this thesis is presented in partial fulfilment of the requirements … · 2018. 4. 11. · justin...

Running Head: CAN WE ENHANCE ATHLETIC PERFORMANCE? i

Can we Enhance Athletic Performance Using Non-Invasive Brain Stimulation?

Thesis supervised by Dr. Hakuei Fujiyama, Dr. Ann-Maree Vallence, and Dr Jeremiah

Peiffer

Word Count – 9803

Murdoch University

Bachelor of Science Honours

Justin Andre

This thesis is presented in partial fulfilment of the requirements for the degree of

Bachelor of Sciences (Honours), Murdoch University, 2017.

Murdoch University Human Research Ethics Committee Approval 2017/021

CAN WE ENHANCE ATHLETIC PERFORMANCE? ii

Declaration

I declare that this thesis is my own account of my research and contains as its main

content work that has not previously been submitted for a degree at any tertiary

educational institution.

Name: Justin Andre Signature: ______________________

CAN WE ENHANCE ATHLETIC PERFORMANCE? iii

Copyright Acknowledgement

I acknowledge that a copy of this thesis will be held at the Murdoch University Library.

I understand that, under the provisions of s51.2 of the Copyright Act 1968, all or part of

this thesis may be copied without infringement of copyright where such a reproduction is

for the purpose of study and research.

This statement does not signal any transfer of copyright away from the author.

__________________

Full Name of Degree: Bachelor of Science with Honours

Thesis Title: Can we Enhance Athletic Performance using Non-

Invasive Brain Stimulation?

Author: Justin Francois Andre

Year: 2017

Signed:

CAN WE ENHANCE ATHLETIC PERFORMANCE? iv

Table of Contents

Title Page .………………………………………………………………….……. i

Declaration ……………………………………………………………………… ii

Copyright Acknowledgement ………………………………………………….... iii

Table of Contents ………………………………………………………………..

List of Figures…..…………………………………………………………….…..

List of Tables…..……………………………………………………………..…..

Acknowledgements ………………………………………………………….…..

List of Abbreviations………………………………………………………….….

Abstract...…………………………………………………………………………

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Introduction ……………………………………………………………………...

Fatigue………………………………………………………………….……

The relationship between RPE and fatigue………………………….……

The relationship between RPE and measures of performance (power

output and time of completion), and heart rate…………………………...

Understanding How RPE Influences an Athlete’s Performance………….…

Transcranial Direct Current Stimulation…………………………………….

Primary Motor Cortex and Fatigue………………………………………….

Dorsolateral Prefrontal Cortex and Fatigue…………………….……………

Cyclists as a Population………………………………………………….…..

Importance of Study………………………………………………………....

The Present Study…………………………………………………………....

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Method ……………………………………………………………….………….. 11

CAN WE ENHANCE ATHLETIC PERFORMANCE? v

Ethics Approval…………………………………………………….………..

Sampling Methodology……………………………………………………...

Participants…………………………………………………………….….

Inclusion criteria……………………………………………………….…

Exclusion criteria…………………………………………………….…...

Research Design……………………………………………………………..

Procedures…………………………………………………………………...

Session 1: graded exercise test……………………………………….…...

tDCS time trials…………………………………………………………..

Sessions 2 – 4 :tDCS…………………………………………….…….

Sessions 2 – 4: time trials………………………………………….…..

Data Analysis…………………………………………………………….…..

Performance: power output………………………………………….……

HR and RPE………………………………………………………….…...

Warm-up………………………………………………………………

Time trial………………………………………………………….…...

Sleep and tDCS sensation questionnaires…………………………….…..

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Results……………………………………………………………………….…....

Performance: Power Output…………………………………………....…

HR and RPE……………………………………………………………....

Warm-up………………………………………………………………

Time trial……………………………………………………………....

Sleep and tDCS sensation questionnaires………………………………...

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Discussion ………………………………………………………………………..

Hypothesis One: Effects of A-tDCS to M1 on Performance……………..

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CAN WE ENHANCE ATHLETIC PERFORMANCE? vi

Hypothesis Two: Effects of A-tDCS to M1 on HR and RPE…………….

Hypothesis Three: Effects of A-tDCS to DLPFC on Performance………

Hypothesis Four: Effects of A-tDCS to DLPFC on HR and RPE………..

Exercise Protocol Considerations………………………………………...

tDCS Considerations……………………………………………………...

Practical Recommendations and Future Directions………………………

Conclusion………………………………………………………………...

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References …………………………………….…………………………………. 37

Appendix A – Ethics Approval Letter……………….…....................................... 49

Appendix B – Information Letter……………………..…………………………. 51

Appendix C – Consent Form……………………………………………….……. 54

Appendix D – tDCS-screening Questionnaire.……..……………………............. 55

Appendix E – Par-Q+ Questionnaire………………………………...……...

Appendix F – Graded Exercise (GXT) Test: Headgear and Metabolic Cart……..

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Appendix G – Beam F3 Software……………………………………………... 59

Appendix H – tDCS sensation questionnaire.…………...………………….……. 60

Appendix I – Sleep Questionnaire…………………………................................. 62

Appendix J – Project Summary…………………….……………...................... 63

Appendix K – SPSS Output ………………........................................................

Appendix L – Assumption of Homogeneity of Variance, Fmax……………...…

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CAN WE ENHANCE ATHLETIC PERFORMANCE? vii

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List of Figures

Figure 1 Equipment used in the Graded Exercise Test. Female riding

a cycle ergometer (Velotron), while wearing the headgear….

Figure 2 Electrode placement of (a) F3 (left DLPFC). (b) Cz (M1) and

Oz (V1). (c) FP2 (right supraorbital region)……………….

Figure 3 Mean ± SD pattern of power output (PO) throughout the Time

Trial (TT) across cortical areas: V1, M1, and

DLPFC……...............................................................................

CAN WE ENHANCE ATHLETIC PERFORMANCE? viii

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List of Tables

Table 1 Participant Demographic………………………………………… 12

Table 2 Mean + SD HR and RPE Across Cortical Areas During 30% and

50% PPO Warm…….……………………………………………

Table 3 Mean + SD HR and RPE Across Cortical Areas Every 4km

throughout the 16.1km TT……………………………………..…

Table 4 Scores for Sleep and tDCS sensation Questionnaires………….…

CAN WE ENHANCE ATHLETIC PERFORMANCE? ix

Acknowledgements

I first want to acknowledge all the wonderful friendships made during my undergraduate

degree, and newly form friendships while undertaking Honours at Murdoch University.

We provided each other with daily doses of laughter and motivation, and continuous

support with our late night study sessions. This made my university experience more

musical and enjoyable. I would also like to express my immense gratitude to my parents.

Thank you for the constant reassurance and for believing in me. I am also hugely grateful

to my supervisors: Dr. Hakuei Fujiyama, Dr. Ann-Maree Vallence, and Dr Jeremiah

Peiffer. Thank you for imparting your wisdom, your guidance, and for you support

throughout the year. I also want to give a huge shout out to the PhD students down at the

exercise labs. Thank you for reassuring, communicating, and helping me. This made

running my experiments down in the labs more enjoyable. Lastly, I would like to thank

my dog, Mr Floop. Thank you for always staying by my side during my late nights.

Thank you all for believing in me. I could not have done this year without the wonderful

social support from all of you, and for this, I thank you.

CAN WE ENHANCE ATHLETIC PERFORMANCE? x

List of Abbreviations

A-tDCS……………………… Anodal Transcranial Direct Current Stimulation

DLPFC………………………………………….. Dorsolateral Prefrontal Cortex

GXT……………………………………………………….Graded Exercise Test

HR…………………………………………………………………….Heart Rate

KM…………………………………………………………………...Kilometres

M1……………………………………………………......Primary Motor Cortex

MVC…………………………………………...Maximal Voluntary Contraction

PPO………………………………………………………….Peak Power Output

PO………………………………………………………………....Power Output

RPE………………………………………………..Rating of Perceived Exertion

RPM…………………………………………………….Revolutions Per Minute

S…………………………………………………………………………Seconds

TT……………………………………………………………………..Time Trial

TTF……………………………………………………………...Time to Fatigue

tDCS…………………………………...Transcranial Direct Current Stimulation

V1…………………………………………………………………Visual Cortex

W……………………………………………………………………….....Watts

CAN WE ENHANCE ATHLETIC PERFORMANCE? xi

Abstract

Recent research has shown athletic performance to be enhanced using non – invasive

brain stimulation. One factor influencing an athlete’s performance is their perception of

how hard an exercise task is, known as their rating of perceived exertion (RPE). Research

has shown RPE to be modulated by fatigue. There is evidence to suggest that when fatigue

occurs, there is reduced output from the primary motor cortex (M1) and the dorsolateral

prefrontal cortex (DLPFC) to the muscles, which contributes to an increase in an athlete’s

RPE. Therefore, using anodal transcranial direct current stimulation (A-tDCS) to increase

cortical excitability could prolong the development of fatigue, and accordingly reduce

RPE. If less effort is needed to perform the physical activity, then heart rate (HR) will

decrease and performance will be enhanced. To test whether A-tDCS can enhance athletic

performance and reduce RPE and HR, 10 athletic cyclists volunteered to complete four

sessions. The first session was a Graded Exercise Test, and sessions two — four involved

A-tDCS administered at one cortical site (M1, DLPFC, or Visual Cortex [control

stimulation]) before participants completed a warm up, followed by a 16.1km Time Trial

(TT). In each TT, HR, RPE, power output (PO), and time to complete the TT were

recorded. Results showed no significant differences in RPE, HR, PO, or time to complete

the TT between cortical sites. This study suggests that A-tDCS was unable modulate

fatigue, and consequently, athletic performance, RPE, and HR remained unaffected.

Reasons behind these findings are discussed, with suggestions for future research.

Keywords: Perceived exertion, RPE, transcranial direct current stimulation, tDCS, primary motor cortex, M1, dorsolateral prefrontal cortex, fatigue, cycling, time trial

CAN WE ENHANCE ATHLETIC PERFORMANCE?

1

Can we Enhance Athletic Performance Using Non-Invasive Brain Stimulation?

The world of sport acknowledges that athletes are constantly trying to reach

optimal performance. Athletes frequently attest to the idea that perfect performances

exist in sport, whether it is the perfect hit, jump, or run (Koivula, Hassme, & Fallby,

2002). Athletes are told continuously to increase their performance, and that ‘practice

makes perfect’ (Koivula et al., 2002). When athletes are not able to achieve their optimal

performance, they are at an elevated risk of being dropped from an elite squad, which in

turn can affect their psychological and physical wellbeing (Hughes & Leavy, 2012).

Therefore, finding ways to increase athletic performance is vital to athletes. One factor

known to influence an athlete’s performance is their perception of how hard an exercise

task is, known as their rating of perceived exertion (RPE). Research suggests that RPE

is modulated by fatigue (Abbiss, Peiffer, Meeusen, & Skorski, 2015).

Fatigue

Fatigue is a multidimensional concept comprising of both physiological and

psychological aspects, and accordingly, definitions of fatigue vary between disciplines

(Abbiss & Laursen, 2005). In the discipline of psychology, definitions of fatigue might

focus on sensations of tiredness. In the discipline of exercise physiology, definitions of

fatigue typically focus on time-related loss of power during physical exercise (Abbiss &

Laursen, 2005), and physical changes in the muscles that affect muscle output (Gandevia,

2001). Furthermore, the cause of muscle fatigue can be linked to peripheral and central

fatigue. Peripheral fatigue is associated with changes at the neuromuscular junction.

Alternatively, central fatigue is the failure of the central nervous system to recruit active

muscle groups, which occurs when there is reduced output from the brain to maintain the

muscles activation during physical activity (Gandevia, 2001; Taylor & Gandevia, 2008).

For the purpose of this study, fatigue is defined as a loss of power and an increase in


2

perceived exertion during physical exercise, consistent with current definitions in the

exercise physiology literature (Abbiss & Laursen, 2005; Abbiss et al., 2015).

The relationship between RPE and fatigue. RPE is a subjective measure of

intensity and fatigue experienced during physical activity (Abbiss et al., 2015). In the

literature, fatigue is a factor known to influence RPE, and therefore, RPE can be used to

investigate the development of fatigue (Abbiss et al., 2015; Berchicci, Menotti, Macaluso,

& Di Russo, 2012; de Morree & Marcora 2013). Berchicci et al. (2012) used RPE and

muscle force measures, i.e., maximal voluntary contraction (MVC), to investigate the

development of fatigue during high intensity submaximal lower limb isometric

contractions. Participants performed four blocks of the isometric exercise at 40% MVC,

with each block consisting of 60 two-second contractions. RPE and MVC were

measured before and in-between blocks of exercise. Compared to baseline, RPE

significantly increased while MVC significantly decreased following the four exercise

blocks. The authors suggested that the significant decline in MVC and increase in RPE

indicated the development of fatigue. Furthermore, Berchicci and colleagues (2012)

suggested that in the presence of fatigue, there is increased activation of the lower limb

muscles needed to maintain the isometric contractions, consequently exacerbating RPE.

A large body of literature has replicated the finding that during exercise, higher

RPE is positively associated with an increase in fatigue (de Morree, Klien, & Marcora,

2012; de Morree & Marcora 2013; Shortz, Pickens, Zheng, & Mehta, 2015). This is

theorised as fatigue impairing the ability of the muscle to generate force, thus more effort

is needed to maintain the physical task, causing a subsequent increase in RPE (Berchicci

et al., 2012). In line with this, if RPE can be reduced, this would reflect the offset of

fatigue, and correspondingly enhance performance (de Koning et al., 2011).


3

The relationship between RPE and measures of performance (power output

and time of completion), and heart rate. As described above, in this thesis, fatigue is

defined as an increase in RPE and a loss of power output during physical exercise, where

power output (PO) refers to the energy being created during performance (Abbiss &

Laursen, 2005). In the literature, RPE has a strong relationship with PO (Cohen et al.,

2013; de Koning et al., 2011; de Jong et al., 2015). In addition, RPE is strongly associated

with other performance measures, such as time taken to finish an exercise task (Cohen et

al., 2013; de Koning et al., 2011; de Jong et al., 2015; Faulker, Gaynor, Parfitt, & Eston,

2011; Tucker, 2009), as well as heart rate (HR) (Green et al., 2005; Green et al., 2006).

De Koning et al. (2011) investigated the influence of RPE on athletic

performance. To accomplish this, de Koning and colleagues (2011) integrated

performance and RPE data from nine separate experiments, where cyclists or runners had

to complete a set distance in the shortest amount of time possible. After compiling the

data, de Koning and colleagues (2011) found that RPE increased with remaining distance.

Additionally, as RPE increased, PO decreased throughout the exercise. The authors

suggested that if RPE were higher than expected, performance would diminish, i.e., lower

PO and the time taken to complete an exercise task would increase (de Konining et al.,

2011). Furthermore, as RPE increases, more effort is needed to maintain the exercise task,

consequently increasing HR (Chen, Chen, Hsua, & Lin, 2013; Ekblom & Goldarg, 1971;

Green et al., 2006; Pinto et al., 2015). In contrast, de Koning and colleagues (2011)

suggested if RPE could be lowered, performance would be enhanced, i.e., increased PO

and decreased time taken to complete a task. If less effort is needed to maintain the

exercise task, then HR will subsequently decrease (Green et al., 2006).

Understanding how RPE Influences an Athlete’s Performance


4

As described above, high RPE is accompanied with inflated levels of fatigue

(Berchicci et al., 2012). It is possible that performance is diminished, reflected by lower

PO and increased time to complete a task, due to fatigue impairing the muscle’s ability

to generate force (de Koning et al., 2011); consequently, more effort is needed to perform

the same function and thus, HR increases (Green et al., 2006; Shortz et al., 2015).

Alternatively, low RPE increased PO and decreased time taken to complete an exercise

task, leading to enhanced performance (de Koning et al., 2011; Okano et al., 2013). It is

plausible that a lower RPE is associated with lower levels of fatigue experienced during

performance, and this resulted in less effort needed to maintain the physical task

(Berchicci et al., 2012).

Transcranial Direct Current Stimulation

There is evidence to suggest that reduced output from the brain to the muscles

during physical activity contributes to fatigue (Liu et al., 2002). Therefore, it is plausible

that increasing brain activity might lead to a reduction in fatigue, as indicated by a lower

RPE. If RPE can be reduced, performance will be enhanced (Okano et al., 2013). One

way to increase brain activity is through Transcranial Direct Current Stimulation (tDCS)

(Nitsche & Paulus, 2000). Transcranial Direct Current Stimulation is a non-invasive

method of neural stimulation that has been used for centuries (Antal, Polania, Schmidt –

Samoa, Dechent, & Paulus, 2011). Applied to the scalp using two electrodes, an anode

and a cathode, a low voltage electrical current is passed through (Nitsche & Paulus, 2000).

Neuronal excitability increases in the cortical area under the anode, and neuronal

excitability decreases in the cortical area under the cathode. Therefore, researchers apply

anodal tDCS (A-tDCS) to increase excitability and cathodal tDCS to decrease excitability

in the cortical area under the cathode (Nitsche & Paulus, 2000). These changes in

neuronal excitability do not cause the cortical neurons to fire directly; instead, they


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change the membrane potential, which increases or decreases the likelihood of neuronal

firing (Cogiamanian et al., 2010; Nitsche & Paulus, 2000). Although tDCS is not entirely

understood, tDCS functions by eliciting synaptic changes in the cortical area stimulated

(Amadi, Ilie, Johansen-Berg, & Stagg, 2014; Boros, Poreisz, Munchau, Paulus, &

Nitsche, 2008; Das, Holland, Frens, & Donchin, 2016; Raimundo, Uribe, & Brasil-Neto,

2012).

Application of tDCS for 10-20 minutes has been shown to induce changes in

cortical excitability of the stimulated area (i.e. the area under the electrodes), and these

changes in cortical excitability can persist for one hour or more (Lang et al., 2005; Nitsche

& Paulus, 2001). Numerous studies have used tDCS to alter cortical excitability, and

examined the influence of this on behaviour, such as psychomotor functions (Antal,

Terney, Kunhl, & Paulus, 2010; Boggio et al., 2006; Fregni et al., 2005). Due to the

potential benefits of transiently altering cortical excitability using tDCS, preliminary

evidence suggests that tDCS might prolong the development of fatigue, which, in turn,

could reduce RPE and enhance athletic performance (Lattari et al., 2016; Okano et al.,

2013).

Primary Motor Cortex and Fatigue

The primary motor cortex (M1) is the brain area that is responsible for the

execution of movement. As described above, a reduction in output from the brain to the

muscle can contribute to fatigue (Hou et al., 2016). Not surprisingly, many studies have

examined the role of M1 excitability in modulating RPE (Angius et al., 2016, Angius et

al., 2017, Williams, Hoffman, & Clark, 2013). During exercise, there is an initial increase

in cortical activity within M1, which results in an increase in motor output to the muscles

(Hou et al., 2016; Tanaka & Watanabe, 2012). An increase in motor outputs to the

muscles allows the exercise task to be maintained (Hou et al., 2016; Gandevia, 2001).


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After the initial increase in M1 activity, there is a decrease in cortical activity from M1

that results in reduced motor output to muscles, causing a decline in muscle activity (Hou

et al., 2016; Liu et al., 2002). Therefore, a decrease in motor outputs is thought to

contribute to the development of fatigue (Liu et al., 2002). If this reduction in output from

the M1 contributes to fatigue, then an intervention that increases activity in this cortical

area could plausibly reduce fatigue and RPE, enhancing performance (Angius et al., 2016;

Williams et al., 2013).

Several studies have examined the effect of A-tDCS applied to M1 during whole

body exercises such as cycling at a constant pace, on RPE and athletic performance

(Angius, Hopker, Marcora, & Mauger, 2015; Angius et al., 2017; Vitor-Costa et al.,

2015). Angius et al. (2017) examined whether A-tDCS to M1 could alter RPE and

enhance cycling performance. Participants underwent A-tDCS to M1 for 10 minutes with

the strength of the current set at 2.0 mA as well as a sham condition; sham acts as a control

condition in which the electrical current is turned on for only 30 seconds, and then

switched off (Nitsche et al., 2008). After tDCS, participants completed a time to fatigue

(TTF) cycling protocol at 70% peak power output (PPO), which consists of participants

riding at a constant power until they are fatigued. HR and RPE were monitored during

TTF protocol. Results showed that TTF was significantly longer and RPE was

significantly lower after A-tDCS compared to sham; no significant differences for HR

were observed between A-tDCS and sham. The authors suggested that following A-tDCS,

an increased excitability of M1 could have augmented the output to the working muscles,

causing participants to use less effort during the exercise protocol. As less effort was

perceived, participants reported a lower RPE and cycled longer, enhancing performance.

Angius et al. (2017) show promising results of A-tDCS to M1 being able to enhance


7

performance and reducing RPE. However, this finding has not been consistent in the

literature (Angius et al., 2015; Vitor-Costa et al., 2015).

Vitor-Costa et al. (2015) also investigated the effect of A-tDCS on TTF in a

cycling task. Participants underwent A-tDCS to M1 for 13 minutes with the strength of

the current set at 2.0 mA as well as a sham condition. After tDCS, participants completed

a TTF cycling protocol at 80% PPO. During each TTF cycling protocol, RPE and HR

were recorded. Results showed that TTF was significantly longer following A-tDCS

compared to sham, but there was no significant difference in RPE and HR between A-

tDCS and sham. Although the longer TTF indicated an improvement in athletic

performance, tDCS did not influence perceptual and physiological variables (that is, RPE

or HR). Vitor-Costa and colleagues (2015) suggested that improvements in athletic

performance were due to an increased M1 excitability; enhancing motor outputs to the

muscles needed to maintain physical activity. However, given that the tDCS parameters

used in their study did not affect RPE, the authors suggested that future research is

necessary to determine the stimulation intensity and stimulation location that affects RPE.

There is some evidence to suggest that A-tDCS to M1 might be effective in reducing RPE

and enhancing athletic performance (Angius et al., 2017). However, as presented above,

results are inconsistent and warrant further investigation (Vitor-Costa et al., 2015).

Dorsolateral Prefrontal Cortex and Fatigue

The dorsolateral prefrontal cortex (DLPFC) is another cortical region that has

been implicated in the development of fatigue and has been examined in modulating RPE

(Lattari et al., 2016). As described above, a decrease in M1 output to the muscles is

theorised to contribute to the development of fatigue. Evidence suggest that the DLPFC

facilitates M1 motor output during fatiguing exercise, and thereby, might play a role in

compensating for fatigue and maintaining the exercise task (Tanaka, Ishii, & Watanabe,


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2016; Tanaka, Ishii, & Watanabe, 2013). In line with this, if the excitability of the DLPFC

is reduced, this could lead to a reduction in the output from DLFPC to M1, which in turn,

might lead M1 to decrease outputs to the muscles resulting in fatigue (Tanaka et al., 2016;

Tanaka et al., 2013). If a decrease in output from the DLPFC contributes to fatigue

occurring, then an intervention that increases activity to this brain region could possibly

reduce fatigue and RPE, enhancing performance.

To date, one study has examined the effect of A-tDCS applied to DLPFC on

athletic performance and RPE (Lattari et al., 2016). Lattari et al. (2016) aimed to evaluate

the efficacy of A-tDCS in enhancing performance. Participants underwent A-tDCS to

the left DLPFC for 20 minutes with the strength of the current set at 2.0 mA as well as a

sham condition. After A-tDCS, participants completed a fatiguing flexion exercise of the

elbow, which involves participants lifting a barbell until fatigue. RPE and the total

number of times the weight was lifted (repetitions) were recorded. Results showed a

reduction in RPE and an increase in the number of repetitions following A-tDCS than

following sham. The authors suggested that the decrease in RPE was due to A-tDCS

modulating fatigue in the DLPFC (Tanaka, Hanakawa, Honda, & Watanabe, 2009). As

only one study has investigated the effects of A-tDCS applied to the DLPFC in

performance (Lattari et al., 2016), this warrants more research of using A-tDCS in another

type of exercise protocol, and under a different intensity and stimulation.

Cyclists as a Population

The literature reviewed above provides some evidence for the efficacy of A-tDCS

applied to M1 and DLPFC to modulate RPE and athletic performance. However, there

are some inconsistencies in the results of this literature, which warrants further

investigation of the effect of tDCS on athletic performance, RPE, and HR. Majority of

the literature examining the effect of tDCS on athletic performance reviewed above used


9

whole body exercise protocols, such as cycling at a constant pace (70% or 80% PPO) and

measuring TTF. When it comes to athletic performance, the sensitivity of a test to

determine performance changes is affected by different types of exercise protocols

(Hopkins, 2000). A cycling protocol, called a time trial (TT), consists of cyclists riding a

fixed distance in the shortest amount of time possible, and is a well-established protocol

that is sufficiently sensitive to detect changes in performance (Bellinger & Minahan,

2014; Paton & Hopkins, 2001; Sparks et al., 2016). Sparks et al. (2016) reported that the

most frequently used distance in road-based TT competitions is 16.1km (kilometers), and

suggested that a 16.1km TT should be used as an exercise performance criterion because

it represents the most directly related and valid assessment of actual cycling performance.

Additionally, a TT conducted in a controlled environment, such as in a laboratory, enables

the control of potential confounding variables such as heat. In cycling research, many

studies have noted the variation in results stemming from different temperatures (Peiffer

& Abbiss, 2011). Exercising in hot (>30*C) or cold (<21*C) conditions can cause

variation of results in TT, PO, HR, and RPE (Peiffer & Abbiss, 2011). Despite the high

validity from cyclists being familiar with the distance and the ability to control the testing

environment, the 16.1 km TT is rarely used in experiments (Jones et al., 2015; Williams

et al., 2015), and has not been used on any studies examining the efficacy of tDCS to

enhance performance.

Importance of Study

This current study is the first to examine the effect of applying A-tDCS to two

cortical areas that are thought to be associated with perceived exertion, namely M1 and

DLPFC, on athletic performance of a 16.1 km TT. Understanding which cortical areas

are related to perceived exertion can have real-world applications in enhancing athletic

performance; cyclists who compete in regular TT may be able to use tDCS to manipulate


10

perceived exertion, potentially providing a way for cyclists to improve their performance

in a TT. More importantly, this research is necessary as modern day technology is

currently being marketed to cyclists, incorporating tDCS into headgear, similar to

headbands worn at the gym or at training (Business Insider, 2017). However, the

published scientific research that can support the use of tDCS in enhancing cycling

performance is limited and conflicting (Angius et al., 2015; Angius et al., 2017;Vitor-

Costa et al., 2015).

The Present Study

To date, studies have found the application of A-tDCS over the M1 or DLPFC

reduced RPE and enhanced athletic performance (Angius et al., 2017; Lattari et al., 2016).

However, there is limited evidence and some conflicting results regarding the efficacy of

A-tDCS on performance, RPE, and HR (Angius et al., 2015; Lattari et al., 2016; Vitor-

Costa et al., 2015). The aim of this study was to determine if A-tDCS to M1 or DLPFC

affected athletic performance, RPE, or HR, applied before a set-intensity warm up and a

16.lkm cycling TT. In examining this, there are four hypotheses: the first hypothesis was

that A-tDCS to M1 (but not Visual Cortex [V1]) will increase PO and decrease time of

TT (Angius et al., 2017; Vitor-Costa et al., 2015); the second hypothesis was that A-tDCS

to M1 (but not V1) will reduce RPE and HR during both set intensity warm-up and TT

(Angius et al., 2017); the third hypothesis was that A-tDCS to DLFPC (but not V1) will

increase PO and decrease time of TT (Lattari et al., 2016); and the fourth hypothesis was

that A-tDCS to DLPFC (but not V1) will reduce RPE and HR during both set intensity

warm-up and TT (Lattari et al., 2016).

Methods


11

Ethics Approval

Murdoch University’s Human Research Ethics Committee approved this study

before data collection (2017/021) (refer Appendix A).

Sampling Methodology

Participants. Following a similar sample size used in previous research

investigating the effect of tDCS on cycling performance (e.g., Angius et al., 2015; Angius

et al., 2017 Vitor-Costa et al., 2015), 1 experienced trained female and 9 experienced

trained male cyclists volunteered for this study. Participants’ demographic information

obtained from the first session is presented in Table 1. Participants were recruited through

the Cycling Time Trial Association Facebook group. Each participant was informed of

the procedures, benefits, and risks (refer Appendix B) before giving written informed

consent to participate in the study (refer Appendix C). Thirteen participants were initially

recruited, but three participants were unable to complete all sessions. Outside of testing

conditions, one of the participants injured their leg, and the other two became sick.

Participants were instructed to avoid strenuous physical activity (i.e., interval training or

time trailing) and to maintain a similar diet the day before and day of testing for all

experimental sessions.

Inclusion criteria. This study had three inclusion criteria: Cyclists had to ride a

minimum of 200km a week, have completed one TT in the past 12 months, and be aged

between 18 – 50.

Exclusion criteria. Participants had to complete two screening questionnaires.

The first was a tDCS-screening questionnaire. Due to the nature of tDCS, cyclists did not

participate in this study if they had undergone brain surgery, were pregnant, had metal in

the brain, or an implanted neurostimulator (refer Appendix D). The second was a Par-Q+


12

questionnaire. This study utilised a high-intensity cycling protocol, as such, cyclists with

cardiovascular problems did not participate in this study (refer Appendix E).

Table 1

Participant Demographic

Research Design

This study used a repeated measures within-subjects design. The independent

variable was the site of stimulation (V1, M1, or DLPFC), and the dependent variables

were PO, RPE, HR, and time of completion. Using a repeated measures design would

reduce individual differences that may contribute to type I or type II errors (Girden, 1992).

Additionally, the allocation of stimulation site was counterbalanced to control for

carryover effects that are enhanced by a repeated measures design. Cyclists were blinded

to the type of stimulation in each session, and this will ensure a control effect. As the

study required a specific population of cyclists, a repeated measures design reduces the

amount of participants needed for the data to be valid (Girden, 1992).

In this study, cyclists completed four exercise sessions at Murdoch University’s

exercise physiology laboratory, with a minimum of two days between each session. The

first session was a graded exercise test (GXT), while the remaining three sessions were

Males Female

M SD M SD

Age (years) 36 6 34 0

Height (cm) 178 9 167 0

Weight (kg) 74 7 72 0

V02max (mL.kg-

1.min-1) 57.60 5.57 43.20 0

PPO (watts) 397 45 299 0


13

tDCS time trials. In each of the three tDCS time trial sessions, participants underwent 20

minutes of A-tDCS applied at a moderate intensity, 1.5mA (Fujiyama et al. 2017), to one

of the three target cortical regions: DLPFC, M1, or V1 (control site). After stimulation,

participants completed a 10-minute standardised warm up on a cycle ergometer, and a

16.1km TT directly after. During the TT, RPE, PO, HR, and time to complete each TT

was recorded.

Procedures

Prior to commencement of the experiment, the researcher (JA) obtained a first aid

certificate to deal with any health concerns that could arise from cyclists participating in

a high intensity exercises. After the certificate was obtained, recruitment of cyclists

commenced.

Session 1: graded exercise test. Participants were sent an information letter that

explained the study, and were required to answer two screening questionnaires, i.e., tDCS

screening and Par-Q+. Before starting the GXT, the participant’s weight and height

measurements were collected, and the electronically braked cycle ergometer (Velotron;

RacerMate, USA) was modified to accurately match the dimensions of the road bicycle

measurements of the participant.

Once the participant was comfortable on the Velotron, they completed a 10-

minute warm-up protocol at a self-selected power to habituate them to the Velotron.

Upon completion of the warm-up, the GXT started at a resistance of 70 Watts (W) with

increases of 35W per minute in resistance for males, and 25W per minute for females

(Peiffer & Abbiss, 2011). During the GXT, participants wore a headgear with a

mouthpiece attached and a nose clip to record oxygen consumption (See Figure 1).

Additionally, a Polar Heart Rate Monitor (T31; Finland) was attached to the chest of the

participant. Throughout the GXT, HR and breathe-by-breathe ventilation were recorded


14

using Parvo TrueOne metabolic cart (ParvoMedics; USA) (refer Appendix F). This type

of metabolic cart has been shown to be valid and reliable (Crouter, Antczak, Hudak,

DellaValle, & Haas, 2006; Macfarlane & Wu, 2013). Before each use, the metabolic cart

was calibrated using gases of known concentration (4.0% CO2 and 16.0% O2), and

through a range of flow rates using a Hans Rudolph 3L Syringe. Also, verbal

encouragement was provided to the participants, and they were instructed to ride at a

cadence above 60 Revolutions Per Minute (RPM). The test was terminated when the

participant reached volitional fatigue, as indicated through cadence dropping below 60

RPM. After completion of the GXT, participants completed a ‘cool down protocol’ on

the Velotron, where participants rode at a self-selected pace. The first session concluded

with the participant being debriefed: answering any questions, ensuring the participant

was in a stable condition, explaining confidentiality of results, and booking the next

session.

After the first session was completed, PPO was obtained from the GXT results,

and from this, 30% and 50% PPO was calculated and used in the next three subsequent

sessions for the warm-up protocol. Data collected from the GXT was also used for to

determine maximal oxygen consumption (VO2max).


15

Figure 1: Equipment used in the Graded Exercise Test. Female riding a cycle ergometer

(Velotron), while wearing the headgear. (Image adapted from NyVelocity, 2007).

tDCS Time Trials.

Headgear


16

Sessions 2 - 4: tDCS. The remaining three sessions were completed using

identical methodology, except for the target tDCS site (DLPFC, M1, or V1), and was

completed in a counterbalanced order. Upon arrival, participants were seated in the

environmental chamber for 20 minutes, during which A-tDCS was administered at one

of the target sites, with a standard current intensity of 1.5 mA (Fujiyama et al. 2017).

A-tDCS was delivered by a battery-driven Dual Channel Iontophoresis System

(Chattanooga Ionto, USA). During sessions 2 – 4, a total of four electrodes were placed

and attached with bandages on the participant’s head; however, only one cortical site was

stimulated per session. This was to blind the participants to what area was being

stimulated. Each electrode was wrapped in a sodium chloride (saline) soaked sponge, and

was lined with conductive gel. In determining electrode positioning, the

electroencephalogram electrodes 10–20 international systems were used (Klem, Luders,

Jasper, & Elger, 1999). For M1 stimulation, the anode was placed over Cz; for DLPFC

stimulation, the anode was placed over F3; and for V1 stimulation, the anode was placed

over Oz. While each cortical area was being stimulated, the cathode was always placed

on Fp2 (right supraorbital area) (see Figure 2).

F3 position was determined by the “Beam method/ Beam F3 - System” (Beam,

Borckardt, Reeves, & George, 2009); and in this method, F3 position was based of three

head measurements: the distance between left and right pre-aurical point, nasion – inion

distance, and head circumference. These values were entered into the “Beam F3”

software, and based on the coordinates calculated by the software, the DLPFC placement

was located (refer Appendix G). Neuroimaging studies have shown the Beam F3 system

to be accurate in locating the left DLPFC (Halper, Yagi, Manevitz, Nishimoto & Onishi,

2016; Mir-Moghtadaei et al., 2015). Additionally, while stimulating the left DLPFC, a


17

common reference site for the cathode is the right supraorbital region, located above the

left eye socket (Hsu, Zanto, & Gazzaley, 2015; Lattari et al., 2016).

Figure 2. Electrode placement of (a) F3 (left DLPFC). (b) Cz (M1) and Oz (V1). (c) Fp2

(right supraorbital region). (Image adapted from TotaltDCS, 2015).

Cz placement was chosen based on the chosen cycling exercise, since applying

the large anode over Cz would stimulate the M1 representations of lower limb muscles

in both hemispheres (Vitor-Costa et al., 2015). Furthermore, while stimulating M1 a

common reference site for the cathode is the right supraorbital region. This type of

electrode placement has been shown to increase motor output (Stagg & Nitsche, 2011).

V1 was located through Oz placement. V1 acted as the active control condition in

this study. There is no evidence to suggest that V1 has a role in fatigue, and therefore,

stimulation of V1 was not expected to induce any changes in RPE, PO, HR, or time of

completion.

Sessions 2 – 4: time trial


18

Immediately after the completion of the tDCS, participants completed a 10-minute

standardised warm up on the Velotron cycle ergometer inside a heat chamber; the first 5-

minutes was at 30%PPO and the next 5-minutes was at 50%PPO. The environmental

chamber remained at a constant temperature of 24°C and 40% relative humidity. During

the mid and end point of each 5-minute stage, RPE and HR was measured. HR and PO

was recorded at a beat-by-beat frequency using a Garmin (model 500; USA). RPE was

recorded using Borg’s (1990) Scale of Perceived Exertion (10-point scale; 1 = no exertion

and 10 = maximal exertion). A meta-analysis showed this scale to have strong

psychometric properties; the scale is valid and reliable in measuring perceived exertion

in a healthy athletic population (Chen, Fan, & Moe, 2002), and across cultures (Leung,

Chung, & Leung, 2002).

After completing the warm-up, participants immediately completed a 16.1 km TT

on the same Velotron cycle ergometer; participants were instructed to complete the TT in

the “shortest time possible.” The Velotron gears were standardised to start at 52/17, where

the gears determined the amount of resistance applied to Velotron. After the TT began,

participants were able to change gear intensity to their preference. When the TT

commenced, a large fan placed directly in front of the participant was immediately started

at the lowest power setting. During the TT, only feedback about the distanced completed

was provided. At 4km intervals, average PO and average HR was collected from the

Garmin, along with the participants’ RPE. Additionally, the mean PO of the TT and time

to complete the 16.1km TT was extracted from the Garmin.

Fifteen minutes after completing the TT, participants were asked to provide their

RPE. They were also asked to fill out a tDCS sensation questionnaire (refer Appendix H)

and a sleep questionnaire (refer Appendix I); the sleep questionnaire also had questions

on the total amount of caffeine and alcohol consumed in the last 12 hours. This


19

questionnaire has been used in tDCS research (Fujiyama et al., 2017; Vancleef, Meesen,

Swinnen, & Fujiyama., 2016) to assess if performance is influenced by other variables,

i.e., lack of sleep or tDCS sensations. Even though the psychometric properties of these

questionnaires are unknown, these questionnaires have provided valid and reliable results

in past research (Fujiyama et al., 2017; Vancleef et al., 2016). All data obtained from the

TT was stored using Excel 2013 (Microsoft Corp., Redmond, Washington, USA). Once

participants have completed all sessions, the summary of the project will be available

online on the Murdoch University’s Library Research Repository for viewing (refer

Appendix J).

Data Analysis

All analyses were performed using the Statistical Package for the Social Sciences

(V.21.0, Chicago, USA), and significance was based on an alpha level of .05. For full

SPSS output, refer to Appendix K. Significant main effects and interactions were further

explored with post hoc analysis with Least Significant Difference corrections. Partial eta-

squared (partial η2) values were provided as measures of effect sizes, with values of .01,

.06, and .14 constituting small, medium, and large effects respectively (Cohen, 1988).

The following statistical analyses were conducted to ensure the assumptions

underlying a repeated-measures ANOVA were not violated. Due to the small sample size

(N = 10), the assumption of normality was assessed with Shapiro-Wilk’s test.

Additionally, Mauchly’s test was used to test the assumption of sphericity. In all cases

when the assumption of sphericity was violated, degrees of freedom were adjusted using

the Huynh-Feldt Epsilon (Tabachnick & Fidell, 2007). Lastly, homogeneity of variance

was assessed using the Fmax statistic. If Fmax statistic was less than ten, the assumption

of homogeneity is met, as the variability in each of scores is approximately equal

(Tabachnick & Fidell, 2007). Appendix L indicates that the assumption of homogeneity


20

of variance was not violated. Furthermore, upon inspection, the assumption of normality

was violated for RPE measures at 30% and 50%PPO warm-up, and RPE measures

throughout the TT. However, a repeated measure ANOVA is robust against the violation

of normality; this encroachment should not threaten the interpretation of analysis (Glass,

Peckham, & Sanders, 1972; Harwell, Rubinstein, Hayes, & Olds, 1992).

Performance: power output. To determine the effect of A-tDCS to overall

performance when applied before a 16.1km TT, one-way repeated measures ANOVAs

were performed with a within-subject factor of CONDITION (three levels: V1, M1,

DLPFC), with separate ANOVAs performed for PO and time to completion of the 16.1km

TT. To further investigate the effect of tDCS on performance, mean PO was calculated

across different points of the TT: 0 – 3 km, 4 – 7 km, 8 – 11 km, and 12 – 16 km. Two-

way repeated measures ANOVAs were performed on the mean PO data with within

subject factors of CONDITIONS (three levels: V1, M1, DLPFC) and TIME (four levels:

4km, 8km, 12km, 16km).

HR and RPE.

Warm-up. To determine the effect of A-tDCS on HR during the set-intensity

warm-up, one-way repeated measures ANOVAs were performed with a within-subject

factor of CONDITION (three levels: V1, M1, and DLPFC), with separate ANOVAs

performed on mean HR for 30%PPO warm-up and mean HR for 50% PPO warm-up. To

determine the effect of A-tDCS on RPE during the set-intensity warm-up, separate

Friedman’s two-way ANOVAs were performed on mean RPE for 30%PPO warm-up and

mean RPE for 50%PPO warm-up.

Time trial. To further investigate the effect of tDCS on HR and RPE throughout

the 16.1km TT, mean HR and RPE was calculated across different points of the TT: 0 –

3 km, 4 – 7 km, 8 – 11 km, and 12 – 16 km. Separate two-way repeated measures


21

ANOVAs were performed on mean RPE and HR with within subject factors of

CONDITIONS (three levels: V1, M1, DLPFC) and TIME (four levels: 4km, 8km, 12km,

16km).

Sleep and tDCS sensation questionnaires. To determine if sleep, caffeine,

alcohol, and tDCS sensations influenced performance, the mean values for number of

numbers of hours slept, sleep quality, alcohol consumed, caffeine intake, and tDCS

sensations were analysed across stimulation conditions. Separate one way repeated

measures ANOVAs were conducted to assess if CONDITIONS (three levels: V1, M1,

and DLPFC) different significantly in the number of numbers of hours slept, sleep quality,

tDCS sensations, alcohol consumed, and caffeine intake.

Results

Performance: Power Output

Mean PO across the TT was not different between conditions in which tDCS was

applied to V1 (M = 278.60, SD = 42.17 W), M1 (M = 273.90, SD = 42.37 W), and DLPFC

(M = 280.20, SD = 39.13 W), F(2,18) = 2.892, p = .081, partial η 2 = .243. Additionally,

the mean completion time for the TT was not different between conditions in which tDCS

was applied to V1 (M = 1434.80, SD = 79.58 s [seconds]), M1 (M = 1433.70, SD= 80.99

s), and DLPFC (M = 1428.40, SD = 70.97 s), F(2,18) = 3.107, p = .069, partial η 2 = .257

Figure 3 shows the mean distribution of PO across 4km intervals, and upon

inspection, show a constant distribution of power across intervals for all three

experimental conditions (i.e., V1, M1, and DLPFC). The results of the ANOVA show no

significant main effect of condition F(2,18) = 1.498, p = 2.50, partial η 2 = .14. However,

significant main effect in PO was observed for time, F(1.702, 15.319) = 23.116, p = <.

001, with a large effect size of partial η 2 =. 720. Pairwise comparisons further revealed

that 4km PO (M = 292.60, SD = 34.53) was significantly greater than 8km (M = 273, SD


22

= 37.44), 12km (M = 266.50, SD = 34.01), and 16km (M = 272.76, SD = 39.12).

Additionally, 8km PO was significantly greater than 12km PO. Lastly, 12km PO was

significantly lower than 16km. There was no significant interaction of time and condition

F (6, 54) = .973, p = .451, partial η 2 =. 098.

Figure 3. Mean + SD pattern of power output (PO) throughout the Time Trial (TT) across

cortical areas: V1, M1, and DLPFC. *Significant differences in PO over time: PO greater

at 4km than 8km, 12km, and 16km; PO greater at 8km than 12km; PO lower at 12km

than 16km.

HR and RPE

Warm-up. Mean HR and RPE during set intensity warm-up at 30% and 50% PPO

are presented in Table 2; HR and RPE remained similar between conditions during 30%

and 50% PPO warm-ups. There were no significant effect of condition during 30% PPO

warm-up for either HR, F(1.163, 10.463) = 2.358, p = .153, partial η2 = .208, or RPE, χ2

(2) = .000, p = 1.000. Additionally, there were no significant effect between conditions

during 50%PPO warm-up for either HR F(2,18) = 1.010, p = .384, partial η2 =.101, or for

RPE χ2 (2) = .857, p = .651.


23

Table 2

Mean + SD HR and RPE Across Cortical Areas During 30% and 50% PPO Warm-up

Time trial. Mean differences in RPE and HR between conditions and over the

16.1km TT are shown in Table 3. Mean HR and RPE increased throughout the TT. The

repeated measures ANOVA showed a significant main effect for time on the measure of

HR, F(1.452, 13.070) = 53.314, p < .001, with a medium effect size of partial η 2 =. 09.

Pairwise comparisons further revealed that 4km HR (M = 161, SD = 8) was significantly

lower than 8km (M = 169, SD = 7), 12km (M =172, SD = 8), and 16km (M = 175, SD =

8). Additionally, 8km HR was significantly lower than 12km and 16km. Lastly, 12km

HR was significantly lower than 16km. Also, a significant main effect of time was

Warm-Up

30%PPO

50%PPO

Cortical Areas HR RPE HR RPE

Visual Cortex 113 + 19 2 + 1 135 + 14 3 + 1

Motor Cortex 110 + 12 2 + 1 136 +13 3 + 1

Dorsolateral

Prefrontal Cortex

107 + 12 2 + 1 134 + 13 3 + 1


24

observed for the measure of RPE, F(2.028,18.253) = 65.553, p<.001, with a large effect

size of partial η2 =. 87. Pairwise comparisons further revealed that 4km RPE (M = 6, SD

= 2) was significantly lower than 8km (M = 7, SD = 1), 12km (M = 8, SD = 1), and 16km

(M = 9, SD = 2). Additionally, 8km RPE was significantly lower than 12km and 16km.

The ANOVAs showed no significant main effect of conditions for either HR, F(2, 18) =

.035, p = .965, partial η 2 =004, or RPE, F(2, 18) = .604, p = .558, partial η 2 = .063.

Additionally, there were no significant interactions between time and conditions for either

HR, F(6,54) = 1.154, p = .345, partial η 2 =. 114, or RPE, F(6,54) = .680, p = .666, partial

η 2 = .070.

Table 3

Mean + SD HR and RPE Across Cortical Areas Every 4km throughout the 16.1km TT.

Sleep and tDCS Sensation Questionnaires

Table 4 shows the means and standard deviations for the units of caffeinated

drinks consumed 12 hours, sleep quality, number of hours slept (night prior to testing

session), units of alcohol consumed in the last 12 hours, and sensations from tDCS. There

were no significant differences between conditions in terms of: (a) units of caffeinated

drinks consumed, F(1.173, 10.560) = .796, p = .413, partial η 2 = .08; (b) sleep quality,

Distance (km)

HR

RPE

Cortical Areas 4km 8km 12km 16km 4km 8km 12km 16km

Visual Cortex 163 + 8 170 + 7 172 + 7 176 + 7 6 + 2 7 + 2 8 + 1 9 + 1

Motor Cortex 161 + 10 170 + 9 172 + 8 176 + 8 6 + 2 7 + 1 8 + 1 9 + 2

Dorsolateral

Prefrontal Cortex

161 + 9 169 + 10 173 + 8 175 + 8 6 + 1 7 + 1 8 + 1 9 + 2


25

F(2, 18) = .675, p = .521, partial η 2 = .07; (c) number of hours slept, F(2, 18) = .911, p

=. 420, partial η 2 = .420 (d) adverse sensations of tDCS, F(2, 18) = 1.00, p = .387, partial

η 2 = .10, and (e) units of alcohol consumed.

Table 4

Scores for Sleep and tDCS sensation Questionnaires

Note. Mean scores for Adverse Sensations of tDCS can range from 1 – 4, with lower

scores indicating weaker sensations felt during tDCS: tingling, burning-sensations,

fatigue, un-comfortableness, or concentration problems.

Discussion

The aim of this study was to determine if A-tDCS to M1 or DLPFC affected

athletic performance, RPE, or HR, applied before a set-intensity warm up and a 16.lkm

cycling TT. The current study has two main results. First, the effect of A-tDCS applied

to M1 or DLPFC on performance was not different to A-tDCS applied to V1 (control

stimulation). Second, the effect of A-tDCS applied to M1 or DLPFC on RPE and HR was

not different to A-tDCS applied to V1 (control stimulation). These results suggest A-

Cortical Areas

Visual Cortex Motor Cortex Dorsolateral

Prefrontal Cortex

Units of caffeinated drinks

consumed

3.00 + 2.00 2.66 + 1.32 2.77 + 1.30

Sleep quality 7.22 + .83 7.33 + .86 6.83 + 2.29

Number of hours slept 7.25 + .25 7.36 + .67 7.16 + .61

Units of alcohol consumed 0 0 0

Adverse Sensations of tDCS 1.10 + 1.10 1.20 + 1.20 1.10 + 1.10


26

tDCS to M1 or DLPFC was not able to enhance athletic performance, nor reduce

perceptual and physiological variables.

Hypothesis One: Effects of A-tDCS to M1 on Performance

It was hypothesised that A-tDCS to M1 (but not V1) would increase PO and

decrease time of TT. This hypothesis was not supported, as results from this present study

showed mean PO and mean time to completion were non-significant between M1 and V1

during the TT. Furthermore, when PO was examined in 4km intervals throughout TT,

results of this further analysis showed a normal distribution of power typically seen in a

16.1km TT (Atkinson & Brunskill, 2000; Jones et al., 2016). Specifically, during a

16.1km cycling TT, cyclists maintain a high PO for the first 4km, which is followed by a

steady decline in PO typically observed at 8km and 12km, and an increase PO is typically

observed in the last 4km to finish the 16.1km TT. This typical distribution of PO that was

observed across the TT was not different for the M1 and V1 conditions, suggesting that

A-tDCS to M1 does not enhance TT performance.

Results from this study differed from past research, which showed enhanced

athletic performance following A-tDCS to M1 (Anguis et al., 2017). Anguis et al. (2017)

applied A-tDCS to M1 at 2.0mA for 10 minutes and found an increase in time taken for

participants to become fatigued in constant paced cycling compared to a sham condition.

Anguis and colleagues (2017) suggested that A-tDCS increased excitability of M1, and

this could have augmented the output to the working muscles, causing participants to use

less effort and continuing cycling for longer. This, in turn, could have mediated the

enhanced performance. Since the current results show that A-tDCS to M1 did not change


27

athletic performance, it is possible that neuronal excitability was not sufficiently altered

following stimulation.

In the current study, we did not directly measure the excitability of M1 following

A-tDCS. It has been suggested that stimulation of the leg representation of M1 might be

less affected by tDCS than the upper limb representations in M1, because the leg

representation is located deeper in the underlying brain tissues than the upper limb

representations (Jeffery, Norton, Roy, & Gorassini, 2007). Therefore, it is possible that

A-tDCS to M1 did not affect M1 output in this current study, which is essential for

maintaining physical activity. Future research should directly measure the excitability of

M1 following A-tDCS, which can be done using a non-invasive brain stimulation

technique called transcranial magnetic stimulation.

After A-tDCS to M1, an alternative explanation of why there were inconsistent

results in performance between this study and previous research can be due to the

differences in samples. We used high-level athletic cyclists, while previous research

(Anguis et al., 2017) used ‘healthy physically active’ participants. Athletic cyclists

participated in this study as a high-intensity cycling protocol was used, and is this protocol

is valid and reliable in assessing cycling performance within a cyclist population.

However, it is possible that there was no effect of tDCS on performance because of a

ceiling effect in the high-level cyclists recruited for this study; tDCS might not be able to

improve performance because high-level cyclists might not have room to improve.

Hypothesis Two: Effects of A-tDCS to M1 on HR and RPE

It was hypothesised that A-tDCS to M1 (but not V1) would reduce RPE and HR

during both set intensity warm-up and 16.1km TT. Results from this study do not support

this hypothesis; there was no significant difference in mean RPE and mean HR after

applying A-tDCS to M1 and V1, during set-intensity warm up, i.e., at 30%PPO and


28

50%PPO, and throughout the TT. Furthermore, after A-tDCS to M1 and V1, results of

this current study showed HR and RPE to continue to increase throughout the set –

intensity warm up and 16.1km TT. Irrespective of tDCS intervention, this is to be

expected, as the literature shows RPE to rise with increasing HR (Green et al., 2005;

Green et al., 2006). A higher RPE is due to fatigue impairing the muscles ability to

generate force, requiring more effort to perform the same function (Berchicci et al., 2012),

correspondingly increasing HR (Green et al., 2005; Green et al., 2006). Therefore, A-

tDCS to M1 was not able to modulate RPE and HR.

Results of this study are inconsistent with previous research (Anguis et al., 2017),

which showed after A-tDCS to M1, a reduction in RPE was observed compared to sham.

The authors indicated that A-tDCS increased excitability of M1, which augmented the

motor outputs to the working muscles. An increase in motor outputs to the muscles caused

participants to use less effort, and this caused the task to be perceived as easier, decreasing

RPE. Therefore, an increase in cortical activity of M1 after A-tDCS might have led to a

reduction in perceived exertion throughout the exercise task (Anguis et al., 2017).

In this current study, the lack of difference in RPE when A-tDCS was applied to

M1 compared to V1 might be due to a lack of change in M1 excitability following A-

tDCS. As described above (Hypothesis One: Effects of A-tDCS to M1 on Performance),

it was suggested that stimulation of the leg representation of M1 might be less affected

by tDCS than the upper limb representations in M1, because the leg representation is

located deeper in the underlying brain tissues than the upper limb representations (Jeffery

et al., 2007). Therefore, it is possible that A-tDCS to M1 did not affect M1 output in this

study, and correspondingly, not modulating fatigue within M1. Since fatigue is a factor

known to influence RPE (Berchicci et al., 2012), and it is possible that A-tDCS did not

increase M1 excitability, it is possible that A-tDCS did not modulate fatigue (Liu et al.,


29

2002) and, therefore, did not change RPE. Additionally, if RPE remained unchanged,

and given the positive association RPE has with HR within the literature (Green et al.,

2005; Green et al., 2006), no change in HR would be observed. If perceived exertion

cannot be reduced, another explanation for the lack of improvements in performance

could be due to perceived exertion not being influenced throughout the set-intensity warm

up and TT; accordingly, optimal performance cannot be achieved (de Koning, 2011; de

Jong et al., 2015).

Hypothesis Three: Effects of A-tDCS to DLPFC on Performance

It was hypothesised that A-tDCS to DLFPC (but not V1) would increase PO and

decrease time of TT. This hypothesis was not supported; after A-tDCS to DLPFC and

V1, results from this study showed no significant changes in mean PO and mean time of

TT completion. Additionally, when PO was examined in 4km intervals throughout TT,

the normal distribution of power throughout the TT, i.e., a decrease and then final increase

in PO, was present in the DLFPC condition (as well as V1 and M1 as described above).

This typical distribution of PO that was observed across the TT was not different for the

DLPFC and V1 conditions, suggesting that A-tDCS to DLPFC does not enhance TT

performance.

Results from this study are in contrast to past research showing A-tDCS to DLPFC

was able to enhance athletic performance (Lattari et al., 2016). Lattari et al. (2016) applied

A-tDCS to DLPFC at 2.0mA for 20 minutes and found an increase in the number of times

participants lifted a barbell compared to a sham condition. Lattari and colleagues (2016)

suggested that the DLPFC was an area related to fatigue, and A-tDCS was able to

modulate existing neural connections in prolonging the development of fatigue and

enhancing performance. Since the current results show that A-tDCS to DLPFC did not

change athletic performance in this study, it is possible A-tDCS to DLPFC did not affect


30

DLPFC output, which is essential for maintaining physical activity and modulating

fatigue.

Hypothesis Four: Effects of A-tDCS to DLPFC on HR and RPE

It was hypothesised that A-tDCS to DLPFC (but not V1) would reduce RPE and

HR during both set intensity warm up and 16.1km TT. Results showed no significant

differences in mean RPE and mean HR between DLPFC and V1 during set intensity

warm-up or TT, rejecting the hypothesis proposed. Furthermore, after A-tDCS to DLPFC

and V1, results of this current study showed that HR and RPE continued to increase

throughout the set – intensity warm up and 16.1km TT. As described above between M1

and VI, irrespective of tDCS intervention, this is to be expected, as the literature shows

RPE to rise with increasing HR (Green et al., 2005; Green et al., 2006). Therefore, results

from this current study suggest that A-tDCS to DLPFC was unable to modulate RPE and

HR.

Findings from this study are in contrast to research reporting a reduction in RPE

after A-tDCS to DLPFC compared to sham (Lattari et al., 2016). Lattari and colleagues

(2016) suggested that the DLPFC is implicated in fatigue, and A-tDCS to the DLPFC in

their study possibly increased cortical activity, prolonging the development of fatigue and

lowering RPE throughout exercise. Since the results show that A-tDCS to DLPFC did

not modulate RPE in this current study, it is possible that A-tDCS to DLPFC did not alter

cortical excitability, which is essential in prolonging the development of fatigue.

Therefore, if fatigue is not modulated, this could explain the lack of differences in RPE

when A-tDCS was applied to DLPFC compared to V1. If RPE remained unaffected,

similar to the RPE and HR interpretations in M1, no changes in HR would be observed;

since HR and RPE are positively associated (Green et al., 2005; Green et al., 2006).


31

Furthermore, if perceived exertion cannot be reduced, another explanation of why there

were no improvements in performance may be due to perceived exertion not being

influenced throughout the TT and set-intensity warm up; accordingly, optimal

performance could not be achieved (de Koning, 2011; de Jong et al., 2015).

An alternative explanation for the lack of differences in RPE during the set-

intensity warm up and TT might be due to DLPFC or M1 not being related in perceiving

exertion. Instead of DLPFC or M1, Okano et al. (2013) suggested that the temporal cortex

and insular cortex are areas related in perceiving exertion. They applied 20 minutes of A-

tDCS to the temporal cortex, and reported a reduction in RPE and enhancements in

athletic performance during an incremental cycling test, compared to sham. Their

findings suggest that A-tDCS over the temporal cortex modulates perceived exertion and

exercise performance. Future research should consider stimulating other cortical sites

related to perceived exertion.

Exercise Protocol Considerations

The differences in results from this study and past literature (Anguis et al., 2017;

Lattari et al., 2016) could be due to exercise protocols. When it comes to athletic

performance, the sensitivity of a test to determine performance changes is affected by

different types of exercise protocols (Hopkins, 2000). The literature examining athletic

performance applying A-tDCS to M1 or DLPFC have used a constant paced exercise

protocol (Anguis et al., 2017; Vitor-Costa et al., 2015) or a weight lifting task (Lattari et

al., 2016); the current study is the first study to assess the effect of A-tDCS on

performance using a cycling TT protocol. A 16.1km cycling TT protocol should be used

as an exercise performance criterion because it represents the most valid assessment of

actual cycling performance, as cyclists are familiar with riding this distance (Sparks et

al., 2016). Additionally, using a TT protocol allows the ability to control the testing


32

environment (Peiffer & Abbiss, 2011). However, it is possible that divergent findings

from the literature and this study could be due to different exercise protocols. Possibly,

the effects of tDCS may only be exerted in strength and conditioning exercises and

cycling at a constant pace, i.e., riding at 70%PPO until the cyclist become fatigued; and

may be indiscernible where cyclists have the option to change the amount of power

distributed throughout an exercise, also known as riding at a self-selected pace during the

TT. As a result, it needs to be acknowledged that the differences in exercise tasks from

the current study and the literature may be the cause of divergent findings.

tDCS Considerations

It is well known that changes in neuronal excitability are dependent on amplitude

and length of stimulation (Jacobson, Koslowsky, & Lavidor, 2012). Previous studies have

applied A-tDCS to M1 ranging from 10 – 13 minutes and at a stimulation intensity of

2.0mA (Anguis et al., 2015; Anguis et al., 2017; Vitor-Costa et al., 2015); this is the first

study to examine applying A-tDCS to M1 for 20 minutes and at 1.5mA. However, longer

or more intensive stimulation does not necessarily increase the efficacy of tDCS

(Batsikadze, Moliadze, Paulus, Kuo & Nitsche, 2013). Batsikadze et al. (2013) aimed to

explore the effects of 2.0mA tDCS on cortical excitability. Participants underwent anodal

and cathodal tDCS to the left primary cortex for 20 minutes with the strength of the

current set at 2.0mA. Additionally, participants completed a sham condition, but also had

1mA cathodal tDCS applied to the left primary motor cortex. Cortical excitability after

tDCS was monitored through transcranial magnetic stimulation. Results showed anodal

tDCS as well with cathodal tDCS at 2.0mA resulted in significant increases in cortical

excitability, whereas cathodal tDCS at 1.0mA decreased cortical excitability. These

results suggest that the application of cathodal tDCS at 2.0mA resulted in cortical

excitability enhancement instead of inhibition. The authors concluded that enhancement


33

of tDCS intensity, i.e., cathodal stimulation of 1.0mA to 2.0mA, does not necessarily

increase to efficacy of stimulation, but instead, shifts the direction of excitability

(Batsikadze et al., 2013). Since the effects of tDCS intensity on cortical excitability are

unclear, it is important for future research to examine the effects of tDCS using a range

of stimulation intensities, on performance of a cycling TT.

It should also be acknowledged that tDCS could modulate neuronal activity in a

relatively larger area than that directly targeted by the electrodes (Lang et al. 2005). Lang

and colleagues (2005) examined if tDCS could alter regional neuronal activity in M1.

Participants underwent anodal or cathodal stimulation to M1 for 10 minutes with the

strength of the current set at 2.0mA as well as a sham condition. After tDCS,

neuroimaging techniques, i.e., positron emission tomography, was used to observe

changes in cerebral blood flow. Neuroimaging results showed that tDCS extended to

other areas of the brain, known as a spatial effect. When compared to sham, anodal

stimulation increased cerebral blood flow in cortical areas such as M1, sensory motor

cortex, and somatosensory areas, and subcortical brain areas such as the red nucleus and

reticular formation. Lang and colleagues (2005) concluded that tDCS provides localised

changes under the area of the electrode, but also extends to other areas of the brain.

This present study applied A-tDCS to the M1 and DLPFC, while placing the

cathode on the right supraorbital area. Placing the cathode on the right supraorbital region

is a common reference site for providing A-tDCS to the DLPFC and the M1 (Hsu, Zanto,

& Gazzaley, 2015; Lattari et al., 2016), and is known in the literature to increase cortical

excitability (Stagg & Nitsche, 2011). However, it is possible that anodal and cathodal

stimulation may have migrated to the other cortical and subcortical areas (Lang et al.,

2005). For example, cathodal stimulation of the right supraorbital area could have spread

to close cortical regions such as the right dorsolateral prefrontal cortex, and possibly


34

decreased cortical activity in this region. The right dorsolateral prefrontal cortex is

involved in mood, emotion regulation and modulating fatigue, and accordingly is part of

a system that regulates exercise tolerance and termination (Anguis et al., 2015). Possibly,

an increase in cortical activity of M1 or left DLPFC from A-tDCS, which the literature

has shown to reduce RPE and enhance athletic performance (Anguis et al., 2017; Lattari

et al., 2016), could have been negatively counteracted by the placement of the cathode in

this present study, decreasing excitability of the right dorsolateral prefrontal cortex.

However, this study did not measure cortical excitability, and this is all speculative.

Practical Recommendations and Future Directions

Findings of this study have increased our understanding of the potential effect of

tDCS to enhance cycling performance; based on the results of this study, do not support

the use of tDCS in enhancing cycling performance (Business Insider, 2017). Furthermore,

it is plausible to generalise the findings of this study to other athletic populations;

however, this needs further testing. As acknowledged above, different types of exercises

and tDCS protocols must be considered. Future research should explore whether tDCS

applied to M1 and DLPFC for different durations and at different intensities affect cycling

TT performance. Additionally, future research should consider other cortical areas

possibly related to perceived exertion, and whether modulation of excitability in these

areas with tDCS can affect athletic performance and RPE.

Although the results of this study suggest that A-tDCS does not enhance athletic

performance, there are limitations within this study that could affect the generalisability

of these results. Firstly, tDCS mechanism is not entirely understood, and it is believed

that tDCS functions by eliciting synaptic changes in the cortical area stimulated.

However, this study lacked neuroimaging methods to assess cortical excitability, and it is

unknown if tDCS intervention played a role in offsetting fatigue at a neuronal level.


35

Additionally, this study speculated that tDCS could have spread to other areas of the

brain, which may have counteracted effects on perceived exertion. The data obtained from

this study cannot confirm whether this occurred, but the effects of tDCS onto other areas

of the brain cannot be discounted. Future research should incorporate techniques such as

neuroimaging techniques or transcranial magnetic stimulation, to determine if tDCS

provided sufficient stimulation to the cortical areas targeted, or if tDCS migrated to other

cortical areas. Secondly, it should be acknowledged that when tDCS is applied for longer

than 10 minutes, changes in excitability occur up to an hour (Nitsche & Paulus, 2001). In

this study, tDCS was applied prior exercise procedures and not during. If tDCS did

modulate cortical excitability, it is possible that changes in cortical activity would not

have lasted throughout the cycling 16.1km TT, since participants also had to complete a

10-minute warm-up prior cycling TT. Future studies should consider applying tDCS

during the exercise protocol, rather than prior to the exercise protocol.

Lastly, it should be recognised that in examining athletic performance, we applied

A-tDCS to M1 and DLPFC compared to an active control site, V1. When the goal is to

demonstrate that stimulation of a cortical area induces a particular effect, an active control

site is used; this is, stimulation over an area irrelevant for the task under study (Woods et

al., 2015). However, previous research that examined athletic performance often used A-

tDCS to M1 or DLPFC compared to sham. During sham, tDCS currents are passed

through electrodes for a brief period, such as 30 seconds. This type of stimulation does

not appear to alter the brains function and acts as a control condition (Nitsche et al., 2008).

A sham condition provides a baseline to compare the effects from applying A-tDCS to

M1 and DLPFC (Nitsche et al., 2008). Thus, future research should use a sham condition

to examine the efficacy of tDCS, as well as an active control site.

Conclusion


36

This current study is the first to examine two cortical areas associated with

perceived exertion, namely M1 and DLPFC. The results demonstrate that A-tDCS applied

to M1 and DLPFC does not affect performance, RPE, or HR compared to the control

stimulation site, V1. These results make some contribution to our understanding of the

cortical areas that contribute to perceived exertion. Future studies should investigate

different tDCS length and stimulation intensity on M1 or DLPFC, consider using a sham

condition and a control stimulation site, incorporate neuroimaging techniques, measure

cortical excitability, and examine other cortical areas related with RPE. The results from

the current study have real-world applications for athlete populations, as technology

companies are already marketing headgear that incorporates tDCS to cyclist and other

athletes (Business Insider, 2017). The results of the current study do not provide any

evidence for the use of such devices for athletes. Future research is necessary before tDCS

use becomes widespread in athletic populations.


37

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49

Appendix A

Ethics Approval Letter


50


51

Appendix B

Information Letter


52


53


54

Appendix C

Consent Form


55

Appendix D

tDCS-screening questionnaire


56

Appendix E

Par-Q+ questionnaire


57


58

Appendix F

Graded Exercise (GXT) Test: Headgear and Metabolic Cart


59

Appendix G

Beam F3 software


60

Appendix H

tDCS sensation questionnaire


61


62

Appendix I

Sleep Questionnaire


63

Appendix J

Project Summary

Researchers: Justin Francois Andre (Honours Student), Dr. Hakuei Fujiyama

(Supervisor); Dr. Ann-Maree Vallence (Co-supervisor); Dr Jeremiah Peiffer (Co-

supervisor)

Thesis Title: Can we Enhance Athletic Performance using Non-Invasive Brain

Stimulation?

Ethics Code: 2017/021

Month and Year: October 2017

Description: The world of sport acknowledges that athletes are constantly trying to reach

optimal performance. Athletes frequently attest to the idea that perfect performances

exist in sport, whether it is the perfect hit, jump, or run (Koivula, Hassme, & Fallby,

2002). Thus, finding ways to increase athletic performance is vital to athletes. One factor

known to influence an athlete’s performance is their perception of how hard an exercise

task is, known as their rating of perceived exertion (RPE), which is modulated by fatigue

(Abbiss, Peiffer, Meeusen, & Skorski, 2015). There is evidence to suggest that when

fatigue occurs, there is reduced output from the Primary Motor Cortex (M1) and the

Dorsolateral Prefrontal Cortex (DLPFC) to the muscles. Accordingly, the development

of fatigue contributes to an increase in an athlete’s RPE. Therefore, using anodal

transcranial direct current stimulation (A-tDCS), a non-invasive stimulation technique

that increases cortical excitability, could prolong the development of fatigue and reduce

RPE. Therefore, this honours thesis looked at enhancing athletic performance using non

– invasive brain stimulation, i.e, A-tDCS.


64

Methods: In an attempt to enhance athletic performance, this study recruited 10 volunteer

athletic cyclists. Each cyclist completed four sessions. Session one was a Graded Exercise

Test, and values obtained from the graded exercise test were provided to the cyclists as

compensation for them volunteering. Sessions 2 – 4 were the tDCS Time Trials (TT)

completed in a heat chamber. Transcranial Direct Current Stimulation was applied at

1.5mA for 20 minutes to the M1, DLPFC, or Visual Cortex (V1) per session; where the

VI acted as a control condition. After stimulation, cyclists completed a 10-minute warm-

up on the Velotron, i.e., the first 5-minutes was at 30%PPO and the next 5-minutes was

at 50%PPO. After the warm up, cyclists completed a 16.1km cycling TT. During the

warm up, HR and RPE was recorded. Additionally, during the TT, mean HR and PO, and

RPE were recorded every 4km; mean PO and time to complete the TT was also recorded

for analysis.

Result/Conclusions: The aim of this study was to determine if A-tDCS to M1 or DLPFC

affected athletic performance, RPE, or HR, applied before a set-intensity warm up and a

16.lkm cycling TT. The current study has two main results. First, the effect of A-tDCS

applied to M1 or DLPFC on performance was not different to A-tDCS applied to V1

(control stimulation). Second, the effect of A-tDCS applied to M1 or DLPFC on RPE and

HR was not different to A-tDCS applied to V1 (control stimulation). These results suggest

A-tDCS to M1 or DLPFC was not able to enhance athletic performance, nor reduce

perceptual and physiological variables (that is, RPE and HR).


65

Appendix K

SPSS Output

Heart Rate: 30% and 50% warm up outputs

Within-Subjects Factors Measure: MEASURE_1

Conditions

Dependent

Variable

1 DLPCEND50HR

2 MCEND50HR

3 VCEND50HR

Within-Subjects Factors Measure: MEASURE_1 Conditions Dependent Variable

1 DLPCEND30HR

2 MCMID30HR

3 VCEND30HR

Tests of Within-Subjects Effects Measure: 30% warm up (HR)

Source

Type III Sum of

Squares df Mean Square F Sig.

Partial Eta

Squared

Conditio

ns

Sphericity Assumed 194.600 2 97.300 2.358 .123 .208

Greenhouse-Geisser 194.600 1.116 174.298 2.358 .155 .208

Huynh-Feldt 194.600 1.163 167.394 2.358 .153 .208

Lower-bound 194.600 1.000 194.600 2.358 .159 .208

Error(C

ondition

s)

Sphericity Assumed 742.733 18 41.263 Greenhouse-Geisser 742.733 10.048 73.916 Huynh-Feldt 742.733 10.463 70.989 Lower-bound 742.733 9.000 82.526

Tests of Within-Subjects Effects Measure: 50% warm up (HR)

Source

Type III Sum of

Squares df Mean Square F Sig.

Partial Eta

Squared

Conditions Sphericity Assumed 36.867 2 18.433 1.010 .384 .101


Huynh-Feldt 36.867 2.000 18.433 1.010 .384 .101

Lower-bound 36.867 1.000 36.867 1.010 .341 .101

Error(Conditions) Sphericity Assumed 328.467 18 18.248 Greenhouse-Geisser 328.467 16.039 20.479 Huynh-Feldt 328.467 18.000 18.248 Lower-bound 328.467 9.000 36.496


66

RPE: 30% and 50% warm up outputs

16.km TT: HR with 4km split

Ranks Mean Rank

DLPCEND30RPE 2.00

MCEND30RPE 2.00

VCEND30RPE 2.00

Test Statisticsa N 10

Chi-Square .000

df 2

Asymp. Sig. 1.000

a. Friedman Test Test Statisticsa N 10

Chi-Square .857

df 2

Asymp. Sig. .651

a. Friedman Test

Ranks Mean Rank

DLPCEND50RPE 1.85

MCEND50RPE 2.15

VCEND50RPE 2.00

Tests of Within-Subjects Effects Measure: MEASURE_1

Source

Type III Sum

of Squares df Mean Square F Sig.

Partial Eta

Squared

Conditions Sphericity Assumed 3.050 2 1.525 .035 .965 .004

Greenhouse-Geisser 3.050 1.790 1.704 .035 .954 .004

Huynh-Feldt 3.050 2.000 1.525 .035 .965 .004

Lower-bound 3.050 1.000 3.050 .035 .855 .004

Error(Conditions) Sphericity Assumed 778.450 18 43.247 Greenhouse-Geisser 778.450 16.113 48.311 Huynh-Feldt 778.450 18.000 43.247 Lower-bound 778.450 9.000 86.494

Tiime Sphericity Assumed 3148.425 3 1049.475 53.314 .000 .856


Huynh-Feldt 3148.425 1.452 2168.023 53.314 .000 .856

Lower-bound 3148.425 1.000 3148.425 53.314 .000 .856

Error(Tiime) Sphericity Assumed 531.492 27 19.685 Greenhouse-Geisser 531.492 11.843 44.878 Huynh-Feldt 531.492 13.070 40.665 Lower-bound 531.492 9.000 59.055


67

Estimates Measure: Time

Tiime Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

1 161.700 2.710 155.570 167.830

2 169.500 2.594 163.632 175.368

3 172.400 2.240 167.332 177.468

4 175.500 2.248 170.414 180.586


68

16.1km TT: RPE with 4km splits

Conditions * Tiime Sphericity

Assumed

24.750 6 4.125 1.154 .345 .1

Greenhouse-

Geisser

24.750 2.891 8.562 1.154 .345 .1

Huynh-Feldt 24.750 4.404 5.619 1.154 .347 .1

Lower-bound 24.750 1.000 24.750 1.154 .311 .1

Error(Conditions*Ti

ime)

Sphericity

Assumed

193.083 54 3.576

Greenhouse-

Geisser

193.083 26.017 7.421

Huynh-Feldt 193.083 39.640 4.871 Lower-bound 193.083 9.000 21.454

Pairwise Comparisons Measure: Time

(I) Tiime (J) Tiime

Mean Difference

(I-J) Std. Error Sig.b

95% Confidence Interval for

Differenceb


1 2 -7.800* .783 .000 -9.571 -6.029

3 -10.700* 1.238 .000 -13.500 -7.900

4 -13.800* 1.660 .000 -17.555 -10.045

2 1 7.800* .783 .000 6.029 9.571

3 -2.900* .928 .012 -5.000 -.800

4 -6.000* 1.365 .002 -9.089 -2.911

3 1 10.700* 1.238 .000 7.900 13.500

2 2.900* .928 .012 .800 5.000

4 -3.100* .497 .000 -4.224 -1.976

4 1 13.800* 1.660 .000 10.045 17.555

2 6.000* 1.365 .002 2.911 9.089

3 3.100* .497 .000 1.976 4.224

Based on estimated marginal means

*. The mean difference is significant at the .05 level.

b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).


69

Within-Subjects Factors Measure: MEASURE_1

Conditions Tiime

Dependent

Variable

1 1 DLPC4KMRPE

2 DLPC8KMRPE

3 DLPC12KMRPE

4 DLPC16KMRPE

2 1 MC4KMRPE

2 MC8KMRPE

3 MC12KMRPE

4 MC16KMRPE

3 1 VC4KMRPE

2 VC8KMRPE

3 VC12KMRPE

4 VC16KMRPE

Descriptive Statistics Mean Std. Deviation N

DLPC4KMRPE 6.00 1.247 10

DLPC8KMRPE 6.950 1.4991 10

DLPC12KMRPE 7.50 1.354 10

DLPC16KMRPE 8.60 1.713 10

MC4KMRPE 6.10 1.524 10

MC8KMRPE 7.10 .994 10

MC12KMRPE 7.80 1.398 10

MC16KMRPE 8.90 1.595 10

VC4KMRPE 5.90 1.524 10

VC8KMRPE 6.70 1.567 10

VC12KMRPE 7.60 1.350 10

VC16KMRPE 9.00 1.414 10


Source

Type III Sum

of Squares df

Mean

Square F Sig.

Partial Eta

Squared

Conditions Sphericity Assumed 1.029 2 .515 .604 .558 .063

Greenhouse-

Geisser

1.029 1.811 .568 .604 .543 .063

Huynh-Feldt 1.029 2.000 .515 .604 .558 .063

Lower-bound 1.029 1.000 1.029 .604 .457 .063

Error(Conditions) Sphericity Assumed 15.346 18 .853 Greenhouse-

Geisser

15.346 16.299 .942

Huynh-Feldt 15.346 18.000 .853 Lower-bound 15.346 9.000 1.705

Tiime Sphericity Assumed 128.723 3 42.908 65.553 .000 .879

Greenhouse-

Geisser

128.723 1.684 76.448 65.553 .000 .879

Huynh-Feldt 128.723 2.028 63.470 65.553 .000 .879

Lower-bound 128.723 1.000 128.723 65.553 .000 .879

Error(Tiime) Sphericity Assumed 17.673 27 .655 Greenhouse-

Geisser

17.673 15.154 1.166

Huynh-Feldt 17.673 18.253 .968 Lower-bound 17.673 9.000 1.964


70

Pairwise Comparisons Measure: Time

(I) Tiime (J) Tiime

Mean Difference



Differenceb


1 2 -.917* .216 .002 -1.404 -.429

3 -1.633* .225 .000 -2.142 -1.125

4 -2.833* .307 .000 -3.529 -2.138

2 1 .917* .216 .002 .429 1.404

3 -.717* .086 .000 -.912 -.522

4 -1.917* .188 .000 -2.342 -1.491

3 1 1.633* .225 .000 1.125 2.142

2 .717* .086 .000 .522 .912

4 -1.200* .166 .000 -1.576 -.824

4 1 2.833* .307 .000 2.138 3.529

2 1.917* .188 .000 1.491 2.342

3 1.200* .166 .000 .824 1.576




Average Power output for 16.1km TT

Estimates

Measure: Time

Tiime Mean Std. Error



1 6.000 .398 5.101 6.899

2 6.917 .395 6.022 7.811

3 7.633 .399 6.731 8.536

4 8.833 .487 7.731 9.936

Tests of Within-Subjects Effects Measure: Average PO


71

Overall time for 16.1km TT

Tests of Within-Subjects Effects Measure: Time Taken

Source

Type III Sum

of Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Conditions Sphericity

Assumed

1180.867 2 590.433 3.107 .069 .257

Greenhouse-

Geisser

1180.867 1.428 826.979 3.107 .091 .257

Huynh-Feldt 1180.867 1.622 728.186 3.107 .083 .257

Lower-bound 1180.867 1.000 1180.867 3.107 .112 .257

Source

Type III Sum

of Squares df

Mean

Square F Sig.

Partial Eta

Squared


Assumed

214.467 2 107.233 2.892 .081 .243

Greenhouse-

Geisser

214.467 1.766 121.417 2.892 .090 .243

Huynh-Feldt 214.467 2.000 107.233 2.892 .081 .243

Lower-bound 214.467 1.000 214.467 2.892 .123 .243

Error(Condition

s)

Sphericity

Assumed

667.533 18 37.085

Greenhouse-

Geisser

667.533 15.897 41.990



DLPC_PO_OVERALL 280.20 39.137 10

MC_PO_OVERALL 273.90 42.378 10

VC_PO_OVERALL 278.60 43.556 10


DLPC_TIMETAKENSECS 1428.40 70.973 10

MC_TIMETAKENSECS 1443.70 80.990 10

VC_TIMETAKENSECS 1434.80 79.582 10


72

Error(Condition

s)

Sphericity

Assumed

3420.467 18 190.026

Greenhouse-

Geisser

3420.467 12.851 266.156


Power output in 4km splits for 16.1km TT


Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial Eta

Squared


Assumed

473.117 2 236.558 1.498 .250 .143

Greenhouse-

Geisser

473.117 1.625 291.165 1.498 .253 .143

Huynh-Feldt 473.117 1.932 244.877 1.498 .251 .143

Lower-bound 473.117 1.000 473.117 1.498 .252 .143

Error(Conditions) Sphericity

Assumed

2842.550 18 157.919

Greenhouse-

Geisser

2842.550 14.624 194.373



DLPC4KMPO 295.70 34.535 10

DLPC8KMPO 275.50 37.447 10

DLPC12KMPO 268.10 34.018 10

DLPC16KMPO 270.80 39.132 10

MC4KMPO 286.80 40.995 10

MC8KMPO 271.50 43.922 10

MC12KMPO 264.10 38.697 10

MC16KMPO 272.00 39.688 10

VC4KMPO 295.30 44.585 10

VC8KMPO 274.10 41.165 10

VC12KMPO 267.30 40.144 10

VC16KMPO 275.50 38.771 10


73

Time Sphericity

Assumed

11428.225 3 3809.408 23.116 .000 .720

Greenhouse-

Geisser

11428.225 1.480 7721.846 23.116 .000 .720

Huynh-Feldt 11428.225 1.702 6714.167 23.116 .000 .720

Lower-bound 11428.225 1.000 11428.225 23.116 .001 .720

Error(Time) Sphericity

Assumed

4449.525 27 164.797

Greenhouse-

Geisser

4449.525 13.320 334.052


Conditions * Time Sphericity

Assumed

323.550 6 53.925 .973 .452 .098

Greenhouse-

Geisser

323.550 2.660 121.626 .973 .413 .098

Huynh-Feldt 323.550 3.881 83.377 .973 .433 .098

Lower-bound 323.550 1.000 323.550 .973 .350 .098

Error(Conditions*T

ime)

Sphericity

Assumed

2991.450 54 55.397

Greenhouse-

Geisser

2991.450 23.942 124.946


Estimates Measure: Power (across 4km split)

Time Mean Std. Error



1 (4km) 292.600 12.398 264.553 320.647

2 (8km) 273.700 12.784 244.780 302.620

3 (12km) 266.500 11.781 239.849 293.151

4 (16km) 272.767 12.160 245.258 300.275

Pairwise Comparisons

Measure: Power across different times

(I) Time (J) Time

Mean Difference



Differenceb



74

1 2 18.900* 3.712 .001 10.503 27.297

3 26.100* 4.396 .000 16.156 36.044

4 19.833* 4.638 .002 9.342 30.324

2 1 -18.900* 3.712 .001 -27.297 -10.503

3 7.200* 1.888 .004 2.928 11.472

4 .933 2.282 .692 -4.229 6.095

3 1 -26.100* 4.396 .000 -36.044 -16.156

2 -7.200* 1.888 .004 -11.472 -2.928

4 -6.267* 1.593 .003 -9.870 -2.663

4 1 -19.833* 4.638 .002 -30.324 -9.342

2 -.933 2.282 .692 -6.095 4.229

3 6.267* 1.593 .003 2.663 9.870




tDCS/ Sleep Questionnaires

Tests of Within-Subjects Effects Measure: tDCS sensations

Source

Type III Sum

of Squares df

Mean

Square F Sig.

Partial Eta

Squared

Conditions (V1,

M1, DLFPC)

Sphericity Assumed .067 2 .033 1.000 .387 .100

Greenhouse-Geisser .067 1.000 .067 1.000 .343 .100

Huynh-Feldt .067 1.000 .067 1.000 .343 .100

Lower-bound .067 1.000 .067 1.000 .343 .100

Error(Condition

s)

Sphericity Assumed .600 18 .033 Greenhouse-Geisser .600 9.000 .067 Huynh-Feldt .600 9.000 .067 Lower-bound .600 9.000 .067


75

Tests of Within-Subjects Effects Measure: Sleep quality

Source

Type III Sum

of Squares df Mean Square F Sig.

Partial Eta

Squared

Conditions (V1,

M1, DLFPC)

Sphericity Assumed .617 2 .308 .675 .521 .070

Greenhouse-Geisser .617 1.603 .385 .675 .493 .070

Huynh-Feldt .617 1.897 .325 .675 .514 .070

Lower-bound .617 1.000 .617 .675 .432 .070

Error(Conditions

)

Sphericity Assumed 8.217 18 .456 Greenhouse-Geisser 8.217 14.428 .569 Huynh-Feldt 8.217 17.072 .481 Lower-bound 8.217 9.000 .913

Tests of Within-Subjects Effects

Measure: Sleep hours

Source

Type III Sum

of Squares df

Mean

Square F Sig.

Partial Eta

Squared

Conditions (V1,

M1, DLFPC)

Sphericity Assumed .713 2 .356 .911 .420 .092

Greenhouse-Geisser .713 1.426 .500 .911 .394 .092

Huynh-Feldt .713 1.619 .440 .911 .404 .092

Lower-bound .713 1.000 .713 .911 .365 .092

Error(Condition

s)

Sphericity Assumed 7.038 18 .391 Greenhouse-Geisser 7.038 12.836 .548 Huynh-Feldt 7.038 14.572 .483 Lower-bound 7.038 9.000 .782


76

Appendix L

Assumption of Homogeneity of Variance, Fmax

Note. Heart Rate (HR); Rating of Perceived Exertion (RPE); Power Output (PO); Time Trial (TT)

Independent Variable

Fmax

HR at 30% 3.36589 HR at 50% 1.053124 HR over TT/4km split

2.252412

RPE over TT/4km split

2.935872

PO over TT/4km split

1.717755

PO overall for TT 1.238572 Time to complete TT

1.310139

this thesis is presented in partial fulfilment of the requirements … · 2018. 4. 11. · justin...

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