This document provides an abstract for a doctoral thesis that investigated the physiological response to maximal speed training. The thesis included 4 studies. Study 1 examined the reliability of various jump variables commonly used to assess neuromuscular function. Studies 2-4 profiled the neuromuscular, physiological and endocrine responses over 24 hours to a maximal speed training session, the effect of performing additional weight training afterwards, and the impact of training order. The thesis found some jump variables like average rate of force development lacked reliability. It also reported bimodal neuromuscular recovery patterns and differences in metabolic responses depending on training protocol and order. The thesis provided a detailed examination of responses to maximal speed training and factors like additional sessions and session placement

1. The physiological response to
maximal speed training: Influence
of session number and order
Michael John Johnston
570022
Submitted to Swansea University in
fulfilment of the requirements for the
Degree of Doctor of Philosophy
Swansea University
2014

2. ii
ABSTRACT
While speed has been shown to be a key physical characteristic for success in elite
sport, little literature exists on the response to maximal speed training, along with the
optimal placement of maximal speed training within the training week. This thesis
set out to investigate this via a series of studies. Study one investigated the reliability
of several key jump variables that have been previously used in neuromuscular
research. The majority of variables were deemed to have acceptable reliability
(coefficient of variation < 10%), however, two variables routinely used in previous
research, average rate of force at 50 ms and 100 ms, returned coefficient of variations
of 29.2% and 17.0% respectively and were not used in subsequent studies. Study 2
profiled the neuromuscular, physiological and endocrine response over 24-hours to a
maximal speed training (6 x 50 m with five minutes between repetitions).
Neuromuscular performance underwent a bimodal recovery pattern with depressions
immediately after the session followed by recovery at 2-hours post and a secondary
decrease at 24-hours post. Study 3 investigated the effect of performing a weight
training session two hours after maximal speed training (Speed/Weights) compared
to just performing maximal speed training (Speed Only) on recovery at 24-hours post
and found that, while peak force was depressed and muscle soreness elevated to a
greater extend in response to the speed/weights protocol, there was no difference in
any of the other markers. Study 4 investigated the effect of training order on these
markers and found that, while the weight training and maximal speed training
produced different metabolic responses, there was no difference in the
neuromuscular and endocrine responses across the 24-hour period. This thesis
provides a detailed look at the response to maximal speed training, the effect of
performing an additional weight training session on the same training day and the
effect of training order on the neuromuscular, physiological and endocrine responses
over a 24-hour period.
Keywords: Speed training, weight training, neuromuscular response, testosterone,
cortisol, creatine kinase, lactate, muscle damage

3. iii
DECLARATION
This work has not previously been accepted in substance for any degree and is not
being concurrently submitted in candidature for any degree.
Signed ...................................................................... (candidate)
Date ........................................................................
STATEMENT 1
This thesis is the result of my own investigations, except where otherwise stated.
Where correction services have been used, the extent and nature of the correction is
clearly marked in a footnote(s).
Other sources are acknowledged by footnotes giving explicit references. A
bibliography is appended.
Signed ..................................................................... (candidate)
Date ........................................................................
STATEMENT 2
I hereby give consent for my thesis, if accepted, to be available for photocopying and
for inter-library loan, and for the title and summary to be made available to outside
organisations.
Signed ..................................................................... (candidate)
Date ........................................................................

4. iv
TABLE OF CONTENTS
Abstract ii
Declarations and statements iii
Table of Contents iv
Acknowledgements vi
List of tables ix
List of figures xi
1. General Introduction 1
1.1. Introduction 2
2. Review of literature 7
2.1 Introduction 8
2.2. Defining neuromuscular fatigue 8
2.2.1 Mechanisms of neuromuscular fatigue 12
2.2.1.1. Mechanisms of peripheral fatigue 12
2.2.1.2. Mechanisms of central fatigue 13
2.2.3. Post activation potentiation 17
2.2.4. Role of the endocrine system in neuromuscular
function
19
2.2.4.1 Testosterone 20
2.2.4.2 Cortisol 24
2.2.5. Muscle Temperature 26
2.2.6. Summary 29
2.3. Measurement of the neuromuscular system 29
2.3.1. Laboratory-based measurements 30
2.3.2. Dynamic measurement of neuromuscular
performance
37
2.3.2.1. Jump variables to assess performance 37
2.3.2.2. Limitations in research using force plates
to assess jumping
46
2.3.3. Summary 48
2.4. Neuromuscular response to training 49
2.4.1. Neuromuscular response to resistance training 49
2.4.2. Neuromuscular response to plyometric training 57
2.4.3. Neuromuscular response to speed training 59
2.4.4. Multiple sessions 61
2.4.5. Effect of training order 64
2.4.6. Summary 67
2.5. Chapter conclusions 69
2.6. Research aims 71
3. General Methods 73
3.1. Introduction 74
3.2. Participants 74
3.3. Training sessions 74
3.4. Neuromuscular performance 76
3.5. Hormonal analysis 81
3.6. Indirect markers of muscle damage 82
3.7. Lactate 83

5. v
3.8 Statistical analysis 83
4. The reliability of jump variables used in the assessment of neuromuscular
function
84
4.1. Introduction 85
4.2. Methods 88
4.2.1 Participants 88
4.2.2. Design 88
4.2.3 Methodology 88
4.2.4. Statistical analysis 89
4.3. Results 89
4.3.1. Countermovement jump 90
4.3.2. Squat jump 90
4.4. Discussion 94
4.4.1. Countermovement and Squat jump Reliability 94
4.4.2. Reliability of average rate of force development 96
4.4.3. Systematic bias 98
4.5. Conclusions 98
4.6. Practical applications 99
5. The neuromuscular, physiological and endocrine responses to a maximal
speed training session in elite games players
100
5.1. Introduction 101
5.2. Methods 102
5.2.1. Participants 103
5.2.2. Design 103
5.2.3. Methodology 106
5.2.4. Statistical analysis 107
5.3. Results 108
5.3.1. Sprints 108
5.3.2. Endocrine responses 108
5.3.3. Muscle soreness, lactate and markers of muscle
damage
111
5.3.4. Neuromuscular response 113
5.3.5. Ear temperature 115
5.3.6. Correlational analysis 115
5.4. Discussion 117
5.4.1. Relationship between speed and jump performance 117
5.4.2. Neuromuscular response to maximal speed training 117
5.4.3. Endocrine response to maximal speed training 121
5.5. Conclusions 122
5.6. Practical applications 122
6. The neuromuscular, physiological and endocrine responses to a single
session versus double session training day in elite athletes
123
6.1. Introduction 124
6.2. Methods 126
6.2.1. Participants 126
6.2.2. Design 126
6.2.3. Methods 127

6. vi
6.2.4. Statistical analysis 128
6.3. Results 130
6.3.1. Sprints 130
6.3.2. Endocrine response 130
6.3.3. Creatine kinase, lactate and muscle soreness 132
6.4.3. Neuromuscular performance 135
6.4. Discussion 138
6.4.1. Neuromuscular performance 138
6.4.2. Endocrine response to speed only and speed/weights
protocols
142
6.5. Conclusions 144
6.6. Practical applications 144
7. The effect of training order on neuromuscular, physiological and endocrine
response to maximal speed and weight training sessions over a 24-hour
period
145
7.1. Introduction 146
7.2. Methods 148
7.2.1. Participants 148
7.2.2. Design 148
7.2.3. Methods 149
7.2.4. Statistical analysis 151
7.3. Results 152
7.3.1. Training analysis 152
7.3.2. Endocrine response 154
7.3.3. Creatine kinase, lactate and muscle soreness 156
7.3.4. Neuromuscular performance 159
7.4. Discussion 161
7.4.1. Neuromuscular response to session order 161
7.4.2. Endocrine response to session order 165
7.5. Conclusions 168
7.6. Practical applications 169
8. Synthesis of research findings 170
8.1. Synthesis 171
Appendices
Appendix 1: Consent forms 181
Appendix 2: Participant information sheets 186
Appendix 3: Ethical approval documentation 196
Appendix 4: Likert scale 201
References 203

7. vii
ACKNOWLEDGEMENTS
I would, first and foremost, like to thank Dr Liam Kilduff for his guidance and
support during the course of this PhD. Your knowledge and good humor has helped
make this journey both enjoyable and rewarding. Prof Christian Cook for being an
unofficial second supervisor and constructively reviewing my work. Thank you for
being so generous with your time and expertise.
I would also like to thank Nick Owen for his expertise, particularly around jump
analysis and Dr Rodney Kennedy for helping me achieve ethical approval from the
University of Ulster and for both challenging and supporting my initial ideas.
The English Institute of Sport for the financial support provided towards my final
year and Sports Institute Northern Ireland for providing me with the time to
undertake this PhD. Special thanks must also go to Declan Gamble, Ricky McCann
and Damian Martin from the SINI physiology department who gave up their time
(and sleep) to help with the data collection and to Ellie Duly and Dr Tom Trinnick
from the Ulster hospital for kindly providing the analysis of the blood samples.
The’ original’ SINI S&C team Mark Kilgallon, Lisa Costley, Giovanni Capello,
Ryan Whitley and Scott Pollock, not just for your help with the data collection but
also for your hard work and thirst for knowledge which helped inspire me day-to-day
to keep pushing forward.
Ulster Rugby and, in particular, David Drake for not just providing the participants
for this thesis but also for recognising the importance of applied research to the
development of the next generation of players. SUFTUM.
My Mum and Dad who have supported me, not just through the process of this PhD,
but also through my entire life. Thank you for everything you have done and
continue to do for me, I’m eternally grateful.

8. viii
My son Caleb. Thank you for all the smiles and telling Daddy ‘NO!’ when you saw
me trying leave my office during the write-up. I hope to make you as proud of me as
I am of you.
Finally and, most importantly, a massive thanks to my wife and proof reader Julie for
not only being a source of positive energy during this process but also for all you
have done to provide me with the space and time to complete this thesis. Without
your love, support and critical eye I wouldn’t be where I am today.

9. ix
LIST OF TABLES
Table 2.1 Definitions of strength, hypertrophy and explosive power
training
51
Table 2.2 Comparison of hypertrophy, strength and power schemes utilised
in McCaulley et al. (2009) (McCaulley et al., 2009)
53
Table 2.3 Afternoon strength, speed and power performance following
three different conditions (control, morning speed training, morning weight
training) (Cook et al., 2013)
66
Table 4.1 Intersession reliability statistics for the variables calculated
during the countermovement jump
92
Table 4.2 Intersession reliability statistics for the variables calculated
during the squat jump
93
Table 5.1 Average time across the 6 x 50m sprints in team sport athletes 109
Table 5.2 Total testosterone, free testosterone and cortisol at four different
time points (pre, immediately post, 2-hours post and 24-hours post 6 x 50m
sprints) in team sport athletes
110
Table 5.3 Perceived muscle soreness, creatine kinase and lactate at four
different time points (pre, immediately post, 2-hours post and 24-hours post
6 x 50 metre sprints) in team sport athletes
112
Table 5.4 Squat and countermovement jump variables at four different time
points pre, immediately post, 2-hours post and 24-hours post 6 x 50 metre
sprints) in team sport athletes
114
Table 6.1 Total testosterone, Free testosterone and cortisol response to
speed only and speed/weights protocols
131
Table 6.2 Lactate, creatine kinase and perceived muscle soreness response
to speed only and speed/weights protocols
133
Table 6.3 Neuromuscular responses to speed only and speed/weights
protocols
136
Table 7.1 Total Tonnage lifted and rate of percieved effort for the weight
training sessions and 10m and 50m times for the two protocols
153
Table 7.2 Total testosterone, free testosterone and cortisol responses to the
speed/weights and weights/speed protocols
155
Table 7.3 Creatine kinase and perceived muscle soreness responses to the
speed/weights and weights/speed protocols
158

10. x
Table 7.4 Neuromuscular responses to the speed/weights and
weights/speed protocols
160
Table 8.1 Possible weekly workout schedule for a track and field sprinter
incorporating the findings of this thesis and based on the model proposed in
Francis (2008; two intensive days model)
176
Table 8.2 Possible weekly workout schedule for a track and field sprinter
incorporating the findings of this thesis and based on the model proposed in
Francis (2008; three intensive days model)
176
Table 8.3 Possible off-season weekly workout schedule for a rugby team
incorporating the findings of this thesis
176

11. xi
LIST OF FIGURES
Figure 2.1 Fatigability of the knee extensors during 60 second maximal
voluntary contraction’s at 40 degrees and 80 degrees knee extension before
and after eccentric exercise induced muscle damage (Byrne & Eaton, 2002)
10
Figure 2.2 Changes in isometric strength at 40 degrees and 80 degrees knee
extension after eccentric exercise induced muscle damage (Byrne & Eaton,
2002)
11
Figure 2.3 Effect of intrathecal fentanyl modified afferent feedback on
integrated electromyography and power output during a 5km cycling time-
trial (Amann, Proctor, Sebranek, Pegelow, & Dempsey, 2009).
16
Figure 2.4 Scatter graph showing the pooled correlation between pre-
workout salivary testosterone and voluntary workload (Cook et al., 2013)
22
Figure 2.5 The relationship between change in core temperature and
change in lower body power in elite rugby players (West, Cook, Beaven &
Kilduff, 2014)
28
Figure 2.6 Contractile rate of force development and electromyography
during maximal isometric contraction before and after 14 weeks of
resistance training (Aagaard, 2002)
35
Figure 2.7 Vertical force time curves obtained during countermovement
jump stretch shortening cycle contractions (Jakobsen et al., 2012)
44
Figure 3.1 Time aligned force, power, velocity and displacement traces 80
Figure 5.1 Time line for the experimental protocol in Chapter 5 105
Figure 5.2 The relationship between sprint performance and (a)
countermovement jump relative peak power, (b) Squat jump relative peak
power, (c) squat jump height and (d) countermovement jump height
116
Figure 6.1 Perceived muscle soreness pre, immediately post, 2-hours post
and 24-hours post the two protocols
134
Figure 6.2 Example of a bimodal recovery pattern using peak power at the
four different time points for both the speed only and speed/weights
protocols
136
Figure 7.1 Lactate response to speed/weights and weights/speed protocols
at PRE, IP1, 2-hours post, IP2 and 24-hours post
157

12. 1
Chapter 1
General Introduction

13. 2
1. INTRODUCTION
The ability to generate strength, power and speed have been shown to be important
physical characteristics for performance in a range of sports (Baker, 2001; Gabbett,
2009; Haff et al., 2005). However, whilst considerable research has been undertaken
into both the acute and chronic responses to training sessions aimed at strength and
power development (Ahtiainen, Pakarinen, Alen, Kraemer, & Hakkinen, 2003;
Andersen et al., 2005; Beaven, Cook, & Gill, 2008; Burgess, Connick, Graham-
Smith, & Pearson, 2007; Cormie, McGuigan, & Newton, 2010; Linnamo et al., 2000;
McCaulley et al., 2009), limited research has focused on speed development
(Duffield, Cannon, & King, 2010; Jimenez-Reyes, Molina-Reina, Gonzalez-
Hernandez, & Gonzalez-Badillo, 2013; Perrey, Racinais, Saimouaa, & Girard, 2010;
Pullinen, MacDonald, Pakarinen, Komi, & Mero, 2005). Given that speed has been
shown to differentiate between standard of play in soccer (Haugen, Tonnessen, &
Seiler, 2013), American football (Black & Roundy, 1994; Garstecki, Latin, &
Cuppett, 2004) and rugby league (Gabbett, Kelly, Ralph, & Driscoll, 2009), this
represents a major gap in our current understanding of the development of athletic
performance.
Speed can be separated into three different components, each of which have varying
levels of importance to athletes depending on their sport: namely acceleration,
maximum velocity and speed endurance (Ross, Leveritt, & Riek, 2001). Acceleration
can be defined as the rate of change in velocity per unit time, with the ability to
accelerate being an important physical characteristic for both track sprinters and
team sport athletes. In track and field, for example, time to three metres (m) has been
identified as a key variable for success in a 100 m race (Mann, 2011), while time
motion analysis studies in both soccer and rugby have reported that the vast majority
of sprints performed during games are less than 20 m in distance (Gabbett, 2012;
Haugen, Tonnessen, Hisdal, & Seiler, 2014). Maximum velocity is the peak speed an
athlete can run at, with elite sprinters reaching velocities of 11.5-12 metres/sec (m.s-
1
) (Mann, 2011) and games players reaching velocities of 8.5-9 m.s-1
(Haugen et al.,
2014). Maximum velocity is a key determinate in many sprint based track and field
events, however, when one considers that a significant proportion of sprints in team
sports are initiated from jogging, striding or non-stationary conditions (Gabbett,

14. 3
2012; Haugen et al., 2014), its relevance to games players is potentially increased.
The importance of maximum velocity sprinting in team sports may also increase
depending on playing position, with Gabbett (2012) reporting that over a third
(33.7%) of sprints performed by outside backs in rugby league were greater than 21
m in distance. In addition, the importance of activities requiring maximal speed
should also be considered as it is suggested that they tend to occur at critical game-
defining moments (Shalfawi, Haugen, Jakobsen, Enoksen, & Tonnessen, 2013).
Finally, speed endurance refers to the ability to maintain maximum velocity. Recent
research by Johnson (2011) suggests that elite sprinters begin to decelerate at 60 to
70 m, making speed endurance very relevant to that population, however, the
distances run in team sports mean it is potentially of lesser importance to team sport
athletes. Therefore, it seems logical that speed training for team sport athletes should
encompass distances that aim to develop both acceleration and maximum speed.
The extent to which speed can be developed in team sport athletes remains unclear.
Hansen, Cronin et al. (2011) reported considerable discrepancies in strength and
power but not in speed between junior and senior rugby players within the same
club, suggesting that players do not get faster as they progress into the senior sides.
Indeed, a recent longitudinal study into changes in the physiological characteristics
in 156 American football players during their time in the Division 1 college system,
reported that speed did not change over a 4-year period (Jacobson, Conchola, Glass,
& Thompson, 2013). While both these studies point to speed being a largely genetic
quality, the findings may also be due to team sport athletes traditionally undertaking
sprint training as part of larger technical sessions (Haugen et al., 2014), which may
result in an interference effect similar to that reported with concurrent training
(Hakkinen et al., 2003). Additionally, the difference in session design utilised within
team sports (Dupont, Akakpo, & Berthoin, 2004; Ebben & Blackard, 2001; Little &
Williams, 2007) when compared to those suggested by elite track and field coaches
(Francis, 2008) may also be a contributing factor, with many team sport coaches
using short recoveries (e.g. 30 seconds (s)) and high total running volumes (e.g.
greater than 600 m) that are reported to limit adaptation to speed training (Ross et al.,
2001). Indeed, both acceleration and maximum velocity have been shown to improve
in soccer players who undertook 10 weeks of isolated sprint work (40 m in distance)
with appropriate volumes (480 m) and recoveries (2–10 minutes; Tonnessen,

15. 4
Shalfawi, Haugen, & Enoksen, 2011), with the authors suggesting that the
improvements were likely due to the specificity of the session itself. Furthermore, to
date, no research has been undertaken into the fatigue response to a speed session. A
clear understanding of the fatigue response to maximal speed training is required as
maximising adaptations to training is reliant on avoiding excessive fatigue, with
neural adaptations, in particular, having been reported to be sensitive to training
intensity (Tan, 1999). This may be especially relevant to speed as most of the factors
associated with it are influenced by the neuromuscular system (Ross et al., 2001).
Team sport players are required to train multiple physical characteristics
simultaneously, with various methods relating to the planning of training having
been previously suggested (e.g. Issurin, 2010; Prestes, De Lima, Frollini, Donatto, &
Conte, 2009). However, regardless of the method of periodisation utilised, in order to
achieve the desired outcome, it will be necessary for several different training
sessions, aimed at the development of different parameters (e.g. strength and speed),
to be applied in the same training week and often on the same training day
(Hakkinen, 1992; Hakkinen & Kallinen, 1994; Hakkinen, Pakarinen, Alen,
Kauhanen, & Komi, 1988; Hartman, Clark, Bemben, Kilgore, & Bemben, 2007). In
order for the athlete to adapt to multiple training sessions, the loads must be applied
in an order or spacing that allows the athlete to have recovered to a point where they
are able to meet or exceed the requirements of the next training session (Bishop,
Jones, & Woods, 2008). While periods of functional overreaching (where the athlete
undertakes intensive training with reduced recovery intentionally with the aim of
inducing a short term period of reduced performance from which the athlete will
rebound from after a period of recovery) may be planned into a training block
(Coutts, Reaburn, Piva, & Rowsell, 2007), if training is continually set up in a
manner where the athlete is given insufficient time to recover from the fatigue
accumulated from the previous session, non-functional overreaching can occur
(Meeusen et al., 2006), resulting in decreased strength and power (Moore & Fry,
2007). Therefore, it is important that, not just the time between sessions, but also the
accumulative effect of multiple training sessions, is considered when designing
training programmes.

16. 5
This process of structuring training is further complicated by studies, which have
demonstrated that the degree and duration of the neuromuscular, endocrine and
physiological responses are specific to the stimulus applied (Enoka & Duchateau,
2008; McCaulley et al., 2009). For example Doma and Deakin (2013) reported a
significantly different degree of neuromuscular fatigue immediately post a strength
session when compared to an endurance session, while McCaulley et al. (2009)
reported that rate of force development recovered slower after a strength session
when compared to a hypertrophy session and that hypertrophy training resulted in a
significantly different endocrine post exercise compared to strength or power
training. In addition hypertrophy training has been demonstrated to result in
significantly greater metabolic accumulation (McCaulley et al., 2009) and muscle
damage than strength training (Uchida et al., 2009). All of these responses will
have implications for training session order when multiple sessions are performed on
the same day. Furthermore, it has been reported that performing endurance training
six hours before strength training resulted in greater fatigue the following day than
when the order was reversed (Doma & Deakin, 2013), possibly due to both the type
of fatigue generated and the time taken to recover from each session being different.
In contrast, Ekstrand, Battaglini, McMurray and Shields (2013) reported that the
ability to perform an overhead shot throw was actually improved by performing a
morning strength training session, possibly as a result of improved neuromuscular
function.
Consequently, based on the above information it is clear that, while speed is a key
physical characteristic, little is currently known about the neuromuscular response to
this type of training. As discussed, this is important, not just with regard to the
adaptive process but also to ensure that the timing and make up of both subsequent
and preceding training sessions is appropriate. This neuromuscular response to
training can be measured via a range of different methods, with one of the most
commonly used in applied research being the assessment of kinematic and kinetic
changes in jump performance. However, while several variables produced during
jumping have been reported to be sensitive to fatigue (Cormack, Newton, McGuigan,
& Doyle, 2008; Thorlund, Michalsik, Madsen, & Aagaard, 2008) or representative of
changes at central or peripheral levels (Jakobsen et al., 2012; Thorlund et al., 2008),
in many cases, there is also limited information available regarding their reliability.

17. 6
This makes it difficult for coaches and scientists to draw meaningful conclusions
regarding the magnitude of changes they observe post-training.
Therefore, the purpose of this thesis was to examine how speed training may be best
integrated into a training programme. This was done by:
I. Assessing the reliability of several key variables produced during the
countermovement and squat jump;
II. Quantifying the fatigue response to a maximal speed training session over a
24-hour period;
III. Comparing the response to a training day consisting solely of a speed training
session to one consisting of a speed training session plus a strength training
session;
IV. Investigating the effect of varying speed and strength training session order
on performance over a 24-hour period.

18. 7
Chapter 2
Review of Literature

19. 8
2.1. INTRODUCTION
The following literature review is separated into three main sections. Section 2.2
aims to provide the theoretical background to this thesis. It begins with a discussion
around neuromuscular fatigue and the various central and peripheral mechanisms
that contribute to decreased neuromuscular output. From there, it moves to provide
an overview of the mechanisms through which exercise can acutely enhance
neuromuscular output, specifically, acute changes in testosterone and cortisol, post
activation potentiation and elevated muscle temperature.
Section 2.3 provides a review of the various laboratory and field-based methods
popularly used in the assessment of neuromuscular fatigue and function, before
focusing on the various jump variables currently used in neuromuscular research. In
so doing, I aim to provide a justification for the use of field-based over laboratory
based measures in applied settings, while demonstrating that further research is
required into the reliability of these variables prior to conclusions based on their
response to a given stimulus being made.
Finally, section 2.4 reviews the current literature available regarding the
neuromuscular response to single and multiple daily training sessions and session
order. This section aims to demonstrate that research to date has not provided a
detailed understanding of the neuromuscular response to training aimed at
developing maximal speed and highlights key limitations in our understanding
regarding multiple versus single training sessions and training order.
2.2. DEFINING NEUROMUSCULAR FATIGUE
Perhaps the most characterised response to exercise is an acute decrease in
performance as a result of fatigue. While several different models have been
proposed to explain the causes and effects of fatigue produced during training
(Abbiss & Laursen, 2005), this review will focus primarily on the neuromuscular
fatigue model.

20. 9
Within the field of muscle physiology, there is a general agreement that
neuromuscular fatigue is a reduction in the force-generating capacity of the muscle
(Gandevia, 2001; Place, Yamada, Bruton, & Westerblad, 2010; Weir, Beck, Cramer,
& Housh, 2006). However, there is a lack of consistency with regard to the time-
frame this reduction is seen to occur within. Gandevia (2001) defines fatigue as “any
exercise-induced reduction in the ability to exert muscle force or power, regardless
of whether or not the task can be sustained” (p.1732). In this definition, the emphasis
is on the change in force production occurring during the activity itself. This method
of defining and measuring the fatigue response is different to that used by several
others who did not perform an analysis of the muscle’s force generating capacity
during the activity and instead defined the post exercise loss of function compared to
the pre training levels as neuromuscular fatigue (Andersson et al., 2008b; Bosco,
Colli, Bonomi, Von Duvillard, & Viru, 2000a; Cormack, Newton, McGuigan, &
Cormie, 2008). Alternatively, Place et al. (2010) do not make such a distinction and
instead view fatigue as decreased force/power generating capacity during and
following prolonged or repeated muscle activity. While these differences may seem
subtle, it is important to differentiate between studies that assessed a post-exercise
drop in force production capacity and those that assessed fatigability of the muscle.
A study by Byrne and Eston (2002a), examined the neuromuscular fatigue response
to 100 eccentric barbell squats via changes in both peak force and rate of fatigue
during a 60-second maximal voluntary contraction at one hour and one, two, three
and seven days post squatting (Figures 2.1 and 2.2). If the Gandevia (2001)
definition was applied to the results reported in Figure 2.1 then, from the decrease in
fatigability at one hour post compared to pre, the conclusion could be drawn that
resistance to fatigue improved post exercise. However, when these results are viewed
in conjunction with Figure 2.2, it can be seen that what the authors are actually
reporting is a decrease in the degree of loss of force production in a muscle from its
depressed post exercise force generating capacity (one hour post) rather than
considering it in relation to its pre-exercise capacities. While all definitions are valid,
the current thesis will focus on post exercise change in function and the
investigations undertaken will focus in the degree of change and the time-frames
associated with return to or above baseline levels.

21. 10
Figure 2.1: Fatigability of the knee extensors during 60-second MVCs at 40° (short;
solid bars) and 80° (optimal; open bars) knee extension before and after eccentric
exercise-induced muscle damage. Fatigability is expressed as the regression
coefficient (b); the less negative the coefficient, the less fatigable the muscles. *
Significantly different (P < 0.05) from 40°. **Significantly different (P<0.05) from
pre-exercise (Reproduced from Byrne & Eaton, 2002).

22. 11
Figure 2.2: Changes in isometric strength at 40° (short; solid bars) and 80° (optimal;
open bars) knee extension after eccentric exercise-induced muscle damage. Values
are means (± standard deviation) expressed as a percentage of pre-exercise strength.
* Significantly different (P < 0.05) from pre-exercise (Reproduced from Byrne &
Eaton, 2002).

23. 12
2.2.1 MECHANISMS OF NEUROMUSCULAR FATIGUE
Reductions in force-generating capacity are the result of changes occurring
somewhere between the brain and the muscle fibre (Weir et al., 2006). Traditionally,
decreases in force-generating capacity with origins distal to the neuromuscular
junction are defined as peripheral fatigue, while changes with origins occurring in
the spinal cord and brain are defined as central fatigue (Weir et al., 2006).
2.2.1.1. MECHANISMS OF PERIPHERAL FATIGUE
Peripheral fatigue can occur within the muscle fibre itself and within the
neuromuscular junction and terminal branches of the motor axons (Babault,
Desbrosses, Fabre, Michaut, & Pousson, 2006a). Within the muscle fibre itself, it
appears that the dysfunction occurs in the excitation-contraction mechanisms, which
is seen to occur with repeated low frequency stimulation. This has been termed low
frequency fatigue (Abbiss & Laursen, 2005) and results in more central drive being
required to maintain the same levels of force. While low frequency fatigue is
traditionally measured by the ratio of force generated by a twitch of 10 (or 20) Hz
and 50 Hz (Vollestad, 1997), evidence for the existence of low frequency fatigue can
also be found in Tomazin, Sarabon, and Strojnik (2008), who demonstrated increased
surface electromyography during a maximum voluntary contraction post-exercise
without a corresponding increase in force, suggesting more neural input was required
to maintain the same level of force. In addition, Bosco, Colli, Bonomi, von Duvillard
and Viru (2000b) reported that electromyography activity was maintained alongside
decreased power outputs post-training.
Although the exact mechanisms causing low frequency fatigue are unclear, post-
exercise decreases in the concentrations of adenosine triphosphate has been reported
to result in a reduction of free calcium in response to each action potential
(Skurvydas et al., 2007). Elevated concentrations of inorganic phosphate may also
play a direct, along with a more indirect, inhibitory role in the contractile processes
of the muscle fibre (Baker, Kostov, Miller, & Weiner, 1993), with previous research
reporting a relationship between declined functioning of the excitation-contraction
mechanism and metabolic accumulation (Tomazin et al., 2008). Calcium release in

24. 13
response to an action potential can also decrease in the hours post-exercise over time
periods by which any metabolic by-products would have been expected to dissipate.
One possible explanation for this is an impaired link between the T tubule and
sarcoplasmic reticulum as a result of the stress induced by exercise (Skurvydas et al.,
2007).
In addition to low frequency fatigue, there is also the possibility of high frequency
fatigue. High frequency fatigue has been suggested to be the result of decreased
sarcolemmal excitability as a result of the high firing rate required to activate fast
twitch fibres (Tomazin et al., 2008) and can be seen to occur when force production
in motor neurons stimulated at frequencies greater than 50 Hz decreases. However,
the vast majority of voluntary contractions are maintained with motor neurons
discharging at no more than 30hz (Chiu, Fry, Schilling, Johnson, & Weiss, 2004)
and, as such speed, power and strength activities are unlikely to induce it. Indeed,
while decreased sacrolemmal excitability has been reported after metabolically
demanding activities (Perrey et al., 2010), it has not been shown to occur after one-
off explosive activities (Tomazin, Morin, Strojnik, Podpecan, & Millet, 2011),
casting doubt around this being a relevant mechanism of impairment for these type
of activities.
Finally, at the neuromuscular junction and the terminal branches of the motor axons,
fatigue has been suggested to occur as a result of decreased membrane sensitivity
which, in turn, is due to increased intercellular lactate and extracellular potassium
concentrations (Abbiss & Laursen, 2005). The increased potassium concentrations
have been attributed to insufficient activation of the sodium-potassium pumps
(Abbiss & Laursen, 2005).
2.2.1.2 MECHANISMS OF CENTRAL FATIGUE
Currently, three mechanisms have been identified which may lead to decreased
central drive as a result of exercise (Taylor & Gandevia, 2008): (a) a decrease in (or
sub-optimal) output from the cortex; (b) an increase in inhibitory input; or (c) a
decrease in responsiveness of the motor neurons through a change in their intrinsic
properties.

25. 14
Decreased output from the cortex
Biochemical changes within the brain during exercise have been proposed to result in
decreased central drive at the level of the cortex (Abbiss & Laursen, 2005). While
dopamine and gamma aminobyturic acid have been investigated as contributing
factors (Siebner, Dressnandt, Auer, & Conrad, 1998), the most popular and
investigated theory suggests that the central drive decreases are because of increased
brain serotonin levels during exercise (Newsholme, Acworth, & Blomstand, 1987).
Serotonin is produced in the brain from the amino acid tryptophan. However, only
unbound tryptophan can pass through the blood brain barrier and, under normal
conditions, the majority of tryptophan circulates loosely bound to albumin. Even in
its unbound state, tryptophan must compete with branch chain amino acids for
transport across the blood brain barrier. Newsholme et al. (1987) propose that
exercise-induced elevations in circulating free fatty acid levels result in increased
brain serotonin levels during exercise. This is due to albumin having a greater
affinity for free fatty acids than tryptophan, resulting in an increase in the free
tryptophan available to be transported across the blood brain barrier. This, along with
the decreased levels of branch chain amino acids levels that have been demonstrated
to occur during prolonged exercise (Blomstrand, Celsing, & Newsholme, 1988),
means that a greater concentration of tryptophan faces less competition for transport
across the blood brain barrier, with the net result of greater brain serotonin
concentrations. Several studies have investigated Newsholme et al. (1987) through
chemical manipulation of serotonin concentrations and, while a direct impact was
demonstrated on performance during an endurance cycling session performed at 80%
maximal oxygen uptake (Marvin et al., 1997), no effect was found on a cycling
protocol performed at 60% maximal oxygen uptake (Strachan, Leiper, & Maughan,
2004).
Increased inhibitory input
Peripheral feedback has also been proposed to affect central drive (Taylor &
Gandevia, 2008). Mechanical and biochemical changes resulting from exercise
stimulate the terminal ends of thinly myelinated (Type III afferents) and
unmyelinated (Type IV afferents) nerve fibres found in muscle fibres (Amann, 2012;
Nicol, Avela, & Komi, 2006). These Type III and IV afferents are known to affect
muscle spindle and Golgi tendon organ activity (Gandevia, 2001). While, during

26. 15
extended exercise, Type III and IV afferents are regulated by the cardiovascular and
ventilatory systems (Amann, 2012), they are also believed to be sensitive to several
markers of metabolic fatigue (potassium, lactate and phosphate) accumulated during
exercise and through muscle damage (Amann, 2012; Taylor & Gandevia, 2008). The
role that Type III and IV afferents play in modulating central drive has been
demonstrated by a study by Amann, Proctor, Sebranek, Pegelow and Dempsey
(2009). Using a double-blind study design, the authors investigated 5 km cycle time
performance under three different conditions performed in a randomised order. In
condition one, participants performed a simple 5 km cycle which acted as a control
sample whereas, in condition two, participants were injected with saline placebo and,
in condition three, participants were injected with fentanyl to inhibit Type III and IV
afferents. As can be seen in Figure 2.3, the fentanyl protocol resulted in significantly
greater electromyography activity and power during the first half of the 5 km cycle.
This data supports the role that the ascending pathways from the muscle play in
terms of imposing an inhibitory influence on the central nervous system as the
central nervous system does not sense the accumulation of metabolic by-products.
What is unclear is if the decreased central-drive results at a conscious or
subconscious level. It should also be noted that the decreased afferent feedback and
subsequent sustained higher power outputs during the first 2.5 km resulted in greater
metabolic accumulation which, in turn, are suggested to have decreased performance
during the second 2.5 km through the mechanisms outlined in section 2.1.1.1.

27. 16
Figure 2.3. Effect of intrathecal fentanyl modified afferent feedback on integrated
electromyography (iEMG) and power output during a 5km cycling time-trial
(Reproduced from Amann, Proctor, Sebranek, Pegelow, & Dempsey, 2009).

28. 17
Decreased motor neuron responsiveness
Activation during maximal voluntary contractions has been shown to decrease the
responsiveness of motor neurons to synaptic input through a process called spike
frequency adaptation (or late adaptation; Nordstrom, Gorman, Laouris, Spielmann, &
Stuart, 2007; Taylor & Gandevia, 2008). During this process, the motor neuron
responsiveness is not only affected by the excitatory and inhibitory potentials
(ionotropic effects) but also due to synaptic or hormonal inputs acting via receptors
on the membrane (metabotropic effects; Nordstrom et al., 2007). A variety of
metabotropic agents (neuromodulators) have been identified, among them serotonin,
norepinephrine and adenosine (Nordstrom et al., 2007) and, as a result, the motor
neuron output in response to a given stimulus can vary depending on the degree of
neuromodulator activity. The responsiveness has been shown to recover within two
minutes of cessation of firing (Taylor & Gandevia, 2008). However, while late
adaptation has been demonstrated in animal studies, there is limited evidence from
human studies (Nordstrom et al., 2007) and, as such, its role in explosive muscle
action is currently theoretical.
2.2.3 POST ACTIVATION POTENTIATION
In addition to the declines in performance associated with neuromuscular fatigue,
acute improvements have also been reported immediately after exercise. For
example, counter movement peak power was found to be enhanced eight minutes
after subjects performed one set of three repetitions back squat (Kilduff et al., 2011),
while sprint performance has been shown to improve after five sets of five to eight
repetition max half squats (Tsimachidis, Patikas, Galazoulas, Bassa, &
Kotzamanidis, 2013) and overhead backwards shot put throw performance has been
shown to be enhanced by throwing a heavy shot put during warm-up (Judge et al.,
2013). It has been suggested that such enhancements may last up to 30 minutes but,
depending on factors such as the intensity of the activity and the training status of the
subject, may take two minutes plus to manifest (Wilson et al., 2013). In all of these
examples, the improvements are suggested to be the result of post activation
potentiation. Post activation potentiation can be defined as an enhanced
neuromuscular output in response to previous contractile activity (Sale, 2002). Two
primary theories to explain post activation potentiation have been proposed by

29. 18
Hodgson, Docherty and Robbins (2005). These are (a) twitch potentiation and (b)
reflex potentiation.
Twitch Potentiation
If twitch potentiation occurs, the force output in response to a single or series of
action potentials is increased (Hodgson et al., 2005). This is explained by a change in
the rate at which the myosin cross-bridges generate force. This increased rate of
force production is a result of increased calcium sensitivity which, in turn, occurs in
response to the phosphorylation of myosin regulatory light-chains via myosin light-
chain kinase (Babault, Maffiuletti, & Pousson, 2008; Hodgson et al., 2005).
Reflex Potentiation
Reflex potentiation can be defined as enhanced motor unit excitability in response to
a stimulus (Kilduff et al., 2011). Three different mechanisms have been suggested to
contribute to reflex potentiation (Misiaszek, 2003): (a) Prior activity results in an
alteration in the excitability of the motor neurons; (b) the afferent terminals change
the amount of neurotransmitter they release post-activation; (c) the intrinsic
properties of the motor neurons are altered by the release of certain
neurotransmitters. However, while many studies have promoted post activation
potentiation as a mechanism to explain the enhanced performance they observe, few
have attempted to assess if either twitch or reflex potentiation actually occurred.
Indeed, it is unclear as to how much either twitch or reflex potentiation actually
contributes to performance. Folland, Wakamatsu and Fimland (2008), for example,
investigated the effect of a 10-second isometric contraction on changes in twitch and
reflex potentiation up to 18 minutes post in eight physically active men. During the
same study, they also assessed the performance of the knee extensors at five minutes
post. While twitch potentiation was observed at 13 and 18 minutes post respectively,
knee extensor performance did not improve. This led the authors to question the
functional benefit of reflex potentiation, in particular, and post activation potentiation
as a whole.
In contrast, other research (such as that by Baudry & Duchateau, 2007), has reported
performance to be enhanced in the presence of post activation potentiation. In
Baudry and Duchateau’s study, it was reported that maximal voluntary contraction

30. 19
was enhanced in the thumb extensors of 10 male subjects at two and five minutes
after a six-second maximal contraction. This coincided with an observed potentiation
of peak twitch.
Only two studies were found which assessed both dynamic performance and post
activation potentiation (Mitchell & Sale, 2011; Pearson & Hussain, 2014). Using
rugby players, Mitchell and Sale (2011) examined if one set of a 5RM squat would
result in an enhanced countermovement jump performance and, on a separate testing
day, if it would result in a twitch potentiation during an evoked isometric contraction
of the knee extensors. While they found both an improved jump height and evidence
of twitch potentiation, no correlation between the two variables was found. While
factors such as the difference between the dates of collection and the movement
patterns assessed may have contributed to the lack of correlation found, additional
factors may also have be involved in enhancing the improved dynamic
neuromuscular performance reported post contraction. Pearson and Hussain (2014)
also found no enhancement of jump performance, even though twitch torque was
enhanced after an isometric loading protocol. Given these results, it is most likely
unwise to use the term post activation potentiation to explain post-exercise
improvements like the ones reported by Seitz, de Villarreal and Haff (2014) and,
instead, they should be seen as a change in neuromuscular performance, which is
multi-factorial in nature.
2.2.4 ROLE OF THE ENDOCRINE SYSTEM IN NEUROMUSCULAR
FUNCTION
Acute changes in certain steroid hormones have also been linked to changes in
neuromuscular performance (Bosco et al., 2000b; Cook, Kilduff, Crewther, Beaven,
& West, 2013; Crewther, Cook, Lowe, Weatherby, & Gill, 2011; Crewther, Kilduff,
et al., 2011). The actions of steroid hormones can be split into direct (genomic) and
indirect (non-genomic) effects. Genomic effects occur through the hormone
attaching to its specific receptor within a cell and stimulating slow occurring changes
in structure, expression and functioning of certain proteins via transcription (Strelzyk
et al., 2012). In addition, steroid hormones also appear to have several significant

31. 20
and rapidly occurring non-genomic effects, which are suggested to occur through
interactions with classical steroid receptors, membrane-based receptors (e.g. G-
protein receptors) and the cell membrane itself (Falkenstein, Tillmann, Christ,
Feuring, & Wehling, 2000). These interactions can also indirectly result in non-
genomic actions by generating a series of secondary messenger systems, which
themselves have effects on cell function (Falkenstein et al., 2000). While it has been
suggested that this distinction between genomic and non-genomic effects is slightly
simplistic (Hinson, Raven, & Chew, 2007), it is taken as the generally accepted
definition in the literature in this area. Furthermore, while many steroid hormones
could potentially have effects on neuromuscular performance, it is beyond the scope
of the current chapter to review each of these. Instead, the focus will be on two
particular steroid hormones that have received significant interest in the area of
strength and power, namely Testosterone and Cortisol.
2.2.4.1. TESTOSTERONE
Testosterone is a steroid hormone primarily produced by the Leydig cells in the
gonads, with smaller concentrations (<5%) also produced in the ovaries and the
adrenal glands (Vingren et al., 2010). The gonadal release of testosterone is
stimulated by the hypothalamus which serves as a direct link between the endocrine
and nervous systems (Vingren et al., 2010). Indeed, the system of signalling, which
starts at the hypothalamus and results in the release of testosterone, is termed the
hypothalamic-pituitary gonadal axis. The primarily genomic effects of testosterone
are on sexual libido, muscle hypertrophy, bone development and facial hair growth
(Hinson, Raven et al. 2007). In addition, testosterone plays a role in development of
the nervous system and is involved in the development of the motor neuron
(Crewther, Cook, Cardinale, Weatherby, & Lowe, 2011). In terms of its non-
genomic effects, testosterone has been shown to have several rapidly occurring
effects which have implications for neuromuscular performance. These include:
 Increasing levels of intracellular calcium as a result of either testosterone
directly binding to unique receptors on the cell membrane or by acting
through other signalling pathways (Estrada, Espinosa, Muller, & Jaimovich,
2003).
 Stimulation of inorganic phosphate concentrations which, in turn, have been

32. 21
found to affect the force produced by each cross-bridge (Guette, Gondin, &
Martin, 2005).
 Lowering the threshold for transmission across the neuromuscular junction
(Cardinale & Stone, 2006; Hamdi & Mutungi, 2010).
There is a growing body of research into the acute effects of testosterone on
neuromuscular performance and several studies have reported that a relationship
between testosterone and explosive performance exists (Cardinale & Stone, 2006;
Crewther, Lowe, Weatherby, Gill, & Keogh, 2009; Viru & Viru, 2005).
Interestingly, a relationship between decreased testosterone concentrations post-
training and peripheral fatigue has also been demonstrated (Bosco et al., 2000a). In
this study by Bosco et al (2000), the authors investigated the relationship between
change in testosterone and change in electromyography/power ratio in trained
sprinters after a high volume power session and reported a strong negative
correlation between decreases in testosterone and electromyography/power ratio,
suggesting that decreased testosterone concentrations require increased muscle
activation to maintain the same output. While it should be noted that the number of
subjects in this group was small (n=6), the authors also reported that the weightlifting
group, who experienced an elevated concentration of testosterone post exercise, did
not experience the same increased electromyography/power ratio.
Aside from potentially decreasing or masking the degree of fatigue experienced,
elevated testosterone concentrations may also improve subsequent performance
through the non-genomic effects outlined previously. Cook and Crewther (2012)
demonstrated this in a study using different video clips (erotic, training, neutral, sad,
humorous and aggressive) to stimulate differing pre-training concentrations of
testosterone. They found that, not only did the erotic, aggressive and training video
clips stimulate higher pre-training concentrations of testosterone than the control;
they also experienced a significantly greater improvement in three repetition max
squat performance. Testosterone concentrations have also been shown to be related
to training motivation in males and females (Cook, Crewther, & Kilduff, 2013). In
this study, the authors found the voluntary selected workloads in the back squat and
bench to correlate with pre-exercise testosterone concentration in 15 elite rugby
players (Figure 2.4) across a number of training sessions.

33. 22
Figure 2.4: Scatter graph showing the pooled correlation between pre-workout
salivary, testosterone and voluntary workload (Reproduced from Cook et al., 2013).

34. 23
Furthermore, increases in testosterone have been linked to improved mood prior to a
tennis match (Booth, Shelley, Mazur, Tharp, & Kittok, 1989) and competitive
aggression in judo players (Salvadora, Suay, Martinez-Sanchis, Simon, & Brain,
1999). Such a relationship would have implications not just for the athletes readiness
to train on the day of the initial session but, given that testosterone has been reported
to be depressed (Hakkinen & Pakarinen, 1993), elevated (Chatzinikolaou et al.,
2010) and unchanged (Ahtiainen et al., 2011) 24-hours after training, may have
implications for recovery/readiness to undertake training the following day.
In general, testosterone follows a normal circadian pattern of highs in the morning
followed by gradual decreases during the day until early evening (Teo, McGuigan, &
Newton, 2011). Given the growing volume of evidence supporting the role
testosterone can play in optimum neuromuscular and cognitive function, altering this
rate of decline may potentially create an environment later in the day when the
ability to generate speed and power is enhanced. However, while elevations in
testosterone have been constantly demonstrated immediately after exercise
(Ahtiainen, Pakarinen, Kraemer, & Hakkinen, 2004; Beaven et al., 2008; Cook et al.,
2013; Crewther, Cook, Lowe, et al., 2011; Hakkinen & Pakarinen, 1993; Kraemer et
al., 1999; McCaulley et al., 2009), the majority of studies report testosterone to have
returned to baseline within 60-minutes (Ahtiainen et al., 2011; Goto et al., 2009;
Hakkinen & Pakarinen, 1993; McCaulley et al., 2009). While these findings would
suggest that exercise does not change the circadian pattern associated with
testosterone, Kraemer et al. (1990) did report that testosterone may ‘rebound’ back
up at between 90 and 120 minutes post, depending on the type of resistance training
applied. Indeed, a study by Cook et al. (2013) examining the effect of different
morning training sessions (sprints and strength) versus a control on the circadian
pattern of testosterone in elite athletes also reports a sustained effect. They found that
both the sprint session, consisting of 5 x 40 metre sprints with 1-minute recovery,
and the weights session, consisting of 3 x 50% (of 3RM); 3 x 80% (of 3RM); 3 x
90% (of 3RM) and 3 x 100% (of 3RM) in the back squat and bench press, both
resulted in an altered circadian pattern for testosterone versus the control (p<0.05).
On top of this, afternoon testosterone was reported to be significantly higher after the
weights protocol versus the sprint protocol. Performance in the squat, bench press,
40-metre sprint and peak power during a countermovement jump were all tested in

35. 24
the afternoon (six hours post), the weights protocol and the speed protocol. After
preforming the speed protocol in the morning, the subjects ran their 40-metre sprints
significantly quicker than after a morning rest. However, after performing the
weights protocol in the morning, bench press, squat, sprint and countermovement
jump performance were all enhanced versus the rest protocol. These results suggest
that changing the circadian pattern of testosterone will have a positive effect on
neuromuscular performance. While these results are promising and have
implications for training design, more research is required to see if the results can be
replicated.
2.2.4.2 CORTISOL
Cortisol is a steroid hormone produced in the adrenal cortex and, like testosterone, its
receptors lie within the muscle cell (Hinson et al., 2007). Cortisol release is
stimulated via the hypothalamic pituitary adrenal axis with its primary functions
relating to immune function, gluconeogenesis and maintaining blood glucose and
glycogen concentrations in a fasted state (Hinson et al., 2007). The constant increase
in cortisol observed post-exercise plays an important role in preparing the body for
the next training session (Hakkinen & Pakarinen, 1993; Hayes, Bickerstaff, & Baker,
2010; McCaulley et al., 2009; Schumann et al., 2013; Taipale & Hakkinen, 2013;
Uchida et al., 2009; West, Cunningham, et al., 2014). While chronic elevations in
cortisol causes inhibition of growth-hormone secretion (Solomon & Bouloux, 2006)
and is linked to Type II muscle fibre atrophy (Solomon & Bouloux, 2006), short-
term elevations generate some rapidly occurring non-genomic effects (Falkenstein et
al., 2000; Haller, Mikics, & Makara, 2008) which may have relevance to short-term
changes in neuromuscular function. In particular, these relate to brain function,
behaviour, energy metabolism and cellular function (Crewther, Cook, Cardinale, et
al., 2011; Falkenstein et al., 2000; Haller et al., 2008; Makara & Haller, 2001;
Strelzyk et al., 2012).
Pre-event elevations in cortisol has been demonstrated to correlate to competitive
outcome of judo matches (Salvador, Suay, Gonzalez-Bono, & Serrano, 2003; Suay et
al., 1999), rowing performance (Snegovskaya & Viru, 1993) and weight lifted in a
weightlifting competition (Crewther, Heke, & Keogh, 2011; Passelergue, Robert, &

36. 25
Lac, 1995). In the most recent of these studies, the authors compared this relationship
in a simulated Olympic weightlifting competition compared to an actual Olympic
weightlifting competition (Crewther, Heke et al. 2011). The results from this study
demonstrated that cortisol concentrations (pre- and post-competition), assessed by
saliva and lifting performance were significantly greater in the actual competition.
While the small sample size (n= 9) and the fact that that the actual competition was
performed later in the day than the simulated competition should be considered, the
study does suggest that increases in cortisol levels are linked to superior
performance. It should also be noted that the athletes did not undergo any significant
structural changes (e.g. increases in lean muscle mass) between competitions so
change in performance would appear to be due to neuromuscular function. While the
exact mechanisms through which cortisol contributed to this improvement in acute
explosive neuromuscular performance are unclear, it has been demonstrated that
increasing cortisol to concentrations still within a physiologically normal range does
affect central nervous system functioning (Strelzyk et al., 2012). Specifically, it has
been reported that cortisol acts directly on the brain to suppress the processing of
non-relevant background information, thereby allowing the subject to focus on task-
specific sensory information (Strelzyk et al., 2012).
If cortisol is to play a role in an athlete’s neuromuscular performance and thereby the
athlete’s recovery, its response during the time period after the exercise must also be
considered in addition to that of the exercise stimulus itself. Several studies into
cortisol response have collected data in the period up to 30 minutes post-exercise and
suggest that, if the protocol is capable of stimulating an elevation in cortisol, it will
still be elevated at this point (Ahtiainen, Pakarinen, Alen, Kraemer, & Hakkinen,
2003; Ahtiainen et al., 2004; Goto et al., 2009; Kon et al., 2010), after which the
evidence would suggest that it starts to decline towards resting levels. Whilst the
exact time required for cortisol to return to baseline is unclear, Izquierdo et al. (2009)
report cortisol to be elevated 45-minutes post a hypertrophy type training session and
most studies which demonstrate a significant increase immediately after training
report changes to be no longer significant by 60-minutes post (Kon et al., 2010;
McCaulley et al., 2009). One notable exception to this is Crewther, Cronin, Keogh
and Cook (2008), who found cortisol to still be significantly elevated at one hour

37. 26
post a hypertrophy session, although this may be due to the time lag between saliva
and blood markers (Cadore et al., 2008).
As with studies reporting on testosterone, caution should be taken when comparing
post-exercise concentrations of cortisol to pre-exercise baselines, as it would be
expected to naturally decline during the day (Teo et al., 2011). Therefore, while
concentrations may not be significantly elevated from the baseline, they may be
significantly higher than expected for that time of day. Indeed, in a study by
Hakkinen and Pakarinen (1993) that compared the exercise-induced elevations to the
circadian pattern found on a control day, it was reported that, while cortisol was on
the decline at this point, it had not reached the concentrations found at the same time-
point on the control day.
Finally, as with testosterone, there is evidence of cortisol being elevated (Uchida et
al., 2009), depressed (Schumann et al., 2013) and unchanged (West et al., 2014) 24-
hours post-exercise. One possible explanation for this may be the variation in the
degree of muscle damage experienced as previous research has reported a
relationship elevations in creatine kinase and cortisol (Uchida et al., 2009; West et
al., 2014). Alternatively, it has been suggested that there is a training load ‘threshold’
upon which the hypothalamic-pituitary adrenal axis is activated (Nemet et al, 2009;
Cadore, Pinheiro et al. 2013) and it is possible that variations in the intensity and
volume used in the protocols may have contributed to the findings.
2.2.5 MUSCLE TEMPERATURE
One of the by-products of physical exercise is an increase in muscle temperature. It
has been demonstrated that an increase in temperature of one degree increases power
output in the muscle by 10% at high velocities (Sargeant, 1987). In addition, a meta-
analysis into the effect of warming-up reported that 79% of studies reviewed
returned a positive effect on performance, with the majority of these improvements
mediated by temperature (Fradkin, Zazryn, & Smoliga, 2010). It has also been
demonstrated that declines in temperature post-exercise negatively affect power
output (Faulkner et al., 2013; West et al., 2013). As such, it is clear that the change in

38. 27
muscle temperature both immediately post and during the recovery period post-
training is another factor affecting the neuromuscular system.
It has been suggested that changes in temperature may have an effect of neural
transmission at a central level (Bishop, 2003a, 2003b). However, while a low
correlation between muscle activity (suggesting some change in recruitment) and
skin temperature (r = 0.26; p < 0.05) has been reported after a cycling power test
(Temfemo, Carling, & Ahmaidi, 2011), a more detailed neuromuscular assessment
found that no change in nerve conduction velocity occurred with increased muscle
temperature (Pearce, Rowe, & Whyte, 2012). Therefore, the primary mechanisms
through which increases in muscle temperature affect the neuromuscular system
appear to be peripheral, with two primary mechanisms having been identified.
Firstly, increasing muscle temperature has been shown to result in increased
adenosine triphosphate turnover, which will have an effect on the rate of contraction
(Gray, De Vito, Nimmo, Farina, & Ferguson, 2006; Gray, Soderlund, & Ferguson,
2008). This increased turnover is primarily due to the fact that ATPase activity is
temperature dependent. Secondly, increased muscle fibre contraction velocity has
been demonstrated to occur with elevated temperature (Gray et al., 2006). While this
is in part due to the increased adenosine triphosphate turnover, it is also reported that
temperature directly affects the speed of the outset in depolarisation which, in turn,
results in increased calcium release, leading to faster cross-bridge cycling (Gray et
al., 2006).
Body temperature has also been shown to follow a distinct circadian pattern, with
temperature low in the morning upon waking, gradually increasing during the day,
before finally starting to decline early evening (Guette et al., 2005; Teo et al., 2011).
Given this, normal circadian rhythms may also play a role in affecting the recovery
of neuromuscular function in the hours post-exercise. This relationship has recently
been demonstrated in elite rugby players (West, Cook, Beaven, & Kilduff, 2014). In
this study, both core temperature and countermovement jump peak power were
found to be significantly higher at 17:00 when compared to 10:00 and a strong
relationship between change in core temperature and change in peak power was
reported (Figure 2.5).

39. 28
Figure 2.5: The relationship between change in core temperature and change in
lower body power in elite rugby players (Reproduced from West, Cook, Beaven, &
Kilduff, 2014).

40. 29
2.2.6 SUMMARY
Two distinct origins of change in neuromuscular output have been reviewed in the
previous section. Central, defined as occurring between the brain and the muscle
fibre, and peripheral, defined as occurring distal to the neuromuscular junction. In
turn, both central and peripheral changes have been demonstrated to be multifactorial
in nature, with exercise resulting in changes that can have both positive and negative
effects on neuromuscular output. It appears that these central and peripheral changes
rarely happen in isolation of each other. This is highlighted by the potential role that
metabolic accumulation plays in both the central and peripheral systems. As
discussed above, metabolic accumulation has been shown to induce both peripheral
fatigue, via its effect on the excitation-contraction mechanism (Tomazin et al., 2008),
and central fatigue, via an effect on Type III and IV afferents (Taylor & Gandevia,
2008). However, metabolic accumulation has also been linked to post-exercise
elevations in testosterone (Izquierdo et al., 2009) which, in turn, have been linked to
improved neuromuscular performance (Cook & Crewther, 2012) via both central and
peripheral mechanisms. Finally, this section of the review has shown that there are
varying time-frames associated with the different parameters that affect the
neuromuscular system and that some may have longer lasting effects than others. For
example, while many will have short-term implications for neuromuscular output
(e.g. metabolic accumulation and post activation potentiation) others will generate
longer lasting changes (e.g. muscle damage and endocrine response). Therefore, the
physiological response to a training session will have implications for the recovery of
the neuromuscular system post-exercise and potentially the placement of subsequent
sessions.
2.3 MEASUREMENT OF THE NEUROMUSCULAR SYSTEM
Changes in the neuromuscular system (e.g. post activation potentiation, fatigue) have
been studied using a variety of methods ranging from laboratory-based assessments
of single muscle groups aimed at providing information regarding the contributions
of the central and peripheral mechanisms discussed in sections 2.2.1.1 and 2.2.1.2
(Babault, Desbrosses, Fabre, Michaut, & Pousson, 2006b; Behm & St-Pierre, 1997),

41. 30
through to total body movements aimed at assessing the effect of fatigue on athletic
performance (Cormack, Newton, McGuigan, & Cormie, 2008; Thorlund et al.,
2008a).
2.3.1 LABORATORY-BASED MEASUREMENTS
It is proposed that the use of laboratory-based methods allows the researcher to
quantify the central and peripheral contributions to any induced change (Kent-Braun
& Ng, 1999). However, given the complex nature of acute change in neuromuscular
performance discussed in section 2.1, this can difficult to do accurately.
This section will begin with a review of two of the most commonly used methods for
this purpose: (a) electromyography and (b) the interpolated twitch technique, both of
which are traditionally assessed during an isometric maximal voluntary contraction.
In addition, this section will review the use of contractile rate of force development
to monitor change in neuromuscular function.
Electromyography
The use of electromyography to assess change in neural drive is commonplace in
research studies (Mileva, Morgan, & Bowtell, 2009; Thorlund, Michalsik, Madsen,
& Aagaard, 2008b; Ullrich & Bruggemann, 2008). Electromyography measures the
voltage potential generated across the sarcolemma of muscle fibres in response to
neural activation (De Luca, 1997). While both intramuscular and surface
electromyography can be used to assess activation, intramuscular electromyography
is unable to assess intensive muscle contractions in large muscle groups (Turker,
1993), such as those studied within the research detailed in subsequent chapters of
this thesis. This section will therefore primarily focus on surface electromyography.
Surface electromyography is measured under voluntary contraction and the signal is
quantified to produce a root mean square. The recorded electromyography signal is a
summation of the detected voltage potentials at the skin surface and, therefore,
changes in surface electromyography amplitude during the course of a maximal
voluntary contraction are assumed to represent changes in motor unit recruitment and
firing rates (De Luca, 1997). In order to better quantity the origin of change, many

42. 31
studies have combined electromyography with electrical muscle stimulation. Here, a
single supra-maximal electrical stimulus is used to elicit an action potential in muscle
cells and force is generated. The result is a compound muscle action potential (M-
wave; Place et al., 2010). Decreases in root mean square without a change in M-
wave would suggest decreased central activation. The M-wave can also be used to
assess peripheral fatigue as a decrease in M-wave amplitude would suggest some
decrease in membrane excitability (Abbiss & Laursen, 2005). However, prior to
drawing definitive conclusions from changes in surface electromyography activity, it
is important to recognise the limitations. Notably, electrode placement, electrode
detection volume, blood flow in the muscle, the fibre type composition, the amount
of tissue between the muscle and the electrodes, fibre diameter, signal from
surrounding muscles (cross talk) and the method of normalisation used, can all affect
the signal and its reproducibility (De Luca, 1997). For example, different muscles
and contraction types have been shown to result in intra-class correlation coefficients
ranging between 0.19 – 0.99 and standard error of measurement as a percentage of
the grand mean ranging between 4 – 36% (Dankaerts, O'Sullivan, Burnett, Straker, &
Danneels, 2004), while different methods of normalisation have been shown to result
in intra-class correlation coefficient’s of between 0.55 – 0.78 and coefficient of
variations (CVs) of between 14.4 -16.8% (Buckthorpe, Hannah, Pain, & Folland,
2012).
Interpolated Twitch Technique
The Interpolated twitch technique has also been used to assess changes in
neuromuscular function (Tillin, Jimenez-Reyes, Pain, & Folland, 2010). This method
involves the subject performing a maximal voluntary contraction and, when force is
seen to have reached a plateau, an electrical stimulus being applied to the peripheral
nerve. This is then compared to an electrical stimulus applied at rest (control twitch).
Depending on the ratio of super-imposed twitch to control twitch, the conclusion
may be made that the drive received by the motor neurone from the central nervous
system is sub-maximal and that central fatigue therefore exists. (Place et al., 2010).
However, as with electromyography, there are limitations to this method that should
be recognised. Firstly, it has been demonstrated that the control twitch, when applied
prior to the maximal voluntary contraction, is un-potentiated while the twitch applied
during the maximal voluntary contraction is potentiated (Folland & Williams, 2007).

43. 32
Therefore, the post-twitch change may also represent changes at a peripheral level. In
addition, the degree to which the superimposed twitch is potentiated is affected by
the time-point during the maximal voluntary contraction at which it is applied
(Folland & Williams, 2007). Both of these factors would, in turn, lead to an
inaccurate assessment of voluntary activation. It has also been demonstrated that
intercellular mechanisms can also play a role in generating an increase in force
during the additional stimulus and, as a result, the interpolated twitch technique may
overestimate the contributions of central mechanisms to fatigue (Place, Yamada,
Bruton, & Westerblad, 2008). One major limitation that should be recognised is that
maximal voluntary contraction during, for example, a leg extension, is the result of
global muscle activity. As such, the distribution of central drive to the general area,
rather than drive to the specific muscle under investigation, may occur. In contrast,
changes in evoked muscle force are solely the result of changes in evoked muscle
(Gandevia, 2001). For both electromyography and interpolated twitch technique
studies, the choice of muscle to be assessed is also an important consideration as
certain muscles cannot achieve the same absolute levels of force as others and, as a
result, the degree of loss of force of which they are capable of is lower (Gandevia,
2001). This leads to questions regarding how representative a single muscle is of the
fatigue experienced by the participant’s neuromuscular system as a whole.
Contractile rate of force development
Outside of assessing maximal voluntary contraction and in conjunction with
electromyography and interpolated twitch technique, the laboratory-based
assessment of changes in the muscles’ ability to generate force over short time
periods has been used to assess fatigue (Storey, Wong, Smith, & Marshall, 2012).
This change in force over time has been termed ‘contractile rate of force
development’ (contractile rate of force development) and has been suggested to be an
important parameter in athletic performance (Aagaard, 2003; Aagaard, Simonsen,
Andersen, Magnusson, & Dyhre-Poulsen, 2002). Like isometric maximal voluntary
contraction, contractile rate of force development is predominately measured in an
isometric position. While various methods have been used to assess contractile rate
of force development, one of the most common methods is to look at change in force
across specific time-frames (e.g. 0-50 ms, 0-100 ms; Aagaard et al., 2002; Jakobsen
et al., 2012; Taipale & Hakkinen, 2013; Thorlund, Aagaard, & Madsen, 2009b;

44. 33
Thorlund et al., 2008a) with this method being demonstrated to correlate with
electromyography in a study by Aagaard et al. (2002). In this paper, they investigated
changes in contractile rate of force development in the leg extensors in response to
14-weeks strength training. Specifically, they defined contractile rate of force
development as change in force over 30-, 50-, 100- and 200 ms post the onset of
contraction, with electromyography also being assessed at 0-50 ms and 0-100 ms.
After the strength training intervention, the authors observed concurrent increases in
both contractile rate of force development and efferent neural drive. These changes
in both rate of force development and electromyography were evident between 0-50
ms, through to 0-100 ms (Figure 2.6) and suggest that changes in contractile rate of
force development may occur in conjunction with changes in motor neuron discharge
rate and recruitment.

45. 34
Figure 2.6: Contractile rate of force development and average electromyography
obtained from the vastus lateralis, vastus mediali and the rectus femoris during
maximal isometric contraction before (open bars) and after (closed bars) 14-weeks of
resistance training. Time intervals denote time relative to contraction onset (for rate
of force development) or onset of electromyography (for all electromyography
parameters). * Significantly different (P < 0.05) from pre-strength training
(Reproduced from Aagaard, 2002).

46. 35
Further support for the use of contractile rate of force development as a measure of
change in neuromuscular performance comes from a study that reported that early
contractile rate of force development (0-50 ms) is related to the intrinsic qualities of
the muscle (Andersen & Aagaard, 2006). In this study, the authors assessed isometric
rate of force development across several different time frames (0-10 ms, 0-20 ms, 0-
30 ms…0-200 ms) and correlated them with maximal voluntary contraction and
evoked twitch rate of force development. A moderate correlation was found between
twitch rate of force development and isometric rate of force development up to 50 ms
suggesting that, at least in part, early contractile rate of force development is related
to the intrinsic qualities of the muscle fibre. Given this, it is possible that changes in
this quality would be reflective of changes in the contractile properties of the muscle
and is potentially more sensitive to fatigue than maximal voluntary contraction. This
would seem to be supported by a study looking at the effects of two training sessions
in one day that reported isometric rate of force development was much more
sensitive to the fatiguing sessions of speed squats than maximal voluntary
contraction (Chiu, Fry, Schilling, Johnson, & Weiss, 2004). However, while
contractile rate of force development clearly represents an interesting avenue for the
assessment of neuromuscular fatigue, many of the other limitations associated with
isometric contractions outlined previously hold true. Indeed, while the methodology
described above may provide the researcher with information regarding the origin of
fatigue, it is important to consider (a) the functional impact that such changes have
on dynamic performance and (b) how representative the recovery of isometric
maximal voluntary contraction or rate of force development is of the recovery of
dynamic performance.
Regarding the first point, it has been demonstrated that isometric maximal voluntary
contraction does not relate to performance in jumping and sprinting (Requena et al.,
2009). In addition, a study by Byrne and Eston (2002b) compared dynamic power
and isometric force after 100 repetitions of eccentric squatting and reported isometric
force in the quadriceps (measured using an isometric leg extension) to have
decreased by 30% while dynamic power (measured by a Wingate) only decreased by
13%. In addition, further questions around the relationship between laboratory-based
assessments of the contractile components of the muscle and dynamic performance
are raised by Pearson and Hussain (2013) who reported no relationship between

47. 36
evoked isometric twitch and countermovement jump performance in response to
three different conditioning activities. Furthermore, no correlation was reported
between evoked isometric twitch and countermovement jump performance after a
5RM squat protocol (Mitchell & Sale, 2011). In this study, the authors postulated
that evoked isometric twitch represented the muscles around only one joint, while the
jump performance was the product of multiple joints, a major reason as to why no
relationship was found. Given the questions around the relationship between the two
types of assessments, it has been suggested that there is a limit to how much can be
drawn from such laboratory-based assessments regarding functional human
movement (Bosco et al., 2000a).
There are also questions around the recovery times associated with laboratory-based
assessments of fatigue and how representative they are of functional performance.
Byrne and Eston (2002a) reported on the recovery times associated with explosive
dynamic performance (assessed via the Wingate test) and force produced during an
isometric leg extension. They found differences between the two tests with the
isometric leg extension recovering at a faster rate. Similar findings have also been
reported after a series of soccer games (Andersson et al., 2008a). Peak torque, sprint
performance and jump height all declined immediately after a soccer match before
returning close to baseline after five hours, after which sprint performance remained
at baseline while countermovement jump performance and peak torque experienced a
second decline. From here they were shown to recover at different rates, with peak
torque being fully recovered after 27-hours while countermovement jump
performance was not fully recovered 72-hours post (Andersson et al., 2008a).
Finally, a recent study into the acute effects of combined plyometric and resistance
training reported isometric single joint force production to recover quicker than
jumping performance (Beneka et al., 2013).
The above findings raise an interesting question regarding the relevance of
attempting to isolate assessment of central and peripheral factors if they are not
having an effect on dynamic functional performance. Indeed, while they may be
useful to assess in certain circumstances, in an applied environment it is arguably
more important to identify a change in performance.

48. 37
2.3.2. DYNAMIC MEASUREMENT OF NEUROMUSCULAR PERFORMANCE
Given the potential limitations of laboratory-based assessments to characterise
changes in more dynamic applied environments, many researchers have used
changes in performance during more functional dynamic movements (Bosco et al.,
2000a; Cormack, Newton, & McGuigan, 2008; Ronnestad, Kvamme, Sunde, &
Raastad, 2008). While sprint cycling (Byrne & Eston, 2002a) and sprint running
(Andersson et al., 2008a; Yetter & Moir, 2008) have been used to assess fatigue, one
of the most popular methods is to track changes in jumping performance in response
to fatigue (Chatzinikolaou et al., 2010; Harrison & Gaffney, 2004; McCaulley et al.,
2009; Thorlund et al., 2008b). Jumps containing a counter movement and those
containing only a concentric phase (squat jump) have both been used for this purpose
and the following section will discuss some of the most common jump variables
reported on in the literature.
2.3.2.1 JUMP VARIABLES USED TO ASSESS PERFORMANCE
Jump Height
The most popular variable used is jump height (Chatzinikolaou et al., 2010;
Johnston, Gabbett, Jenkins, & Hulin, 2014; McCaulley et al., 2009; Oliver,
Armstrong, & Williams, 2008; Taipale & Hakkinen, 2013; Thorlund et al., 2009b;
Thorlund et al., 2008a; Tonnessen et al., 2011). Jump height has been found to
display excellent reliability across a range of assessment methods (coefficient of
variation 2.0 -5.0%; Cormack et al., 2008; Crewther et al., 2011; Moir, Garcia, &
Dwyer, 2009). Jump height has also been reported to correlate with sprint
performance (Cronin & Hansen, 2005), playing standard in rugby players (Gabbett,
Kelly, Ralph, & Driscoll, 2009) and the probability of been selected to start in a
Division 1 American football programme (Sawyer, Ostarello, Suess, & Dempsey,
2002). These findings therefore suggest that changes in jump height are relevant to
performance. However, a review of the available literature reveals that, while
several authors report jump height to decrease in response to a fatiguing exercise
(Cadore et al., 2013; Chatzinikolaou et al., 2010; Oliver et al., 2008; Pereira et al.,
2009; Thorlund et al., 2008b), others found no decreases and even improvements
(Cormack et al., 2008; Hoffman, Nusse, & Kang, 2003; Thorlund, Aagaard, &

49. 38
Madsen, 2009a) to protocols aimed at inducing fatigue. While the intensity and
volume of the fatiguing exercise in these studies can play a role in the degree of
fatigue experienced (Brandon, Howatson, Strachan, & Hunter, 2014), there may be
other explanations as to why such a variation in responses has been observed. One
such explanation may be that, under fatigued conditions, a change in jump strategy
may occur to compensate for a sub-optimal ability to generate force at the initiation
of the movement and allow the jumper to still reach the same take-off velocity
(Cormack et al., 2008; Thorlund et al., 2008b). As a result, these initial changes in
force production at the initiation of the movement are not reflected in jump height
(Ugrinowitsch, Tricoli, Rodacki, Batista, & Ricard, 2007b).
Given this limitation, other variables have been proposed as being better markers of
neuromuscular fatigue. These include mean power (Bosco et al., 2000a; Cormack et
al., 2008), peak power (McLellan, Lovell, & Gass, 2011a; West et al., 2014), peak
force (Bagheri, van den Berg-Emons, Pel, Horemans, & Stam, 2012; Hoffman et al.,
2002) and rate of force development (Thorlund et al., 2008b). Similar to jump height,
a strong relationship has been demonstrated between many of these variables and
dynamic performance (Lorenz, Reiman, Lehecka, & Naylor, 2013). Peak power, for
example, has been shown to have a strong correlation with 15-metre sprint time in
swimming (West, Owen, Cunningham, Cook, & Kilduff, 2011), while mean power
has been shown to vary between playing standard in rugby league (Baker & Newton,
2008) and peak force during a countermovement jump has been shown to
differentiate power lifters and Olympic lifters from a control group (McBride,
Triplett-McBride, Davie, & Newton, 1999). Such relationships support their use as a
measure for the assessment of neuromuscular response to training.
Mean Power
Mean power, defined as the average power during the concentric phase of the jump,
has also been used as a marker of neuromuscular fatigue (Bosco et al., 2000a;
Cormack et al., 2008) and has been shown to be depressed in response to activities
such as an AFL game (Cormack et al., 2008) and a marathon (Petersen, Hansen,
Aagaard, & Madsen, 2007). Unfortunately, the authors of one of these studies
(Cormack et al., 2008) seem to have incorrectly defined the end of the eccentric and
start of the concentric phase from the force-time-trace in their methodology. The end

50. 39
of the eccentric and beginning of the concentric should be defined as the point where
velocity begins to change from negative to positive (Cormie, McBride, &
McCaulley, 2008; Linthorne, 2001), at which point, ground reaction force will have
risen significantly above the initial force produced by the participant’s body mass
prior to initiating the jump. However, in this case, the authors have identified the end
of the eccentric phase as the minimum vertical ground reaction force prior to values
increasing again. In reality, what they have identified is the end of the de-load phase
(Thorlund et al., 2008b), a point at which negative displacement and velocity are just
beginning and are not near their end-points. As a result, concentric mean power
would have been incorrectly calculated and any conclusions made or comparisons
drawn from this data rendered invalid.
Decreases in mean power have also been demonstrated in sprinters following a
weight training session (Bosco et al., 2000a). However, the authors in this study only
used displacement data to calculate mean power. The validity of this method has
previously been called into question as calculating jump variables from displacement
alone requires extensive data processing of the displacement data via the double
differentiation method to determine force (Cormie, Deane, & McBride, 2007). It is
also important to recognise other limitations with the use of a single linear position
transducer. For example, if the amount of horizontal displacement of the barbell
increases during the movement, the risk of miscalculating true vertical displacement
increases as well (Cormie et al., 2007), therefore, if the bar where to move
horizontally 10 degrees the vertical velocity would be overestimated by 1.39 m.s-1
.
This, in addition to the extensive data processing involved with using the double
differential method, leads to questions about the measure’s sensitivity to pick up
changes in fatigue. Finally, while these two papers found mean power to decrease in
response to a fatiguing exercise, other studies have not. For example, no change in
mean power was also reported in young males after a soccer game (Thorlund et al.,
2009a). Finally, reliability measures for mean power have been reported to range
from 2.8% (Gathercole, Sporer, Stellingwerff, & Sleivert, 2014) to 7.8% (Hori et al.,
2009).

51. 40
Peak Power
Several studies have used peak power during a jump to track change in
neuromuscular performance (Cook et al., 2013; Cormack et al., 2008; Thorlund et
al., 2009b; Thorlund et al., 2008a; West et al., 2014), with significant changes being
reported in response to a range of activities including resistance training (Beaven,
Gill, Ingram, & Hopkins, 2011), a rugby league match (McLellan et al., 2011a) and
pulling a weighted sled (West et al., 2014). Peak power was also been reported to
correlate with twitch peak force and twitch rate of force development in eight
physically active men after a 5RM back squat (Nibali, Chapman, Robergs, &
Drinkwater, 2013). However, while the authors reported the coefficient of variation
for the twitch technique (between 5-7%) they did not report them for the jump
variables.
In papers that have reported on the reliability of peak power derived from jumping,
CVs of between 9.5% (Sheppard, Cormack, Taylor, McGuigan, & Newton, 2008)
and 2.3% (Hori et al., 2009) have been reported. One factor that contributes to this
difference in reported coefficient of variation may be the difference in methods for
the collection and analysis of peak power found in the literature. Peak power has
been derived from displacement-time data recovered from a linear position
transducer (Bosco et al., 2000b), a single linear position transducer integrated with a
force plate (Sheppard et al., 2008), two LPTs integrated with a force plate (Cormie et
al., 2010a, 2010b, 2010c) and a force plate only (Cook et al., 2013; West et al., 2014;
West et al., 2011). While the limitations associated with the single linear position
transducer method have been discussed previously, there are also major differences
between methods that integrate linear position transducer data with force plate data
and those that only use force plate data. The method used to calculate power from a
linear position transducer and force plate involves velocity data derived from the
displacement time data (collected from the linear position transducer) being
integrated with ground reaction force data (collected from the force plate). This
would appear to have a major advantage over the force plate only method were
velocity has to be calculated from the ground reaction force and will require
additional data processing steps. However, the accurate calculation of power relies
on the ability to record accurate measures of the force applied to the resistance of
interest and its resultant velocity (Lake, Lauder, & Smith, 2012). The linear position

52. 41
transducer and force plate method actually integrates velocity data from the barbell
or dowel to which it is attached and force data from the centre of mass of the
participant performing the jump. Therefore, for this method to be valid, the velocity
data derived from the linear position transducer would have to be representative of
the velocity of the participant’s centre of mass. However, research by Lake et al.
(2012) suggests this is not the case. They calculated velocity from (a) the barbell
using 3D analysis, (b) the centre of mass using 3D analysis and (c) the centre of mass
using ground reaction force and found the velocity calculated from the barbell to be
significantly different from that of the centre of mass when calculated using either
the ground reaction force data or 3D analysis (23% and 18.7% respectively). Given
this, the integrated use of linear position transducer data and ground reaction force
data as a valid method for the assessment of power during jumping is questionable
and, as such, it is unsurprising that there is such a range in the reliability reported.
Peak Force
Peak force has also been reported in studies investigating post-exercise change in
neuromuscular performance (Bagheri et al., 2012; Hoffman et al., 2002; Thorlund et
al., 2009b; Thorlund et al., 2008a). Peak force has been demonstrated to be
significantly depressed 40s post vibration training (Bagheri et al., 2012) and in
response to a competitive American football game (Hoffman et al., 2002). However,
it did not change in response to either a handball match (Thorlund et al., 2008b) or a
soccer match (Thorlund et al., 2009a). Regarding reliability, figures of between
6.4% (Gonzalez-Badillo & Marques, 2010) and 3.5% (Sheppard et al., 2008) for CVs
for this measure have been reported.
Rate of Force Development
As has been previously discussed, a strong relationship has been demonstrated
between contractile rate of force development and neural drive (Aagaard et al.,
2002). Therefore, measuring rate of force development during a jump may
potentially provide a robust measure of fatigue. However, while reviewing the
literature, only four studies were found which attempted to use rate of force
development during a countermovement jump to measure acute changes in
neuromuscular performance (Bagheri et al., 2012; Jakobsen et al., 2012; Thorlund et
al., 2009a; Thorlund et al., 2008b). Both peak rate of force development and average

53. 42
rate of force development have been assessed during jumping. Peak rate of force
development is defined as the greatest value of the first derivative with respect to
time (Moir et al., 2009). Reliability research into peak rate of force development
tends to report poor CVs, with values of 35.5% (Moir et al., 2009), 17.9%
(McLellan, Lovell, & Gass, 2011b) and 24% (Hori et al., 2009) reported. Given the
poor reliability associated with this method this review will focus on research
conducted using average rate of force development.
Average rate of force development, calculated as the force at a given time point
divided by the time it took to reach it, has been used to both monitor both adaptation
(Cormie et al., 2010a) and fatigue (Thorlund et al., 2009a; Thorlund et al., 2008b).
However, there is a lack of consistency in how the start point, in particular, is
identified, with studies calculating from the start of the eccentric phase (Cormie,
McBride, & McCaulley, 2009), the point of lowest force (Ugrinowitsch, Tricoli,
Rodacki, Batista, & Ricard, 2007a), the point where the ground reaction force returns
to equal body weight ( Thorlund et al., 2008a) and the start of the concentric phase
(Moir et al., 2009). The effect of variation in the identification of the start point is
highlighted by Hansen, Cronin and Newton (2011), who examined the effect of
analysing the same jump with three different methods of identifying the starting
point (the start of the eccentric, the point of lowest force and the start of the
concentric). They found the same group of jumps to yield average RFDs over 100 ms
ranging from -3109.3 to 9720.4 Newtons per second (N.s-1
). Taking this into
account, two papers (Jakobsen et al., 2012; Thorlund et al., 2008a) have suggested
that the average rate of force development method described by Thorlund et al.
(2008a) may represent a possible field measurement of changes in the contractile
components of the muscle. This method identifies the start point for the calculation
of average rate of force development as the end of the de-load phase, i.e. the point
when the ground reaction force has returned to equal body weight (identified at ep-
dec in Figure 2.7). In the first of these two studies, Thorlund et al. (2008a) assessed
dynamic rate of force development (0-50 ms and 0-100 ms) during a
countermovement jump followed by maximal voluntary contraction and average rate
of force development at 50 and 100 ms during an isometric leg extension and found a
positive correlation between contractile rate of force development between 0-100 ms

54. 43
and countermovement jump rate of force development between 0-100 ms (r = 0.64-
0.65 pre-post; p < 0.05).

55. 44
Figure 2.7: Vertical force time curves obtained during countermovement jump
stretch–shortening cycle contractions (Reproduced from Jakobsen et al., 2012).

56. 45
This same method was also used in a study by Jakobsen et al. (2012) who
investigated the effects of strength training in 49 untrained participants. One of many
jump variables used in this study was average rate of force development, defined as
the average change in force 0–50 ms, and a significant correlation between change in
hamstring electromyography activity and countermovement jump rate of force
development was reported.
When both the Thorlund et al. (2008a) study and the Jakobsen et al. (2012) study are
considered together, it appears that this method of calculating rate of force
development during a countermovement jump may be a valid field-measure of post-
exercise changes in neuromuscular performance. Indeed, it has already been used to
assess changes post handball (Thorlund et al., 2008a) and soccer activities (Thorlund
et al., 2009b). However, to date, none of the studies that have used these methods
have reported on their reliability. This represents a significant limitation that needs
addressed before this method is further used to characterise change in neuromuscular
performance.
In addition to the method used by Thorlund et al. (2008a), another method to ensure
a consistent starting force may be to use a squat jump to provide a dynamic
assessment of neuromuscular fatigue, as the force at the start position will be
consistent (i.e. system mass x 9.81). In theory, achieving consistent pre-tension
should allow the participant to produce more reliable average rate of force
development data. This is supported by a study looking into the effect of
familiarisation on the reliability of the squat jump which reported that average rate of
force development is a reliable measure using the squat jump once the participant is
familiar with the technique (Moir, Sanders, Button, & Glaister, 2005a). However,
until the reliability of this method, or indeed the method used by Thorlund et al.
(2008a), is assessed, they should both be viewed with caution.
An alternative viewpoint regarding the interpretation of average rate of force
development during jumping has been proposed by Moir et al. (2009). They suggest
that, rather than being a field-measure of the intrinsic qualities of the muscle, rate of
force development generated during a jump instead characterises the execution of the
jump. This would suggest that this variable is more related to the movement pattern

57. 46
executed than the contractile capabilities of the muscles involved. This does not rule
it out as a useful measure of neuromuscular performance, however, as acute changes
in movement patterns may occur to compensate for fatigued muscles.
2.3.2.2 LIMITATIONS IN RESEARCH USING FORCE PLATES TO ASSESS
JUMPING
It is also clear from the preceding section that, while there are jump variables that
have been used in the assessment of neuromuscular performance, there is also
considerable variability in the reliability scores reported for these. While some
variable-specific reasons for this have already been commented on, there are several
additional factors that may contribute to the varying levels of reliability reported.
However, given that force-only calculations performed using the double integration
method represents the most valid method for the assessment of jump variables (Lake
et al., 2012), the remainder of this section will focus on sources of error associated
with that method.
The primary factors that contribute to random error being accumulated during a jump
have been identified as (Street, McMillan, Board, Rasmussen, & Heneghan, 2001;
Vanrenterghem, De Clercq, & Van Cleven, 2001):
 the sampling frequency used;
 the method used for the measurement of body mass;
 the identification of the start of the jump and the start of integration.
Firstly, there is a lack of consistency in the sampling frequency used to collect the
data for jump analysis, with rates of 100 Hz (Bagheri et al., 2012), 200 Hz (Cormack,
Newton, McGuigan, & Doyle, 2008; Gathercole et al., 2014; Nibali et al., 2013; Teo
et al., 2011), 300Hz (AragonVargas & Gross, 1997), 500 Hz (Feldmann, Weiss,
Schilling, & Whitehead, 2012; Hansen, Cronin, Pickering, & Douglas, 2011; Tillin,
Pain, & Folland, 2013), 750 Hz (Rousanoglou, Georgiadis, & Boudolos, 2008), 800
Hz (Coh & Mackala, 2013; Richter, Rapple, Kurz, & Schwameder, 2012) and 1000
Hz (Andersson et al., 2008b; Oliver et al., 2008; Pereira et al., 2009; Thorlund et al.,
2009a; Thorlund et al., 2008b) being reported. The reliability of sampling at lower

58. 47
rates has been questioned (Hori et al., 2009; Street et al., 2001) and, for the analysis
of jumps, a minimum sampling frequency of 1000 Hz is recommended (Owen,
Watkins, Kilduff, Bevan, & Bennett, 2013; Street et al., 2001).
Secondly, the impulse momentum method is very sensitive to correct body weight
determination (Vanrenterghem et al., 2001). In a study into the sources of error
associated with calculating jump performance via ground reaction force data, Street
et al. (2001) reported that even a small error of 0.13% in body weight results in the
accumulation of 3.3% error in jump height due to integration process. To minimise
this, they recommend a pre-jump weighting phase of ≥ 1.5 s or 1500 samples.
However, few of the reviewed studies provide a description of how they have
calculated body weight. Of those that do, body weight has been reported to be
calculated from the mean of 44 samples (Moir, Sanders, Button, & Glaister, 2005b)
though to the mean of 4000 samples (Buckthorpe, Morris, & Folland, 2012).
Finally, correctly identifying the start of the jump has also been identified as key to
ensuring reliable and valid jump data is returned and failure to do so can increase the
degree of random error encountered (Street et al., 2001; Vanrenterghem et al., 2001).
To ensure an accurate identification of the start of the jump, Street et al. (2001)
suggest body mass ± 1.75 times the peak residual during the weighting period, while
Vanrenterghem et al. (2001) suggest adding the standard deviation of the GRFs
during the weighting phase to the maximum force recorded during that period. Both
methods ensure that the noise (either from the equipment or the subject) produced
prior to the start of the jump is accounted for. While variations of these methods
have been used in the research to date (Kilduff et al., 2011; Lamas et al., 2012; Moir,
2008; Moir et al., 2009; Owen et al., 2013; West et al., 2014; West et al., 2011a;
West et al., 2011b), other methods which do not account for the noise produced prior
to the start of the jump have also been reported. These include identifying the start
time as a 10-Newton change from body weight (McLellan et al., 2011b), a 5%
reduction in ground reaction force (Cormack et al., 2008; Nibali et al., 2013) and
body mass minus 5% (Ugrinowitsch et al., 2007a). Such methods may lessen the
likelihood of correctly identifying the start of the jump, which would have
implications for variables such as length of eccentric phase. The accurate
identification of the start of the jump also has implications for the time point at

59. 48
which integration is started. Street et al. (2001) recommend the integration process
within 0.2 s of the start of the jump in order to minimise random error. However, of
the studies reviewed, only West et al. (2011) identified in their methodology when
the integration process began.
Finally, the reliability of jump variables will also be potentially affected by the
expertise of the subjects. While no familiarisation was reported to be required, along
with a low coefficient of variation for jump height (5.6%) for physical active college
aged males (Moir et al., 2009), inter-session CVs of up to 14.48% have been reported
for jump height when assessing young males (mean age 13.5 ± 0.5 years; Lloyd,
Oliver, Hughes, & Williams, 2009), suggesting that the need for familiarisation may
be population specific.
2.3.3 SUMMARY
The previous section highlights some significant issues associated with methods
aimed at identifying the origin of neuromuscular fatigue. In addition to the
limitations of the key methods currently used in the research reviewed, it has been
demonstrated that the changes observed during laboratory-based measurements may
not be reflective of the changes in functional dynamic performance. Given this in
addition to the multiple contributions of the central and peripheral nervous systems,
along with the potential influence played by post activation potentiation, changes in
muscle temperature and the endocrine system in acute changes in neuromuscular
function, it is suggested that a more dynamic functional movement may provide a
better representation of changes in the neuromuscular system. In particular, it is
proposed that jump performance may represent a more valid measure. Given the
various factors that may be contributing to changes in jump performance, it is more
appropriate to view such tests as changes in neuromuscular performance rather than
as direct measures of neuromuscular fatigue.
From reviewing the literature, peak power and, in particular, the Thorlund et al.
(2008a) average rate of force development method, have been suggested to be valid
field-measures of changes in the contractile components of the muscle. While this is
interesting, the research into average rate of force development is limited by the lack
of published reliability data on this method for assessing average rate of force

60. 49
development. Given this, and the general low reliability reported for average rate of
force development measures assessed over short time periods, it is suggested that
further research is required into this method prior to it being used in the assessment
of change in neuromuscular performance.
This section also demonstrates that there is a lack of consistency in the methodology
used to collect and calculate other commonly reported jump variables. In addition to
the random error introduced to the calculations by variations in sampling frequency,
measurement of BW, identification of the start of the jump and the start of
integration, there are potentially variations in the degree of systematic bias that
results from differences in the populations used in the studies. Given this, there is an
identified need to undertake research into the reliability of the jump variables of
interest with a participant group representative of the one to be used in future studies
in order to ensure the robustness of these measures.
2.4 NEUROMUSCULAR RESPONSE TO TRAINING
To date, there has been considerable research performed into the acute
neuromuscular response to a wide range of activities including competitive marathon
running (Petersen et al., 2007), rugby (Duffield, Murphy, Snape, Minett, & Skein,
2012; West et al., 2014), soccer (Andersson et al., 2008a) and AFL (Cormack et al.,
2008) through to novel training methods, for example, sled pulling (West et al.,
2014) and occlusion training (Beaven, Cook, Kilduff, Drawer, & Gill, 2012). To
provide a detailed review is beyond the scope of this thesis and, instead, this section
will focus on the acute neuromuscular response to training aimed at developing
strength, speed and power. Specifically, this will focus on resistance training aimed
at developing hypertrophy, maximal strength and power; plyometric training sessions
and training sessions aimed at developing sprinting speed.
2.4.1. NEUROMUSCULAR RESPONSE TO RESISTANCE TRAINING
While resistance training can be used to generate a wide range of adaptations, this
review will focus on types of resistance training sessions aimed at developing
strength, hypertrophy or power. Several studies have investigated the neuromuscular

61. 50
response to strength, hypertrophy and power-focused resistance-training sessions,
however, there is a lack of continuity in terms used to describe the protocols. For
example, in a study by Linnamo et al. (2000) five sets of 10 repetitions with a load of
70-75% of 1RM was classed as heavy resistance training, while a near identical
protocol of four sets of 10 repetitions at 70% of 1RM was classified as hypertrophy
in another study (Beaven et al., 2008). In terms of sets and repetitions, strength
protocols have ranged from the ones previously described to include three sets of
five repetitions at 85% of 1RM (Beaven et al., 2008) to 20 sets of two to four
repetitions at 70% of 1RM (Bosco et al., 2000a). This creates difficulty in comparing
results as variations in intensity, time under tension and rest period even when
volume is normalised across protocols, have been shown to affect the neuromuscular
system differently (McCaulley et al., 2009).
As such, for the duration of this review, the following definitions, summarised in
Table 2.1, will be used (Kraemer, Duncan, & Volek, 1998). Strength/heavy power
training will be taken to mean protocols which utilise six or less repetitions, have a
recovery between sets of equal or greater than three minutes and, where load is
reported, utilise a load of 80% of 1RM or more. Hypertrophy training will include
studies which report using eight or more repetitions, have a recovery of two minutes
or less and use loads of 70 to 75% of 1RM. Finally, explosive power exercises will
have no more than 10 repetitions, have recoveries of equal to or greater than three
minutes and have loads no greater than 45% of 1RM.

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Table 2.1: Definitions of strength hypertrophy and explosive power training
Grouping Repetitions Recovery between
sets (minutes)
Load (% of 1RM)
Strength/heavy power ≤6 ≥3 ≥80%
Hypertrophy ≥8 ≤2 70 to 75%
Explosive power ≤10 ≥3 ≤45%
RM = repetition max

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Immediate post resistance training response
To date, only one study has compared the neuromuscular fatigue response of
resistance exercise using protocols that match these definitions (McCaulley et al.,
2009). In this study, peak force and average rate of force development at 200ms
during an isometric squat where compared pre, immediately post, 60 minutes post,
24 hours post and 48 hours post three different resistance training sessions.
 Session one was a hypertrophy training session consisting of four sets of 10
repetitions at 75% of 1RM and 90 s recovery between sets.
 Session two was a strength session consisting of 10 sets of three repetitions
at 90% of 1RM with 5 minutes recovery between sets.
 Session three was an explosive power session consisting of eight sets of six
repetitions at body weight with 3 minutes recovery.
All sessions were performed using the squat or, in the case of the power session, the
jump squat. Participants had a minimum of two years training history and the
schemes where set up to match each other in terms of total work done (Table 2.2).

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Table 2.2: Comparison of hypertrophy, strength and power schemes utilised in
McCaulley et al. (2009). Values expressed as means (standard deviation)
Reproduced from McCaulley et al. (2009)
Hypertrophy Strength Power
Work (J) x 10-3
84.2 (8.5) 84.2 (19.7) 77 (22.5)
Intensity (% of 1RM) 72.8 (2.47)a
89.3 (1.36)a,b
0 (0)
Total repetitions 37 (3)c
33 (0) 48 (0)b
Rest period (minutes) 1.5 5 3
Velocity (m.s-1
) 0.83 (0.12) 0.82 (0.04) 3.67 (0.09)b,c
Force (N) 2468.8 (104.5)a
2890.5 (55.0)a,b
2185.9 (92.2)
Power (W) 1882.1 (492.0) 2143.9 (144.3)b
5831.5 (211.7)b,c
Time/rep (m.s-1
) 2298.4 (311.7)a
2856.5 (214.3)a,b
736.9 (132.6)
a
= significant (P < 0.05) difference from power
b
= significant (P < 0.05) difference from hypertrophy
c
= significant (P < 0.05) difference from strength

65. 54
This study reported that peak force and rate of force development declined in
response to the strength and hypertrophy sessions but not in response to the power
session. electromyography was also measured and it was reported that the strength
session resulted in significant decreases in muscle activity, while the hypertrophy
session resulted in a slight increase in muscle activity and no change was found with
the power session. These results would appear to suggest that, while strength and
hypertrophy loading both produce similar changes in neuromuscular performance,
the mechanisms may be different. As noted previously, these protocols involved the
participants performing a similar amount of total work. The results from this study
would, therefore, seem to suggest that intensity, repetitions per set and recovery
between sets all play a more important role than volume on neuromuscular
performance. However, it is possible that these results are linked to the differing
degrees of metabolic accumulation which may have occurred due to the longer sets
and shorter recovery periods associated with the hypertrophy protocol. In addition, it
seems possible that the longer recovery periods and higher intensity may have
resulted in greater muscle damage during the strength session. Nevertheless, in terms
of this specific study, both suggestions remain theoretical as markers of muscle
damage and metabolic accumulation were not taken.
Similar findings regarding the response to hypertrophy training were reported in a
study by Bosco et al. (2000b) which investigated mean lower body power and
electromyography in six bodybuilders pre- and post a session consisting of 12
compound sets of half squat, leg extension and leg curl at loads of 70-75% 1RM.
Each exercise was performed for 8-12 repetitions and one to two minutes was
allowed between compounds. The study reported an increase in electromyography
activity with no change in force output fatigue post training, suggesting changes
primarily occurred at a peripheral level. In the same study, male sprinters performed
a strength session consisting of six series of three sets of squat with six, six and four
repetitions at a load of 80% 1RM and with three minutes between sets and eight
minutes between series. The study found that muscle activity, assessed using
electromyography amplitude, was maintained while average power per repetition
decreased. These results would suggest that the strength protocol resulted in
peripheral fatigue and, therefore, is in contrast to the findings of McCaulley et al.,
(2009).

66. 55
As previously suggested, one explanation for the difference in response to strength
and hypertrophy schemes is the differing degree of metabolic disturbance caused.
Significant increases in lactate have been found to occur post hypertrophy type
loadings due to the high volume load performed (Brandenburg & Docherty, 2006;
Linnamo, Hakkinen, & Komi, 1998). Both studies report these increases in lactate to
correlate with decreases in force production and metabolic accumulation has been
suggested as a cause of peripheral fatigue.
Isometric force and electromyography have also been compared pre- and post a
bilateral knee extension explosive power loading protocol (Linnamo et al., 1998).
However, in contrast to McCaulley et al., (2009), this study reported significant
decreases in electromyography activity during the 0-100 ms phase of the isometric
contraction after the explosive power loading suggesting a degree of fatigue. The
fact that both studies found different responses to explosive resistance training
requires further examination. While it is possible that the use of an isometric squat
lacks the sensitivity of a knee extension, it is also possible that the differences
between the protocols played a role. In the Linnamo et al. (1998) study, a load of
45% 1RM was used while McCaulley et al. (2009) used body weight, which has
been shown to equate to around 30% 1RM when equated of system mass (Dugan,
Doyle, Humphries, Hasson, & Newton, 2004). As such, this raises the possibility
that, in the absence of volume, intensity may play a role in stimulating a fatigue
response. Finally, the potential role of post activation potentiation, change in muscle
temperature and possible non-genomic endocrine contributions cannot be ruled out in
explaining why a lack of decline immediately post was observed in McCaulley et al.
(2009).
Post resistance training recovery
While these studies help us to identify the acute response to strength, power and
hypertrophy training, they tell us little about the more prolonged changes that may
occur and may impact training performance. Indeed, post-exercise changes in
neuromuscular performance are of significant interest to both coaches and athletes
when designing a training plan as they look to ensure each training load is applied at
an optimal time. Limited research has been conducted on the specific recovery
pattern post strength, power and hypertrophy training. Only one study has looked at

67. 56
the recovery pattern after a strength training session (McCaulley et al., 2009) and
found both average rate of force development at 200 ms and peak force to have
recovered from their initial decline and returned to baseline 60-minutes post.
However, when they were reassessed after 24-hours, rate of force development had
significantly declined again while peak force was unaffected. This type of bimodal
recovery pattern is not just limited to resistance training and has also been found to
occur after long duration continuous activity like marathon running (Avela,
Kyrolainen, Komi, & Rama, 1999) and in intermittent sprint sports like Australian
rules football (Cormack et al., 2008) and soccer (Andersson et al., 2008a). The exact
reasons for a bimodal recovery pattern are not clear. However, the secondary decline
would not be linked to metabolic factors, as sufficient time would have passed to
allow metabolic recovery. Instead, it is possible that these secondary decreases could
be linked to inflammatory or remodelling processes that are not initiated until two to
six hours post-exercise as a result of exercise-induced muscle damage (Dousset et al.,
2007). However, the recovery could also be linked to factors outlined in section 2.1,
such as post activation potentiation, endocrine response and/or changes in muscle
temperature. Indeed, the neuromuscular system has actually been shown to be
enhanced in the hours post strength-focused protocols (Cook et al., 2013). While this
study has already been discussed in detail, it does demonstrate how neuromuscular
performance in the hours post-training may be linked to alterations to the normal
testosterone circadian pattern. However, in the McCaulley et al., (2009) study, a
bimodal recovery pattern was not reported after the hypertrophy protocol. Here, the
initial decreases in both average rate of force development and peak force recovered
after 60-minutes and showed no further decline. Other studies, however, have
reported time-frames of up to 48-hours to recover from a hypertrophy type session
(Linnamo et al., 1998).
It appears, therefore, that the immediate neuromuscular response and subsequent
recovery in the hours and days post resistance training are sensitive to the type of
session (i.e. strength, hypertrophy or power) undertaken. However, while there may
be a general trend even within these protocols, there is a lack of consistency
regarding the response. While this may be due to slight variations in the protocols
used, other factors related to the participant population may affect the response. For
example, weak and untrained muscle has been shown to react differently to loading

68. 57
than trained muscle (Pingel, Moerch, Kjaer, & Langberg, 2009) and strength-trained
athletes have been shown to experience more fatigue post-exercise than non-
strength-trained participants (Ahtiainen & Hakkinen, 2009).
2.4.2 NEUROMUSCULAR RESPONSE TO PLYOMETRIC TRAINING
Plyometric training represents another method which has been shown to improve
explosive leg power (Cormie et al., 2009; Markovic, Jukic, Milanovic, & Metikos,
2007; Vissing et al., 2008). Plyometric training activities are normally characterised
by rapid stretch shorting cycle muscle activities (Cormie, McGuigan, & Newton,
2011).
Immediately post plyometric training
Decreases in neuromuscular response have been demonstrated to occur immediately
after a plyometric training session consisting of alternate single-leg bounds (3 x 20),
jumps over 40 cm cones (8 x 5), alternate leg power skips (3 x 20), lateral hopping
with two jumps each direction over 30cm cones (4 x 10) and depth jumps from a 60
cm height (4 x 3; Drinkwater, Lane, & Cannon, 2009). Using a combination of
isometric knee extension and evoked twitch, the authors reported a significant
decrease in performance under evoked conditions while, at the same time, failing to
observe any change during voluntary activation. This lack of change in central drive
or voluntary activation, coupled with the decrease evoked muscle force production
and muscle relaxation time, would suggest the loss of force was due to changes in the
peripheral system.
The effect of different volumes on foot contacts on acute changes in neuromuscular
performance following plyometric training has also been investigated (Cadore et al.,
2013). In this study, the acute neuromuscular, metabolic and endocrine responses to
hurdle jump sessions consisting of either 100, 200 or 300 jumps were investigated in
rugby players. Participants were assessed at four different time-points: pre,
immediately post, 8 hours post and 24 hours post. This study found no difference
between the neuromuscular, hormonal or metabolic responses across any of the
protocols at any time-point. This would seem to suggest that there might be a
‘threshold’ upon which the neuromuscular response to training is induced and this, in

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turn, may have implications for the addition of other training on the same training
day. However, it is unclear from this study as to what effect volume would have had
if the jumps had been of either a higher or lower intensity.
Post plyometric training recovery
In Drinkwater et al. (2009), the decline in neuromuscular performance which
occurred immediately after a high volume plyometric training session was found to
be relatively short lasting and performance returned to baseline two hours post. A
similar initial recovery pattern was also found in a study that investigated the effect
of performing drop jumps until the participants could no longer sustain 70% of their
peak height (Dousset et al., 2007). However, while function was initially returned
relatively quickly, a secondary decrease in performance occurred after the two hour
mark, resulting in a bimodal pattern of recovery similar to that discussed in 2.3.1.
The time-point at which this secondary decline occurs is unclear. While Dousset et
al. (2007) suggest that the second decline is linked to the onset of inflammatory
processes (occurring two to six hours post), jump performance was found to be
unaffected eight hours post in one study before undergoing a decline at 24 hours post
(Cadore et al., 2013), and it is possible that changes in temperature and hormonal
status etc. may have prevented its onset.
However, not all studies report a bimodal recovery pattern in response to plyometric
exercise. For example, Twist and Eston (2005) reported declines in power output
from 30 minutes to 72 hours post 10 sets of 10 maximal vertical jumps with a one
minute recovery between sets. Furthermore, the study by Chatzinikolaou et al.
(2010) found no initial decline in squat jump, countermovement jump or peak force.
However, when the variables where reassessed 24 hours post, declines were evident
in both jumps. This is similar to the findings of Cadore et al. (2013) who also did not
observe any decline in jump performance until 24 hours post. These findings
highlight the importance of monitoring the neuromuscular response to training in the
hours and days post-training, even in the absence of an immediate post-exercise
change.

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2.4.3 NEUROMUSCULAR RESPONSE TO SPEED TRAINING
Speed is a key physical characteristic that has been shown to separate elite athletes
from non-elite athletes (Lorenz et al., 2013). While undertaking specific speed
sessions is a common practice in athletics, it is also a practice used by other sports at
the elite level. Indeed, this approach is supported by recent research that
demonstrated significant improvements in the 40 m time of soccer players due to
separate speed sessions within the training week (Tonnessen et al., 2011). The
authors concluded that these improvements were likely related to the specificity of
the training session.
Immediate post speed training response
However, despite the obvious importance of speed and of the use of speed training
sessions, only a limited number of studies have examined the acute neuromuscular
response to sprint training. Duffield et al., (2010) tested isometric force production of
the knee extensors immediately after a training session that consisted of a
combination of a 20 m sprint followed immediately by 10 m bounds, repeated every
minute for 10 minutes. Isokinetic voluntary performance and peak twitch force in
the quadriceps were measured and depressions in both were found both immediately
and 2 hours post the session. Perrey et al., (2010) employed the same methods to
assess fatigue after a set of 12 x 40 m sprints with a 30-second recovery and reported
decreased neuromuscular performance immediately after.
It is important to highlight that both these protocols differ significantly from those
used to develop speed in elite level sport (Francis, 2008). Indeed, the short recoveries
and high total running volume would not be recommended for elite athletes as they
would produce a less than optimal training adaptation (Ross et al., 2001) due to the
high metabolic accumulation that occurs with such short recovery times. As a result,
the athlete’s ability to produce the velocities required to optimise adaptation becomes
limited. This was clearly demonstrated in the Duffield et al. (2010) study where
sprint times declined 10.6 ± 7.5%, 10-metre bound distance declined 10.0 ± 4.4%
and lactate reached values of 19.6 ± 3.2 mmol/l. As a result, neither of the studies
documented above involved protocols which could be seen to be representative of
sprint training sessions designed to facilitate improvement in maximum speed and,

71. 60
instead, are more likely to enlist anaerobic adaptations. As such, few conclusions
regarding the degree and/or origin of fatigue developed post sprint training can be
made.
To date, only one study has come close to assessing neuromuscular fatigue after a
protocol that allowed sufficient recovery (Pullinen et al., 2005). In this study,
electromyography and isometric maximal voluntary contraction where collected
immediately before and after a session of 10 x 50 m sprints, with a four minute
recovery between each. While maximal voluntary contraction decreased,
electromyography did not change, suggesting peripheral mechanisms. Interestingly,
this session also produced significant levels of lactate (13.8 ± 2.1 mmol/l) which,
while not as high as the levels reported by Duffield et al. (2010), were still slightly
higher than those previously reported after a hypertrophy session (Linnamo et al.,
1998). This would suggest that the metabolic response to speed training may be more
similar to hypertrophy type resistance training than to strength or explosive power
training, even when four minutes recovery between repetitions is allowed. It is
important to note, however, that 500 m of maximal speed training in a session is
significantly higher than would be recommended by elite speed coaches (Francis,
2008).
Recovery post speed training
While the limited relevance of the Duffield et al. (2010) protocol to the type of speed
training untaken by elite athletes has already been discussed, the study does report on
the recovery pattern displayed by its participants. In the study, data on voluntary
contraction and evoked force in the quadriceps was collected immediately, 2 hours
and 24 hours post. In this study, no bimodal recovery response was found, with the
participants’ voluntary contraction and evoked force being significantly depressed
immediately and 2 hours post, before returning to baseline 24 hours post. Finally, as
discussed previously, acute elevations in neuromuscular performance have, however,
been demonstrated following a speed type workout (Cook et al., 2013).

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2.4.4 MULTIPLE SESSIONS
Many sports require elite athletes to undertake more than one session per day
(Cormack et al., 2008; Hakkinen, Pakarinen, Alen, Kauhanen, & Komi, 1988;
Hoffman, Kang, Ratamess, & Faigenbaum, 2005). Indeed, this practice is supported
by previous research which, using this multiple daily session approach, demonstrated
improvements in the isometric peak force of the knee extensors in both female (4.8 ±
5.0%) (Hakkinen & Kallinen, 1994) and male (5.1 ± 10.2%) weight lifters (Hartman
et al., 2007), along with peak power, mean power and onset of blood lactate
accumulation during cycling (Ijichi et al., 2014). However, as discussed in sections
2.4.1 to 2.4.3, different training methods result in different degrees of neuromuscular
fatigue and damage. Therefore, it is important to consider the combined effect of two
sessions, and their order, on both the recovery and fatigue profiles to determine if the
second training session results in higher levels of fatigue in the hours or days that
follow as this would have implications for the subsequent training days and for
competition preparation. Given this, it is quite surprising that, to date, only a limited
number of studies have assessed the neuromuscular and/or endocrine response to
multiple training sessions (Chiu et al., 2004; Cook et al., 2013; Hakkinen, 1992;
Hakkinen et al., 1988; Skurvydas, Kamandulis, & Masiulis, 2010a, 2010b). In the
earliest of these, the neuromuscular response to a morning session, performed
between 0900 and 1100, and an afternoon session, performed between 1500 and
1700, was investigated in eight weightlifters (Hakkinen et al., 1988). Both sessions
were similar and contained a mix of Olympic and strength lifts, with slightly higher
intensity and lower volume in the morning session. The study reported
neuromuscular performance, as measured by isometric maximal voluntary
contraction and electromyography, to decline after the first session, recover between
the sessions, and then undergo a decline after the second session, with no difference
between the degrees of fatigue experienced reported.
A further study by the same group of authors had participants perform four sets of
six repetitions of between 70-80% 1RM during the first session (performed between
1000 and 1100) and four sets of two to three repetitions with loads between 70 and
100% 1RM during the second session (performed between 1500 and 1600;
Hakkinen, 1992). The first session resulted in change in maximal voluntary

73. 62
contraction without any change in electromyography, while the more intensive lower
volume second session induced decreases in the males but not in the females.
However, the participants had recovered between the two sessions. Both studies
suggest that five hours is sufficient for acute neuromuscular recovery regardless if
the weights sessions is maximal strength in nature or has a higher volume bias.
Interestingly, a more recent study reported that contractile rate of force development
(at 30 ms and 200 ms) was significantly higher prior to the start of the second
compared to the first session of the day (Storey et al., 2012). This occurred in
response to the participants performing two sessions, four to six hours apart, of 10
front squats, with each repetition separated by two minutes, at 90% 1RM. The exact
reasons as to why this occurred are not clear. As discussed in section 2.1.4, muscle
temperature enhances the contractile components of the fibre and, as a result, there is
a strong correlation between circadian changes in force production and temperature.
Indeed, the authors speculate that normal circadian increases in force production may
counteract the fatigue produced in session one and therefore explain the recoveries
reported in the literature. Two studies report performance in the afternoon may not
only to be maintained but actually be enhanced after a morning session (Cook et al.,
2013; Ekstrand et al., 2013). Cook et al. (2013) was discussed in detail in section
2.1.4.1 and linked the improved afternoon performance to a change in the circadian
pattern of testosterone, demonstrating an important influence of the endocrine system
on neuromuscular performance. In Ekstrand et al. (2013), 14 college-aged throwers
performed an early morning weight training session (between 0800 and 1000) where
they built to a 1RM in the back squat and a 4RM in the power clean. They returned
four to six hours later and tested backward overhead shot throw and
countermovement jump. Results were compared to a separate day on which no
morning weights sessions were performed (control day). Backward overhead shot
throw was significantly greater for the control day; however, there was no difference
in countermovement jump performance. It is unclear why backward overhead shot
throw performance was improved and countermovement jump was not. One
explanation put forward by the authors is that the backward overhead shot throw held
greater biomechanical similarity with the clean, resulting in greater transfer. It can
also be speculated that the neuromuscular recovery reported at the start of session
two, may be the result of the neuromuscular fatigue element being masked by other

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factors (e.g. endocrine and temperature), rather than actual recovery. This may be an
additional explanation for the bimodal recovery pattern previously discussed and
further highlights the importance in tracking neuromuscular performance into the
following day. However, to date, only two of the reviewed studies that used multiple
daily sessions reported on the neuromuscular recovery in the following days
(Skurvydas et al., 2010a, 2010b).
Skurvydas et al. (2010b) examined the effect of two bouts of 50 maximal effect
jumps separated by 60-minutes on neuromuscular performance up to 48-hours post.
While it was reported that the second bout did not result in any additional loss in
performance, the study also found neuromuscular performance to be depressed 48-
hours post. In the second study, the subjects also undertook a second protocol where
they performed 30-second maximal effort cycling sprints, again separated by 60-
minutes (Skurvydas et al., 2010a). While neither protocol resulted in any additional
fatigue after the second session, there was evidence of better neuromuscular recovery
48-hours post the cycling protocol. In both studies, it is suggested that the repeat
bout effect may protect the athlete from any further damage. The repeat bout effect
occurs when an initial bout of eccentrically biased physical activity, though causing
muscle damage itself, is shown to provide protection against additional damage from
a subsequent bout of eccentric exercise by inducing less severe delayed onset of
muscle soreness and a lessened elevation of markers of muscle damage (Chen, 2003;
Nosaka & Newton, 2002b; Nosaka, Sakamoto, Newton, & Sacco, 2001b), along with
a decreased negative effect of performance (Chiu et al., 2004). How long this
protection lasts after the initial bout is unclear, with time frames up to nine months
(Nosaka, Sakamoto, Newton, & Sacco, 2001a) having been reported. While the exact
mechanisms behind the repeat bout effect are unknown, one possible explanation is
that the damage during the first bout occurs only to the weaker areas of certain fibres
and, as a result, the fibres susceptible to mechanical stress are already damaged prior
to the start of the second session (Byrne & Eston, 2002b). A second possibility is
that improved neuromuscular recruitment patterns result in more effective dissipation
of force across recruited muscle fibres (McHugh, Connolly, Eston, & Gleim, 1999).
In all the studies referenced above, the second stimulus was effectively the same as
the first. When the second stimulus is of a higher intensity than the first, the outcome

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is not as clear. One study suggests this may result in a slight retardation of the
recovery process (Nosaka & Newton, 2002a) while another concludes that muscle
damage is not elevated and recovery is not retarded, even when the second session is
of a higher intensity (Chen, 2003).
Indeed, the training of many athletes often requires them to undertake multiple
sessions containing both lifting and running elements on the same day (Cormack et
al., 2008; McLean, Coutts, Kelly, McGuigan, & Cormack, 2010). It remains
unknown what effect sessions training different elements would have on each other,
for example, would there be a repeat bout effect when a speed session precedes a
strength session or vice versa. While answers to this question would provide coaches
with critical information relating to the planning of such sessions, to date no research
has been carried out in this area and it represents an important area for future study.
It is also difficult to verify that a repeat bout effect actually occurred during
Skurvydas et al. (2010a) and Skurvydas et al. (2010b), as there was not a one day
session to compare it to. In both studies, creatine kinase was used as the indirect
marker of muscle damage. Given that creatine kinase does not peak until 48-72 hours
post (Deschenes et al., 2000), it is impossible to truly know if the addition of a
second session did or did not exasperate the response. This represents a major
limitation regarding the conclusions that can be drawn.
2.4.5 EFFECT OF TRAINING ORDER
Along with the type and number of sessions performed on a given training day, a
third factor which could potentially affect performance and adaptation is session
order. It has been shown that exercise order can affect some of the cell signalling
pathways and gene expressions related to training adaptation (Coffey, Pilegaard,
Garnham, O'Brien, & Hawley, 2009) and that the interference resulting from sprint
intervals is greater than from endurance training (Coffey, Jemiolo, et al., 2009).
Indeed, the potential importance of session order is illustrated by Cook et al. (2013).
While this study has been reviewed in-depth elsewhere in this review, it is interesting
to compare the effect of a speed training session in the morning on afternoon strength
to that of the effect of a strength training session in the morning to afternoon speed

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performance. It is clear from the results presented in Table 2.3 that, while performing
a weights session in the morning enhanced afternoon sprint performance, performing
a speed session in the morning did not enhance sprint performance. While this study
was primarily focused on inducing afternoon performance, neural adaptations in
particular are reported to be sensitive to training intensity (Tan, 1999). Therefore, it
is important that sessions requiring maximal effort are performed when the athletes’
nervous system is in the optimal state.

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Table 2.3: Afternoon strength, speed and power performance following three
different conditions (control, morning speed training, morning weight training).
Reproduced from Cook et al. (2013)
Condition Performance
3RM Bench
3RM Squat 40m sprint Countermovement
jump Power
Control 139 ± 12 168 ± 10 5.23 ± 0.17 4292 ± 365
Sprint 140 ± 14 169 ± 12 5.19 ±0.19a
4317 ± 422
Weights 144 ± 14b
175 ± 13b
5.16 ± 0.16a,b
4408 ± 378b
a
Indicates different to control
b
Indicates different to all other conditions

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In the course of this review, no other study was found which provided any
information on the effect of sprint and strength training session order. Indeed, while
research has been conducted looking at the effect of exercise order within a training
session (Chaves et al., 2013) and into the effect of performing a strength session
either immediately before or after an aerobic or anaerobic session (Cadore et al.,
2012; Coffey, Jemiolo, et al., 2009; Coffey, Pilegaard, et al., 2009; Rosa et al., 2012;
Schumann et al., 2013; Taipale & Hakkinen, 2013), only one other study to date was
found that investigated the effect of session order where a break between sessions
was inserted to allow recovery (Doma & Deakin, 2013). This study examined the
effect of strength training followed six hours later by endurance training compared
with an endurance training session followed 6 hours later by a strength training
session. The endurance training session consisted of 20 minutes steady state running
followed by four, one and a half minute intervals of increasing rest periods, while the
strength training session involved six sets of six repetitions in the incline leg press
and four sets of six repetitions in the leg extension and leg curl machines. The study
found that the cost of running was significantly higher at 24 hours post when the
strength session preceded the endurance session. It is unclear as to why this occurred
but it is possible that performing the weights session prior to running resulted in the
running session generating more fatigue or that the endurance session generated
more fatigue and that there was not sufficient time to recover from it. Previous
research has reported that performing endurance training after resistance training
exasperates inflammation when compared to endurance training followed by weights
(Coffey, Pilegaard, et al., 2009), which may offer a possible explanation for the
decreased performance at 24 hours post.
2.4.6 SUMMARY
The previous section provides an overview of the current literature on the acute
responses to single and multi-session training days aimed at developing strength,
speed and power. It is clear that, even within the spectrum of strength, power and
hypertrophy focused resistance-training sessions, there is significant variation in both
the degree of fatigue and pattern of recovery experienced. When the resistance
training response is considered in conjunction with the responses to plyometric and
speed training, a somewhat consistent pattern begins to emerge. It would appear that,

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if the training sessions result in significant metabolic fatigue, an initial depression in
performance will emerge. It also appears that, if the session results in significant
muscle damage or inflammation, decreased performance is also likely to be observed
24 - 48 hours post.
Several studies also report neuromuscular recovery and, in some cases enhancement,
in the time-frame after metabolic fatigue would have dissipated and, potentially,
before the inflammation processes would have been initiated. While this bimodal
recovery pattern may represent a period of recovery between these two processes, it
is also possible that other factors, such as changes in circadian pattern of testosterone
and cortisol and/or changes in muscle temperature, may be involved.
The review of the available literature on training aimed at developing speed has
demonstrated that, to date, really only one study has been published which
investigated the acute response to an appropriately designed speed session (Pullinen
et al., 2005). However, while this study provided information of the changes that
occur immediately after speed training, it did not provide information on the
recovery or potential secondary decline that may or may not have occurred. This
represents a real limitation in our current understanding of the recovery pattern
associated post sprint training.
The research into multiple training sessions being performed on the same day
suggests that a second session being performed on the same day does not result in
any additional fatigue. However, there are clear limitations in this research as, to
date, no study has compared the recovery from one session versus two over a 24-
hour period. In addition, the majority of research looking at multiple sessions have
used protocols were both sessions are the same. While this is a common practice in
weightlifting, it is not representative of sports like athletics or rugby.
Finally, it is clear from previous research that session order may significantly affect
performance across the training day (Cook et al., 2013). This is an important
consideration when ensuring that adaptation is optimised. It has also been
demonstrated that the order that endurance and strength training sessions are
performed in can affect recovery the following day (Doma & Deakin, 2013).

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However, it is unclear if variations in the order of sprint training and strength
training would have similar effects and this represents an area in need of further
study.
2.5 CHAPTER CONCLUSIONS
It is clear from this review that exercise can induce a range of physiological
responses which, in turn, have an effect on the neuromuscular system at both central
and peripheral levels. These physiological responses have been shown to both
enhance the neuromuscular system (e.g. post activation potentiation, increased
muscle temperature and change in testosterone and cortisol concentrations) and
inhibit it (e.g. muscle inflammation, accumulation of H+ etc.). To further complicate
matters, while both positive and negative contributions to the neuromuscular system
can be occurring at the same time-point, the time-frames during which they have an
effect on the neuromuscular system will vary. For example, the effects of
testosterone may last for several hours (Cook et al., 2013), while the effects of post
activation potentiation will last only several minutes (Wilson et al., 2013). This
creates a challenge to the researcher in identifying either the degree of fatigue
induced or the mechanisms contributing to it as, at any given time, there are several
different factors adding to and/or subtracting from the neuromuscular output.
Therefore, it is argued that it is more appropriate to talk in terms of ‘change in
neuromuscular performance’ as this term accounts for all the various potential
contributions.
There are a number of potential methods available to the researcher for the
assessment of change in neuromuscular performance. These include both laboratory-
based (electromyography, interpolated twitch technique etc.) and field-based
measures (jumping, running etc.). While there are many benefits to the use of
laboratory-based measures (e.g. the potential to identify the central or peripheral
nervous system as the origin of fatigue) there are also some major limitations
associated with their use most notably, Requena et al. (2009) have demonstrated that
changes observed during laboratory-based measurements may not be reflected in
functional dynamic performance. In addition, it has also been reported that
laboratory-based parameters and measures of functional dynamic performance may

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recover at different rates (Andersson et al., 2008a). Both these factors limit their
usefulness for researchers interested in assessing the ability to compete or optimally
undertake training. Given this, it was concluded from the review that more dynamic
and functional measures of change in neuromuscular performance should be used for
this purpose.
Jumping performance has been shown to correlate with both playing standard (Baker
& Newton, 2008) and sprint performance (Hansen, et al., 2011), suggesting it is an
appropriate method for the measurement of change in neuromuscular performance.
However, it is also clear from this review that there are significant variations in the
equipment used to collect the data, the methods used to calculate the variables and
how the various variables have been defined. This, in turn, has resulted in a
significant range in the reliability reported for different jump variables. Given this, it
is suggested that, prior to using jump variables to measure neuromuscular
performance, the researcher should undertake their own study into the reliability of
the equipment, protocol and methodology they choose.
One jump variable of particular interest is average rate of force development and this
is calculated using a method proposed by Thorlund et al. (2008a). This method has
been reported to correlate with both changes in contractile rate of force development
(Thorlund et al., 2008a) and changes in muscle activation (Jakobsen et al., 2012).
However, while this would support the use of this variable in the assessment of
change of neuromuscular performance, no study to date has reported on the
reliability of this method. This should be addressed prior to future use of this
variable.
From reviewing the research that has been done into the neuromuscular response to
training aimed at developing strength, speed and power, it is clear that there is
considerable variation in the responses to different types of training. However, what
is perhaps most interesting, is the lack of research that has been conducted into
training sessions aimed at developing maximal speed. Only one study was found that
came close to reflecting the type of sprint training sessions undertaken by elite
athletes (Pullinen et al., 2005). However, this study only reported on the changes that
occurred immediately after the session. It is clear from the studies into resistance and

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plyometric training that neuromuscular performance can respond in a variety of
patterns. Therefore, our current lack of understanding regarding the neuromuscular
response to speed training represents a major limitation in our understanding of the
training process.
While many athletes undertake multiple training sessions on the same day, there has
been limited study performed in this area. Indeed, the majority of research has been
performed using weightlifting protocols where the two sessions performed are very
similar. While it is generally reported that the fatigue response to the second session
is similar to that of the first, it is unclear if this would be the case if the sessions had
been very different in nature. It is also notable that the 24-hour response to a single
session training day has not been compared to the 24-hour response to a double
session training day. A greater understanding of this may help the coach decide how
to most appropriately distribute sessions across a training week.
Finally, it is clear from this review that, while concurrent training has been the
subject of considerable investigation, limited research has been performed into the
effect of session order. However, the type of session performed in the morning has
been shown to effect neuromuscular performance in the afternoon (Cook et al., 2013)
and the order of strength and endurance training sessions have been shown to effect
the degree of recovery 24 hours post (Doma & Deakin, 2013). Given this, there is a
clear need for additional research into session order and, in particular, the order of
speed and resistance training sessions as this may potentially effect adaptation and
recovery.
2.6 RESEARCH AIMS
After reviewing the available literature the following research aims have been
identified for this thesis:
 To undertake a methodological study with the aim of:
o Establishing the reliability of several squat and countermovement
jump variables using methods derived from Street et al. (2001).
o Establishing the reliability of average rate of force development
variables proposed by Thorlund et al. (2008a) and Jakobsen et al.

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(2012) as being valid measures of contractile rate of force
development and change in muscle activation.
 To describe the neuromuscular response to speed training over a 24-hour
period. Specifically, the thesis will aim to answer the following questions:
o What is the immediate neuromuscular response to this type of
session?
o What pattern does neuromuscular performance take over a 24-hour
period post?
o What are the physiological and hormonal responses to speed training
and how are they linked the neuromuscular pattern observed?
 To describe the neuromuscular response to two sessions (speed and weights)
on the same training day. Specifically, the thesis will aim to answer the
following questions: -
o What effect does performing a weights session after a speed session
have on the neuromuscular system when compared to a training day
where only a speed session was performed?
o What effect does training order (speed followed by weights versus
weights followed by speed) have on performance during the second
session?
o What effect does training order (speed followed by weights versus
weights followed by speed) have on neuromuscular performance over
a 24-hour period?
o What are the physiological and hormonal responses to these protocols
and how are they linked the neuromuscular pattern observed?

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Chapter 3
General Methods

85. 74
3.1 INTRODUCTION
This thesis features a reliability study of the variables assessed via both
countermovement and squat jump performance in addition to three progressive
studies designed to investigate the response to sprinting training and the optimisation
of it within a training week. While a range of different methods were used to achieve
this, there was considerable overlap across the experimental studies and, as such, this
chapter will provide a description of those methods.
3.2 PARTICIPANTS
Participants were recruited from within the Ulster rugby academy system. The Ulster
rugby academy system annually selects the best 10 to 20 players in their year group,
regardless of playing position based of physical and technical ability. These players
are selected from both the club and school systems from across the province of
Ulster. A short presentation was given outlining the details in the participant
information sheet (appendix 2), which they were also be given to read. Players were
then given the opportunity to opt in or out of the study, with it being made clear by
both the coaching and research teams that any decision to partake in the study was
their own and that they were under no obligation to either Ulster Rugby or Sports
Institute Northern Ireland to do so. Those who opted to participate where informed of
the associated risks and benefits before providing informed consent. While a more
detailed description of the physical characteristics of the subjects can be found in
each of the experimental chapters, all participants had a minimum of one to two
years resistance and speed training experience under the supervision of a
professional strength and conditioning coach and all studies were conducted at
Sports Institute Northern Ireland at Jordanstown, with ethical approval being
provided by the University of Ulster Research Ethics Committee (Appendix 3).
3.3. TRAINING SESSIONS
Standardised Warm-up
A standardised warm-up was performed during all of the experimental chapters. This
consisted of 10-minutes of ergometer cycling (Keiser M3, Keiser Corp, USA) at a

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gradually increasing intensity. The first five minutes was performed at 100 W and
the second five minutes consisted of alternating 30 s efforts at 150 and 100 W. The
protocol was based on a warm up reported in previous research (Faulkner et al.,
2013), and was reflective of the warm up regularly used by the participants in their
normal training week.
Speed training session
The same maximal speed training session was used in Chapters 4, 5 and 6.
Participants proceeded to the indoor track and, after a running specific warm-up
consisting of a mix of mobility and running drills, performed four sub-maximal 50 m
sprints with two minutes recovery. This was followed by the maximal speed training
session itself, which consisted of six maximal 50 m sprints with five minutes
recovery. Each sprint started 30 cm behind the start line and was timed at 10 m
(Chapters 5 and 6) and 50 m (Chapters 4, 5 and 6) using light gates (Brower timing
system, Salt Lake City, UT, USA), which were set at hip height.
A distance of 50 m was chosen for the sprints to ensure there was sufficient distance
to allow the participants to develop maximum velocity whilst also aiming to ensure
that they did not undergo significant deceleration at the end of each repetition. This
is supported by previous research that reports distances of greater than 40 m are
required to develop maximum velocity (Haugen et al. 2014) and that trained sprinters
begin to decelerate around 60 m (Choukou, Laffaye, & Heugas-De Panafieu, 2012).
A total session volume of 300 m was selected based on the recommendations of elite
track and field coaches (Francis, 2008) and five minutes recovery between repetition
was utilised to allow creatine phosphate replenishment and maximise the opportunity
of motor skill development (Merlau, 2005). These training parameters also reflected
the participants’ normal training sessions.
Strength training session
The same lower body strength training session was used in Chapters 5 and 6, with
the exercise choice, volumes and intensities reflecting those used by the participants
in their normal training session whilst still being in line with the guidelines for the
development of strength outlined in Chapter 2. Specifically, the session consisted of

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five sets of four repetitions of the parallel back squat and five sets of four repetitions
of the Romanian dead lift, all at 85% of current 1RM and with four minutes recovery
between sets. Each exercise was preceded by two sets of four at 50% and 70% 1RM
by way of a warm-up. Participants were regularly tested on their 1RM data, from
tests performed within the last 3 weeks, were provided by the Ulster Academy’s lead
strength and conditioning coach.
The session was supervised by a United Kingdom Strength and Conditioning
Association (UKSCA) accredited strength and conditioning coach to ensure
appropriate technique was maintained throughout. On a limited number of occasions,
the barbell load was either reduced or increased by two and a half to five kilograms
for the next set of the exercise at his discretion.
3.4 NEUROMUSCULAR PERFORMANCE
Neuromuscular performance was assessed over the course of this thesis via change in
various jump variables which were calculated from the vertical component of the
ground reaction force.
Data collection
The ground reaction force was collected using a portable force platform with a built
in charge amplifier (Type 9286BA, Kistler Instruments Ltd., Farnborough, United
Kingdom). The platform was factory calibrated and its calibration checked before
each testing session using known masses. In accordance with previous research
(Owen et al., 2013), the vertical force range was set at 20kN and the ground reaction
force was sampled at 1000 Hz through a 16-bit analogue to digital converter (Kistler
Instruments Ltd., Farnborough, United Kingdom) using Kistler’s Bioware (version
3.2.7.0). Both countermovement and squat jumps were performed, with
countermovement jumps being performed in Chapters 4, 5, 6 and 7 and squat jumps
being performed in Chapters 4 and 5.
Countermovement Jump
The participant was instructed to stand on the plate and stand as still as possible, at
which point they indicated they were ready to begin and sampling was initiated

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(Street et al. 2001). After a two second countdown, each participant performed a
countermovement jump, during which they were encouraged to gain maximum
height. Depth was self-selected by the participants and, in order to isolate the lower
limbs, participants kept hands on hips throughout (Owen et al., 2013). Three jumps
were performed consecutively with a rest period of 45 s between each one.
Squat Jump
Sampling began when the participant was standing on the plate with hands on hips,
at which point they were instructed to squat down to a 90-degree knee angle. This
position was then checked using a goniometer (Smith and Nephew, Hull, United
Kingdom). When the correct angle was achieved, the subject was then given a four
second countdown, after which they jumped. The four second hold was used to
eliminate the contribution of the stretch shortening cycle to the jump (Hoffman et al.,
2002). The force-time trace was then immediately checked for jumps that displayed a
noticeable decrease in force. These jumps were discarded and repeated. After
collection, the first two seconds of the vertical component of the ground reaction
force-time history was removed. This meant that the initial counter movement was
removed from the ground reaction force-time history and was not involved in the
analysis process. As with the countermovement jump, three jumps were performed
consecutively with a rest period of 45 s between each one.
Calculation of body mass and identification of the start and end of the jump
Once collected, both the countermovement jump and squat jump data was exported
to a custom-built Excel spread sheet for analysis. Body weight was calculated as the
average force during the initial one and a half seconds of the ground reaction force
trace in accordance with the recommendations in Street et al. (2001). The start of the
jump was calculated using a previously published method (Street et al., 2001). This
process involved initially identifying the point at which force deviated above or
below body weight by more than one threshold. The threshold was defined as 1.75
times the peak residual found during the body weight averaging period. At this point,
a backwards search was performed until the time-point at which force passed through
body weight was identified. This point was marked as the start of the jump and the
point at which integration began. This method aimed to ensure that only the jump
signal was used in the calculation of the variables. Take-off was defined as the first

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intersection of force with the take-off threshold force. The take-off threshold was
defined as the offset force plus the peak residual during the 0.4 second offset period
(Street et al., 2001). The offset was, in turn, determined by finding the 0.4 second
moving average during the flight phase with the smallest standard deviation (Street et
al., 2001).
Calculation of Impulse and Velocity
The impulse momentum relationship was applied to the force-time trace at the start
of both the CMJs and SJs to calculate power. This involved impulse being calculated
by multiplying the vertical ground reaction force minus body mass by 0.005 s
(sampling frequency). Instantaneous velocity was calculated by for each time point
by dividing the impulse by body weight (in kg). Summing the instantaneous
velocities over time then produced a velocity-time profile.
Countermovement jump variables
Three key variables were calculated in each of the four experimental chapters from
the force and velocity data. The first, countermovement jump height, was calculated
by multiplying the velocity at each sampling point by the time (0.005 s). Jump
height (in m) was then defined as the difference between vertical displacement at
take-off and maximal vertical displacement. The second variable, countermovement
jump peak power, was calculated by multiplying the force collected at each sampling
point with its corresponding velocity and identifying the highest value. Finally,
Relative peak power (W.kg-1
) was calculated by dividing the peak power by the body
weight in KG.
Three different countermovement jump average rate of force development measures
were then calculated using a published method (Thorlund et al., 2008): (i) average
rate of force development (Total), (ii) average rate of force development (50) and
(iii) average rate of force development (100). Average rate of force development
(Total) was defined as the change of force during the eccentric rise phase divided by
the time of the eccentric rise phase. average rate of force development (50) was
defined as the change in force 50 ms after the start of eccentric rise phase divided by
50 ms, while average rate of force development (100) was defined as the change in
force 100 ms after the start of eccentric rise phase divided by 100 ms. The start of the

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eccentric rise phase was defined as the time point during the eccentric phase when
the force passed through body weight (Figure 3.1). average rate of force development
(Total), average rate of force development (50) and average rate of force
development (100) were all measured in Chapter 4, while only average rate of force
development (total) was measured in Chapters 5, 6 and 7.
Two further variables were also measured in all four experimental chapters.
Countermovement jump peak force which was defined as the peak force value
produced prior to take-off and countermovement jump Peak Velocity which was
defined as the peak velocity value produced during the performance of the jump
(Figure 3.1).

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Figure 3.1: Time aligned force, power, velocity and displacement traces. Broken line
equals the start of the eccentric rise phase. Solid line equals peak eccentric force.
aRFD (Total) was calculated between these two points.

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Squat jump variables
Squat jump height, peak power, W.kg-1
, peak force and peak velocity were all
calculated using the same methods described previously for the countermovement
jump, and all of these variables were measured in Chapters 4 and 5.
In order to calculate average rate of force development over 100 ms, 150 ms and
total time, force at the start of the jump was first subtracted from the force at 100 ms,
the force at 150 ms and the peak force during the movement, respectively. The
average rate of force development at 100 ms, 150 ms and total time were then
calculated using the same methods described for the countermovement jump.
Average rate of force development at 100 ms, 150 ms and total time were all
measured in Chapter 4, while only average rate of force development total was
measured in Chapter 5.
3.5 HORMONAL ANALYSIS
Blood samples were taken in every experimental chapter with the exception of
Chapter 4. After 10-minutes of lying supine to stabilise the effects of postural change
on blood volume, a 5 ml blood sample was taken by trained practitioners via
venipuncture. After collection, the samples were transferred to a local hospital
where they underwent analysis. Samples were centrifuged at 3000 rpm for 10
minutes at room temperature and plasma was drawn for further analysis.
Testosterone, cortisol, sex hormone binding globulin and albumin were analysed
using commercially available kits (Roche Diagnostic Limited, Charles Avenue,
Burgess hill) on a Cobas C8000 analyser (Roche Diagnostics, Switzerland).
Receptors in the target tissues are exposed to the specific serum levels of hormone
concentrations, therefore, hormone concentrations were not adjusted due to changes
in plasma blood volume (Rubin et al. 2005). The inter-assay coefficients of variation
for testosterone, cortisol, sex hormone binding globulin and albumin were 5.3, 3.7,
7.5 and 6.3%, respectively. Both Albumin and sex hormone binding globulin were
determined in order to allow for the calculation of Free Testosterone. Free
testosterone was calculated using the following equation: -

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[( ) ( )] √[ ( ) ( ⟧ ⟦ ) ( )⟧
( )
3.6 INDIRECT MARKERS OF MUSCLE DAMAGE
Perceived muscle soreness
In all experimental chapters, perceived muscle soreness was collected via a 7-point
Likert scale (appendix 4; Andersson et al., 2008; Morgan, Costill, Flynn, Raglin, &
O'Connor, 1988). The scale was designed to measure perceived soreness in the lower
body. Evidence of the construct validity of this measure has been provided by
Impellizzeri and Maffiuletti (2007) and it has been utilised in previous research in
this area (Andersson et al., 2008; Morgan et al., 1988). The participants were asked
to base their scores on perceived soreness during normal movement, and were alone
when questioned in order to reduce the desire to provide favourable scores in front of
their peers.
Creatine kinase analysis
Creatine Kinase is an enzyme that catalyses the transfer of phosphate from
phosphocreatine to adenosine diphosphate (Koch, Pereira, & Machado, 2014).
Given that creatine kinase does not typically diffuse through the membrane of an
undamaged muscle cell, increases in creatine kinase in the blood stream are
considered to be the result of increased membrane permeability due to damage
(Jamurtas et al., 2000). Therefore, while acknowledging the various limitations
associated with creatine kinase responses (e.g. variance across individuals and
protocols), changes in creatine kinase concentrations can be considered an indirect
marker of muscle damage (Koch, Pereira, & Machado, 2014).
Blood samples were taken in every experimental chapter, with the exception of
Chapter 4. After 10-minutes of lying supine to stabilise the effects of postural change
on blood volume, a 5 ml blood sample was taken by trained practitioners via
venipuncture. After collection, the samples were transferred to a local hospital
where they underwent analysis. Samples were then centrifuged at 3000 rpm for 10
minutes at room temperature and plasma was drawn. Creatine kinase was analysed

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using commercially available kits (Roche Diagnostic Limited, Charles Avenue,
Burgess hill) on a Cobas C8000 analyser (Roche Diagnostics, Switzerland). The
inter-assay coefficient of variation for creatine kinase was 1.4%.
3.7 LACTATE
Blood lactate was measured by fingertip blood sampling in Chapters 5, 6 and 7.
Whole blood was collected via fingertip puncture using a spring-loaded disposable
lancet (Safe-T-Pro Plus, Accu-Chek; Roche Diagnostics GmBH, West Sussex,
Germany). Whole blood lactate concentrations were analysed using a lactate analyser
(Lactate pro, Arkray, Japan).
3.8 STATISTICAL ANALYSIS
Due to variations in the statistical methods being used across the experimental
chapters the statistical methods will be described in each chapter.

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Chapter 4
The Reliability of Jump Variables used
in the Assessment of Neuromuscular
Function

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4.1 INTRODUCTION
Both squat and countermovement jumps are commonly used to assess chronic (i.e.
changes in response to a training block; Cormie et al., 2010b; Coutts, Reaburn, Piva,
& Murphy, 2007) and acute (i.e. changes occurring immediately post-exercise and
lasting several hours or days) responses (Cormack et al 2008b; Kilduff et al., 2011;
West et al., 2014) to training and competition. Performing these activities on a force
plate allows the researcher and coach to generate a range of jump variables that aim
to provide a greater understanding regarding the adaptation or fatigue observed
through the training and competition process. While jump height remains the most
commonly reported jump variable used for this purpose, its sensitivity in detecting
changes in neuromuscular performance (both acute and chronic) has been questioned
(e.g. Cormack et al., 2008a). Therefore, researchers have suggested using additional
jump variables to assess change with three of the most commonly used being mean
power (Bosco et al., 2000; Cormack et al., 2008b), peak power (McLellan et al.,
2011a; West et al., 2014) and peak force (Bagheri et al., 2012; Hoffman et al., 2002).
However, even within studies that have used force plate data, there is considerable
variability in the level of reliability reported for these variables (Gathercole et al,
2014; Gonzalez-Badillo & Marques, 2010; Hori et al., 2009; Sheppard et al., 2008).
Reliability concerns the reproducibility of a measure when it is repeated (Hopkins,
2000a) and is affected by the degree of systematic bias (e.g. fatigue and change in
physical capacity) and random error (e.g. inconsistency in how the protocol is
performed and mechanical variation; Atkinson & Nevill, 1998). The primary factors
that contribute to random error during a jump performed on a force plate have been
identified as the sampling frequency used, the method used for the measurement of
body weight, the identification of the start of the jump and the time point at which
integration is started (Street et al., 2001; Vanrenterghem et al., 2001). It has been
reported, for example, that a drop in the sampling frequency from 1000 to 900 Hz
can result in a miscalculation in the start time of the jump (Street et al., 2001).
Furthermore, a small error of 0.13% in the calculation of body weight can result in a
3.3% error in the calculation of jump height, leading to either an over or
underestimation of the participant’s physical capabilities on a given day. In the
studies published to date, sampling rates ranging from 100 Hz (Bagheri et al., 2012)

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up to 2000 Hz (Buckthorpe et al., 2012) have been reported. In addition, few of the
reviewed studies have provided a description of how they have calculated body
weight. Of those that did, body weight was reported to be calculated from the mean
of 44 samples (Moir et al., 2005a) through to the mean of 4000 samples (Buckthorpe
et al., 2012). Finally, variation in the threshold used to identify the start of the jump
has been shown to result in significant differences in the variables calculated
(Meylan, Nosaka, Green, & Cronin, 2011). A range of methods have been reported
in regards to the identification of the start of the jump, from body weight ± 1.75
times the peak residual during the weighting period (Street et al., 2001) to identifying
the start time as a 10-Newton change from body weight (McLellan et al., 2011b) or a
5% reduction in ground reaction force (Cormack et al., 2008; Nibali et al., 2013).
The accurate identification of the start of the jump also has implications for the time-
point that data integration is started. However, of the studies reviewed, only West et
al. (2011) identified in their methodology when the integration process began.
The reliability of the variables can also be affected by the level of sporting
experience of the participants. For example, while jump height coefficient of
variation has been reported to be 5.6% in physically active college aged males (Moir
et al., 2009), it has been reported to be 14.48% in young males aged 13.5 (± 0.5
years; Lloyd et al., 2009). It would therefore appear that a differing degree of
systematic bias and random error will occur depending on both the participant group
and methodology used. Therefore, prior to undertaking any experimental research
into the neuromuscular response to training, the reliability of the variables that are to
be utilised in the subsequent studies should first be assessed, using procedures based
on best practice, in a subject group representative of those to be used.
In addition to the above mentioned jump variables, another jump variable that should
be considered in addition to jump height, peak power and peak force is rate of force
development. It has been reported that a change in contractile rate of force
development (as measured during an isometric contraction within a single muscle) is
the most important functional benefit induced by training as it will make it possible
for the muscle to generate higher forces and velocities during the rapid movements
involved in many sporting activities (Aagaard, 2003). Furthermore, changes in
contractile rate of force development have also been associated with increased motor

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unit recruitment and firing frequency (Aagaard et al., 2002) and have been suggested
to be sensitive to the changes in neural drive caused by muscle damage (Penailillo,
Blazevich, Numazawa, & Nosaka, 2014). However, contractile rate of force
development during an isometric contraction and dynamic rate of force development
produced during a jump should not necessarily be considered to be reflective of each
other. Indeed, while a number of different methods have been used to calculate rate
of force development during both countermovement jump (Hori et al., 2009; Moir et
al., 2005; Moir et al., 2009) and squat jump (Moir et al., 2005), only one method has
been reported to correlate with contractile rate of force development (Thorlund et al.,
2008a) and reflect change in neuromuscular activity in the hamstrings (Jakobsen et
al., 2012). However, while several studies have reported the coefficient of variation
of rate of force development derived from other methods to range from 13.2 – 17.9%
(McLellan et al., 2011b; Moir et al., 2009), to date there is no information on the
reliability of this method and further research is required to assess if this is indeed a
reliable method for the assessment of rate of force development.
Therefore, the aim of the present study is to assess the reliability of the proposed
methods for the collection and calculation of commonly used jump variables in order
to refine those used in subsequent studies. In addition, the reliability of the method
used by Thorlund et al. (2008) and Jakobsen et al. (2012) to calculate rate of force
development was also investigated.

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4.2 METHODS
4.2.1 PARTICIPANTS
Thirty academy rugby players from a professional rugby club were recruited for this
study (mean ± standard deviation: age 20 ± 1.3 years, mass 94.12 ± 7.6 kg, height
187.2 ± 7.2 cm). Each player had been involved in a professional academy system
for a minimum of 1 year, during which time they were exposed to regular strength,
power and speed training and testing. This study was undertaken at the beginning of
the pre-season and participants were performing one to two sessions per day, five
days a week. Participants provided informed consent and the University of Ulster
Research Ethics Committee provided ethical approval.
4.2.2 DESIGN
The study used a single-group, repeated measures experimental design. The
participants performed three testing sessions across a three-week period, with each
session separated by a week. The participants reported for testing at the same time
each day to control for circadian rhythm (Bird & Tarpenning, 2004) and the day
prior to each collection was designated a rest day. The participants undertook a flat
loading training design and as a result training frequency, volume and intensity did
not vary across the data collection period. Within each session, the participants
completed three SJs and three CMJs.
4.2.3 METHODS
Counter movement jump
The countermovement jump tests were performed on a force platform (Type
9287CA, Kistler Instruments Ltd., Farnborough, United Kingdom). Please refer to
chapter 3.4 for more detail.

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Squat jump
The countermovement jump tests were performed on a force platform (Type
9287CA, Kistler Instruments Ltd., Farnborough, United Kingdom). Please refer to
chapter 3.4 for more detail.
4.2.4 STATISTICAL ANALYSIS
All results are presented as mean ± standard deviations. After tests for normal
distribution, systematic bias and familiarisation between consecutive testing sessions
were assessed using a repeated measures analysis of variance using the statistical
package IBM SPSS (Version 19 SPSS, Inc., Chicago, IL), with the significance level
set at p < 0.05. Subsequent significant intersession differences were identified using
repeated contrasts. ICCs and CVs were calculated alongside 90% confidence
intervals (CI) for each variable. These calculations were performed in a spreadsheet
designed for analysis of reliability of consecutive pairs of trials (Hopkins, 2000b).
All the data was log transformed prior to analysis. Statistical significance for all
analyses was defined as p ≤ 0.05. While it is accepted that there are limitations with
setting a cut-off threshold for reliability (Atkinson & Nevill, 1998), for the purposes
of this study a variable was considered to have high reliability if it has a coefficient
of variation ≤ 10% and an intra-class correlation coefficient 0.90, while a variable
was considered to have sufficient reliability if it had a coefficient of variation ≤ 15%
and an intra-class correlation coefficient 0.80.

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4.3. RESULTS
4.3.1 COUNTERMOVEMENT JUMP
Table 4.4 shows the coefficient of variation and intra-class correlation coefficient
values for the countermovement jump variables across the three sessions.
countermovement jump height (sessions one to two and one to three), average rate of
force development (total) (sessions one to two), average rate of force development
(50 ms; sessions one to three), average rate of force development (100 ms; sessions
one to three) and peak velocity (sessions one to two and sessions one to three) all
showed evidence of systematic bias. Therefore, intra-class correlation coefficient and
coefficient of variation statistics for these variables were not calculated across these
sessions. However, there was no evidence of systematic bias between sessions two
and three.
Between sessions two and three, peak power, jump height, average rate of force
development (Total), body weight, peak force, peak velocity and relative peak power
were all found to display a high degree of reliability (coefficient of variation ≤ 10%;
intra-class correlation coefficient 0.90; Table 4.1). However, average rate of force
development (50 ms; coefficient of variation = 29.16%; intra-class correlation
coefficient = 0.54) and average rate of force development (100 ms; coefficient of
variation = 16.97%; intra-class correlation coefficient = 0.74%) were found to lack
sufficient reliability.
4.3.2 SQUAT JUMP
There was no evidence of systematic bias between sessions one to two, two to three
or one to three in any of the squat jump variables. In general, the reliability of the
variables was better between sessions two to three than sessions one to two,
however, this was non-significant (Table 4.2).
Between sessions two to three, relative peak power was found to display a high
degree of reliability (coefficient of variation ≤ 10%; intra-class correlation
coefficient 0.90), while peak power, jump height, body weight, peak force and

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peak velocity were all found to display a sufficient degree of reliability (coefficient
of variation ≤ 10%; intra-class correlation coefficient 0.80). However, average
rate of force development (100 ms; coefficient of variation = 24.20%; intra-class
correlation coefficient = 0.74), average rate of force development (150 ms;
coefficient of variation = 15.94%; intra-class correlation coefficient = 0.61) and
average rate of force development (Total; coefficient of variation = 10.73%; intra-
class correlation coefficient = 0.67) were found to lack sufficient reliability.

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Table 4.1: Intersession reliability statistics for the variables calculated during the countermovement jump
Sessions 1-2 Sessions 2-3 Sessions 1-3
CV% 90% CI ICC 90% CI CV% 90% CI ICC 90% CI CV% 90% CI ICC 90% CI
Peak power (W) 2.49 2.05-3.20 0.96 0.92-0.98 2.96 2.44-3.80 0.94 0.88-0.97 2.73 2.25-3.50 0.95 0.90-0.97
Jump height (m) - - - - 4.50 3.70-5.79 0.93 0.87-0.96 - - - -
aRFD Total (N.s-1
) - - - - 8.29 6.80-10.73 0.92 0.85-0.96 9.20 7.53-11.92 0.90 0.81-0.94
aRFD 50ms (N.s-1
) 28.68 23.14-38.10 0.61 0.38-0.77 29.16 23.52-38.74 0.54 0.28-0.72 - - - -
aRFD 100ms (N.s-1
) 16.64 13.55-21.77 0.76 0.59-0.86 16.97 13.81-22.21 0.75 0.58-0.86 - - - -
Body weight (kg) 0.92 0.76-1.17 1.00 0.99-1.00 0.84 0.69-1.08 1.00 0.99-1.00 1.00 0.82-1.28 1.00 0.99-1.00
Peak force (N) 4.66 3.83-6.00 0.91 0.85-0.95 4.06 0.34-5.22 0.93 0.87-0.96 5.05 4.15-6.50 0.90 0.82-0.94
Peak velocity (m.s-1
) - - - - 1.88 1.55-2.41 0.94 0.89-0.97 - - - -
Rel. Power (W.kg-1
) 2.43 2.00-3.12 0.96 0.93-0.98 3.00 2.47-3.86 0.94 0.89-0.97 2.62 2.15-3.36 0.95 0.92-0.98
CV = Coefficient of variation %; ICC = Intra-class correlation coefficient; 90% CI = 90% confidence limits; aRFD = average rate of force development

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Table 4.2: Intersession reliability statistics for the variables calculated during the squat jump
Sessions 1-2 Sessions 2-3 Sessions 1-3
CV% 90% CI ICC 90% CI CV% 90% CI ICC 90% CI CV% 90% CI ICC 90% CI
Peak Power (W) 4.31 3.54-5.55 0.89 0.81-0.94 4.09 3.36-5.26 0.88 0.79-0.93 4.59 3.77-5.91 0.86 0.75-0.92
Jump height (m) 7.21 5.91-9.31 0.83 0.70-0.90 5.88 4.83-7.58 0.89 0.81-0.94 7.45 6.11-9.63 0.82 0.69-0.90
Body weight (kg) 0.79 0.88-1.00 1.00 0.99-1.00 0.88 0.73-1.13 1.00 0.99-1.00 1.00 0.83-1.28 1.00 0.99-1.00
Peak force (N) 4.51 3.71-5.80 0.91 0.83-0.95 4.55 3.74-5.86 0.86 0.75-0.92 5.64 4.64-7.28 0.83 0.70-0.90
Peak vel. (m.s-1
) 3.20 2.64-4.12 0.81 0.67-0.89 2.61 2.15-3.26 0.87 0.77-0.93 3.28 2.70-4.21 0.80 0.66-0.89
aRFD 100ms (N.s-1
) 37.15 29.80-49.83 0.40 0.11-0.62 24.20 19.59-31.96 0.74 0.57-0.85 36.38 29.19-48.75 0.38 0.09-0.61
aRFD 150ms (N.s) 18.93 15.38-24.83 0.48 0.21-0.68 15.94 12.98-20.83 0.61 0.37-0.77 21.30 17.28-28.04 0.37 0.07-0.60
aRFD Total (N.s-1
) 12.74 10.41-16.59 0.56 0.31-0.74 10.73 8.78-13.94 0.67 0.46-0.81 14.54 11.86-18.97 0.44 0.17-0.66
Rel. Power (w.kg-1
) 4.04 3.32-5.19 0.89 0.81-0.94 4.02 3.30-5.17 0.91 0.84-0.95 4.40 3.62-5.66 0.88 0.79-0.94
CV = Coefficient of variation %; ICC = Intra-class correlation coefficient; 90% CI = 90% confidence limits; aRFD = average rate of force
development

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4.4 DISCUSSION
The main finding of the current study was that a large number of variables obtained
during both the squat jump and countermovement jump exhibited high reliability
(Tables 4.1 and 4.2) and can be used as markers of neuromuscular performance is
subsequent studies. Interestingly, in both jump types, jump height, while still
displaying sufficient reliability, was not as reliable as some other variables, notably
peak power and peak velocity in the countermovement jump and peak force in the
squat jump. This may reflect the technical element involved in optimising velocity at
take-off or the potential limitations of the impulse momentum method (Cormack et
al., 2008a). Given this finding, it would appear that peak power, peak velocity and
peak force might be superior for the tracking of acute and long-term changes in
athletic performance and should certainly be used, at least in conjunction with jump
height, to provide a clearer picture of the neuromuscular response.
4.4.1. COUNTERMOVEMENT JUMP AND SQUAT JUMP RELIABILITY
The current study used elite athletes and found countermovement jump jump height
to be more reliable than studies who used untrained subjects between the ages of 55-
65 (coefficient of variation =7.1%; Ditroilo, Forte, McKeown, Boreham, & De Vito,
2011) or young males aged 13.5 ± 0.5 years (coefficient of variation = 14.48%;
Lloyd et al., 2009). One possible explanation for this is that age and training
experience directly affect the reliability of jump performance (Benton, Raab, &
Waggener, 2013; Ditroilo et al., 2011). The potential effect of training experience
and skill on the reliability of a test highlights the importance of conducting
independent reliability studies using participants representative of those to be used in
further experimental work.
Squat jump height was found to be less reliable than countermovement jump height.
While this was in line with the findings of Markovic, Dizdar, Jukic and Cardinale
(2004), it is in contrast to the findings of Arteaga, Dorado, Chavarren and Calbet
(2000) who reported squat jump height to be a more reliable variable. In addition to
this, while the degree of reliability observed for the squat jump variables in the
current study are in line with previous research (Moir et al. 2005), they were, on the

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whole, less reliable than those produced during the countermovement jump. It is
unclear why the current study found the countermovement jump variables to be more
reliable than squat jump variables. Again, it is possible that the expertise and training
level of the participants played a role. For example, it is suggested that elite athletes
exhibit superior utilisation of eccentric phase (Aagaard, 2003) and it is therefore
possible that this may, in turn, have resulted in a more consistent countermovement
jump performance and therefore superior reliability. In addition, the method used to
identify the start of the squat jump in the current study may have played a role. The
current study identified the start of the jump by finding the peak residual in the
stance phase and multiplying it by 1.75 to identify the start of the jump. It is possible
that having the athlete maintain a 90-degree squat position during the stance phase of
the squat jump resulted in greater peak residuals, due to the difficultly in maintaining
the position. In turn, this could have effected subsequent identification of the start of
the jump and increased the amount of random error in the squat jump protocol.
To date, only one study has reported the reliability of squat jump variables (Moir et
al., 2005). This study reported peak force to have a coefficient of variation 2.4% and
intra-class correlation coefficient of 0.96; peak power to have a coefficient of
variation of 3.3% and intra-class correlation coefficient of 0.97 and average rate of
force development to have a coefficient of variation of 6.5% and an intra-class
correlation coefficient of 0.84%. It is unclear why this study reported better
reliability of the squat jump variables than the current study. It seems unlikely that
this was due to the methods used to collect and calculate the data, as Moir et al.
(2005) (a) sampled at 250 Hz (b) calculated body mass for 44 samples and (c) used a
threshold of change in force of 10 N to identify the start of the jump, all of which
would have been expected to negatively effect the reliability (Street et al., 2001). It
was interesting however, that peak force showed less variation during the Moir et al.
study. Given that peak force is derived directly from the force trace itself and is
unaffected by the aforementioned factors (identification of start time etc.) it suggests
that the participants in that study were perhaps more consistent in their performance
of the jumps. The reason for this is not clear, although the sample size in Moir et al.
(n=9) may have played a role, as their study may have lacked sufficient power to
have generated reliable results (Hopkins, 2000b).

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The reliability of the variables assessed during the countermovement jump, on the
other hand, are in line, and in many cases superior, to those reported in other force
plate studies (Cormack et al., 2008; Ditroilo et al., 2011; Hori et al., 2009; Moir et
al., 2009). This provides support for the calculation of these variables using the
methods described in subsequent studies.
4.4.2 RELIABILITY OF AVERAGE RATE OF FORCE DEVELOPMENT
While a number of studies have previously published reliability data on the dynamic
average rate of force development produced during jumping, the methods used to
identify the start and end of the time period from which it is calculated has varied
across studies. These have ranged from calculations being begun at the start of the
eccentric phase (Cormie et al., 2009), the point of lowest force (Ugrinowitsch et al,
2007a), the point where the ground reaction force returns to equal body weight
(Thorlund et al., 2008) and the start of the concentric phase (Moir et al., 2009). This
study is the first to investigate a method for calculating average rate of force
development proposed by Thorlund et al. (2008). This method was chosen because
previous research has established a positive correlation between average rate of force
development (100 ms) calculated using this method and contractile rate of force
development produced by the quadriceps during an isometric contraction (r = 0.64-
0.65 pre-post; Thorlund et al., 2008). The method is also supported by another
research paper that reported a relationship between changes in neuromuscular
activity and changes in average rate of force development (50 ms; Jakobsen et al.,
2012). Viewed together, these studies suggest that changes in this variable are
reflective of changes occurring at the muscular level. However, in the current study,
countermovement jump average rate of force development at both 50 and 100 ms
were found to be highly variable (Table 4.4). Early rate of force development
measures during the squat jump were also investigated and they too where also found
to lack sufficient reliability (Table 4.5). Given the high CVs and low intra-class
correlation coefficient reported in the current study for the rate of force development
measures, it is suggested that conclusions drawn from average rate of force
development 50 ms and 100 ms regarding changes at the level of the muscle should
not be made. In addition, it would appear that there are limited conclusions that can

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be drawn from rate of force development variables over 100 ms and 150 ms during
the squat jump. As such, none of these measures will be used in subsequent studies.
The reliability of average rate of force development measures did, however, improve
(in both the countermovement and squat jump) the greater the time-frames from
which they were generated (Tables 4.1 and 4.2). Indeed, when total average rate of
force development was calculated during the countermovement jump, CVs of < 10%
where observed, suggesting it may have sufficient reliability to be used to track acute
changes in neuromuscular performance. This figure was better than those reported in
previous studies, with investigated average rate of force development reliability
during a countermovement jump (coefficient of variation 13.2 – 17.9%; McLellan et
al., 2011b; Moir et al., 2009).
Given that variations in starting force have been shown to effect rate of force
development (Viitasalo, 1982), it is possible that the utilisation of a consistent
starting force for the countermovement jump in the current study contributed to this.
The constant starting force was achieved in the countermovement jump by starting to
calculate rate of force development at the point during the eccentric phase when the
force passed through body weight. This represents a departure from previous
research studies that tend to start the calculation at the point of minimum force, a
time-point which, in itself, is reported to have a degree of variability (coefficient of
variation = 10; intra-class correlation coefficient = 0.63; Moir et al., 2009). It
should also be noted that the average rate of force development values observed
during jumping may represent information regarding the technical execution of the
jump (AragonVargas and Gross 1997, Moir et al. 2009) as opposed to changes in the
contractile capacities of the muscle as suggested by Thorlund et al. (2008) and
Jakobsen et al. (2012). If Moir et al. (2009) are correct, then the observed changes in
average rate of force development (total) during jumping would still be of use to the
coach or researcher. However, rather than be seen to be providing information on the
contractile capabilities of the muscle, it would instead be providing information
regarding change in jump technique due to fatigue/soreness or improved skill.

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4.4.3 SYSTEMATIC BIAS
The current study looked at the reliability of countermovement and squat jump
variables over a three-week period. In contrast to previous research which reported a
lack of systematic bias in physically active men and woman (Moir et al. 2004, Moir
et al. 2009), some of the jump variables assessed in this study were found to show
evidence of systematic bias (Table 4.1). Interestingly, the bias occurred in a negative
direction. All the participants were at the start of a heavy pre-season training period
at the time of the study. While recovery was factored into their training week, it is
possible that this was insufficient for the volume of training they were undertaking,
with the resulting fatigue affecting some of the jump variables. The majority of
variables decreased between weeks one and two, after which they stabilised,
suggesting that the initial week produced the greatest shock to the system. Similar
results were reported by Alemany et al. (2005), who reported declines in peak power
during the squat jump between sessions one and four. It is clear that excessive
physical strain should be avoided during a reliability study and, therefore, while it
appears that familiarisation trials are not required for a squat or countermovement
jump, care should be taken performing reliability studies with elite athletes during
heavy training periods.
4.5 CONCLUSIONS
In conclusion, the purpose of this study was to assess the intersession reliability of
several squat and countermovement jump variables in elite academy rugby players.
The results of this study demonstrate that several of the variables have excellent
reliability and are sensitive enough to detect acute training-induced changes in
athlete performance. In addition, it was found that when the assessment of average
rate of force development was performed over short time-frames, reliability was
poor. However, all measures improved as the time-frames over which they were
calculated expanded and average rate of force development (total) calculated during
a countermovement jump was found to have sufficient reliability to be included in
subsequent experimental work. .

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4.6 PRACTICAL APPLICATIONS
As few studies into the reliability of jump variables have previously been conducted
using elite junior rugby players, the findings of the current study provide information
that can aid coaches and researchers in the assessment of elite athletes. Hopkins
(2000) suggests that a change in the region of one and a half to two times the
coefficient of variation represents a real change in performance. By applying this to
the results of the current study, several countermovement jump jump variables (peak
power, jump height, average rate of force development (Total), body weight, peak
force, peak velocity, relative peak power) and several squat jump jump variables
(peak power, jump height, body weight, peak force, peak velocity, relative peak
power) have be identified which would be sensitive enough for assessing acute
changes in response to training. Furthermore, it does not appear that familiarisation
sessions are required for either test when using an elite population and, therefore,
coaches and researchers can utilise these tests with confidence early in the
assessment process. Finally, it is recommended that researchers do not undertake
data collection when participants are undergoing a change in training load as this
may result in systematic bias.

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Chapter 5
The Neuromuscular, Physiological and
Endocrine Responses to a Maximal
Speed Training Session in Elite Games
Players.

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5.1. INTRODUCTION
Successful sporting performance has been shown to be related to a range of physical
components (Baker, 2001; Gabbett, 2002). One such essential component is speed
which has been linked to both performance and playing level in elite games players
across a range of different sports (Baker & Newton, 2008; Black & Roundy, 1994;
Gabbett et al., 2009; Haugen et al., 2013) with, in rugby league for example, first
grade players being reported to be significantly faster than second grade players
(Gabbett, 2002). While longitudinal studies have reported speed to be less responsive
to training than other physical qualities (Jacobson et al., 2013), improvements have
been demonstrated in games players as a result of undertaking speed training (Lockie
et al., 2012; Tonnessen et al., 2011). For example, 10-weeks of isolated speed
training was found to improve 40 m time by 0.06 s in soccer players, with the authors
suggesting that this was most likely due to the specificity of the training session
(Tonnessen et al., 2011). Indeed, such findings have resulted in a growing trend for
elite athletes to undertake specialised speed training sessions in isolation of technical,
repeated sprint ability or strength training elements in order to maximise the
specificity of the training session.
However, while considerable research has examined the acute post-exercise
neuromuscular, endocrine and physiological responses induced by strength (Bosco et
al., 2000; Hakkinen, 1992) and endurance (Daly, Seegers, Rubin, Dobridge, &
Hackney, 2005; Petersen et al., 2007) training sessions, little is known about the
response to maximal speed training. Only one study, to date, has profiled the acute
neuromuscular, physiological and endocrine responses to maximal speed training
(Pullinen et al., 2005), an alarming finding given the role played by acute post-
exercise responses in the neuromuscular and endocrine systems in the athlete’s
adaptation to and recovery from training. (Bosco et al., 2000; Kraemer & Ratamess,
2005; Ross et al., 2001) Furthermore, while this study observed declines in isometric
maximal voluntary contraction and elevations in lactate alongside elevations in
testosterone and cortisol immediately post training, it did not provide information on
recovery in the hours and days that followed, which is vital information for both the
sports scientist and coach to ensure the training week is planned effectively. A
review of the neuromuscular responses to stretch shortening cycle exercise (SSC;

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Nicol et al., 2006) highlights the importance of observing neuromuscular
performance in the hours post training. Several of the reviewed studies (Avela et al.,
1999; Dousset et al., 2007; McCaulley et al., 2009), while reporting an initial decline
immediately after the stimulus, observed an initial recovery followed by a second
decrease in performance 24-48 hours post. The mechanisms behind this pattern may
be linked to the time frames associated with the onset of and recovery from
metabolic disturbance and muscle damage (Kuitunen, Avela, Kyrolainen, & Komi,
2004). For example both increased levels of perceived muscle soreness (Burt et al.,
2014) and muscle damage (Doussett et al., 2007) have been linked to declines in
performance in the days following exercise. In addition both testosterone (Hakkinen
& Pakarinen., 1993) and cortisol release over a 24-hour period (Cormack et al.,
2008) may be affected by intensive training which may in turn have implications for
both acute neuromuscular performance (Cook et al., 2013) and chronic adaptation
(Ahtiainen et al., 2003).
Therefore, the aim of the current study was to profile the neuromuscular and
endocrine responses to a maximal speed training session over 24 hours. In addition,
changes in several physiological parameters linked to recovery were also tracked. It
is intended that the findings of this study regarding the nature of recovery post-
maximal speed training can be used by both coaches and athletes to inform the
planning and placement of maximal speed training within the training week.

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5.2 METHODS
5.2.1 PARTICIPANTS
Eighteen academy players, from a professional rugby club, were recruited for this
study (mean ± standard deviation: age 20.5 ± 1.2 years, mass 99.4 ± 8.6 kg, height
186.4 ±7.5 cm). Each player had been involved in a professional academy system for
a minimum of one to two years, during which time they were exposed to regular
strength, power and speed training and testing (squat 1RM 150 ± 22 kg; bench press
1RM 121 ± 15 kg; 10m sprint time 1.75 ± 0.1 s). This study was undertaken during
the regular playing season and participants were performing one to two sessions per
day on four to five days a week in addition to playing in one competitive game per
week. Participants provided written informed consent and the University of Ulster
Research Ethics Committee provided ethical approval.
5.2.2 DESIGN
The experimental protocols were completed over two days (Figure 5.1). Prior to
arriving on day one, participants were given two days off training and had undergone
a 12-hour fast in order to control for inter- and intra-subject variations in nutritional
intake which may have, in turn, affected endocrine response (Hackney & Viru,
2008). Upon arrival (PRE time point), participants filled out a questionnaire on
perceived muscle soreness and a blood sample was collected for subsequent analysis.
Blood lactate was also taken at this time point. Participants then performed a 10-
minute standardised warm-up before reporting to the testing area where the two
neuromuscular tests of countermovement jump and squat jump were each performed
three times in this pre-determined order. Participants were given 30 s recoveries
between jumps. It has previously been reported that functional measures, similar to
those used herein, correlate well with dynamic performance (West et al., 2011),
making them relevant markers for the assessment of neuromuscular response.
Immediately after this initial testing, participants reported to the indoor track to
perform a maximal speed training session, details of which can be found in section
3.3. Upon completing the final sprint, lactate and perceived muscle soreness were
recorded. Immediately after the neuromuscular tests and blood collection were

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repeated. Two hours after the completion of their sprints, each participant returned to
complete testing protocols (2 hours post). Finally, on day two, the subjects returned
to complete the testing protocols again following a 12-hour fast (24 hours post). The
arrival times on days one and two were standardised for each participant.
Immediately after the sprint session, participants consumed a standardised recovery
shake consisting of 70 grams of carbohydrate and 35 grams of protein (2:1:1
recovery, Optimum nutrition), a normal practise for elite athletes (Lun, Erdman,
Fung, & Reimer, 2012). A standardised nutritional intake was also used to minimise
the effect of nutrition on endocrine response (Hackney & Viru, 2008). Consumption
of water was also allowed throughout the testing and training periods. The
temperature in the testing area was maintained between 20-24 degrees.

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Figure 5.1: Time line for the experimental protocol in Chapter 5
Day 1
• 07:30
• Athlete arrives
• 07:35
• Blood Collection 1 begins
• 07:50
• Warm up and jumps
• 08:25
• Sprint session begins
• 09:05
• jumps
• 09:17
• Blood collection 2 begins
Break
• 2 hour break
• 11:05
• Blood collection 3 begins
• 11:20
• Warm up and jumps
Day 2
• 07:30
• Athlete arrives
• 07:35
• Blood Collection 1 begins
• 07:50
• Warm up and jumps

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5.2.3 METHODOLOGY
Neuromuscular performance
The squat and countermovement jump tests were performed on a force platform
(Type 9287CA, Kistler Instruments Ltd., Farnborough, United Kingdom). Please
refer to chapter 3.4 for more detail. Based on the findings presented in Chapter 4,
jump height, peak power, Relative peak power, peak force and peak velocity were
calculated from both the countermovement jump and the squat jump with average
rate of force development (total) also calculated from the countermovement jump to
assess change in neuromuscular performance in response to the maximal speed
training session.
Hormonal response
Hormonal responses were calculated from blood samples collected from the
antecubital vein after 10-minutes of lying supine. For more detail please refer to
chapter 3.5.
Creatine kinase response
Creatine kinase responses were calculated from blood samples collected from the
antecubital vein after 10-minutes of lying supine. For more detail please refer to
chapter 3.6.
Perceived Muscle Soreness
Perceived muscle soreness was recorded at each data collection point, using a 7-point
Likert scale designed to measure soreness in the lower body. Please refer to chapter
3.6 for more detail.
Lactate response
Blood lactate was analysed using a lactate analyser (Lactate pro, Arkray). For more
detail please refer to chapter 3.7.
Tympanic temperature
Tympanic temperature was collected using a digital tympanic thermometer
(ThermoScan Type 6022, Braun, Germany). The collection involved the athletes

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sitting or standing still for 10 s and a member of the research team collecting two
measurements from the left ear (holding the sensor in each ear for 5 s; Childs,
Harrison, & Hodkinson, 1999). The two measurements were then averaged to the
resultant figure which was used in analysis. The use of this method to assess
tympanic temperature is supported by previous research in the area (Teo et al., 2011).
5.2.4 STATISTICAL ANALYSIS.
Data is expressed as the mean ± standard deviation. After tests for normal
distribution and prior to any further statistical analysis, creatine kinase recorded
values were log transformed due to large inter-participant variability. Statistical
analysis was carried out using a repeated measures analysis of variance on the
various measures. Where significant effect was observed, paired comparisons were
used in conjunction with a Bonferroni correction to control for Type I error to
determine significant differences. Effect size (ES) was determined using partial eta-
squared, with a ES of approximately 0.2 considered small, approximately 0.5
considered medium and approximately 0.8 considered large (Cohan, 1988).
Relationships between variables were explored using Pearson-product-moment
correlation coefficients. The level of significance was set at p ≤ 0.05 for the present
study and all statistics were performed using SPSS 17.1 (SPSS Inc., Chicago, IL).

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5.3. RESULTS
5.3.1 SPRINTS
A time effect was found for sprinting speed across the protocol (Effect Size eta2
:
0.252; F = 5.790, p < 0.05). A significant increase in sprint time was found when
sprint one was compared to sprint five (6.58 + 0.33 s vs. 6.65 + 0.34 s ; p=0.007;
Table 5.1), there was no other difference in sprint times across the session.
5.3.2 ENDOCRINE RESPONSES
As seen in Table 5.2, the maximal speed training session had a significant time effect
on total testosterone (Effect Size eta2
: 0.712; F = 41.991, p < 0.05) free testosterone
(Effect Size eta2
: 0.696; F = 38.967, p < 0.05) and cortisol (Effect Size eta2
: 0.778; F
= 59.555, p < 0.05). At 2 hours post, significant decreases in total testosterone, free
testosterone and cortisol levels were found with all values returning to baseline at the
24 hours post time-point (Table 5.2).

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Table 5.1: Average time across the 6 x 50m sprints in team sport athletes. Data presented as mean ± standard
deviation.
Sprint 1 Sprint 2 Sprint 3 Sprint 4 Sprint 5 Sprint 6
Time (s) 6.60 ± 0.31 6.61 ± 0.31 6.61 ± 0.31 6.64 ± 0.32 6.72 ± 0.43* 6.65 ± 0.31
* = Significant to 0.05 vs. sprint 1.

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Table 5.2: Total testosterone, free testosterone and cortisol at four different time points (Pre, immediately post, 2 hours
post and 24 hours post 6 x 50 m sprints) in team sport athletes. Data presented as mean + standard deviation.
Pre
speed
Immediately
post
2 hours
post
24 hours
post
Testosterone (nmol/l) 20.1 ± 6.12 20.7 ± 6.36 15.5 ± 6.08* 20.8 ± 6.53
Free Testosterone (pmol/l) 415 ± 96 413 ± 89 303 ± 86* 429 ± 110
Cortisol (nmol/l) 543 ± 119 500 ± 104 267 ± 61.2* 531 ± 96.1
* Significant to 0.05 vs. pre speed values

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5.3.3 MUSCLE SORENESS, LACTATE AND MARKERS OF MUSCLE
DAMAGE
As seen in Table 5.3, maximal speed training had a significant time effect on creatine
kinase (Effect Size eta2
: 0.888; F = 39.465, p < 0.05), lactate (Effect Size eta2
: 0.945;
F = 289.422, p < 0.05) and perceived muscle soreness (Effect Size eta2
: 0.305; F =
7.472, p < 0.05). Immediately post, lactate, perceived muscle soreness and creatine
kinase increased significantly (Table 5.3) compared to baseline with values for
perceived muscle soreness and creatine kinase remaining significantly evaluated at
the 2 hour post time point. At 24 hours post, there was a significant increase in both
creatine kinase and perceived muscle soreness when compared to pre-training levels
(Table 5.3).

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Table 5.3: Perceived muscle soreness, creatine kinase and lactate at four different time points (Pre, immediately post, 2
hours post and 24 hours post 6 x 50 metre sprints) in team sport athletes. Data presented as mean + standard deviation.
Pre
speed
Immediately
post
2 hours
post
24 Hours
post
Muscle soreness (likert) 1.00 + 1.19 2.39 + 1.14* 2.11 + 1.41* 2.56 + 1.62*
Creatine kinase (u l) 420 + 360 514 + 406* 615 + 437* 990 + 703*
Lactate (mmol/l) 1.58 + 1.06 10.6 + 1.58* 2.06 + 1.07 1.16 + 0.35
 Significant to 0.05 vs. pre training values

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5.3.4 NEUROMUSCULAR RESPONSE
Table 5.4 shows the time effect the maximal speed training had on neuromuscular
performance. During the countermovement jump, jump height (Effect Size eta2
:
0.557; F = 21.376, p < 0.05), peak power (Effect Size eta2
: 0.481; F = 15.767, p <
0.05), relative peak power (Effect Size eta2
: 0.463; F = 14.678, p < 0.05), average
rate of force development (total) (Effect Size eta2
: 0.259; F = 5.941, p < 0.05) and
peak velocity (Effect Size eta2
: 0.623; F = 28.130, p < 0.05) were all significantly
affected. Countermovement jump height, peak power, relative peak power and peak
velocity were significantly decreased immediately after (Table 5.4).
Countermovement jump height, peak velocity and average rate of force development
(total) were also significantly decreased 24 hours post, while countermovement jump
peak power displayed a very strong tendency towards depression at 24 hours post (p
= 0.051; Table 5.8). Only peak velocity was significantly different to pre training
levels at 2 hours post. Average rate of force development (total) was found to be
significantly lower at 24 hours post when compared to 2 hours post.
During the squat jump, significant time effects were found for peak power (Effect
Size eta2
: 0.468; F = 14.961, p < 0.05), jump height (Effect Size eta2
: 0.312; F =
7.696, p < 0.05), Relative peak power (Effect Size eta2
: 0.441; F = 13.416, p < 0.05),
peak force (Effect Size eta2
: 0.300; F = 7.293, p < 0.05) and peak velocity (Effect
Size eta2
: 0.329; F = 8.330, p < 0.05). Subsequent paired comparisons revealed peak
power, relative peak power, peak velocity and jump height to be significantly
decreased immediately and 24 hours post but not 2 hours post (Table 5.4). Peak
power, Relative peak power and peak force were all significantly higher at 2 hours
post when compared to 24 hours post.

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Table 5.4: Squat and countermovement jump variables at four different time points (Pre, immediately post, 2 hours post
and 24 hours post 6 x 50 metre sprints) in team sport athletes. Data presented as mean ± standard deviation.
Pre
speed
Immediately
post
2 hours
post
24 hours
post
Countermovement Jump Height (m) 0.40 ± 0.05 0.36 ± 0.06* 0.39 ± 0.05 0.38 ± 0.06*
Countermovement Peak Power (W) 5193 ± 461 4963 ± 562* 5154 ± 503 5106 ± 508
Countermovement Rel. Power (W.kg-1
) 54.3 ± 6.37 51.8 ± 6.34* 53.9 ± 6.7 53.4 ± 6.41
Countermovement aRFD (N.s-1
) 4557 ± 1014 4333 ± 1282 4579 ± 1077 3891 ± 936*^
Countermovement Peak Force (N) 2281 ± 345 2277 ± 300 2196 ± 301 2203 + 307
Countermovement Peak Velocity (m.s-1
) 2.90 ± 0.18 2.80 ± 0.19* 2.86 ± 0.18* 2.83 ± 0.19*^
Squat Jump Height (m) 0.33 ± 0.04 0.31 ± 0.05* 0.32 ± 0.05 0.31 ± 0.06*
Squat jump Peak Power (W) 5042 ± 479 4837 ± 574* 4964 ± 537 4742 ± 541*^
Squat jump Rel. Power (W.kg-1
) 52.6 ± 6.14 50.5 ± 6.75* 51.8 ± 6.77 49.6 ± 6.60*^
Squat jump Peak Force (N) 2234 ± 186 2270 ± 246 2269 ± 215 2158 ± 174^
Squat jump Peak Velocity (m.s-1
) 2.56 ± 0.16 2.45 ± 0.21* 2.51 ± 0.20 2.45 ± 0.24*
* significant to 0.05 vs. Pre speed values
^ significant to 0.05 vs. 2 hours post values

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5.3.5 EAR TEMPERATURE
Statistical analysis following the maximal speed training session revealed a
significant time effect on tympanic temperature (Effect Size eta2: 0.443; F = 19.377,
p < 0.05). Immediately after the maximal speed training there was no change in
tympanic temperature. However, when the temperature at 2 hours post was compared
to the baseline measure, it was found to be significantly elevated whereas there was
no significant difference between baseline and the 24 hour post time point.
5.3.6 CORRELATIONAL ANALYSIS
No relationship was found between countermovement jump peak power and sprint
time, however, when body weight was accounted for and peak power expressed as
W.kg-1
(relative peak power) a strong relationship was found (p < 0.01; r =-.793;
Figure 5.2). A similar pattern was found for squat jump peak power which also
displayed a significant relationship to best 50 m sprint performance when expressed
as W.kg-1
(p < 0.01; r=-.744; Figure 5.2). jump height for both squat jump and
countermovement jump significantly correlated with sprint performance (p < 0.05;
r=.728 and p < 0.05; r =.859 respectively; Figure 5.2).

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Figure 5.2: The relationship between sprint performance and (a) countermovement
jump Relative peak power, (b) squat jump relative peak power, (c) squat jump height
and (d) countermovement jump height

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5.4 DISCUSSION
The current study is the first to document the neuromuscular, endocrine and
physiological responses to a maximal speed training session over a 24-hour period.
The main finding of this study was that several neuromuscular markers followed
complex recovery patterns post-training and were still declined at 24 hours post.
5.4.1 RELATIONSHIP BETWEEN SPEED AND JUMP PERFORMANCE
Several of the countermovement and squat jump measures investigated were
depressed by the maximal speed training session. It has previously been reported that
functional measures, similar to those used in the current study, correlate well with
dynamic performance (Dal Pupo et al., 2013; Haff et al., 1997; Khamoui et al., 2011;
Stone et al., 2003; Stone et al., 2005; West et al., 2011). Likewise, in this study,
significant correlations were found between sprint performance and several
measures; namely countermovement jump relative peak power (r = -.744); squat
jump relative peak power (r = -.793); squat jump height (r = .728) and
countermovement jump height (r = .859; Figure 5.6). Given these relationships,
along with the high degree of reliability established for these variables in Chapter 4,
it may be reasoned that depressions in these variables would have an adverse effect
on performance, making them relevant markers for the assessment of the
neuromuscular response to a sprint training session.
5.4.2 NEUROMUSCULAR RESPONSE TO MAXIMAL SPEED TRAINING
Initially, it was found that the majority of variables declined immediately after the
maximal speed training. While the lack of consistency in terms of variables used to
assess changes in neuromuscular performance in different studies makes it difficult
to draw definitive conclusions, the initial decrements in countermovement jump
relative peak power observed were lower than those reported after a marathon
(Armstrong, 1990; 4.4% vs. 11%). However, the 10% decline in countermovement
jump height found immediately post was similar to the 8% decrease in
countermovement jump height reported after a strength training session (Walker,
Ahtiainen, & Hakkinen, 2010) and the 8% decrease found after a drop jump session

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(Skurvydas, Dudoniene, Kalvenas, & Zuoza, 2002). While more research is required,
it seems possible that the greater immediate post-exercise loses in performance
experienced by the marathon runners were related to the high volume of running and
it is possible that initial depressions in performance are more sensitive to volume
than intensity.
Two hours after the protocol only countermovement jump peak velocity differed
from PRE levels, suggesting that the neuromuscular performance had recovered.
However, when observed 24 hours post, seven of the 11 markers of neuromuscular
performance were again significantly depressed compared to pre-training levels. This
would suggest that maximal speed training effects neuromuscular performance in a
bimodal pattern similar to that described by Komi (2000). In previous studies, both
decreased levels of testosterone (Bosco et al., 2000) and increased afferent feedback
in response to muscle damage (Nicol et al., 2006) have been suggested to be
involved in the immediate post-exercise loss of neuromuscular performance.
However, in the current study, no depression in either total or free testosterone was
found immediately after exercise and, while both creatine kinase and perceived
muscle soreness were significantly elevated, both were also found to be still rising at
two hours post. It seems unlikely that performance would have improved in the face
of further increases in markers of muscle damage if these had been a major
contributor to the initial declines in performance observed. A more likely
explanation for the initial decreases observed may be decreased functioning of the
contractile mechanisms of the muscle fibre in the presence of the metabolites
produced during exercise (H+, ADP, inorganic phosphate) (Skurvydas et al., 2007).
Indeed, blood lactate was significantly elevated upon completion of the sprints and,
while not a direct marker of metabolic fatigue; significant increases in lactate may be
viewed as an indicator of metabolite accumulation (Skurvydas et al., 2006). These
findings suggest that a maximal speed training session consisting of 6 x 50 m, with
five minute recoveries between repetitions, results in significant metabolic stress
(Table 5.3) and produces lactate levels higher than would be expected from strength
training protocols using similar recoveries (Skurvydas et al., 2002). This high lactate
would also suggest a high requirement for energy production via glycolysis during
the last repetition (Choukou, Laffaye, & Heugas-De Panafieu, 2012) and would seem
to support previous research which reported that lactate begins to accumulate during

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sprints greater than 40 m (Hirvonen, Rehunen, Rusko, & Harkonen, 1987).
Therefore, the metabolic stress associated with maximal speed training should be
considered when planning volumes and recoveries.
On the surface, it seems curious that neuromuscular performance had recovered by 2
hours post while creatine kinase and perceived muscle soreness continued to rise.
However, it is likely that it is the inflammatory responses to muscle damage, as
opposed to the muscle damage itself, which ultimately affects neuromuscular
performance (Dousset et al., 2007). This inflammatory response is not initiated until
between two and six hours after the exercise (Armstrong, 1990). Taking this time-
frame into account, it is hypothesised that the recovery in neuromuscular
performance observed at 2 hours post occurred prior to the initiation of the
inflammatory response and was most likely due to the removal of the metabolic by-
products that had initially built up immediately after MST (Dousset et al., 2007).
This is supported by the fact that lactate had returned to baseline by 2 hours post. It
is further suggested that the secondary decline in muscle performance, observed 24
hours post, was primarily related to the inflammatory response that would be well
underway at this time-point. This is supported by the 135% elevations in creatine
kinase from pre training levels observed, suggesting that maximal speed training
resulted in significant muscle damage.
It was also observed that, at 2 hours post, tympanic temperature was significantly
elevated versus pre training levels. This occurred despite the participants undertaking
the same warm-up at each time-point. Tympanic temperature was collected as a field
measurement of body temperature. An increase in temperature during the day is a
normal circadian event (Hayes et al, 2010; Teo et al., 2011) but its potential effects
on neuromuscular performance at the 2 hour post time-point must also be considered
as even small increases in muscle temperature have been demonstrated to increase
nerve conduction velocity (Ferrario, Tredici, & Crespi, 1980), thereby improving the
muscle’s capacity to generate explosive force (Hayes et al., 2010) and maximal force
(Jasper, Haussler, Baur, Marquardt, & Hermsdorfer, 2009). Interestingly, there was a
non-significant decline in tympanic temperature at the end of the maximal speed
training. This suggests that the session itself was not inducting any further increases
in temperature and that the participants were slowly losing heat. This, in turn, may

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have implications for athletes undertaking this type of training and coaches may want
to consider some potential methods of maintaining heat during the relatively long
breaks in activity required for this type of training.
The declines in neuromuscular performance at 24 hours post also have implications
for training design. Previous research has shown both neuromuscular performance
and 10m sprint performance to be depressed when training performed 24 hours prior
results in significant muscle damage and soreness (Highton, Twist, & Eston, 2009).
In contrast, however, 2000m rowing performance was not affected by a heavy
strength training session the previous day, even in the presence of decreased
countermovement jump performance and elevated levels of muscle soreness (Gee et
al., 2011). Given this, alongside the significantly higher levels in five of the jump
variables 2 hours post when compared to 24 hours post observed in the current study,
it would seem that it is better to place explosive/maximal effort training relatively
close together and coaches may consider sequencing their training in a manner that
takes advantage of this. However, further research is required into the effect of
performing additional training in this window on muscle damage, neuromuscular
fatigue and recovery time. In addition, given that, as long as the participants are
familiarised with the type of training performed (Burt, Lamb, Nicholas, & Twist,
2013), performance in submaximal activities would appear to be unaffected at this
time, a strategy of alternating high intensity explosive training days containing
multiple sessions with days emphasising submaximal activities may take advantage
of the observed pattern of neuromuscular recovery.
Interestingly, 24 hours post was the only time point at which countermovement jump
average rate of force development (total) was depressed during the jumps. As
discussed in Chapter 3, this parameter has been suggested to reflect changes in jump
strategy (Moir et al., 2009), neuromuscular activity (Jakobsen et al., 2012) and
contractile rate of force development (Thorlund et al., 2008). It is possible, therefore,
that the observed declines in average rate of force development in the current study
were due to the inflammatory responses to the muscle damage produced the previous
day resulting in a change in central drive by affecting Type III and IV afferents
(Dousset et al., 2007). Such changes may also have implications for the type of
training performed at this time-point as changes in muscle activation as a result of

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inflammatory processes may also result in changes in sensorimotor control which
may potentially increase the risk of injury (Twist, Gleeson, & Eston, 2008).
However, the fact that no in-depth data on muscle activation and inflammation was
collected means that this remains theoretical.
5.4.3 ENDOCRINE RESPONSE TO MAXIMAL SPEED TRAINING
In the current study, maximal speed training did not cause any acute elevations in
total testosterone, free testosterone or cortisol immediately after the session had been
completed. This lack of endocrine response immediately post is in contrast to
previous work on maximal speed training where trained sprinters undertook 10 x 50
m sprints with five minutes between efforts (Pullinen et al., 2005). Pullinen et al.
(2005) reported a 19% increase in testosterone and a 31% increase in cortisol
immediately after the session. There appear to be several mechanisms linked to post-
exercise elevations in testosterone and cortisol. Previous research has reported a link
between the secretion of both testosterone (Walker et al., 2010) and cortisol
(Spiering et al., 2008) and metabolic accumulation. However, given that Pullinen et
al. (2005) and ourselves reported similar elevations in lactate post-exercise, this does
not explain the differences between our findings. Instead, the lower sprint volume
performed in the current study (6 x 50 m versus 10 x 50 m) may have played a role.
In resistance training, the testosterone response has been linked to volume load
(McCaulley et al., 2009) so it is possible that the different hormonal responses are a
response to the different volume of maximal speed training performed. Indeed,
studies into endurance running suggest high volumes of SSC activities can cause
fluctuations in endocrine levels in the days following the training stimulus (Daly et
al., 2005).
While total testosterone, free testosterone and cortisol all were found to be
significantly lower than PRE values when measured at 2 hours post, these
depressions are in line with the normal circadian variations previously reported in the
literature (Kraemer et al., 2001). Therefore, it seems unlikely that these depressions
were a direct response to the training stimulus. However, the lack of non-exercise
control data in the current study means that this cannot be confirmed. This finding is
in contrast to that of Cook et al., (2013) who reported that a morning sprint session

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changed the circadian pattern of testosterone but not cortisol. It is not clear as to why
no such response was found in the current study, however, research suggests both
training background (Ahtiainen et al, 2004) and training experience (Kraemer et al.,
1992) can influence the testosterone response to a workout. Indeed, the participants
in Cook et al. (2013) were required to have had a minimum of three years of
monitored training compared to one to two years in the current study.
5.5 CONCLUSIONS
From the current study, it appears that neuromuscular performance undergoes a
bimodal recovery pattern in response to maximal speed training, with an initial loss
in performance being found to occur after training. Performance was then found to
have recovered by 2 hours post, before undergoing a second decrease 24 hours post.
These results suggest that periods longer than 24 hours are required to allow full
neuromuscular recovery from maximal speed training. However, the endocrine
system was unaffected by maximal speed training and it appears that 24 hours is
sufficient for it to recover from this type of training. The fact that several of the
neuromuscular and physiological parameters had not fully recovered by 24 hours
post represents a limitation in the current study. However, it is also the reality of elite
sport that a further training stimulus would be undertaken prior to full recovery.
5.6 PRACTICAL APPLICATIONS
Coaches should consider the timing and type of sessions undertaken after a maximal
speed training session. It appears that the 2 hours post time point would seem to
represent a superior training window for explosive type activities than the 24 hour
post time-point, while training undertaken at 24 hour post should be more
submaximal in nature. This, in turn, may also have implications for the current
popular use of ‘priming’ activities and, if the competition activity is explosive and
maximal in nature, care should be taken regarding both the nature of the priming
activity and the time it is performed prior to the game/competition.

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Chapter 6
The Neuromuscular, Physiological and
Endocrine Responses to a Single
Session Versus Double Session Training
Day in Elite Athletes.

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6.1 INTRODUCTION
Elite athletes will often undertake a block of training involving multiple high
intensity training sessions being repeated over the course of a week to improve
overall performance (Cormack et al., 2008). Neural adaptations, in particular, are
reported to be sensitive to training intensity (Tan, 1999) and it is therefore important
that sessions aimed at inducing neural adaptations are performed when the athletes
are in the most optimal state. A number of studies have successfully shown that
performing multiple training sessions on the same day will achieve this (Hakkinen &
Kallinen, 1994; Hartman et al., 2007; Cook et al., 2013; Ijichi et al., 2014). Indeed, in
Chapter 4 of this thesis, neuromuscular performance was found to be better two
hours after maximal speed training when compared to 24 hours after, suggesting this
may be the most appropriate time point to perform a second intensive neuromuscular
training session.
However, intensive dynamic training sessions result in inflammatory processes,
which, in turn, can affect performance on subsequent training days (Asp, Daugaard,
Kristiansen, Kiens & Richter, 1998; Marcora & Bosio, 2007). In addition, very
intensive sessions may result in changes in testosterone (Hakkinen & Pakarinen
1993) and cortisol release over a 24-hour period (Cormack et al. 2008,
Chatzinikolaou, Fatouros et al. 2010) that may, in turn, influence both neuromuscular
function (Cook et al., 2013) and adaptation (Ahtiainen et al., 2003). Therefore, it is
important to consider the combined effect of two sessions on both the recovery and
fatigue profiles, to determine if the second training session results in higher levels of
fatigue in the hours or days that follow. If this were found to be the case, it would
also have implications for both the subsequent training days and competition
preparation.
To date, only a few studies have examined the effects of multiple training sessions
on neuromuscular performance and recovery (Hakkinen, 1992; Chiu et al., 2004;
Skurvydas et al., 2010a, 2010b). Of these, only two performed any sort of follow-up
in the days post-training (Skurvydas et al., 2010a, 2010b). In both studies, the loss of
performance evident after the second bout of exercise was no greater than the loss
after the first (Skurvydas et al., 2010a, 2010b). This led the authors to conclude that

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this was due to the initial bout damaging the weak fibres and the stimulus from the
second session being insufficient to produce any additional damage. However, it is
unclear from these studies as to how neuromuscular performance was affected 24
hours post and if any changes in neuromuscular performance at these time-points
would be different than those resulting from a single session. Having this
information would better allow the coach to make informed decisions about the use
of twice daily training and the placement and type of sessions they wish to have the
athlete perform during the rest of the training week.
Furthermore, the majority of research conducted to date has used similar exercises
and loadings in both training sessions (Hakkinen, 1992; Hakkinen & Kallinen, 1994;
Hartman et al., 2007; Skurvydas et al., 2010a, 2010b). While a multiple daily
resistance session approach is commonly used by weightlifters (Hartman et al.,
2007), the weekly training of an elite games player and sprinter often requires them
to undertake both lifting and running sessions on the same day (Cormack et al., 2008,
McLean et al., 2010). To date, no studies have investigated the effect of a training
day containing speed and weight training sessions. Given that it has been suggested
that changes in the contraction type (Coffey, Pilegaard, Garnham, O'Brien &
Hawley, 2009) and variations in stimulus (Skurvydas et al. 2010a) are factors that
exacerbate the inflammatory response, it is possible that a second session containing
such a significant change in stimulus may result in more damage and a greater loss in
neuromuscular performance.
The aim of this study, therefore, was to investigate the effect of a two session
training day (speed and weights) verses a one session training day (speed only) on
neuromuscular performance, markers of muscle damage and hormonal response.

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6.2. METHODS
6.2.1. PARTICIPANTS
Fifteen academy level rugby players were recruited for this study (mean ± standard
deviation: age 21 ± 1.3 years; 100.5 ± 10.5 kg; height 185.7 ± 6.6 cm). Each player
had been involved in the professional academy system for a minimum of one to two
years, at which time they were exposed to regular strength, power and speed training
and testing (mean ± standard deviation: Squat 1RM 170 ± 20 kg, Bench 1RM 135 ±
10 kg, 10 m sprint time 1.75 ± 0.1 s). The study was undertaken at the end of the
regular playing season and participants were performing physical training four days
per week, consisting of speed, strength and conditioning sessions. Participants
provided written informed consent and ethical approval for the study was provided
by the University of Ulster Research Ethics Committee.
6.2.2 DESIGN
This study profiled the responses to a training day consisting solely of a maximal
speed training session and a training day consisting of maximal speed training
followed by a heavy weight training session 2 hours post (speed/weights) to
determine if the second session resulted in a different metabolic, endocrine or
neuromuscular response. The study was designed as a randomised crossover study
and each experimental protocol was completed over two days.
Prior to arriving on day one of each protocol, participants were given two days off
training. Each participant was given an arrival and start time that was maintained
throughout the study to account for circadian variation in hormones and body
temperature (Hackney & Viru, 2008). Upon arrival (pre speed training time point),
participants filled out a questionnaire on perceived muscle soreness and a blood
sample was collected for subsequent analysis. Lactate was also taken at this time
point. Participants then performed a 10-minute standardised warm-up before
reporting to the testing area where three CMJs were performed. Results presented in
Chapter 5 demonstrated that countermovement jump correlates well with dynamic
performance, making it a relevant marker for the assessment of neuromuscular

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response. At this point, the participants performed either the speed only or
speed/weights protocol.
With the speed only protocol, participants proceeded to the indoor track and, after a
running specific warm-up, performed a maximal speed training session, details of
which can be found in section 3.3. After completion of the final sprints, Lactate,
perceived muscle soreness, countermovement jump and blood was collected, with
this procedure repeated 2 and 24 hours post also.
In the speed/weights protocol, participants repeated the protocol detailed above, up
until the point they had collected the 2 hours post testing protocol. At this point, the
participants proceeded to the gym to undertake a strength training session, details of
which can be found in section 3.3. After completion of the strength session, the
countermovement jumps were repeated and lactate was again taken (post weights
time point). On day 2, the participants returned and the testing protocols were
completed again 24 hours post speed.
During each protocol, the first day’s breakfast, lunch, snacks and dinner along with
the following day’s breakfast were provided (Soulmate food, Lancashire, UK). Both
calorie intake and food choice were kept the same throughout both the speed only
and speed/weights protocols in order to ensure that the participant’s nutritional intake
was standardised throughout the study. Consumption of water was also allowed
throughout the testing and training periods.
6.2.3 METHODS
Neuromuscular performance
Due to time and personnel constraints only CMJs were collected as markers of
neuromuscular performance. The countermovement jump was chosen over the squat
jump due to the higher degree of reliability reported for countermovement jump
variables in Chapter 4. The countermovement jump tests were performed on a force
platform (Type 9287CA, Kistler Instruments Ltd., Farnborough, United Kingdom).
Please refer to chapter 3.4 for more detail. Based on the findings presented in
Chapter 4, jump height, peak power, Relative peak power, average rate of force

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development (total), peak force and peak velocity were calculated to assess change in
neuromuscular performance in response to the protocols.
Hormonal response
Hormonal responses where calculated from blood samples collected from the
antecubital vein after 10-minutes of lying supine. For more detail please refer to
chapter 3.5.
Creatine kinase response
Creatine Kinase responses where calculated from blood samples collected from the
antecubital vein after 10-minutes of lying supine. For more detail please refer to
chapter 3.6.
Perceived Muscle Soreness
Perceived muscle soreness was recorded at each data collection point, using a 7-point
Likert scale designed to measure soreness in the lower body. Please refer to chapter
3.6 for more detail.
Lactate response
Blood lactate was analysed using a lactate analyser (Lactate pro, Arkray). For more
detail please refer to chapter 3.7.
6.2.4 STATISTICAL ANALYSIS
Data is expressed in its recorded form as the mean ± S.D. After tests for normal
distribution and, prior to any further statistical analysis, creatine kinase recorded
values were log transformed due to large inter-participant variability. Differences
between and within protocol were assessed using a two-way (time point and
protocol) repeated measures analysis of variance. If significant F values were
observed (p ≤ 0.05) a post hoc test with a Bonferroni correction to control for Type I
error was run to determine where significant differences occurred. Effect size was
determined using partial eta-squared, with an effect size of approximately 0.2
considered small, approximately 0.5 considered medium and approximately 0.8
considered large (Cohan, 1988). The level of significance will be set at p ≤ 0.05 for

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the present study and all statistics were performed using SPSS 20.0 (SPSS Inc.,
Chicago, IL).

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6.3 RESULTS
6.3.1 SPRINTS
The mean 10 m and 50 m times for the speed only and speed/weights protocols were
1.80 ± 0.90 s; 6.57 ± 0.32 s and 1.80 ± 1.10 s; 6.55 ± 0.34 s, respectively. There was
no significant interaction effects for either 10m (effect size eta2
= 0.047, F = 0.692, p
> 0.05) or 50 m time (effect size eta2
= 0.012, F = 0.171, p > 0.05), suggesting that
performance was not different between the protocols. There was no significant time
effect on either 10 m or 50 m time suggesting that performance was maintained
across both protocols.
6.3.2 ENDOCRINE RESPONSE
Analysis revealed that the study protocols had a significant time effect on total
testosterone (effect size eta2
= 0.530, F = 15.797, p < 0.05), free testosterone (effect
size eta2
= 0.497, F = 13.839, p < 0.05) and cortisol (effect size eta2
= 0.673, F =
28.824, p < 0.05). However, no interaction was found between protocol and time-
point in total testosterone (effect size eta2
= 0.025, F = 0.366, p > 0.05), free
testosterone (effect size eta2
= 0.034, F = 0.490, p > 0.05) or cortisol (effect size eta2
= 0.049, F = 0.722, p > 0.05).
Both total and free testosterone were significantly elevated immediately after the
maximal speed training performed as part of both the speed only and speed/weights
protocols when compared to baseline values. Cortisol, in contrast, was found to be
significantly lower at the same time point during the speed/weights protocol but
unchanged during the speed only protocol (Table 6.1).
At 2 hours post, neither total nor free testosterone were significantly elevated when
compared to baseline values in either protocols and mean cortisol had dropped
significantly in both protocols. At 24 hours post, total testosterone, free testosterone
and cortisol did not differ from pre-training levels in either protocol (Table 6.1).

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Table 6.1: Total testosterone, free testosterone and cortisol response to speed only and speed/weights protocols. Data
presented as mean ± standard deviation
Pre
speed
Immediately
post
2 hours
post
24 hours
post
Speed Only protocol
Total Testosterone (nmol/l) 16.91 ± 4.16 19.51 ± 4.02* 16.52 ± 4.53 18.02 ± 4.59
Free Testosterone (pmol/l) 361 ± 74 419 ± 84* 349 ± 80 386 ± 85
Cortisol (nmol/l) 526 ± 94 404 ±154 307 ± 83* 530 ± 96
Speed weights protocol
Total Testosterone (nmol/l) 16.31 ± 3.66 18.65 ± 3.97* 15.15 ± 5.06 17.38 ± 3.96
Free Testosterone (pmol/l) 356 ± 69 401 ± 83* 331 ± 100 387 ± 68
Cortisol (nmol/l) 491 ± 103 357 ± 114* 297 ± 73* 520 ± 106
 = Significant to 0.05 when compared to immediately pre speed

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6.3.3 CREATINE KINASE, LA AND MUSCLE SORENESS
Lactate (effect size eta2
= 0.975, F = 540.593, p < 0.05), perceived muscle soreness
(effect size eta2
= 0.537, F = 16.205, p < 0.05) and creatine kinase (effect size eta2
=
0.503, F = 14.155, p < 0.05) were all found to vary significantly across the time
course of the study (Table 6.2). A protocol and time interaction was also found for
perceived muscle soreness (effect size eta2
= 0.253, F = 4.750, p < 0.05) but not for
either lactate (effect size eta2
= 0.017, F = 0.243, p > 0.05) or creatine kinase (effect
size eta2
= 0.160, F = 2.663, p > 0.05).
Significant elevations in lactate were observed immediately post the speed training
sessions performed as part of both protocols. However, lactate did not significantly
differ from baseline values at either 2 or 24 hours post in either protocol. Lactate was
also significantly elevated immediately post the weight training session compared to
baseline values. Furthermore, the lactate responses immediately post the speed
session was also found to be significantly higher than that immediately post the
weights session.
Table 6.2 shows the creatine kinase activity during both protocols. The mean creatine
kinase value was found to be significantly elevated immediately, 2 hours and 24
hours post the speed training session in both protocols. The creatine kinase at 24
hours post was significantly higher than 2 hours post in the speed/weights protocol
but not in the speed only protocol.
Perceived muscle soreness was reported to be significantly higher than baseline
immediately and 2 hours post the speed protocol in both sessions. However, at 24
hours post, soreness was still significantly elevated in the speed/weights protocol but
not in the speed only protocol which was significantly different between the two
protocols (Figure 6.1).

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Table 6.2: Lactate, creatine kinase and perceived muscle soreness response to speed only and speed/weights protocols.
Data presented as mean ± standard deviation
Pre
speed
Immediately
post
2 hours
post
Post
weights
24 hours post
speed
Speed only
Lactate (mmol/l) 1.33 ± 0.38 9.32 ± 1.65* 1.55 ± 1.05 - 1.05 ± 0.71
Creatine Kinase (u.l) 498 ± 284 561 ± 301* 603 ± 302* - 955 ± 876*
Muscle soreness (likert) 1.67 ± 0.72 3.33 ± 1.35* 3.00 ± 0.85* - 2.53 ± 1.25
Speed Weights
Lactate (mmol/l) 1.50 ± 0.72 9.41 ± 1.38* 1.41 ± 0.64 2.45± 1.19*^ 0.89 ± 0.49
Creatine Kinase (u.l) 485 ± 420 582 ± 454* 589 ± 423* n/a 1161 ± 816*
Muscle soreness(likert) 1.67 ± 0.82 3.20 ± 1.01* 3.07 ± 0.80* 4.10 ± 1.95* 3.80 ± 1.21*§
* = Significant difference from pre speed training;
^ = Significant difference from 2 hours post speed training
§
= Significant difference from 24 hours post speed training

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Figure 6.1: Perceived muscle soreness Pre (PRE), immediately post (IP), 2 hours
post (2P) and 24 hours post (24-hours post) speed training the speed only and
speed/weights protocols. *Significant difference between protocols
0
1
2
3
4
5
6
PRE IP 2P 24P
LikertScale
Time point
Speed only
Speed Weights
*

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6.3.4 NEUROMUSCULAR PERFORMANCE
There was a significant time effect for countermovement jump peak power (effect
size eta2
= 0.733, F = 38.456, p < 0.05), jump height (effect size eta2
= 0.575, F =
18.966, p < 0.05), peak force (effect size eta2
= 0.309, F = 6.268, p < 0.05), average
rate of force development (effect size eta2
= 0.170, F = 2.860, p < 0.05), relative peak
power (effect size eta2
= 0.732, F = 38.216, p < 0.05) and peak velocity (effect size
eta2
= 0.608, F = 21.710, p < 0.05) over the course of the study. There was an
interaction effect peak force (effect size eta2
= 0.210, F = 3.712, p < 0.05) and peak
velocity (effect size eta2
= 0.181, F = 3.094, p < 0.05). However, there was no
interaction effect for peak power (effect size eta2
= 0.0.38, F = 0.560, p > 0.05), jump
height (effect size eta2
= 0.160, F = 2.659, p > 0.05), average rate of force
development (effect size eta2
= 0.062, F = 0.919, p > 0.05) or relative peak power
(effect size eta2
= 0.037, F = 0.540, p > 0.05).
Several countermovement jump variables were found to have declined from their
baseline values immediately after the speed training session performed during both
protocols (Table 6.3). However, by 2 hours post, all these variables had returned to
baseline values. When observed 24 hours post, several jump variables across both
protocols were significantly depressed verses pre-training values indicating a second
decline in neuromuscular performance (Table 6.3).
During the speed/weights protocol, an additional measure of jump performance was
taken immediately post the weights session. At this time point, peak power, jump
height, relative PP and peak velocity were found to be significantly lower than both
the pre-training and 2 hour post levels.
There was a significant difference in peak velocity between the protocols
immediately after the speed training sessions in both protocols and a significant
difference in peak force between the protocols at the immediately post and 24 hours
post time-points.

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Figure 6.2: Example of a bimodal recovery pattern using peak power at the four
different time points (Pre, Immediately post, 2-hours post and 24-hours post) for both
the speed only and speed/weights protocols.
* = Significant decrease when compared to PRE.
4000
4200
4400
4600
4800
5000
5200
5400
5600
5800
6000
PRE IP 2P 24P
PeakPower(w)
Time point
Speed only
Speed Weights
* *

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Table 6.3. Neuromuscular responses to the speed only and speed/weights protocols. Data presented as mean ± standard
deviation
Pre
speed
Immediately
post
2 hours
post
Post
weights
24 hours
post speed
Speed only protocol
Countermovement Peak Power (W) 5345 ± 477 5066 ± 467* 5439 ± 437 - 5202 ± 458*
Countermovement Jump height (m) 0.39 ± 0.06 0.35 ± 0.07* 0.39 ± 0.06 - 0.37 ± 0.06*
Countermovement aRFD(total) (n.s-1
) 4688 ± 1570 4591 ± 1004 4838 ± 1535 - 4528 ± 1497
Countermovement Peak Force (N) 2467 ± 400 2377 ± 338§ 2479 ± 405 - 2414 ± 406§
Countermovement Rel. Peak power (W.kg-1
) 54.80 ± 6.76 52.03 ± 6.76* 55.70 ± 6.95 - 53.37 ± 7.23*
Countermovement Peak velocity (m.s-1
) 2.91 ± 0.20 2.81 ± 0.23*§ 2.90 ± 0.22 - 2.85 ± 0.20*
Speed weights protocol
Countermovement Peak Power (W) 5371 ± 452 5109 ± 474* 5408 ± 429 5037 ± 429*^ 5174 ± 415*
Countermovement Jump height (m) 0.40 ± 0.05 0.37 ± 0.06* 0.39 ± 0.06 0.36 ± 0.05*^ 0.37 ± 0.06*
Countermovement aRFD(total) N.s-1
) 4973 ± 1504 4742 ± 944 4913 ± 1218 4492 ± 1194 4342 ± 1102 *
Countermovement Peak Force (N) 2495 ± 484 2466 ± 442§ 2435 ± 386 2400 ± 404 2325 ± 336*§
Countermovement Rel. Peak power (W.kg-1
) 55.42 ± 6.15 52.54 ± 6.95* 55.47 ± 6.78 51.55 ± 5.40*^ 53.32 ± 6.63
Countermovement Peak velocity (m.s-1
) 2.93 ± 0.18 2.88 ± 0.19*§ 2.93 ± 0.21 2.82 ± 0.18*^ 2.84 ± 0.21*
* = significant difference from immediately pre (0.05)
^ = significant difference from 2 hours post (0.05)
§ = significant difference between the protocols at this time point (0.05)

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6.4 DISCUSSION
The present study is the first to compare the temporal responses of various
neuromuscular, physiological and endocrine parameters from a training day
consisting of a speed training session performed in isolation to a training day
containing one speed and one weight training session separated by two hours. The
main finding from the current study was that, while the addition of a lower body
weights session two hours after a speed training session did result in an increase in
perceived muscle soreness, it did not result in any additional changes in hormonal or
biochemical response and had only a minor effect on neuromuscular performance
over the course of the 24-hour measurement period.
6.4.1 NEUROMUSCULAR PERFORMANCE
Immediately after the maximal speed training session in both protocols, several of
the countermovement jump variables were found to be significantly depressed before
returning to baseline by 2 hours post. This confirms the findings reported in Chapter
5 regarding the initial effect of maximal speed training on neuromuscular function.
As in Chapter 5, these initial depressions in neuromuscular performance were
accompanied by significant elevations in creatine kinase, lactate and perceived
muscle soreness. By 2 hours post, lactate had returned to baseline in both protocols
while creatine kinase and perceived muscle soreness continued to rise (Table 6.2).
The finding that lactate had returned to baseline at 2 hours after completion also
supports the findings reported in Chapter 5 and, viewed alongside the recovery of
neuromuscular performance, suggests that, at least in part, the decreased jump
performance observed immediately after the maximal speed training was due to
decreased functioning of the contractile mechanisms of the muscle fibre (Skurvydas
et al., 2007) in the presence of the metabolites produced during exercise.
Several countermovement jump variables were also found to be depressed after the
weight training session performed during the speed/weights protocol (Table 6.2).
When these post-weights session depressions in performance were compared to the
drops experienced immediately after maximal speed training, no significant

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differences were found. This is consistent with the results of previous research
(Hakkinen et al., 1988; Hakkinen 1992; Chiu et al., 2004; Skurvydas et al., 2010a,
2010b) which, while using different measures of neuromuscular performance, also
reported no significant difference in the losses experienced after each of the two
training sessions performed on the same day. However, the current study is the first
to report these findings after a training day consisting of a speed and weight training
session which is a common approach and one recommended by elite coaches
(Francis, 2008).
To date, only Hakkinen et al. (1988) reported the lactate response to multiple
sessions. In their study, they compared two strength sessions containing a mix of
Olympic and strength lifts and found no difference between the post-session
metabolic responses. This is in contrast to our findings, where a significant
difference in the post-session lactate levels was observed. This is an interesting
finding given that the recoveries were the same and the duration of the efforts were
shorter during the sprint training. It appears that, even though the duration of effort
would have been expected to primarily tax the adenosine triphosphate-
phosphocreatine system and the between-effort recoveries should have allowed
significant creatine phosphate replenishment, this did not occur at the end of the
session. A study into 400 m training reported 3 x 100 m sprints to produce greater
lactate levels than one effort of 300 m (Saraslanidis et al., 2009). The authors suggest
that this was a result of the 3 x 100 m protocol allowing the participants to operate at
higher speeds over the distance and that the repeated maximal efforts performed
throughout the speed session most likely resulted in a significant post-effort energy
demand. It has been reported that variations in metabolic demand of exercise can
result in different mechanisms of fatigue even when the decreases in neuromuscular
performance are similar (McCaulley et al., 2009). For example, it is suggested that
the depressions in neuromuscular performance that occur immediately after high
intensity strength training may be the result of central rather than peripheral
mechanisms (Hakkinen, 1992; McCaulley et al., 2009), while maximal effort fast
stretch shortening cycle activities like sprinting produce impaired propagation of
muscle action potential (Tomazin et al., 2008). Therefore, while similar decreases in
neuromuscular performance were observed after both sessions in our study, it is

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possible that different mechanisms may have contributed to the losses in
performance.
In the current study, peak power, jump height and relative peak power were all
depressed 24 hours after the speed only protocol, while jump height, average rate of
force development(total), peak force and peak velocity were all depressed 24 hours
after the speed/weights protocol when compared to pre-training levels (Table 6.3).
However, peak force was the only variable where a difference between the protocols
was observed. Given this, the results from the countermovement jump suggest that
the addition of a weight training session 2 hours after maximal speed training did not
result in a greater loss in neuromuscular performance at 24 hours post. The finding
that several jump variables underwent a secondary decline in response to the speed
only protocol confirms the findings of Chapter 5, and suggests that maximal speed
training induces a bimodal recovery pattern in this population (Figure 6.2). The
depressions in performance at 24 hours post were accompanied by elevations in both
creatine kinase and perceived muscle soreness in both protocols, indicating
significant muscle damage. It has been reported that it is the inflammatory response
to muscle damage as opposed to the muscle damage itself that ultimately affects
muscle performance (Dousset et al., 2007). While absent 2 hours post, this
inflammatory response would be expected to be well underway by 24 hours post
(Armstrong, 1990) and represents the most likely explanation for the decline in
neuromuscular performance observed in both protocols.
Interestingly, at 24 hours post, there was a significant difference between the
protocols in terms of perceived muscle soreness but not creatine kinase. While
perceived muscle soreness is often presented as a marker of muscle damage (Nguyen
et al. 2009), it is important to draw a distinction between the two as perceived muscle
soreness has be shown to provide a poor reflection of muscle damage and
inflammation (Nosaka et al., 2002). Given this, their roles in the development of
fatigue may be different. Previous research has demonstrated a significant decrease
(15%) in maximal voluntary torque to correlate with elevated perceived muscle
soreness (Racinais, Bringard, Puchaux, Noakes & Perrey, 2008), with the suggestion
that this was due to the participant reducing exercise intensity on a conscious and
unconscious level, rather than by an acute exercise-related physiological or

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biochemical alteration. However, in the current study, the majority of jump variables
showed no difference in the degree of decline despite the difference in perceived
muscle soreness. One possible explanation for this may be that elite athletes’
performance is less affected by perceived muscle soreness. This is supported by the
findings of a study which tracked maximal voluntary isometric force and perceived
muscle soreness in both trained and untrained subjects for five days after an eccentric
protocol designed to induce muscle damage (Newton, Morgan, Sacco, Chapman &
Nosaka, 2008). The study reported that, while both groups reported similar levels of
perceived muscle soreness, neuromuscular performance returned much quicker in the
trained group.
Overall, our results suggest that the performance of a weights session 2 hours after
the completion of a maximal speed training session did not exacerbate the loss of
functional performance or muscle damage experienced 24 hours post, potentially
suggesting that mechanisms previously attributed to the repeat bout effect (Nosaka,
et al. 2001) may have be involved. While these mechanisms been extensively studied
across sessions separated by several days or weeks (Nosaka et al. 2001; Nosaka &
Newton 2002), limited research has been performed into multiple daily sessions. Of
the research that has been performed, our results are consistent with the findings on
multiple daily resistance sessions (Skurvydas et al., 2010a) and cycling sessions
(Skurvydas et al., 2010b) but is the first to demonstrate it occurs during a training
day combining two different modalities.
It is also important to highlight that, in the current study, the weight training session
followed the maximal speed training. Eccentric stress is reported to be one of the
main mechanisms behind muscle damage/inflammation (Chatzinikolaou et al., 2010)
and the repeat bout effect has been demonstrated not to occur when the second
session of a significantly higher intensity than the first (Nosaka & Newton 2002). It
is unclear if changing the exercise session order would have had an effect on the
degree of muscle damage, perceived muscle soreness and loss of performance
experienced 24 hours post.

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6.4.2 ENDOCRINE RESPONSE TO SPEED ONLY AND SPEED/WEIGHTS
PROTOCOLS
This study also set out to profile the effect of the two training protocols on total
testosterone, free testosterone and cortisol response and, therefore, potentially
recovery. Levels of testosterone and free testosterone were observed to significantly
increase immediately after the MST session (Table 6.1), while cortisol was
unaffected after the speed only protocol and significantly depressed after
speed/weights protocol. While the testosterone responses are in line with the results
of previous research in the area, the lack of increase in cortisol post-sprinting is not
(Pullinen et al., 2005). However, an increase in testosterone coupled with a
decrease/lack of change in cortisol, has been reported after both a resistance training
(Beaven et al., 2008) and a repeated sprint training session consisting of 4 x 250 m
sprints of three minutes recovery (Meckel et al., 2009).
Considering the maximal speed training undertaken in both the current chapter and in
Chapter 5 were identical, it is curious that, in the current study, both the sprint
protocols resulted in an elevation in total testosterone while in Chapter 4 it did not.
Total testosterone response to a training stimulus is reported to be dependent on both
training background (Ahtiainen et al., 2004) and training experience (Kraemer et al.,
1992). In the current study, the participants had more experience with both speed and
weight training and this may have contributed to the post exercise elevations
observed. This can be seen when the strength levels are compared across the two
studies, with the participants in Chapter 5 having a 1RM squat and bench of 150 ± 22
kg and 121 ± 15 kg, respectively, compared to 170 ± 20 kg and 135 ± 10 kg in this
chapter.
It should also be considered that, in order to fully replicate a full training day, the
participants in the current study were allowed to take breakfast while, in Chapter 4,
the training was performed fasted. It has been demonstrated that diet can affect
resting testosterone (Volek, 2004). While it is unlikely to have caused an elevation in
either total testosterone or free testosterone post-exercise, it cannot be ruled out that
breakfast resulted in slightly depressed pre-training testosterone levels. The exact
reason why cortisol did not respond immediately after the initial training session

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while total testosterone and free testosterone did is unclear. Given that our initial
baselines were taken immediately prior to exercise, it is possible that they are
unrepresentative of resting cortisol levels due to the participant’s anticipation of the
training sessions. However, a recent study in powerlifting suggests that pre-exercise
anticipation does not cause elevations in cortisol in all cases (Le Panse et al., 2012).
Alternatively, it has been suggested that there is a training load ‘threshold’ upon
which the hypothalamic-pituitary adrenal axis is activated (Cadore, Pinheiro et al.
2013) and it is possible that the low volume (6 x 50 m) in the current study was
insufficient to activate it. Testosterone has been found to have several fast acting
non-genomic effects, including several related to muscle function (Crewther et al.,
2011). It is possible that the post-sprinting increase in total testosterone observed was
an attempt to optimise neuromuscular performance and/or counteract the effects of
peripheral fatigue as opposed to a response to the session volume or metabolic
demand. While the absence of a control group is a limitation of the current study,
given the circadian pattern previously reported with testosterone (Hayes et al., 2010),
the lack of decrease in total testosterone at 2 hours post may actually be viewed as an
elevation versus the concentrations that would have been expected without the sprint
training session. Cortisol on the other hand, appeared to follow the expected
circadian pattern and was significantly lower 2 hours after the maximal speed
training in both protocols. While the degree to which the sustained post-exercise
elevations in testosterone observed may or may not be directly involved in inducing
muscle protein synthesis is subject to controversy (Schroeder, Villanueva, West &
Phillips 2013), it has been suggested they may play a role in other adaptations
relevant to strength/power athletes (Anttila, Manttari & Jarvilehto, 2008). For
instance, it has been demonstrated that altering the normal circadian pattern of
testosterone with a morning weight training session correlated with improved
afternoon performance (Cook et al., 2013). It cannot, therefore, be ruled out that the
post-exercise total testosterone response observed in the current study may have
resulted in a superior training or competitive environment. If so, this may have
implications for training order and potentially pre-competition preparation.
Previously, variations in testosterone and or cortisol hormones in the days following
training have been thought to give an indication of training stress (Chatzinikolaou et
al., 2010). In the current study cortisol, total testosterone and free testosterone were

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found to be no different from pre-training levels 24 hours after the initial training
session in either protocols and there was no difference in the response between the
protocols. This would suggest that the addition of a second training session did not
affect the acute hormonal response observed 24 hours post. However, further
research is required to see if this pattern continues long-term or if continuingly
performing multiple training sessions per day does induce altered hormonal
responses long-term.
6.5. CONCLUSIONS
In conclusion, our primary finding is that the addition of a weights session 2 hours
after a maximal speed training session did result in an increase in perceived muscle
soreness. However, this increase in perceived muscle soreness did not result in a
significant increase in the vast majority of neuromuscular performance variables.
One possible explanation for this is that the weight training was less intensive than
the maximal speed training and, as a result, any damage that was done during the
speed/weights protocol had already been done prior to the weight training session.
However, further research is required to assess if, indeed, these findings were
influenced by session order.
6.6 PRACTICAL APPLICATIONS
Athletes are often required to undertake training sessions aimed at developing
several different physical qualities in the same day and/or week. This study shows
that two hours was sufficient for the neuromuscular system to recover from a
maximal speed training session. In addition, the performance of weight training 2
hours after speed training did not result in any difference in the biochemical or
neuromuscular markers assessed 24 hours post compared to only performing speed
training. This has implications for programming as compressing the weight and
speed training into a single training day does not seem to result in additional fatigue
or damage and may actually promote superior adaptation.

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Chapter 7
The Effect of Training Order on
Neuromuscular, Physiological and
Endocrine Response to a Maximal Speed
and Weight Training Sessions over a 24-
hour Period

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7.1 INTRODUCTION
As discussed in Chapter 6, it is common practice for athletes from a range of sports
to perform speed training and weight training sessions on the same day (Cormack et
al., 2008; McLean et al., 2010). Chapter 6 investigated the effect of performing a
training day consisting of maximal speed training alone compared to a training day
consisting of maximal speed training followed 2 hours later by a lower bodyweight
training session and found that, while the multiple session training day resulted in
greater perceived muscle soreness, it did not result in an increased loss in
neuromuscular performance or a change in endocrine response 24 hours post. As
such, it would appear that the addition of a weight training session 2 hours post
maximal speed training did not result in increased fatigue.
One possible explanation for this finding is that the initial exercise stimulus damages
those fibres susceptible to injury and as a result no further damage results from
subsequent exercise bouts (Skurvydas et al, 2010a, 2010b). Therefore, it is possible
that the weight training session did not result in additional loss in performance due to
the overload provided by the initial maximal speed training. However, increased
damage has been demonstrated to occur during protocols where the second session is
of a significantly higher intensity than the first (Nosaka & Newton, 2002). Another
factor that could potentially affect the recovery is the extent to which the participants
recover between sessions, something that has been demonstrated in a study into the
effect of session order on running performance 24 hours post (Doma & Deakin,
2013). This study found that running performance was impaired to a greater extent
when participants performed a training day consisting of a weights session six hours
prior to a running session when compared to a training day consisting of a running
session six hours prior to a weights session. The authors attributed this to the six
hours between sessions being insufficient for recovery post weights, thereby
resulting in the running session being performed in a greater degree of fatigue. This,
in turn, was suggested to result in increased fatigue 24 hours post.
However, while several studies have examined the order effect on concurrent weight
and endurance training sessions (Cadore et al., 2012; Coffey et al., 2009; Coffey,
Pilegaard, Garnham, O'Brien, & Hawley, 2009; Rosa et al., 2012; Schumann et al.,

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2013; Taipale & Hakkinen, 2013), to date, no studies have examined the order effect
of maximal speed training and strength training. Therefore, it is unclear what effect
changing the session order of maximal speed training and strength training would
have on the degree of muscle damage, muscle soreness and loss of performance
experienced 24 hours post. This represents an important gap in our understanding
when it comes to programme design, especially on training days containing maximal
speed training and weight training sessions as there may be occasions when the
coach finds it preferable to perform strength training prior to speed training. For
example, it has been shown previously that exercise order can affect some of the cell
signalling pathways and gene expressions related to training adaptation (Coffey et
al., 2009a) and that the interference resulting from sprint intervals is greater than
from endurance training (Coffey et al., 2009b). Given this, there may be occasions
when the coach may want to manipulate the order to effect both acute and chronic
training responses. In addition, it has been demonstrated that a morning weight
training session can have a positive effect on afternoon performance (Cook et al.,
2013; Ekstrand et al., 2013), potentially through alterations in the normal circadian
patterns associated with testosterone. While both these papers investigated morning
training as a way to prime afternoon performance, neuromuscular adaptations have
been shown to be sensitive to the intensity of the overload (Tan, 1999) and,
therefore, the potential enhancement of the neural and endocrine systems in the
afternoon would have implications for training as well. However, other research has
demonstrated running performance to be impaired eight hours after a weight training
session (Palmer & Sleivert, 2001), thereby affecting the quality of and, potentially,
the overload produced.
Therefore, the aim of this study was to compare the neuromuscular, endocrine and
biochemical responses to a training day during which maximal speed training was
followed 2 hours later by weight training, compared to a training day during which
weight training was followed 2 hours later by maximal speed training. Specifically,
the study set out to compare morning performance to afternoon performance where it
was preceded by a second session and to see if session order affected recovery at 24
hours post.

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7.2. METHODS
7.2.1 PARTICIPANTS
Fifteen academy level rugby players were recruited for this study (mean ± standard
deviation: age 21 ± 1.3 years; 100.5 ± 10.5 kg; height 185.7 ± 6.6 cm). Each player
had been involved in the professional academy system for a minimum of one to two
years, during which time they were exposed to regular strength, power and speed
training and testing (mean ± standard deviation: Squat 1RM 170 ± 20 kg, Bench
Press 1RM 135 ± 10 kg, 10m sprint time 1.75 ± 0.1 s). The study was undertaken at
the end of the regular playing season and participants were performing physical
training consisting of speed, strength and conditioning sessions four days per week.
Participants provided written informed consent and ethical approval for the study
was provided by the University of Ulster Research Ethics Committee.
7.2.2 DESIGN
This study profiled two training days, one consisting of maximal speed training
followed by a weight training session with a strength development focus 2 hours
later (speed/weights) and one consisting of a weight training session followed by a
maximal speed training session 2 hours later (weights/speed) to determine if session
order resulted in a different metabolic, endocrine or neuromuscular response. The
study was designed as a randomised crossover study and each experimental protocol
was completed over two days.
Prior to arriving on day one of each protocol, participants were given two days off
training. Each participant was given an arrival and start time which was maintained
throughout the study to account for circadian variation in hormones and body
temperature (Hackney & Viru, 2008). Upon arrival, participants filled out a
questionnaire on perceived muscle soreness and, after 10 minutes lying supine, a 5
ml blood sample was collected for subsequent analysis. Participants then performed a
10-minute standardised warm-up before reporting to the testing area where three
countermovement jumps were performed (Pre speed time point) after which they
performed either the speed weights or weights speed protocol.

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In the speed/weights protocol, participants proceeded to the indoor track and, after a
running specific warm-up, performed a maximal speed training session, details of
which can be found in section 3.3.4. After completion of the final sprints, the
participants again provided blood samples and information on perceived muscle
soreness before performing three CMJs (Immediately post session 1 time-point).
Two hours after completion of the maximal speed training, blood, perceived muscle
soreness and countermovement jumps were collected again (2 hours post time-point),
after which, the participants proceeded to the gym to undertake a weight training
session (section 3.3). After completion of this session, the countermovement jump’s
were repeated and blood lactate was taken once again (immediately post session 2
time-point). Lactate, perceived muscle soreness, countermovement jump and blood
were collected again for a final time the following morning (24 post session1 time-
point).
In the weights/speed protocol, the exact same sessions were performed, however, the
order was reversed with the weight training session being performed in the morning
and the speed session in the afternoon. We chose to design the training days in this
manner based on the anecdotal reports of coaches and elite athletes regarding the
structure of their training days.
During each protocol, the first day’s breakfast, lunch, snacks and dinner along with
the following day’s breakfast were provided (Soulmate food, Lancashire, UK). Both
calorie intake and food choice were kept the same throughout both the speed/weights
and weights/speed protocols in order to ensure that the participants’ nutritional intake
was standardised throughout the study. Consumption of water was also allowed
throughout the testing and training periods.
7.2.3 METHODS
Neuromuscular performance
Due to time and personnel constraints, only CMJs were collected as markers of
neuromuscular performance. The countermovement jump was chosen over the squat
jump due to the higher degree of reliability reported for countermovement jump
variables in Chapter 4. The countermovement jump tests were performed on a force

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platform (Type 9287CA, Kistler Instruments Ltd., Farnborough, United Kingdom).
Please refer to chapter 3.4 for more detail. Based on the findings presented in
Chapter 4, jump height, peak power, Relative peak power, average rate of force
development (total), peak force and peak velocity were calculated to assess change in
neuromuscular performance in response to the protocols.
Hormonal response
Hormonal responses were calculated from blood samples collected from the
antecubital vein after 10-minutes of lying supine. For more detail please refer to
chapter 3.5.
Creatine kinase response
Creatine kinase responses were calculated from blood samples collected from the
antecubital vein after 10-minutes of lying supine. For more detail please refer to
chapter 3.6.
Perceived Muscle Soreness
Perceived muscle soreness was recorded at each data collection point, using a 7-point
Likert scale designed to measure soreness in the lower body. Please refer to chapter
3.6 for more detail.
Lactate response
Blood lactate was analysed using a lactate analyser (Lactate pro, Arkray). For more
detail please refer to chapter 3.7.
Resistance training
The participants recorded weights lifted during each of the squat and Romanian
deadlift work sets and total tonnage was calculated from this information. Each
participant also provided a Rate of Perceived Exertion, using the Borg 10 grade
scale, for the weight training sessions performed during each protocol upon
completion (Borg, 1982).

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7.2.4 STATISTICAL ANALYSIS
Data is expressed in its recorded form as the mean ± standard deviation. After tests
for normal distribution and prior to any further statistical analysis, creatine kinase
recorded values were log transformed due to large inter-participant variability.
Differences between and within protocol were assessed using a multi-factorial (time-
point and protocol) repeated measure analysis of variance. If significant F values
were observed (p ≤ 0.05), a post hoc test with Bonferroni corrections to control of
Type I error was run to determine where significant differences occurred. Effect size
(ES) was determined using partial eta-squared, with an effect size of approximately
0.2 considered small, approximately 0.5 considered medium and approximately 0.8
considered large (Cohan, 1988). Finally, differences in afternoon and morning
weight training rate of percieved effort and tonnage lifted, along with average and
maximal speed at 10 m and 50 m were assessed using a two tailed T-Test. The level
of significance was set at p ≤ 0.05 for the present study and all statistics were
performed using SPSS 20.0 (SPSS Inc., Chicago, IL).

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7.3 RESULTS
7.3.1 TRAINING ANALYSIS
No time effect was found for either 10 m (effect size eta2
= 0.084, F = 1.287, p >
0.05) or 50 m (effect size eta2
= 0.068, F = 1.029, p > 0.05) sprint time suggesting
that performances were maintained throughout the protocols. There was no
significant time-protocol interaction for either 10 m (effect size eta2
= 0.030, F =
0.427, p > 0.05) or 50 m (effect size eta2
= 0.070, F = 1.046, p > 0.05) showing that
general performance did not differ across the protocols. The protocols did not differ
with regard to peak 10 m or 50 m time, although peak 10 m time in the afternoon
was faster than peak morning 10 m performance to a point which could be
considered practically significant (p = 0.087; improvement 0.04 s). There was no
significant different in the rate of percieved effort or total volume lifted for the
weight training sessions regardless of the protocol (table 7.1).

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Table 7.1: Total tonnage lifted and rate of perceived effort for the weight
training sessions and 10m and 50m times for the two protocols. Data
presented as mean ± standard deviation
Speed Weights Weights Speed
Rate of perceived effort (scale 1 - 10) 6.87 ± 1.19 6.50 ± 1.18
Tonnage lifted (kg) 2771 ± 279 2812 ± 318
10 m time (s) 1.80 ± 0.11 1.76 ± 0.08
50 m time (s) 6.56 ± 0.34 6.53 ± 0.34

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7.3.2 ENDOCRINE RESPONSE
Analysis revealed that the study protocols had a significant time effect on total
testosterone (effect size eta2
= 0.349, F = 7.520, p < 0.05), free testosterone (effect
size eta2
= 0.415, F = 9.928, p < 0.05) and cortisol (effect size eta2
= 0.751, F =
42.297, p < 0.05) (Table 7.2). However, no protocol time interaction was found in
Total testosterone (effect size eta2
= 0.115, F = 1.822, p > 0.05) free testosterone
(effect size eta2
= 0.021, F = 0.306, p > 0.05) or cortisol (effect size eta2
= 0.026, F =
0.376, p > 0.05. While testosterone was significantly elevated immediately after the
morning maximal speed training in the speed/weights protocol, its rise did not reach
significance after the morning weights session in the weights/speed protocol.
However, there was no significant difference between the two protocols.
Testosterone was not different from baseline measures at any other time point in
either protocol (Table 7.2). Cortisol was found to be significantly declined
immediately post and 2 hours post both the morning maximal speed training and
weights sessions. However, it did not differ from baseline at 24 hours post (Table
7.2).

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Table 7.2: Total testosterone, Free testosterone and cortisol responses to speed/weights and
weights/speed protocols. Data presented as mean ± standard deviation
Pre session 1 Immediately
post session 1
2 hours post
session 1
24 hours post
session 1
Speed/Weights Protocol
Total Testosterone (nmol/l) 16.31 ± 3.66 18.65 ± 3.97* 15.15 ± 5.06 17.38 ± 3.96
Free Testosterone (pmol/l) 356 ± 69 401 ± 83* 331 ± 100 387 ± 68
Cortisol (nmol/l) 491 ± 103 357 ± 114* 297 ± 73* 520 ± 106
Weights/Speed Protocol
Total Testosterone (nmol/l) 17.12 ± 4.93 18.15 ± 4.95 15.63 ± 6.13 17.66 ± 4.55
Free Testosterone (pmol/l) 359 ± 96 397 ± 103 322 ± 112 391 ± 93
Cortisol (nmol/l) 516 ± 99 373 ± 136* 290 ± 103* 514 ± 100
* = Significant to 0.05 when compared to immediately pre

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7.3.3 CREATINE KINASE, LA AND PERCEIVED MUSCLE SORENESS
RESPONSES
The protocols were found to have a significant time effect on lactate (effect size eta2
= 0.923, F = 167.799, p < 0.05), perceived muscle soreness (effect size eta2
= 0.650,
F = 26.032, p < 0.05) and creatine kinase (effect size eta2
= 0.882, F = 105.042, p <
0.05). In addition, there was a significant protocol time interaction for lactate
response (effect size eta2
= 0.932, F = 193.379, p < 0.05) (Figure 7.1) but not for
perceived muscle soreness (effect size eta2
= 0.024, F = 0.343, p > 0.05) or creatine
kinase (effect size eta2
= 0.063, F = 0.940, p > 0.05; Table 7.14). Immediately after
both the morning maximal speed training in the speed/weights protocol and the
morning weights session in the weights/speed protocol significant elevations in
lactate, creatine kinase and muscle soreness were observed (Table 7.3). While no
difference was found in either perceived muscle soreness or creatine kinase at this
time point, the increase in lactate was significantly greater post speed training versus
strength training (Table 7.3). Both creatine kinase and perceived muscle soreness
were found to have continued to rise at both 2 and 24 hours post. However, by 2
hours post, lactate had returned to baseline in both protocols. Immediately after the
afternoon weight training session in the speed/weights protocol and after the
maximal speed training session in the weights/speed protocol, lactate levels were
again found to be elevated and again, as can be seen in both Table 7.3 and Figure
7.1, the lactate response to maximal speed training was significantly greater than that
to weight training. At 24 hours post, lactate had again returned to its pre-training
levels.

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Figure 7.1: Lactate response to speed/weights and weights/speed protocols at Pre
session 1 (PRE), immediately post session 1 , 2 hours post session 1 , immediately
post session 2 and 24 hours post session 1.
0
2
4
6
8
10
12
14
PRE IP1 2P IP2 24P
Lactate(mmol/l)
Time point
Speed weights
weights speed

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Table 7.3: creatine kinase and perceived muscle soreness responses to speed/weights and weights/speed protocols. Data presented as mean
± standard deviation
Pre session
1
Immediately post
session 1
2 hours post
session 1
Immediately post
session 2
24 hours post
session 2
Speed/Weights Protocol
Creatine Kinase (u.l) 485 ± 420 582 ± 454* 589 ± 423* n/a 1161 ± 816*
Muscle soreness (likert) 1.67 ± 0.82 3.20 ± 1.01* 3.07 ± 0.80* 4.10 ± 1.95* 3.80 ± 1.21*
Weights/Speed Protocol
Creatine Kinase (u.l) 508 ± 306 571 ± 319* 607 ± 358* n/a 1122 ± 946*
Muscle soreness (likert) 1.87 ± 0.99 3.20 ± 0.77 3.33 ± 0.90 4.40 ± 0.63* 3.67 ± 1.05
* = Significant to 0.05 when compared to immediately pre

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7.3.4 NEUROMUSCULAR PERFROMANCE
Time effects were found for countermovement jump peak power (effect size eta2
=
0.636, F = 24.416, p < 0.05), jump height (effect size eta2
= 0.629, F = 23.765, p <
0.05), average rate of force development (total) (effect size eta2
= 0.454, F = 11.639,
p < 0.05), peak force (effect size eta2
= 0.353, F = 7.655, p < 0.05), relative peak
power (effect size eta2
= 0.590, F = 20.169, p < 0.05) and peak velocity (effect size
eta2
= 0.645, F = 25.446, p < 0.05). No interaction was found in any of these
measures between protocol and time-point countermovement jump peak power
(effect size eta2
= 0.114, F = 1.796, p > 0.05), jump height (effect size eta2
= 0.061, F
= 0.912, p > 0.05), average rate of force development (total) (effect size eta2
= 0.081,
F = 1.237, p > 0.05), peak force (effect size eta2
= 0.084, F = 1.291, p > 0.05) relative
peak power (effect size eta2
= 0.147, F = 2.410, p < 0.05) and peak velocity (effect
size eta2
= 0.143, F = 2.335, p < 0.05).
Countermovement jump peak power, jump height, relative peak power and peak
velocity all followed the same pattern in response to the training protocols, with
initial depressions being observed immediately after the first training session of the
day before recovering to baseline levels 2 hours post. After the completion of the
second session of the day, all of these variables were again depressed and remained
so when observed 24 hours post (Table 7.4). There was no significant difference
between the losses observed between the protocols. Average rate of force
development was found to be depressed compared to baseline measures at 24 hours
post during both the protocols but not at any other time point, while peak force was
found to be depressed at 24 hours post during the speed/weights protocol but not at
any other time point (Table 7.4).

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Table 7.4: Neuromuscular responses to speed/weights and weights/speed protocols. Data presented as mean ± standard deviation
Pre session
1
Immediately post
session 1
2 hours post
session 1
Immediately post
session 2
24 hours post
session 1
Speed/Weights Protocol
Countermovement Peak Power (W) 5371 ± 452 5109 ± 474* 5408 ± 429 5037 ± 429* 5174 ± 415
Countermovement Jump height (m) 0.40 ± 0.05 0.37 ± 0.06 0.39 ± 0.06 0.36 ± 0.05* 0.37 ± 0.06*
Countermovement aRFD (n.s-1
) 4972 ± 1504 4742 ± 944 4913 ± 1218 4492 ± 1194 4343 ± 1102*
Countermovement Peak Force (n) 2495 ± 484 2466 ± 442 2435 ± 386 2400 ± 404 2325 ± 336*
Countermovement Power (W.kg-1
) 55.42 ± 6.15 52.54 ± 6.95* 55.47 ± 6.78 51.55 ± 5.40* 53.32 ± 6.63
Countermovement Peak vel. (m.s-1
) 2.93 ± 0.18 2.88 ± 0.19* 2.93 ± 0.21 2.82 ± 0.18* 2.84 ± 0.21*
Weights/Speed Protocol
Countermovement Peak Power (W) 5368 ± 446 5073 ± 532* 5363 ± 397 5168 ± 463* 5215 ± 424
Countermovement Jump height (m) 0.39 ± 0.06 0.37 ± 0.05* 0.39 ± 0.06 0.37 ± 0.05* 0.37 ± 0.06*
Countermovement aRFD (n.s-1
) 4943 ± 1204 4713 ± 1338 4775 ± 1221 4709 ± 1345 3965 ± 1194*
Countermovement Peak Force (n) 2458 ± 382 2410 ± 410 2463 ± 387 2462 ± 392 2331 ± 332
Countermovement Power (W.kg-1
) 55.10 ± 6.46 51.83 ± 490* 55.07 ± 6.43 53.18 ± 6.07* 53.48 ± 6.49
Countermovement Peak vel. (m.s-1
) 2.91 ± 0.20 2.83 ± 0.16* 2.90 ± 0.19 2.85 ± 0.17 2.84 ± 0.19*
* = Significant to 0.05 when compared to immediately pre

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7.4 DISCUSSION
To our knowledge, this is the first study to examine the influence of manipulating the
order of maximal speed training and weight training on acute neuromuscular,
physiological and endocrine responses over a 24-hour period. The primary findings
from this investigation was that while the two sessions individually resulted in
significantly different metabolic responses, training order did not result in different
endocrine responses, patterns of muscle soreness, muscle damage or neuromuscular
performance over a 24-hour period.
7.4.1 NEUROMUSCULAR RESPONSE TO SESSION ORDER
In the current study, both the initial maximal speed training and weights sessions
were found to result in similar depressions in neuromuscular performance
immediately post. The response to the morning maximal speed training session in the
speed/weights protocol confirms the findings reported in Chapters 5 and 6 and
suggests that athletes and coaches can expect maximal speed training to result in a
depression in neuromuscular performance immediately post. The finding that there
was no difference in the immediate post-exercise responses to either morning session
is interesting as previous research has shown the acute response to exercise to vary
depending on the nature of the fatiguing activity (Cadore et al., 2012; McCaulley et
al., 2009). Furthermore, a link between metabolic fatigue and loss in neuromuscular
performance has previously been reported (Walker et al., 2012). However, no such
relationship was found in the current study as the sessions differed considerably in
terms of lactate accumulation immediately after the first sessions of the day (speed
9.41 ± 1.38 mmol/l vs. strength 3.15 ± 1.07 mmol/l; Figure 7.1). Instead, it is
possible that the training experience of the participant group in the current study
contributed to the findings, as the participants were elite athletes with considerable
experience with weight training (Squat 1RM 170 ± 20 kg, Bench 1RM 135 ± 10 kg).
It has been demonstrated that strength-trained participants have the ability to
generate significantly more neural fatigue than untrained participants (Ahtiainen &
Hakkinen, 2009) and, therefore, it is possible that the participants in this study
experienced greater depressions in neuromuscular performance immediately after a
maximal strength focused weight-training session than would have been expected
from a non-elite population.

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When performance was reassessed two hours after the morning sessions and
immediately prior to the start of the afternoon sessions, all of the countermovement
jump variables had recovered in both protocols. While the time-frames required for
recovery from different types of resistance training have previously been
demonstrated (McCaulley et al., 2009; Raastad & Hallen, 2000), to our knowledge,
this is the first study to compare the time-frames for recovery from maximal speed
training to a maximal strength-focused weight-training session. These findings
suggest, therefore, that two hours is a sufficient recovery period between sessions
and confirm results reported in Chapters 4 and 5 regarding maximal speed training.
In addition, these results support prior research into the time-frames required for the
acute recovery of neuromuscular performance after a maximal strength-focused
weight-training session (McCaulley et al., 2009).
Given the relationship between exercise intensity and neuromuscular adaptation
(Tan, 1999), it is important that the second session of the day is not performed in a
fatigued state. Chapters 5 and 6 both reported that neuromuscular performance was
superior 2 hours compared to 24 hours post maximal speed training. This study set
out to investigate what effect performing maximal speed training and weight-training
as a first session of the day compared to as the second session of the day had on
performance. The results showed no difference in either total tonnage lifted or rate of
perceived effort when the weight training sessions were compared (Table 7.1),
suggesting that performing a strength-training protocol 2 hours post maximal speed
training does not result in decreased performance and/or overload. While there was
no significant difference between the two protocols in terms of 50m sprint time, 10m
sprint time showed a practically significant improvement when performed 2 hours
after a weights session versus the morning (0.04 second improvement). This
improved performance may have been a result of normal circadian patterns
associated with body temperature. It has previously been demonstrated that an
increase in temperature of as little as one degree increases power output in the
muscle by 10% at high velocities (Sargeant, 1987) via improved neural transmission
(Bishop, 2003) and increased adenosine triphosphate turnover rates (Gray,
Soderlund, & Ferguson, 2008). Body temperature has also been shown to follow a
distinct pattern, with temperature low in the morning upon waking, gradually
increasing during the day, before finally starting to decline early evening (Guette et

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al., 2005; Teo et al., 2011). Given this, it is possible that sprint performance was
merely following its normal circadian pattern. It is also possible that the weight
training itself played a role in improving sprint performance 2 hours post, as previous
research has demonstrated morning weight training to enhance afternoon sprint
(Cook et al., 2013) and backward overhead shot throw (Ekstrand et al., 2013). Cook
et al. (2013) reported morning weight training to result in a change in the normal
circadian pattern of testosterone, resulting in it being significantly elevated prior to
the speed testing versus the same time-point on a day were no morning session was
performed. They suggested that these changes in endocrine status may have played a
role in improving sprint performance via non-genomic processes (Crewther et al.,
2011). In the current study, testosterone, while not significantly elevated
immediately post weight training, was not significantly different from baseline
values 2 hours post, suggesting that weight training may have affected the normal
circadian pattern associated with testosterone. In doing so, it is possible the non-
genomic effects associated with testosterone (Crewther et al., 2011) accentuated the
normal circadian patterns associated with performance and contributed to sprint
performance at this time-point. While the potential for an increase in 10 m speed
may imply that it is optimal to perform strength training followed by maximal speed
training, other factors should also be considered depending on the desired outcome
of the training day. Notably, research has shown that the metabolic changes that
occur in response to sprint training can inhibit some anabolic signalling pathways
and reduce IGF-1 levels for periods of at least three hours (Coffey et al., 2009a).
Therefore, the coach should make the decision regarding exercise order with their
desired adaptation in mind.
In addition to the faster 10m time in the second session of the day, the current study
also found average time at 10m and 50m not to differ across the two protocols,
showing that a morning weight training session, at worst, does not negatively affect
the ability to undertake a maximal speed training session 2 hours post. This may be
linked to the finding that the performance of prior exercise did not affect metabolic
response to either session. This conflicts with the findings of other studies, which
have reported the metabolic response to the second session to be affected by the first
(Coffey et al., 2009; Schumann et al., 2013). Coffey et al. (2009a), for example,
reported that performing weight training after sprint training results in a significantly

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higher lactate response compared to when the resistance training session was
performed first, while Schumann et al. (2013) found the lactate concentrations during
a 30-minute run to be significantly elevated when they were preceded by a resistance
training session. The most likely explanation for the difference between these results
and the current study is the difference in the time between the sessions, with Coffey
et al (2009b) performing their sessions with a 15-minute recovery between them and
Schumann et al. (2013) performing their sessions back-to-back. In contrast, a two-
hour recovery between sessions was utilised in the current study and, as a result,
sufficient time was available for lactate concentrations to return to baseline, in turn,
allowing the participants to sufficiently recover from the first session.
Speed and strength performance were, at minimum, maintained during the second
session of the day, even in the presence of elevated perceived muscle soreness and
creatine kinase. This would suggest that these markers alone did not inhibit or mark
for reduced neuromuscular performance at this time-point and supports the findings
reported in Chapters 5 and 6. Therefore, while differences in population and session
type may have played a role, it seems likely that the 2 hours post time-point
represents a time-frame prior to the initiation of this inflammatory process
(Armstrong, 1990) but after metabolic recovery during which the athlete can
undertake additional explosive type training in a fully recovered state.
At 24 hours post, neuromuscular performance was again found to be significantly
declined versus initial baseline measurements in both protocols, however there was
no difference between the protocols suggesting that session order does not affect the
neuromuscular system at this time point. These findings suggest that those fibres
susceptible to injury were damaged during the first session of the day regardless if it
is a maximal speed training or a weight training session. While previous research has
reported similar findings when the two sessions were identical in make-up
(Skurvydas et al., 2010a, 2010b), this is the first study to suggest that, at least on
weights and speed training days, session order does not seem to be a factor.
However, this finding conflicts with Doma and Deakin (2013) who found a strength
session followed by an aerobic run to have a significantly greater negative effect on
running performance 24 hours post than when an aerobic run was followed six hours
later by a strength session. One possible explanation for this difference between the

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studies is the readiness of the neuromuscular system to undertake the second session
of the day. While in the current study neuromuscular performance had returned to
baseline prior to the start of session two, Doma and Deakin (2013) reported that
maximal voluntary contraction was still depressed six hours after the strength
training session and immediately prior to the start of the run session. This was in
contrast to the running-strength training sequence were maximal voluntary
contraction had fully recovered between sessions. This again highlights the
importance of ensuring neuromuscular recovery prior to beginning session two as
training in a fatigued state will result in greater depressions 24 hours post.
24 hours post was the only time-point at which average rate of force development
was depressed during the jumps, supporting the findings of both Chapters 4 and 5
and potentially suggesting a different mechanism of fatigue to that which occurred
immediately post. While inflammation was not directly measured in the current
study, the depression in neuromuscular performance observed 24 hours post, which
has been linked to inflammation, did not differ between protocols. This would seem
to suggest no difference in inflammation, at least to the point where it affects
neuromuscular performance.
Finally, Chapters 5 and 6 demonstrated neuromuscular performance to recover in a
bimodal pattern post maximal speed training. However, given that the design of the
current study meant that participants undertook a maximal speed training session 2
hours after the weight training session, it is unclear if a secondary decline in
neuromuscular performance would have occurred without the addition of a weight
training session 2 hours post. For example, while a bimodal recovery pattern has
been reported in response to a strength protocol by Raastad and Hallen (2000), a
more linear recovery pattern was found in response to a strength protocol by
McCaulley et al. (2009).
7.4.2 ENDOCRINE RESPONSE TO SESSION ORDER
Immediately after both the morning maximal speed training and weight training
sessions, cortisol decreased significantly while testosterone increased significantly
after the maximal speed training and non-significantly after the weight training

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session. However, most importantly there was no difference in the testosterone
response between the protocols (Table 7.2). This lack of difference in testosterone
response occurred even though the sessions differed significantly in terms of the
metabolic response they inducted, with the maximal speed training session resulting
in significantly greater accumulation of lactate than the weight session (speed 9.41 ±
1.38 mmol/l vs. strength 3.15 ± 1.07 mmol/l). The relationship between the
metabolic and endocrine responses that occur post-exercise has been the subject of
considerable research (Fry & Lohnes, 2010; Izquierdo et al., 2009; Kon et al., 2010;
Kraemer et al., 1999; Spiering et al., 2008; Walker, Taipale, Nyman, Kraemer, &
Hakkinen, 2011). While several studies report a relationship between training-
induced elevations in lactate and post-exercise changes in both testosterone
(Izquierdo et al., 2009; Walker et al., 2011) and cortisol (Spiering et al., 2008;
Walker et al., 2011), others have found elevations to occur in the absence of lactate
(Fry & Lohnes, 2010). Indeed, similar elevations in testosterone and cortisol have
been reported to occur in response to the same resistance training protocol performed
under both hypoxic (thereby accumulating more lactate) versus non-hypoxic
environments (Kon et al., 2010) and in response to volume matched hypertrophy and
strength-training, despite significant differences in post session Lactate accumulation
(McCaulley et al., 2009). The results of the current study suggest that metabolic
accumulation does not affect either testosterone or cortisol in a dose response
manner. However, given that a significant elevation in lactate occurred in response to
the strength-training session in the current study (1.24 ± 0.66 mmol/l Pre vs. 3.15 ±
1.07 mmol/l Immediately post session 1), it cannot be ruled out that lactate plays a
permissive role in testosterone’s response to training.
In the current study, increases in testosterone post weight training did not reach
significance. This is in line with the majority of previous research into weight
training as, while significant post-training elevations in testosterone have
consistently been demonstrated to occur post hypertrophy type protocols (Crewther
et al., 2008; McCaulley et al., 2009), traditionally, strength-focused training
protocols, like the one employed in the current study, are not reported to significantly
elevate testosterone post-training (Crewther et al., 2008; Hakkinen & Pakarinen,
1993; McCaulley et al., 2009). However, the finding that testosterone was not
significantly below baseline values 2 hours after either the maximal speed training or

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weight training sessions is noteworthy as research into the circadian rhythms
associated with testosterone suggests that levels would have been expected to
undergo decline by this time-point (Kraemer et al., 2001; Teo et al., 2011). It seems
possible that the initial training session in both protocols affected the normal
circadian patterns associated with testosterone and that, while the strength training
protocol did not result in a significant elevation in testosterone immediately post, it
may still have altered the normal pattern of its release. While this is in contrast to
research which reported the circadian pattern of testosterone to be unaffected by a
morning session (Kraemer et al., 2001), it does support the findings of Cook et al.
(2013) who found morning training to result in a significant slowing in the rate of
decay in the release of testosterone. Interestingly, Cook et al. (2013) reported that
afternoon testosterone concentrations were significantly higher in the afternoon after
a weight training than after a repeated sprint training session, while the current study
did not.
Cortisol continued to decline during the 2 hours after both the morning maximal
speed training and weight training sessions and was significantly lower than the
levels obtained immediately post session both protocols. While cortisol does appear
to degrade at a faster rate during the day than testosterone (Hayes et al., 2010; Teo et
al., 2011), the lack of a significant decline in testosterone coupled with the changes
in cortisol further suggests that both training protocols had an effect on normal
endocrine circadian rhythms. It is unclear why cortisol was not elevated post session
alongside testosterone, however, as was also discussed in Chapter 5, it is possible
that neither session exceeded the training load ‘threshold’ upon which the
hypothalamic-pituitary adrenal axis is activated (Cadore et al., 2013). The degree to
which post-exercise changes in endocrine status may or may not be directly involved
in inducing muscle protein synthesis is subject to controversy (Ronnestad, Nygaard,
& Raastad, 2011; Schroeder et al., 2013; West & Phillips, 2012), at least in untrained
populations. However, testosterone, in particular, has been found to have several fast
acting non-genomic effects, including several related to muscle function and
aggression (Crewther et al., 2011) and exercise-induced changes in endocrine status
have been suggested to be linked to changes in explosive performance (Cook et al.,
2013; Crewther et al., 2011). As was also discussed earlier, it is possible that the lack

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of degradation in testosterone observed 2 hours post weights could play a role in
optimising both performance and adaptation.
While it has previously been reported that session order can result in differing
endocrine responses 24 hours post (Cadore et al., 2012; Schumann et al., 2013), no
such pattern was found in the current study. A major contributing factor to this may
be that, in both the studies that demonstrated session order to have an effect on the
endocrine system 24 hours post, sessions were performed back-to-back. As discussed
earlier, this lack of recovery between sessions may have contributed to the
differences in outcomes. It is important to note that previous research has shown that
acute exercise-induced changes in endocrine status may take 48 hours post-training
to manifest (Chatzinikolaou et al., 2010). As a result, it cannot be ruled out that the
endocrine values in our participants may have overshot baseline values in the days
that followed the completion of our data collection as muscle damage and perceived
muscle soreness subsided. However, given the elite nature of the subject population
and their high training demand, it would not have been possible to get participants to
refrain from training for an additional 24-hours. Finally, it should also be considered
that, given that the endocrine variations in response to training that have been
reported take considerable time to manifest themselves (Kraemer & Ratamess,
2005), it is unclear how closely the chronic responses to the protocols would mirror
the acute responses.
7.5 CONCLUSIONS
In conclusion, this study showed that two protocols with different session order
resulted in similar neuromuscular, endocrine and biochemical responses over a 24-
hour period in a trained population. This was the case even though the metabolic
response and, potentially, the origin of fatigue were different between the sessions.
This was potentially due to the two-hour time period allowing the participants to
have fully recovered from the first session of the day and/or the first session of day,
regardless of make up, damaging those fibres susceptible to injury.. As a result, there
was no difference between afternoon or morning performance when the session types
were compared and, therefore, no difference in the stimulus being applied.

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7.6 PRACTICAL APPLICATIONS
This study demonstrated that two hours is sufficient for the recovery of
neuromuscular performance after both maximal speed training and weight training
sessions in an elite population. Results further suggest that, as long as time is given
for the athlete to sufficiently recover from the first session, then the coach and athlete
can structure their sessions in either order without fear of negatively affecting either
performance in the second session or neuromuscular, endocrine or biochemical
markers 24 hours post. Given this, it is unlikely that manipulating the training order
of maximal speed training and weight training will affect the rate at which the athlete
recovers from the training day if sufficient intra-session recovery is built in.
While not reaching statistical significance, there was a practically significant
improvement in 10m-sprint performance in the afternoon. While several factors
could have contributed to this, it is possible that by altering the circadian pattern
associated with the release of testosterone, the morning session enlisted some degree
of priming and coaches may want to consider this when designing training days and
pre-competition routines.

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Chapter 8
Synthesis of Research Findings

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8.1 SYNTHESIS
In the following synthesis the main findings from the four experimental chapters and
their practical application to coaches and scientists will be discussed in relation to the
main themes than ran throughout the thesis. These themes included the monitoring of
changes in neuromuscular performance in response to speed and strength training
and its recovery following training days consisting of single or multiple training
sessions, with neuromuscular performance defined as the sum of those factors that
enhance the neuromuscular system (post activation potentiation, endocrine status and
muscle temperature) minus those that inhibit it (central and peripheral fatigue).
In the current thesis changes in neuromuscular performance were measured via
change in jump variables derived from the performance of both squat and
countermovement jumps on a force plate. This decision to use a multi-joint dynamic
movement for the assessment of neuromuscular performance over a more laboratory
based approach was primarily due to questions around the relationship between
laboratory-based assessments and dynamic performance (Pearson and Hussain,
2013). However, while jumping is a valid and reliable measurement of global change
in neuromuscular performance, information regarding the origin of fatigue or any
factors that may be contributing to enhanced performance cannot be derived from it,
thereby limiting our understanding of the mechanisms that resulted in the changes in
jump performance throughout this thesis.
The review of literature demonstrated a lack of consistency in both the methodology
used to calculate, along with the degree of reliability reported for many of the most
commonly used jump variables. Therefore, chapter 4 investigated the reliability of
various jump variables using procedures proposed by Street et al. (2001). From the
findings of chapter 4 it is recommended that coaches adhere to these procedures for
the collection of jump data in order to minimise the amount of random error
generated and to ensure consistency in approaches for subsequent studies in this area.
Specifically, this involves ensuring the athlete stays perfectly still prior to the start
of the jump, getting them to identify when they are ready for data collection to begin
and ensuring a minimum of 1.5 steady stance phase is collected prior to the start of
movement for accurate determination of body mass.

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When the findings of chapter 4 were considered in conjunction with chapter 5, which
investigated the relationship between jumping and sprint performance, and in
chapters 5, 6, and 7, which investigated their response to various training days, it was
found that that W.kg-1
, jump height and peak velocity (i) have the lowest coefficients
of variation, (ii) undergo the greatest changes in response to the training stimulus and
(iii) have the highest correlations with sprint performance. While the exact reasons
for this relationship with sprint performance remains unclear it has been reported that
thigh velocity is a key determinate of sprint performance (Mann, 2011) and it is
speculated that the velocity dependent variables measured in this thesis provide good
a field measurements of this quality. Given this, it is suggested that these variables
are the most useful in the monitoring of neuromuscular response to training and the
use of these measures by coaches and scientists is recommended. Variables from
both countermovement jump and squat jump can be used, in conjunction with other
markers to reliability track changes in neuromuscular performance and both types of
jumps were found to follow similar patterns in response to a training session.
However, on the whole, variables produced from the countermovement jump have
less measurement error and it is recommended that this measure is used when time
constraints are evident.
Of the other variables assessed in chapter 4 there was particular interest in the
reliability of the method for the calculation of average rate of force development
over 0-50 and 0-100 ms (Thorlund et al., 2008), as previous research had reported a
relationship between this variable and change in neural drive. However, Chapter 4
found both measures to lack sufficient reliability (coefficient of variation > 10%) to
be used in subsequent experimental work in the current thesis. Nevertheless, the third
average rate of force development measure, average rate of force development
(total), was found to have sufficient reliability (coefficient of variation = 8.29%), and
to undergo depressions the day following intensive training (chapters 5, 6, and 7).
While the reasons for this pattern are unclear, it is speculated that the inflammatory
response to the training-induced changes in afferent feedback from the muscle which
either decreased the contractile rate of force capabilities of the muscle involved
(Thorlund et al., 2008) or resulted in the athlete changing their jump mechanics to
accommodate this (Moir et al., 2009). Regardless of the mechanisms, it is suggested

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that coaches and scientists consider monitoring changes in average rate of force
development (total) in the days following intensive training sessions as it appears to
be a useful variable for gauging recovery.
A major theme of this thesis was the investigation of the neuromuscular response to
training days containing maximal speed training sessions and this thesis is the first to
quantify the neuromuscular response to a true maximal speed training session.
Throughout the course of the thesis the neuromuscular response to maximal speed
training was found to be consistent, with an immediate decrease in a number of
neuromuscular variables immediately post-session, followed by recovery of these
markers 2 hours post, before a second decrease at 24 hours post. It seems likely that
peripheral mechanisms caused the initial post exercise decline, as the declines were
accompanied by a significant accumulation of lactate which may be viewed as an
indicator of significant metabolic accumulation (Skurvydas et al., 2006). The high
metabolic demand of maximal sprint training demonstrated in this thesis is an
important factor for coaches to consider this when designing training programmes
and it is suggested that sufficient time is left for this to dissipate prior to undertaking
additional training. In the current thesis 2 hours was shown to be a sufficient time
frame for the recovery of neuromuscular performance, however we are limited in our
understanding of the actual time point at when the participants had recovered as
additional measures prior to 2 hours post (e.g. 30 minutes, 1 hour, etc.) were not
taken. Therefore, it cannot be ruled out that recovery occurred prior to this point.
Alternatively, it is possible that neuromuscular performance continued to rise after
the 2 hour post data collection time point and by failing to collect data after this point
we failed to identify an enhancement in neuromuscular performance like that
reported by Cook et al. (2013).
The secondary depression in neuromuscular performance at 24 hours post are
potentially associated with increased afferent feedback potentially as a result of
muscle inflammation (Armstrong, 1990; Dousset et al., 2007). However again the
lack of additional time points (e.g. four, six and eight hours post) limits our full
understanding regarding the secondary depression in neuromuscular performance
and the time-point at which it occurred. Indeed this information may have gone some
way to help explain the differences between the findings of this thesis and those

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reported by Doma and Deakin (2013) who reported decreased neuromuscular
performance 6 hours post a weight training session. Regardless of this limitation it is
clear that coaches and scientists need to monitor neuromuscular performance in the
hours and days after the training session as, while an initial recovery in performance
may occur it may be followed some time later by a secondary decline.
The termination of data collection at 24 hours post, a time-point prior to full
neuromuscular and physiological recovery, represents a further limitation of this
thesis. Indeed it is important to acknowledge that several studies have reported
performance to still be depressed at 48 hours post (Twist & Eston, 2005; Twist &
Eston, 2009; Twist, Gleeson, & Eston, 2008). This early termination was due to the
reality of the training demands of the elite athletes involved in the studies as it was
unrealistic to expect the coaches and athletes to refrain from training for another 24-
hours when their preparation periods were already so limited and would have been
unreflective of their training practices. However it is suggested that future studies
track neuromuscular performance back to baseline in order to add depth to the
findings in the current thesis.
Nevertheless the finding that neuromuscular performance was not fully recovered 24
hours after maximal speed training has important implications for program design,
and it is suggested that training requiring high neuromuscular effort is not performed
at this time-point as performance will be compromised. Previous research has
demonstrated that sub-maximal activities can be performed when the neuromuscular
system is compromised (Gee et al., 2011) without any adverse effects and it is
suggested that these types of activities are performed on the training day that follows
a speed session. However, coaches and athletes should also keep in mind that there
may be a higher metabolic cost both during and after submaximal efforts performed
in the days after intensive activities like strength training (Burt, Lamb, Nicholas, &
Twist, 2014).
The potential role of inflammatory processes inhibiting performance at 24 hours post
is especially relevant for those sports whose competition programme requires them
to repeat explosive activities on consecutive days (e.g. rugby 7’s, basketball and
track and field), and it is suggested that competition recovery processes should be

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aimed at minimising this inflammatory response. Indeed the information presented in
this thesis regarding the neuromuscular, endocrine and physiological response to
maximal speed training in the hours that follow has informed the recovery strategies
used for elite track and field athletes at major international championships (e.g.
World Championships, European Championships and Commonwealth Games).
Given the importance of intensity in inducing neuromuscular adaptation (Tan, 1999),
the finding that neuromuscular performance had not only recovered 2 hours after
maximal speed training but was superior at this time point when compared to 24
hours after, prompted an investigation into the effect of performing a second session
two hours after the first. Chapter 6 found that the addition of a weight training
session at this time point did not result in a significantly greater loss in
neuromuscular performance at 24 post. In addition, testosterone, cortisol and creatine
kinase were not found to be significantly different between the groups. Given these
findings it is suggested that 2 hours post maximal speed training represents a
superior window for maximal effort training that two intensive session can be
performed on a single training without fear of increased loss of performance 24
hours post. Therefore based on this Tables 8.1 to 8.3 provide a suggested weekly
training schedules for both track and field and team sports athletes incorporating the
approaches to training design based on the findings of Chapter 6.

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Table 8.1: Possible weekly workout schedule for a track and field sprinter incorporating the findings of this thesis and
based on the model proposed in Francis (2008; two intensive days model)
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
AM Speed Tempo Off Speed Tempo Tempo Off
PM Weights Weights
Table 8.2: Possible weekly workout schedule for a track and field sprinter incorporating the findings of this thesis and
based of the model proposed in Francis (2008; three intensive days model)
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
AM Speed Tempo Speed Tempo Speed Tempo Off
PM Weights Weights Weights
Table 8.3: Possible off-season weekly workout schedule for a rugby team incorporating the findings of this thesis
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
AM Speed Conditioning Technical Speed Conditioning Technical Off
PM
Weights
(Strength)
Weights
(hypertrophy)
Weights
(Strength)
Weights
(hypertrophy)

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Interestingly, however, perceived muscle soreness was greater on the two session
training day when compared to the one session training day (Chapter 6). Perceived
muscle soreness is reported to be a poor reflection of muscle inflammation (Nosaka
et al., 2002) but may result in participants reducing neuromuscular performance on a
conscious and unconscious level, rather than by any acute physiological or
biochemical alterations (Racinais et al., 2008). The finding that there was no
difference in neuromuscular performance across the protocols despite the difference
in perceived muscle soreness suggests that elite athletes are be able to maintain
performance in the face of increased perceived muscle soreness. Therefore it is
suggested that coaches and scientists are cautious when using is as a marker of
readiness for training and competition in elite populations.
One possible explanation considered for the finding that the addition of a strength
session 2 hours after a maximal speed session was the order in which sessions where
performed, with previous research into the effect of alternating the order of
endurance and strength training demonstrating differing degrees of recovery at 24
hours post (e.g. Doma & Deakin, 2013). Therefore, based on the findings of previous
research ((Cadore et al., 2012; Coffey, Jemiolo, et al., 2009; Coffey, Pilegaard, et al.,
2009; Rosa et al., 2012; Schumann et al., 2013; Taipale & Hakkinen, 2013) and
Chapter 6, the final chapter of the thesis investigated if session order would (i) effect
the performance before or during either the maximal speed training or weight
training sessions and (ii) if session order would result in a different neuromuscular,
endocrine or physiological response at 24 hours post.
An average improvement of 0.04 seconds over 10 metres was found when the
maximal speed session was preceded by the weight training session. Given that
testosterone had not undergone a significant depression prior to the start of the
maximal speed training session in the weights speed protocol, it is possible that the
higher than expected levels of testosterone, in conjunction with the expected
circadian increase in muscle temperature (Teo et al., 2011), may have contributed to
the improved performance. Given this, coaches may want to consider using weight
training or sprinting two hours prior to competition as part of a priming strategy for

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athletes with sufficient training history. This window also allows sufficient time to
remove any metabolic accumulation that may have occurred and is sufficiently prior
to the onset of any secondary decline in neuromuscular performance. The potential
role of a morning session priming afternoon performance represents an interesting
area for future studies and the there is a need to look deeper at the optimal time-
frames between sessions to maximise neuromuscular and endocrine responses.
It was also found that the immediate loss in performance after maximal speed
training is similar to that resulting from a weight training session aimed at maximum
strength development, even though the metabolic response is considerably different.
It can be speculated that this difference was due to the differing contributions of
central and peripheral contributions similar to the differences previously
demonstrated to occur in response to hypertrophy and strength sessions (McCaulley
et al., 2009). The occurrence of decreased neuromuscular performance in the absence
of significant metabolic accumulation after the strength training session may have
been related to the training level of the subjects as it has been demonstrated that
strength-trained participants have the ability to generate significantly more neural
fatigue than untrained participants (Ahtiainen & Hakkinen, 2009). This potential
increased requirement for recovery post session in strength trained subjects is
something that should be considered when planning training programs.
Finally this thesis concluded that session order did not affect recovery 24 hours post,
with no difference in neuromuscular, endocrine or physiological markers reported
between the speed/weights and weights/speed protocols. It was concluded, therefore,
that two hours is sufficient time to recover from both a maximal speed training and
weight training session when performed in the morning and as a result there was no
difference between the protocols in terms of overload generated. Coaches may
therefore consider structuring the order of their speed and strength training days to
best fit their needs.
Both chapters 6 and 7 utilised a randomised crossover design and it is important to
consider the limitations associated with this. Most obvious was the limited time
between protocols and therefore the lack of a wash out period due to the limited
availability of the participants. However, the consistency in the pattern of

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neuromuscular response over 24 hours to maximal speed training observed in both
chapters 5 and 6, when data collection was performed over 12 months apart, would
suggest that the results were not significantly affected by this factor.
Finally, it is important to consider that this thesis has focused on establishing the
acute responses to training days consisting solely of maximal speed training and
those where it is part of a multi-session training day. Further research will be
required to establish the accumulative effect of such training days across a training
block on neuromuscular, endocrine and physiological parameters. Research is also
required into the adaptation to maximal speed training sessions similar to those
investigated in the current study and the effect of combining this type of training
with other training modalities. For example, while research has been carried out into
the interference effect caused when endurance and weight training are combined
(e.g. Bell et al., 2000), to date no research has been conducted into the combined
effect of weight and speed training and, as such, it is unclear if the interference effect
applies to this combination as well.
,

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Appendices

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Appendix 1: consent forms

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CONSENT FORM FOR PARTICIPATION IN RESEARCH PROJECTS
TITLE OF PROJECT: The influence of familiarisation on the reliability of rate of force
measurements during jumping in academy rugby players
OUTLINE EXPLANATION FOR PARTICIPANTS:
The objective of the present study is
You will be required to attend four consecutive Mondays between 8am and 10am; and
complete a warm up followed by 6 jumps (3 Countermovement jumps and 3 Squat Jumps)
per testing session. Data collected will include average rate of force development and peak
rate of force development. All data will be stored on a password protected computer, while
data will be coded ensuring anonymity for all who take part.
I (name) .............................................................................................................................
of (address) ..............................................................................................................................
.............................................................................................................................
hereby consent to take part in the above investigation, the nature and purpose of which have
been explained to me. Any questions I wished to ask have been answered to my satisfaction.
I understand that I may withdraw from the investigation at any stage without being required
to give a reason for doing so.
Signed (Volunteer)
................................................... Date ..............................
(Investigator)
................................................... Date ..............................

194. 183
CONSENT FORM
TITLE: - The acute Neuromuscular, Endocrine and inflammatory response to
maximal velocity sprinting in elite games players’ pre and post a 6 week training
block.
CHIEF INVESTIGATOR: - Dr Rodney Kennedy
Please initial
 I confirm that I have been given and have read and understood the
[ ]
information sheet for the above study and have asked and
received answers to any questions raised
 I voluntarily agree to allow blood to be drawn from me by venepuncture
[ ] or skin puncture. I understand that the blood will be used only for
scientific purposes
 I have not: suffered from hepatitis or jaundice, received blood
[ ] transfusions, undergone dialysis treatment, been refused as a blood
donor, and I am not in a recognised risk group for HIV infection
 I understand that my participation is voluntary and that I am free to
[ ]
withdraw at any time without giving a reason and without my rights
being affected in any way
 I understand that the researchers will hold all information and data
[ ]
collected securely and in confidence and that all efforts will be made
to ensure that I cannot be identified as a participant in the study (except
as might be required by law) and I give permission for the researchers
to hold relevant personal data
 I agree to take part in the above study
[ ]
___________________________________ _______________________________ __________
Name of Subject Signature
Date
__________________________________ _______________________________ __________
Name of person taking consent Signature
Date
__________________________________ _______________________________ __________

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Name of researcher Signature Date
Consent Form for studies involving the use of human tissue/relevant material
Title of Study
Chief Investigator
Please confirm, by marking the boxes, that you agree with the following statements:
1. I have been given and have read and understood the
information sheet for the above study and have asked and
received answers to any questions raised
2. I understand that my participation is voluntary and that I
am free to withdraw at any time without giving any reason
and without my rights being affected in any way
3. I understand that the researchers will hold all information
and data collected during the study securely and in
confidence and that all efforts will be made to ensure that I
cannot be identified as a participant in the study (except as
might be required by law) and I give permission for the
researchers to hold relevant personal data
4. I agree to provide access to up-to-date training records for
the purposes of calculating 1RM’s for use in the training
study.
5. I understand that my blood or other tissues are required
for the purposes of this study and confirm that I have been
given details of the amount(s) to be taken and how it will
be stored, used and the method of disposal
6. I agree to take part in the above study.
Name of Participant (please
print)
Signature Date (dd/mm/yy)
………………………………………
Name of proxy (where
appropriate)
and relationship to participant
Signature Date (dd/mm/yy)
The neuromuscular, endocrine and inflammatory response to
single vs. multiple daily training sessions in elite games players
Dr Rodney Kennedy

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Name of Researcher Signature Date (dd/mm/yy)

197. 186
Appendix 2: Participant information forms

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PARTICIPANT INFORMATION SHEET
TITLE: - The influence of familiarisation on the reliability of rate of force
measurements during jumping in academy rugby players
You are being invited to take part in a research study. Before you decide whether or not to
take part, it is important that you understand what the research is for and what you will be
asked to do. Please read the following information and do not hesitate to ask any questions
about anything that might not be clear to you. Make sure that you are happy before you
decide what to do. This Study will form part of a PhD undertaken at Swansea University.
Thank you for taking the time to consider this invitation.
What is the purpose of the study?
The aim of the study is to investigate the reliability of rate of force measurements collected
during the countermovement jump (CMJ) and squat jump (SJ). Furthermore, the number of
familiarisation sessions required before an accurate measure of reliability is established will
also be assessed.
Why have I been chosen?
You have been proposed as a potential participant by members of the IRFU fitness team who
feel you have the necessary training history to partake.
Do I have to take part?
It is up to you to decide whether or not to take part. If you do decide to take part, you will be
given this information sheet to keep. You will also be asked to sign a consent form. If you
choose to take part, you can change your mind at any time and withdraw from the study
without giving a reason.
What will happen to me if I take part?
If you chose to take part you will be required to turn up at the Sports Institute Northern
Ireland (SINI) on four consecutive Mondays and complete a warm up followed by three
countermovement jumps and three squat jumps with adequate recovery.
What Will I have to do?
You will have to refrain from training the day before however this will be built into your
training week.
Are there any potential risks?
Based on your own sporting background, you are already familiar with the level of physical
stress occurring during the collection of the performance measure (squat jumps and
countermovement jumps), and the general risks inherent in both jumps (such as injuries to
soft tissue as well as joints).
Thus, there are no known risks to your health in taking part in this study and the level of
inconvenience you will experience is minimal.
Are there any potential benefits?
You will gain detailed information regarding your force capacity. This information may
better inform future training programmes.

199. 188
PARTICIPANT INFORMATION SHEET
TITLE: - The acute Neuromuscular, Endocrine and inflammatory response to
maximal velocity sprinting in elite games players.
You are being invited to take part in a research study. Before you decide whether or
not to take part, it is important that you understand what the research is for and what
you will be asked to do. Please read the following information and do not hesitate to
ask any questions about anything that might not be clear to you. Make sure that you
are happy before you decide what to do. This Study will form part of a PhD
undertaken at Swansea University. Thank you for taking the time to consider this
invitation.
What is the purpose of the study?
Proper understanding about the degree of fatigue experienced from a training session
and the time require to recover are important to ensure training sessions are
sequenced to obtain optimal training effect within a given training block. While
significant research has been carried out on endurance and resistance training fatigue
to date none has been carried out on speed training.
This study aims to address this and help us better understand how a speed training
session affects the elite rugby players’ ability to perform explosive exercise. It will
also then track how long it takes to recover from a speed session.
Why have I been chosen?
You have been proposed as a potential participant by members of the IRFU fitness
team who feel you have the necessary training history to partake.
Do I have to take part?
It is up to you to decide whether or not to take part. If you do decide to take part, you
will be given this information sheet to keep. You will also be asked to sign a consent
form. If you choose to take part, you can change your mind at any time and withdraw
from the study without giving a reason.
What will happen to me if I take part?
If you chose to take part you will be required to turn up at the Sports Institute
Northern Ireland (SINI) to provide baseline measures. This will involve you
warming up and providing us with information on the degree of muscle soreness you
are experiencing. 5ml of Blood will be drawn from your forearm which will be
analysed at a local hospital. The hospital will provide information on the levels of
creatine kinase, Cortisol and testosterone which will be used to inform us about your
baseline levels of muscle damage and hormones. No additional tests will be
performed. Your ear temperature will also be taken to give us an estimate of muscle
temperature. You will then perform three maximal vertical jumps and three maximal
isometric mid thigh clean pulls on a force plate. This will be done to give a baseline
of your ability to perform explosive exercise.
Following this you will undertake a speed session consisting of 8 x 50m sprints with
5 minutes recovery in between each sprint. Immediately after the speed session you
will again perform the assessment protocol to allow us to gauge the degree of fatigue
experienced. This assessment protocol will be repeated 3 hours later and 24 hours

200. 189
later to allow us to track your recovery. 5ml of blood will also be drawn at each of
these three (immediately post, 3 hours post and 24 hours post) time points as well.
What Will I have to do?
You will have to refrain from training the day before and the day after the
intervention so your recovery can in accurately assessed. However this will be built
into your training week.
Are there any potential risks?
Based on your own sporting background, you are already familiar with the level
of exhaustion experienced after a speed session, the physical stress occurring
during the collection of the performance measure (squat jumps and isometric
pulls), and the general risks inherent in both activities (such as injuries to soft
tissue as well as joins). Sampling of blood may cause minor discomfort and
temporary bruising to the forearm, and this can be reduced by applying
pressure to the needle wound once sampling has been completed. Finally,
taking ear temperature is absolutely pain free and should not take more than a
few seconds.
Thus, there are no known risks to your health in taking part in this study and the level
of inconvenience you will experience is minimal.
Are there any potential benefits?
Undertaking this study will provide your coaches with information to help ensure
your training week is structured optimally.
What happens when the study ends?
Feedback will go directly to the IRFU fitness team. It is hoped that such insights will
shape more specific interventions that will foster optimal preparation for Irish rugby
players in the future.
What if something goes wrong?
In the unlikely event something goes wrong a chartered physiotherapist will be on
call during the experiments. His/her sole function is to act independently of the study
team to ensure your safety and well being. He/she may terminate the experiment on
medical grounds at any time, and you may consult with him/her any time.
Emergency resuscitation and medical facilities are provided in an adjacent Treatment
Room, in the event that you require any assessment or treatment whilst you are
taking part in the study.
Additionally the university has procedures in place for reporting, investigating,
recording and handling adverse effects. Any complaints will be taken seriously and
should be made to the Chief Investigator, Dr Rodney Kennedy.
Will my part in this study be kept confidential?
Any information obtained during this experiment will remain confidential as to your
identity: if it can be specifically identified with you, your permission will be sought
in writing before it is published. Other material, which cannot be identified with you,
might be published or presented at meetings with the aim of benefiting others. For
these cases and in order to guarantee that you cannot be identified with any

201. 190
individual data, we will provide every participant with a simple number to
anonymous the data set prior analysis.
What will happen to the results of the study?
All information will be participant to the conditions of the Data Protection Act
1998 and subsequent statutory instruments. Experimental records (raw data)
and computer files, will be stored on the password protected PC of the
respective member of the research team tasked with managing the respective
data. Raw data will be stored by research team members for no longer than 10
years. You have right of access to your records at any time. After analysis has
been performed on the blood samples they will be destroyed in line with the
terms of the conditions of the Human Tissue Authority.
Who is organising and funding the research?
This study is been organised by Sports Institute Northern Ireland in conjunction with
the IRFU, Swansea University and the University of Ulster.
Who has reviewed this study?
A full scientific protocol for this experiment has been approved by the University of
Ulster Research Advisory Group. This protocol complies with all current legislation,
including the Draft Additional Protocol to the Council of Europe Convention on
Human Rights and Biomedicine on Biomedical Research (CDBI/INF (2001) 5 dated
18 July 2001). Further details of the approval will be provided to you if you wish,
and you have a right to have a copy of the full protocol to retain, if you so request of
the Project Officer.
Contact details
For more information please feel free to contact:-
Michael Johnston
Head Strength and Conditioning Coach,
Sports Institute Northern Ireland
Email: -michaeljohnston@sini.co.uk
Mobile: - 07973667521

202. 191
PARTICIPANT INFORMATION SHEET
TITLE: - The neuromuscular, Endocrine and inflammatory response to single
vs. multiple daily training sessions in elite games players
You are being invited to take part in a research study. Before you decide whether or
not to take part, it is important that you understand what the research is for and what
you will be asked to do. Please read the following information and do not hesitate to
ask any questions about anything that might not be clear to you. Make sure that you
are happy before you decide what to do. This study will form part of a PhD
undertaken at Swansea University. Thank you for taking the time to consider this
invitation.
What is the purpose of the study?
The purpose of this study is to see how performing speed and resistance training on
the same day affects the elite rugby player. The information gathered will help to
ensure your training week is structured to allow you to gain the most benefit.
Why have I been chosen?
You have been selected as a potential participant as you are a member of the IRFU
Academy and have the necessary training history to partake.
Do I have to take part?
It is up to you to decide whether or not to take part. If you do decide to take part, you
will be given this information sheet to keep. You will also be asked to sign a consent
form. If you choose to take part, you can change your mind at any time and withdraw
from the study without giving a reason.
What will happen to me if I take part?
If you chose to take part you will be required to come to the Sports Institute Northern
Ireland (SINI) building, at the University of Ulster, Jordanstown campus, to perform
3 different training sessions. Each session will require two visits, one on which to
perform the session and one 24 hours after the session to provide additional measures
from which we can gauge your recovery. This process can be seen in the figure on
the following page.

203. 192
The order in which you do the sessions will be chosen at random, and you will be
informed of the order a minimum of 1 week prior to the first session.
Upon arrival each day you will provide baseline measures. This will involve you
warming up and letting us know on a scale of 0-6 how sore your muscles feel. 5ml of
Blood will be drawn from your forearm which will be analysed at a local hospital.
The hospital will provide information relating to muscle damage and hormones. No
additional tests will be performed. You will then perform three maximal vertical
jumps and three squat jumps on a force plate. This will be done to give a baseline of
your ability to perform explosive exercise.
Following this you will undertake one of the three following protocols.
 Session 1: You will perform a speed session consisting of 6 x 50m sprints
with 5 minutes recovery in between each sprint. Each sprint will be timed and
the time will be recorded. Immediately after the speed session you will again
give blood and perform the jumps. This will allow us to see how tired the
sprinting made you. These measures (jumps, bloods, etc.) will be repeated 2
hours later and 24 hours later to allow us to track your recovery. 5ml of blood
will also be drawn at four (immediately pre, immediately post, 2hours post
and 24 hours post) time points in total.
 Session 2: You will perform a speed session consisting of 6 x 50m sprints
with 5 minutes recovery in between each sprint. Each sprint will be timed and
the time will be recorded. Immediately after the speed session you will again
give blood and perform the jumps. This will allow us to see how tired the
sprinting made you. These measures (jumps, bloods, etc.) will be repeated 2
hours later, at which point you will report to the gym to perform a strength

204. 193
training session consisting of 5 sets of 5 repetitions in the Back squat, the
Bench Press and pull ups. 3 minutes of recovery will be provided between
sets. Each will be performed at 85% of your current best lift, which will be
calculated from recent training records. When you finish you will again
perform the jumps. You will return the next morning (24 hours later at speed
session) to again give blood, perform the jumps and tell us how sore you are.
5ml of blood will also be drawn at four (immediately pre, immediately post,
2hours post and 24 hours post) time points in total.
 Session 3: You will undertake a strength training session consisting of 5 sets
of 5 repetitions in the Back squat, the Bench Press and pull ups. 3 minutes of
recovery will be provided between sets. Each will be performed at 85% of
your best lift. Immediately after the strength training session you will again
perform the jumps and give blood. This will allow us to see how tired the
session made you. These measures (jumps, bloods, etc.) will be repeated 2
hours later, at which point you will report to the track to perform a speed
session consisting of 6 x 50m sprints with 5 minutes recovery in between
each sprint. Upon completion you will again perform the squat jumps. You
will return the next morning (24 hours later at speed session) to again give
blood, perform the jumps and tell us how sore you are. 5ml of blood will also
be drawn at four (immediately pre, immediately post, 2hours post and 24
hours post) time points in total.
What will I have to do?
You will have to refrain from training the day before and the day after the
intervention so your recovery can in accurately assessed. You will also be required to
fast 12 hours prior to your first blood sample.
Are there any potential risks?
Based on your own sporting background, you are already familiar with the level
of exhaustion experienced after both a speed session, a strength session, the
physical stress occurring during the collection of the performance measure
(squat jumps and countermovement jumps), and the general risks inherent in
both activities (such as injuries to soft tissue as well as joints).The risks of this
study will therefore be similar to those encountered during a normal training
session. Sampling of blood may cause minor discomfort and temporary bruising
to the forearm, and this can be reduced by applying pressure to the needle
wound once sampling has been completed. All blood samples will be taken by a
fully trained phleobotomist.
Thus, there are no known risks to your health in taking part in this study and the level
of inconvenience you will experience is minimal.
Are there any potential benefits?
Undertaking this study will improve understanding of the effects of exercise on
neuromuscular fatigue.
What happens when the study ends?
Feedback will go directly to the IRFU fitness team. However this feedback will be
on the aggregated data for the whole group and no individual data on specific

205. 194
athletes will be provided. It is hoped that such insights will shape more specific
interventions that will foster optimal preparation for Irish rugby players in the future.
What if something goes wrong?
In the unlikely event something goes wrong a chartered physiotherapist will be on
call during the experiments. His/her sole function is to act independently of the study
team to ensure your safety and well being. He/she may terminate the experiment on
medical grounds at any time, and you may consult with him/her any time.
Emergency resuscitation and medical facilities are provided in an adjacent Treatment
Room, in the event that you require any assessment or treatment whilst you are
taking part in the study.
Additionally the university has procedures in place for reporting, investigating,
recording and handling adverse effects. Any complaints will be taken seriously and
should be made to the Chief Investigator, Dr Rodney Kennedy.
Will my part in this study be kept confidential?
Any information obtained during this experiment will remain confidential as to your
identity: if it can be specifically identified with you, your permission will be sought
in writing before it is published. Other material, which cannot be identified with you,
might be published or presented at meetings with the aim of benefiting others. For
these cases and in order to guarantee that you cannot be identified with any
individual data, we will provide every participant with a simple number to
anonymous the data set prior analysis.
It is important to acknowledge however that the study is being performed on a small
group of participants with a specific sporting background and while no individual
data will be published of shared it is possible some people may surmise who
contributed to the study.
What will happen to the results of the study?
All information will be participant to the conditions of the Data Protection Act
1998 and subsequent statutory instruments. Experimental records (raw data)
and computer files, will be stored on the password protected PC of the member
of the research team tasked with managing the data. Raw data will be stored by
research team members for no longer than 10 years. You have right of access to
your records at any time. After analysis has been performed on the blood
samples they will be destroyed in line with the terms of the conditions of the
Human Tissue Authority.
The data will be written up to form a chapter of a PhD thesis at Swansea
University and will potentially be published as a study in a relevant journal.
However only aggregated data will be written up. Feedback on the outcomes of
the study will be provided to participants prior to the start of the new rugby
season.
Who is organising and funding the research?
This study is been organised by Sports Institute Northern Ireland in conjunction with
the IRFU, Swansea University and the University of Ulster.

206. 195
Who has reviewed this study?
A full scientific protocol for this experiment has been approved by the University of
Ulster Research Ethics Committee. This protocol complies with all current
legislation, including the Draft Additional Protocol to the Council of Europe
Convention on Human Rights and Biomedicine on Biomedical Research (CDBI/INF
(2001) 5 dated 18 July 2001). Further details of the approval will be provided to you
if you wish, and you have a right to have a copy of the full protocol to retain, if you
so request of the Project Officer.
Contact details
For more information please feel free to contact either:-
Michael Johnston Dr Rodney Kennedy
Head Strength and Conditioning Coach, Lecturer in Sport and Exercise,
Sports Institute Northern Ireland University of Ulster
Email: -michaeljohnston@sini.co.uk Email: - r.kennedy@ulster.ac.uk
Mobile: - 07973667521 Mobile: - 07799623996

207. 196
Appendix 3: Ethical approval forms

208. 197

209. 198

210. 199

211. 200

212. 201
Appendix 4: Likert scale of perceived
muscle soreness

213. 202
Please tick the sentence below that best describes your level of muscle soreness you
are currently experiencing.
[ ]0 A complete absence of soreness
[ ]1 A light pain felt only when touched / a vague ache
[ ]2 A moderate pain felt only when touched/ a slight persistent pain
[ ]3 A light pain when walking up or down stairs
[ ]4 A light pain when walking on a flat surface/ painful
[ ]5 A moderate pain, stiffness or weakness when walking/ very painful
[ ]6 A severe pain that limits my ability to move

214. 203
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