (N = 9)
VO2max (mL.min−1) 3220.4 ± 271.8 3332.4 ± 499.1
IVO2max (W) 250.3 ± 25.5 266.9 ± 44.1
P95% (W) 235.7 ± 23.0 252.6 ± 42.7
LT (W) 106.0 ± 31.3 133.1 ± 59.0
LT (%IVO2max) 41 ± 11 48 ± 16
VO2max, maximal oxygen uptake; IVO2max, intensity at VO2max; P95%, power output
relative to 95% IVO2max; LT, lactate threshold.

FIGURE 2 | Pulmonary oxygen uptake (VO2) response of a representative subject to constant-work rate (CWR) exercise (closed circles) compared
with intermittent exercise (open circles) performed with passive (A, short and B, long) and active (C, short and D, long) recovery.
FIGURE 3 | Mean ± SD values of the end-exercise VO2 obtained during
the exercise performed in different conditions for passive (PR) (N = 9)
and active (AR) (N = 9) groups. CWR—constant-work-rate exercise; *p
0.05 in relation to CWR.
exercise condition on end-exercise VO2 values (F = 5.47, p =
0.009, η2 = 0.25), but no effect of group (F = 1.53, p = 0.23, η2
= 0.08) or interaction was detected (F = 1.25, p = 0.29, η2 =
0.07). The end-exercise VO2 values obtained during CWR (PR
= 3236.9 ± 405.8 mL.min−1; AR = 3488.6 ± 415.9 mL.min−1)
were higher than those attained during Short (PR = 2995.2 ±
337.7 mL.min−1; AR = 3205.7 ± 447.2 mL.min−1) and Long (PR
= 3053.3 ± 276.1 mL.min−1; AR = 3149.6 ± 476.3 mL.min−1)
tests (p 0.05).
The mean ± SD values of tlim and VO2 slope during
CWR and intermittent exercises for the PR and AR groups are
presented in Table 2. A group vs. exercise condition interaction
(F = 11.08, p = 0.000, η2 = 0.40) indicated longer tlim obtained
during intermittent exercises (Short and Long) than CWR for
both groups (p 0.05). Considering the duration of the work
and recovery type, tlim at Short was significantly longer than at
Long only for the PR group (p 0.05). Group effect (i.e., PR vs.
AR) was significant only when comparing the Short intermittent
protocols (p 0.05), with no significant difference for Long
conditions (p 0.05). There was a significant main effect for the
exercise condition on VO2 slope values (F = 95.98, p 0.000,
η2 = 0.90), but no group effect (F = 1.86, p = 0.19, η2 = 0.16)
or interaction was detected (F = 0.02, p = 0.99, η2 = 0.01).
VO2 slope was significantly greater at CWR than Short and Long
conditions (p 0.05).
The mean ± SD values of [La] and HR during CWR and
intermittent exercises for the PR and AR groups are presented
in Table 3. There was a significant main effect for the exercise
condition on [La] values (F = 4.72, p = 0.01, η2 = 0.22),
but no effect of group (F = 0.05, p = 0.81, η2 = 0.04) or
interaction was detected (F = 1.76 p = 0.18, η2 = 0.09).
The [La] was significantly lower at Short than CWR and Long
condition (p 0.05). A group vs. exercise condition interaction
(F = 5.00, p = 0.01, η2 = 0.23) indicated that HR was lower
during Short than Long and CWR only for the PR group
(p 0.05).

TABLE 2 | Mean ± SD values of the time to exhaustion (tlim) and the slope
of the oxygen uptake response (Slope) during the constant-work-rate
(CWR) and intermittent exercise conditions (Short and Long), for passive
(PR) and active (AR) recovery groups.
PR (N = 9) AR (N = 9) Significance
CWR Short Long CWR Short Long
tlim (s) 540 1523 984 489 902 886 *F = 11.08
116 411‡,† 260‡ 84 239‡,** 254‡ p = 0.000
Slope (L.min−2) 0.16 0.01 0.02 0.16 0.03 0.01 ++F = 5.34
0.04 0.01‡ 0.01‡ 0.04 0.04‡ 0.01‡ p = 0.01
*Group vs. condition interaction;
‡
p 0.05 relative to the CWR condition;
†
p 0.05
relative to the Long condition; **p 0.05 relative to the Short condition;++Main effect of
exercise condition.
TABLE 3 | Mean ± SD values of the blood lactate concentration ([La]) and
heart rate (HR) during the constant-work-rate (CWR) and intermittent
exercise conditions (Short and Long), for passive (PR) and active (AR)
recovery groups.
PR AR Significance
CWR Short Long CWR Short Long
[La] (mM) 12.4 10.3 12.1 11.2 10.9 11.8 ++F = 4.72
2.83 3.70 3.02 2.35 2.65 2.78 p = 0.01
HR (bpm) 177 14 170 15† 177 11 184 7 183 6 186 5 *F = 5.00 p = 0.01
*Group vs. condition interaction;
†
p 0.05 relative to CWR and Long conditions; ++Main
effect of exercise condition.
DISCUSSION
This, we believe, is the first study to compare the endurance
performance and VO2SC kinetics during severe-intensity
intermittent exercise performed with different durations and
recovery types in active individuals. The data demonstrate that
endurance performance during severe-intensity intermittent
exercise is negatively influenced by active recovery only during
shorter (∼120 s) intermittent exercise. Interestingly, slopes
describing the increases in VO2 with time (i.e., VO2SC)
and end-exercise VO2 were reduced during intermittent
exercise (i.e., CWR vs. intermittent exercise). However, VO2
kinetics (VO2SC and end-exercise VO2) were similar between
work:recovery duration (short vs. long) and recovery type
(passive vs. active) analyzed in the present study, therefore
rejecting our original hypothesis. Thus, the relationship
between VO2 kinetics (VO2SC and end-exercise VO2) and
endurance performance observed during CWR exercise
(Jones et al., 2010; Barbosa et al., 2014a) seems to be
differently regulated during severe-intensity intermittent
exercise.
It has been widely reported that endurance performance
during high-intensity intermittent exercise is improved when
compared with CWR exercise (Demarie et al., 2000; Millet
et al., 2003; Chidnok et al., 2012). However, both endurance
performance and metabolic response are influenced by the
characteristics of the protocol utilized during high-intensity
intermittent exercise. Turner et al. (2006) analyzed the influence
of duty cycle duration with the same work:recovery ratio
(10:20 s, 30:60 s, 60:120 s, and 90:180 s) on pulmonary gas
exchange and blood lactate dynamics during intermittent cycling
exercise performed at 120% IVO2max. At this condition, a
greater metabolic response (elevated blood lactate concentration
and attainment of VO2max) and exercise intolerance (i.e.,
subjects could not complete 30 min of exercise) were observed
only for the longer duty cycles (i.e., 60:120 s, and 90:180 s).
Although our intermittent exercise protocol presents different
characteristics (e.g., work:recovery = 2:1 and exercise intensity
= 95% IVO2max), it was also verified a reduced endurance
performance during longer duty cycles performed with passive
recovery. The intramuscular PCr concentration ([PCr]) kinetics
both during and following high-intensity exercise presents a
curvilinear profile and seems to be closely linked with VO2
kinetics (Rossiter et al., 2002). For instance, under the conditions
of the present study, is very likely that the amplitude of [PCr]
restoration during the 240 s recovery intervals (Long protocol)
was not doubled than what was presented when 120 s periods of
recovery (Short protocol) were allowed. Moreover, Chidnok et al.
(2013) demonstrated that [PCr] restoration become longer as
the intermittent protocol continued. Thus, [PCr] is progressively
lower immediately before each repetition, particularly when
duty-cycle duration is lengthened. The metabolites generated
by muscle contraction at this condition, such as Pi, ADP, and
AMP, increase glycolytic flux and consequently, glycolytic H+
(Adams et al., 1990; Conley et al., 1997) and lactate (Karpatkin
et al., 1964) production. Low values of muscle [PCr] and pH
(i.e., high values of [H+]) and consistently high values of [Pi]
and [ADP] have been associated with fatigue development during
high-intensity exercise (Jones et al., 2008; Vanhatalo et al.,
2010).
Another factor that can influence both endurance
performance and metabolic response is the activity pattern
performed during the recovery intervals between each bout
(Chidnok et al., 2012). Using the CP model, Chidnok et al.
(2012) demonstrated that endurance performance during
intermittent exercise was enhanced only when the recovery
intervals were performed below CP. Active recovery performed
below CP allows a partial PCr reconstitution and/or clearance
of fatigue-related metabolites (Chidnok et al., 2013), with
the former being apparently more important to enhance
endurance performance during high-intensity intermittent
exercise. Indeed, both endurance performance (Chidnok
et al., 2012) and PCr reconstitution (Chidnok et al., 2013)
are higher during intermittent exercise with passive recovery
than during active recovery performed bellow CP condition.
Thus, a lower PCr reconstitution can explain, at least in part,
the impaired endurance performance during short condition
performed with active recovery, as observed in the present
study.
However, a different scenario emerges from the data
obtained during the Long intermittent exercise protocol. At
this condition, endurance performance was not modified by
the active recovery periods. Two different mechanisms, which

can occur simultaneously, could help explain this phenomenon.
Firstly, the negative influence of active recovery on PCr
reconstitution could be time-dependent, i.e., longer duty-cycle
could allow more similar PCr reconstitution than a shorter one.
The curvilinear PCr recovery profile supports this hypothesis
(Harris et al., 1976). Secondly, the clearance of lactate and H+
ions within muscles might be higher during the longer duty-
cycle. A higher muscle pH can reduce, directly or indirectly
(a more favorable metabolic milieu for PCr reconstitution),
fatigue during high-intensity exercise. Alternatively, it is
possible that [PCr] kinetics both during and following high-
intensity intermittent exercise would contribute progressively
less to endurance performance when the duty-cycle duration is
lengthened.
The end-exercise VO2 was not significantly different between
CWR exercise and VO2max measured during the incremental
test. This is consistent with the fact that exhaustive exercise
performed within the severe intensity domain (i.e., above
CP) is characterized by the development of the VO2SC,
which is truncated at VO2max. Some interventional (e.g.,
endurance training and priming exercise) (Jones et al., 2007;
Caritá et al., 2014) and correlational studies (Barbosa et al.,
2014a) have produced evidences that both VO2 kinetics (a
proxy for intramuscular PCr kinetics) (Rossiter et al., 2002)
and VO2max attainment is related to endurance performance
during high-intensity exercise. Thus, it was hypothesized
that VO2SC trajectory, which reflects the interaction between
VO2SC and VO2max attainment, could explain the endurance
performance during high-intensity intermittent exercise. Indeed,
it was demonstrated that VO2SC trajectory was faster during
CWR exercise than during intermittent exercise, regardless of
duration and recovery type. However, similar to the results
found by Chidnok et al. (2012), VO2SC trajectory was not
significantly different among intermittent exercise, and end-
exercise VO2 was lower during these conditions than at CWR
exercise. Thus, substrate utilization/accumulation, VO2 kinetics
(VO2SC trajectory and end-exercise VO2) and endurance
performance during high-intensity exercise seem to present
different relationship during CWR and intermittent exercise.
Priming high-intensity exercise has previously been reported to
reduce the amplitude of VO2SC and an increase in apparent W’
during subsequent exercise (Caritá et al., 2014, 2015; Dekerle
et al., 2015). In this context, each preceding intermittent exercise
bout may have “primed” the muscle (i.e., reduces the amplitude
of VO2SC and/or raise the W’) during subsequent bouts.
These modifications are consistent with enhanced endurance
performance, and could help to explain the apparently different
metabolic regulation imposed by the interaction between
intervals duration and recovery type during intermittent exercise.
Our experimental protocol (i.e., exercise intensity,
work:recovery durations and recovery types) was specifically
designed to investigate the hypothetical association between
intermittent endurance performance and VO2SC kinetics.
Similar to previous studies (Caputo and Denadai, 2008;
Barbosa et al., 2014a), both CWR and intermittent exercise
were performed at 95% IVO2max. As demonstrated in the
present study, exhaustive exercise performed at this intensity is
characterized by the development of the VO2SC and VO2max
attainment. Some studies have utilized the “percentage delta”
(for details please see Lansley et al., 2011) aiming to select
a predetermined exercise intensity domain (i.e., heavy or
severe) and/or to standardize the exercise intensity between
subjects. Indeed, when compared to a more traditional method
(e.g., %VO2max), this approach allows a lower inter-subject
variability of physiological responses to CWR exercise (Lansley
et al., 2011). However, for the first time, the present study
have normalized the wok:recovery durations based on the
individual VO2SC kinetics response. Thus, we are confident that
the inter-subject variability of physiological responses during
the intermittent exercise was attenuated. Finally, this study
presented a possible limitation, since the effect of passive and
active recovery on intermittent exercise was analyzed using
2 different groups of active individuals. Hypothetically, this
experimental design could be influenced by the individual
variability on both endurance performance and VO2SC kinetics.
However, PR and AR groups have presented similar data
during incremental (VO2max, IVO2max, 95% IVO2max and
LT) and CWR exercise (endurance performance and VO2SC
kinetics). Therefore, the possibility of inter-subject variability
influencing the recovery types comparisons was probably
reduced. This limitation comes from the heavy testing required
to be undertaken by each subject to test our research hypothesis.
It is important to note that a short-term training program (6
sessions) involving high-intensity exercise (repeated all-out
sprint training) have reduced the amplitude of the VO2SC and
increased tolerance to high-intensity exercise in recreationally
active subjects (Bailey et al., 2009). Thus, if a repeated measures
design has been utilized in our experimental approach, a
confounding factor could be added to our analysis, since the
volunteers would have to perform 6 bouts of severe-intensity
exercise.
CONCLUSION
The present study showed that under our experimental
conditions (i.e., exercise intensity, work:recovery durations
and recovery type), intermittent exercise enhances endurance
performance during severe-intensity exercise, independently
of intervals duration and recovery type. Passive recovery is
superior in relation to active recovery to enhance endurance
performance only during shorter duty-cycles. Although VO2SC
trajectory is attenuated during high-intensity intermittent
exercise, its alteration does not seem to explain the interaction
effects of intervals duration and recovery type on endurance
performance. Moreover, the end-exercise VO2 was lower
during intermittent exercise than at CWR exercise. Thus,
severe-intensity intermittent exercise performed with different
intervals duration and recovery type seems to modify the
relationship between endurance performance and VO2 kinetics
observed during CWR exercise. Further studies using a
repeated measures design are required to examine the effect
of severe-intensity intermittent exercise on both endurance
performance and VO2SC in trained individuals. A threshold

in the duration of the recovery, from which PCr resynthesis
and/or W’ reconstitution would be less affected by active
recovery could be identified. This can help to explain and
confirm our main results, giving support to elaborate a
more sophisticate interval training programs for different
populations.
AUTHOR CONTRIBUTIONS
Study design: BD and CG. Data acquisition and analysis: LB, BD,
and CG and Writing the paper: LB, BD, and CG.
FUNDING
Supported by Fundação de Amparo à Pesquisa do Estado de São
Paulo (FAPESP) (grant 2009/07700-2 and grant 2016/22907-6),
Conselho Nacional de Desenvolvimento Científico e Tecnológico
(CNPq) and Fundação para o Desenvolvimento da Unesp
(FUNDUNESP).
ACKNOWLEDGMENTS
The authors appreciate the time and effort expended by all
volunteer subjects in this study.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
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ORIGINAL RESEARCH
published: 19 December 2016
doi: 10.3389/fphys.2016.00627
Frontiers in Physiology | www.frontiersin.org December 2016 | Volume 7 | Article 627 |
Edited by:
Alexis R. Mauger,
University of Kent, UK
Reviewed by:
Benjamin Pageaux,
University of Burgundy, France
Daria Neyroud,
University of Lausanne, Switzerland
*Correspondence:
Christian Froyd
christian.froyd@hisf.no
Specialty section:
Accepted: 01 December 2016
Published: 19 December 2016
Citation:
Froyd C, Beltrami FG, Millet GY and
Noakes TD (2016) No Critical
Peripheral Fatigue Threshold during
Intermittent Isometric Time to Task
Failure Test with the Knee Extensors.
doi: 10.3389/fphys.2016.00627
No Critical Peripheral Fatigue
Threshold during Intermittent
Isometric Time to Task Failure Test
with the Knee Extensors
Christian Froyd1, 2
*, Fernando G. Beltrami3
, Guillaume Y. Millet4
and Timothy D. Noakes2
1
Faculty of Teacher Education and Sport, Sogn og Fjordane University College, Sogndal, Norway, 2
Department of Human
Biology, University of Cape Town, Cape Town, South Africa, 3
Exercise Physiology Lab, Department of Health Sciences and
Technology, ETH Zurich, Zürich, Switzerland, 4
Human Performance Laboratory, Faculty of Kinesiology, University of Calgary,
Calgary, AB, Canada
It has been proposed that group III and IV muscle afferents provide inhibitory feedback
from locomotor muscles to the central nervous system, setting an absolute threshold
for the development of peripheral fatigue during exercise. The aim of this study was to
test the validity of this theory. Thus, we asked whether the level of developed peripheral
fatigue would differ when two consecutive exercise trials were completed to task failure.
Ten trained sport students performed two exercise trials to task failure on an isometric
dynamometer, allowing peripheral fatigue to be assessed 2 s after maximal voluntary
contraction (MVC) post task failure. The trials, separated by 8 min, consisted of repeated
sets of 10 × 5-s isometric knee extension followed by 5-s rest between contractions. In
each set, the first nine contractions were performed at a target force at 60% of the pre-
exercise MVC, while the 10th contraction was a MVC. MVC and evoked force responses
to supramaximal electrical femoral nerve stimulation on relaxed muscles were assessed
during the trials and at task failure. Stimulations at task failure consisted of single stimulus
(SS), paired stimuli at 10 Hz (PS10), paired stimuli at 100 Hz (PS100), and 50 stimuli at
100 Hz (tetanus). Time to task failure for the first trial (12.84 ± 5.60 min) was longer (P
0.001) than for the second (5.74 ± 1.77 min). MVC force was significantly lower at
task failure for both trials compared with the pre-exercise values (both P 0.001), but
there were no differences in MVC at task failure in the first and second trials (P = 1.00).
However, evoked peak force for SS, PS100, and tetanus were all reduced more at task
failure in the second compared to the first trial (P = 0.014 for SS, P 0.001 for PS100
and tetanus). These results demonstrate that subjects do not terminate exercise at task
failure because they have reached a critical threshold in peripheral fatigue. The present
data therefore question the existence of a critical peripheral fatigue threshold during
intermittent isometric exercise to task failure with the knee extensors.
Keywords: maximal voluntary contraction, femoral nerve electrical stimulation, neuromuscular activation,
neuromuscular fatigue, evoked peak force, knee extension, electromyography, rating of perceived exertion
20

Froyd et al. Peripheral Fatigue at Task Failure
INTRODUCTION
Neuromuscular fatigue is often defined as a reduction in maximal
voluntary contraction (MVC) force. Both (i) central fatigue,
defined as a reduction in the maximal capacity of the central
nervous system to maximally recruit motor units to produce
force and (ii) peripheral fatigue, defined as the reduction in
force originating from sites at or distal to the neuromuscular
junction (Gandevia, 2001) contribute to neuromuscular fatigue.
Peripheral fatigue is commonly measured as a reduction in
evoked force responses to electrical or magnetic supramaximal
stimulations delivered to the motor nerve to relaxed muscles
(Verges et al., 2009; Millet et al., 2011).
It has been proposed that peripheral fatigue is the critical
event at task failure (Amann et al., 2006; Amann and Dempsey,
2008) and that group III and IV muscle afferents provide
inhibitory feedback from locomotor muscles to the central
nervous system (Taylor and Gandevia, 2008), influencing the
regulation of central motor drive during fatiguing exercise,
and thus playing a key role in determining the moment of
exhaustion (Taylor and Gandevia, 2008; Amann, 2012). It has
been further proposed that a reduction in central motor drive
i.e., a reduction in voluntary descending drive from the primary
motor cortex usually indirectly measured via electromyography
(EMG) (Amann et al., 2013), constrains the development of
peripheral fatigue to a certain “critical” threshold associated with
a given level of intramuscular metabolic perturbation (Amann
et al., 2006). According to this model, humans may not ever
exceed a critical level of peripheral fatigue, leading to the proposal
of a critical peripheral fatigue threshold (Amann et al., 2006;
Amann and Dempsey, 2008). As a result, when the critical
peripheral fatigue threshold is approached, feedback from group
III and IV muscle afferents reduces central motor drive and
thus exercise intensity during self-paced exercise (Amann and
Dempsey, 2008), or triggers task failure during constant load
exercise (Amann et al., 2011).
In support of a critical peripheral fatigue threshold, similar
levels of peripheral fatigue have been reported after constant-
load endurance exercise with different degrees of arterial oxygen
content (Amann et al., 2006), after intermittent isometric knee
extension to task failure at different intensities (Burnley et al.,
2012), after self-paced endurance exercise whether or not subjects
were pre-fatigued before exercise (Amann and Dempsey, 2008),
and after all-out cycling sprints whether or not subjects were pre-
fatigued by electrical stimulation (Hureau et al., 2014). Support
for a critical peripheral fatigue threshold is provided by studies
showing greater levels of peripheral fatigue at the end of exercise
following selective blockade of sensory afferents with intrathecal
fentanyl injection compared to saline (Amann et al., 2009, 2011;
Blain et al., 2016).
However, a critical peripheral fatigue threshold is not
a universal finding, leading some authors to question the
Abbreviations: EMG, electromyography; MVC, maximal voluntary contraction;
PS10, paired stimuli at 10 Hz; PS100, paired stimuli at 100 Hz; PS10/PS100,
evoked peak force for PS10/PS100; RMS, root mean square; RMS·M−1, root mean
square/M-wave peak to peak amplitude; RPE, rating of perceived exertion; SS,
single stimulus; Tetanus, tetanic stimulation, 50 stimuli at 100 Hz = 0.5 s.
importance of peripheral fatigue in regulating exercise
performance (Marcora and Staiano, 2010; Christian et al.,
2014; Froyd et al., 2016; Neyroud et al., 2016). But these
criticisms of this theory have been dismissed on the basis that
some studies employed designs in which the interventions
produced lower levels of peripheral fatigue than did the control
conditions (Johnson et al., 2015). It has been argued (Broxterman
et al., 2015) that to disprove the existence of a critical peripheral
fatigue threshold, an experimental manipulation must cause the
subjects to surpass the threshold, that is, by achieving higher
levels of peripheral fatigue in the intervention condition. If
inhibitory feedback from group III and IV muscle afferents
constrains the extent to which peripheral fatigue develops during
endurance exercise (Amann, 2011, 2012), it follows that trials
of similar intensity, but different pre-fatiguing conditions will
be of different durations, but should finish at similar levels of
peripheral fatigue.
Therefore, the aim of this study was to test the validity of
the critical peripheral fatigue threshold model during exercise
until task failure. Subjects performed isometric knee extension
exercise on a dynamometer, allowing assessment of peripheral
fatigue at task failure. After 8 min of recovery, subjects completed
a second exercise bout, also to task failure. We hypothesized that
evoked peak force would be lower at task failure in the second
trial compared to the first one, showing that the first exercise bout
did not terminate because a critical peripheral fatigue threshold
had been reached.
MATERIALS AND METHODS
Subjects
Ten sport students (five men, five women, mean ± SD age:
24 ± 4 years, body mass: 71 ± 12 kg, height: 176 ± 9 cm)
participated in the study. Subjects were trained in both endurance
and strength exercises and classified as performance level 3 or 4
(De Pauw et al., 2013; Decroix et al., 2016). None of the subjects
had any leg injury or knee pain. Subjects were instructed to
refrain from high-intensity exercise on the day prior to testing
and to refrain from alcohol during the last 24 h before testing.
Subjects were also instructed to eat a light meal 2–4 h before
arrival to the laboratory. The study was approved by the Regional
Ethics Committee in Norway (2011/1634), and the experiments
were performed according to the latest (2013) revision of the
Declaration of Helsinki. The subjects gave their written informed
consent to participate in the study. Subjects were given a full
explanation of the details and rationale of the study and were
informed that they were free to withdraw at any time. The
possibility that electrical stimulation might cause discomfort was
fully explained as was the nature of the risks involved.
Experimental Protocol
Each subject visited the laboratory on two occasions. During
the first visit, the subjects were familiarized with the procedures
that would be used for assessment of neuromuscular function
consisting of electrical stimulation and isometric MVC. In
addition, the subjects were familiarized with the experimental
trial involving intermittent isometric contractions at 60% of

MVC force until task failure with knee extension on the KinCom
dynamometer (Kinematic Communicator, Chattecx Corp.,
Chattanooga, TN). Three to five days after the familiarization
visit, subjects visited the laboratory for the experimental trials.
Trials to Task Failure
Subjects performed two isometric knee extension trials with the
right leg to task failure (Figure 1A), separated by 8 min. One-leg
constant load knee extension exercise has been used to investigate
the critical peripheral fatigue threshold previously (Amann et al.,
2013), but with measurement of peripheral fatigue 2 min after
task failure. In the present study, peripheral fatigue assessments
began within 2 s following completion of the MVC (i.e., within
7 s post task failure), since we have shown that peripheral fatigue
recovers substantially within 1 min after exercise cessation (Froyd
et al., 2013), and it is not known if recovery of peripheral fatigue is
the same after different exercise trials. During the trials, subjects
performed consecutive sets of 10 × 5-s isometric contractions
followed by 5-s rest between contractions (Figure 1B). The first
nine contractions were performed at a target force at 60% of
pre-exercise MVC, while the 10th contraction in each set was
a MVC. Electrical stimulation to assess neuromuscular function
was applied after each MVCs in each set. A target line on a 24-
inch widescreen monitor, positioned in front of the subject, was
used for visual feedback of the force recordings during both trials.
Task failure occurred when the subject could not maintain the
required force for at least 4 s for two consecutive contractions,
with subjects being informed each time they failed to achieve
the required force output. The experimenter made the decision
when task failure had occurred. Following the second missed
contraction, subjects were instructed to produce a final 5-s MVC,
followed (2 s) by the electrical stimulation protocol described
below.
Settings and Warm-Up
On arrival at the laboratory, subjects were secured to the
dynamometer by chest and hip strapping to avoid excessive
lateral and frontal plane movements. The seating was adjusted
FIGURE 1 | Overview of the protocol (A) and detailed description of the trials (B). (A) first and second trials were separated by a break of 8 min including the
neuromuscular function measurements (NMF). NMF, i.e., a maximal voluntary contraction (MVC) followed within 2 s by electrical stimulation (ES), was assessed three
times prior to the first trial (pre-exercise 1), after each set during the trials, at task failure, as well as 1 min before the second trial (pre-exercise 2). (B) trials consisted of
consecutive sets of 10 × 5-s isometric contractions followed by 5-s rest between contractions. The first nine contractions were performed at a target force at 60% of
pre-exercise MVC, while the 10th contraction in each set was a MVC. ES was applied in the 5-s break before the next set of contractions began. Sets of contractions
were repeated until task failure. SS, single stimulus; PS10, paired stimuli at 10 Hz; PS100, paired stimuli at 100 Hz; tetanus, 50 stimuli at 100 Hz.

for each subject, with the right knee femoral epicondyle aligned
with the axis of the dynamometer’s rotation arm. The right
lower leg was attached to the lever arm just above the lateral
malleolus. The left leg was not active at any time and was secured
to the dynamometer by strapping around the upper leg. The
seat’s backrest was reclined 10 degrees, and the dynamometer’s
rotation arm was kept at 90 degrees. Hip and knee angle was
approximately 110 and 80 degrees, respectively. Subjects kept
their hands crossed in front of their upper body and in the same
position during all experiments.
Warm-up consisted of 5-s isometric contractions followed
by 5-s rest. The intensity was 25% of MVC force for five
contractions, 50% of MVC force for five contractions, and
75% of MVC force for two contractions. MVC force from the
familiarization visit was used to determine warm-up intensity.
The rest period between each set was 30 s.
Neuromuscular Function Assessment
Neuromuscular function assessment consisted of a 5-s MVC
followed by a sequence of electrical stimuli. For the MVC,
the subjects were instructed to produce maximal force for 5 s
whilst they received strong verbal encouragement. Femoral nerve
electrical stimulation on relaxed muscles consisted of single
stimulus (SS), paired stimuli at 10 Hz (PS10), and paired stimuli
at 100 Hz (PS100), and assessment started within 2 s after a
MVC. The interval between the stimulation techniques was 1.5 s.
Hence neuromuscular function assessment duration excluding
MVC was approximately 3.5 s. In addition, PS100 was followed
by tetanus (50 stimuli at 100 Hz = 0.5 s) once prior to the first trial
and once at task failure of both trials. Thus, electrical stimulation
lasted from second 2–7 after the MVC at task failure.
Pre-exercise neuromuscular function (Figure 1A) assessment
started 2 min after the warm up. Three isometric MVCs, each
lasting 5 s were performed with a 2 min break between MVCs
and followed by electrical stimulation. Neuromuscular function
was also assessed after each set during the trials, at task failure,
and 1 min prior to the start of the second trial. Power Lab
(ADInstruments Pty Ltd, Bella Vista NSW, Australia) was used
to trigger the electrical stimulation.
Data Collection
Electrical Stimulation
A high voltage (maximal voltage 400 V) constant current
stimulator (DS7AH, Digitimer, Hertfordshire, UK) was used
to deliver square-wave stimuli of 1 ms duration. The femoral
nerve was stimulated percutaneously via a 10 mm diameter self-
adhesive cathode electrode (Skintact, Austria) pressed manually
by the investigator onto the skin at the femoral triangle. The
anode, a 130 × 80 mm self-adhesive electrode (Cefar-Compex
Scandinavia AB, Sweden), was applied to the gluteal fold.
The optimal stimulation intensity for one single stimulus was
determined by increasing the current gradually from 10 mA until
a plateau in force was reached. The current was then increased by
a further 30% (current range: 35–60 mA) to ensure supramaximal
stimulation. The intensity was kept constant for the same subject
for all types of electrical stimulation. The subjects were instructed
to relax fully whilst the electrical stimulation was applied.
EMG Recordings
EMG signals from the vastus lateralis and vastus medialis of
the right leg were recorded via surface electrodes (DE-2.1 single
differential surface sensors, distance between muscle site contacts
= 10 mm; Delsys Inc, Boston, MA). SENIAM (Merletti and
Hermens, 2000) recommendations were used for the placement
of the sensors on the skin. The skin was shaved and wiped
with isopropyl alcohol before the sensors were applied. The
reference electrode was applied to the patella. EMG signals
were sampled at 2000 Hz and amplified (gain = 1000) using
Bagnoli-8 (Delsys Inc). EMG signals were transferred together
with simultaneous force and electrical stimulation recordings
into Power Lab (ADInstruments) and filtered using a band pass
filter with a bandwidth at 15–500 Hz in Lab Chart Pro software
(ADInstruments).
RPE
Perceived exertion (also known as perception of effort) defined
as “the conscious sensation of how hard, heavy, and strenuous
exercise is” (Pageaux, 2016), was assessed after every 8th
contractions in each set for the trials using the ratings of
perceived exertion (RPE) scale (Borg, 1974). Standardized
instructions for the scale were given to subjects before the warm-
up. Subjects were asked to rate how hard they were driving their
leg during the exercise, but not to include an expression of pain
in their legs.
Experimental Variables and Data Analysis
Force Data
Mean of the three successful MVCs prior to the first trial of
exercise was taken as the pre-exercise MVC. Pre-exercise MVC
force was used for calculation of the target force at 60% of MVC
in both trials. MVC force was calculated as the highest average
force sustained for 1 s. Force was also calculated for the first
nine contractions of each set by averaging the force during the
middle 4 s of the 5 s contractions. The force responses to electrical
stimulation are reported as evoked peak force. The mean value in
evoked peak force after the three MVCs was therefore used as the
pre-exercise value. A reduction in evoked peak force, highlighting
peripheral fatigue development, is due to factors distal to the site
of stimulation, that is, at the neuromuscular junction or within
the muscle. PS10/PS100 (evoked peak force for PS10/PS100) was
calculated as an index of low-frequency fatigue (Verges et al.,
2009).
EMG
The root mean square (RMS) of the EMG data of vastus lateralis
and vastus medialis was calculated for 1 s around peak force for
MVC, i.e., 500 ms before and after peak force, and for the middle
4 s of the first nine contractions of each set. M-wave peak-to-
peak amplitude in response to SS was also assessed. RMS during
voluntary contractions was normalized to RMS of pre-exercise
MVC. In addition RMS during voluntary contractions was
divided by the M-wave peak to peak amplitude of the following
SS response to estimate neuromuscular activation (Millet et al.,
2011). To limit the number of MVCs at task failure, voluntary

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