Optimizing Cycle Exercise Performance During Normobaric ...

UNLV Theses, Dissertations, Professional Papers, and Capstones

5-1-2019

Optimizing Cycle Exercise Performance During Normobaric Optimizing Cycle Exercise Performance During Normobaric

Hypoxia Exposure Hypoxia Exposure

Cierra Brittany Ugale

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OPTIMIZING CYCLE EXERCISE PERFORMANCE DURING NORMOBARIC HYPOXIA

EXPOSURE

By

Cierra Brittany Ugale

Bachelor of Science in Community Health Sciences

University of Nevada, Reno

2013

Bachelor of Science in Kinesiology

University of Nevada, Las Vegas

2016

A thesis submitted in partial fulfillment

of the requirements for the

Master of Science – Exercise Physiology

Department of Kinesiology and Nutrition Sciences

School of Allied Health Sciences

Division of Health Sciences

The Graduate College

University of Nevada, Las Vegas

May 2019

Copyright © 2019 by Cierra Brittany Ugale

All Rights Reserved

ii    

Thesis Approval

The Graduate College The University of Nevada, Las Vegas

April 15, 2019

This thesis prepared by

Cierra Brittany Ugale

entitled

Optimizing Cycle Exercise Performance During Normobaric Hypoxia Exposure

is approved in partial fulfillment of the requirements for the degree of

Master of Science – Exercise Physiology Department of Kinesiology and Nutrition Sciences

James Navalta, Ph.D. Kathryn Hausbeck Korgan, Ph.D. Examination Committee Chair Graduate College Dean Gabriele Wulf, Ph.D. Examination Committee Member Will Jonen, Ph.D. Examination Committee Member Szu-Ping Lee, Ph.D. Graduate College Faculty Representative

iii

ABSTRACT

Optimizing Cycle Exercise Performance During Normobaric Hypoxia Exposure

By: Cierra Brittany Ugale

James Navalta, Examination Committee Chair

Associate Professor of Kinesiology

University of Nevada, Las Vegas

Introduction: The purpose of the present study was to examine whether implementing factors of

OPTIMAL Theory: Enhanced Expectancies (EE), Autonomy Support (AS), and External Focus

(EF) during a cycle exercise bout at a simulated altitude of 21,001 feet elevation had an effect on

exercise performance and EPOC response in comparison to a control condition.

Methods: Sixteen participants (n = 8 women, n = 8 men) completed resting oxygen

measurements (resting metabolic rate) between 6:00 A.M. and 8:00 A.M. Cycle exercise to

fatigue at a constant workload was performed (100 W) while breathing air with reduced oxygen

content to simulate exercising at altitude (9.4% fraction of oxygen, equivalent of 6401 m above

sea level). All participants performed under two conditions, an optimized and a control

condition. The order of conditions were counterbalanced. Following cycle to fatigue protocol,

participants were reconnected to the metabolic analysis system and instructed to sit quietly until

they returned to their baseline oxygen values (EPOC duration). EPOC magnitude was

determined by adding up the net oxygen consumption for every minute during the EPOC

duration. Data analysis consisted of paired t-tests.

Results: In summary, the results of this study reveal that cycle exercise performance between

both conditions was significant, p = .03. Performance outcome measures included duration of

iv

cycle exercise to fatigue and mean watts (W). Participants were able to cycle longer in the

optimized condition relative to the control, with exercise carried out at the same absolute

workload. EPOC duration and magnitude in participants (N = 16) who performed cycling

exercise at 100 W under simulated altitude of 6401 m (21,001 ft) to fatigue, resulted in no

statistically significant difference between the following optimized and control conditions.

Therefore, despite longer cycle exercise duration in the optimized condition, EPOC duration and

magnitude in both conditions was not significantly different.

Discussion: The present findings adds to evidence that key variables in the OPTIMAL theory

influence energy expenditure, enhance movement efficiency, and reduce oxygen consumption.

To the best of our knowledge, this is the first study to investigate aerobic exercise performance

and EPOC response where all three variables in OPTIMAL theory are applied consecutively

during exercise. Thus, further investigation is necessary to examine the physiological parameters

of other exercise intensities to asses if similar results are produced.

Keywords: OPTIMAL theory, motivation, exercise performance, simulated altitude, oxygen

consumption

v

ACKNOWLEDGEMENTS

First and foremost, I would like to express my deepest gratitude to my advisor, Dr. James

Navalta, for his continued patience and encouragement throughout my graduate work. Words

cannot describe my appreciation for his guidance and support as he has shared incredible advice

and given me space to develop ideas on my own. He always took an interest, not only in my

academics, but as an individual as well, and I am truly fortunate to have him as a mentor.

I am grateful to Dr. Gabriele Wulf, Dr. Szu-Ping Lee, and Dr. Will Jonen for serving on

my thesis committee. I appreciate their time and valuable feedback from the beginning of this

study development to the completion of my thesis work.

I would also like to acknowledge my fellow graduate students. I am lucky to have such a

supportive and uplifting group of people on my side, which has made this entire process all the

more worthwhile. In addition, I’d also like to thank my research assistants, Nate Gentry and

Cody Kwong, for volunteering their time and committing to the early mornings throughout the

weeks of data collection.

Finally, I could not have finished the work for this degree without the relentless support

of my family and friends. My parents have always encouraged me to pursue my dreams, and

they’ve never stopped believing in me through the years.

vi

TABLE OF CONTENTS

ABSTRACT…………………………………………...………………………………………...iii

ACKNOWLEDGEMENTS…….………………………………………………………….…....v

LIST OF FIGURES……………………………………………………………………………viii

CHAPTER 1 INTRODUCTION………….……………………………….……………..……..1

CHAPTER 2 LITERATURE REVIEW…...………….…………………………...……...…....7

Enhanced Expectancies………………………………………………….…….…….......7

Autonomy…………………………………………………………………………...........9

External Focus…………………..………………………………………….…………...11

OPTIMAL theory of motor learning………………………………………………….13

Excess post-exercise oxygen consumption (EPOC)…………………………………..15

Simulated Altitude……………………………..…….…………………………………17

CHAPTER 3 METHODOLOGY……………………………..………………………..……...20

Participants…………………………………………………………….……….….……20

Equipment……………………………………………………………………….……...21

Procedures………………………………………………….………………….………..21

Statistical Analysis………………………..……………………………………….……24

CHAPTER 4 RESULTS…………..………………………………………...………………….25

CHAPTER 5 DISCUSSION…………....……………………………………………………...29

APPENDIX A IRB Approval……………..………………………………….……………….. 33

APPENDIX B Informed Consent……………………………………….…..…………………34

APPENDIX C ACSM Health Risk Questionnaire……………….………...…….…………...37

REFERENCES…………………………………………………………………….……...…….38

vii

CURRICULUM VITAE…………….……………………………….…………….…………...44

viii

LIST OF FIGURES

Figure 1. Schematic of the OPTIMAL theory………………………………………………….....5

Figure 2. Performance outcomes in the optimized and control conditions……………………....26

Figure 3. Excess post-exercise oxygen consumption duration…………………………………..26

Figure 4. Excess post-exercise oxygen consumption magnitude…………….…………………..27

Figure 5. Exercise performance comparison between visits……………………………………..27

Figure 6. RMR comparison between visits………………………………………………………28

1

CHAPTER 1

INTRODUCTION

It is first imperative that we define the three variables involved in the OPTIMAL theory

of motor learning. The OPTIMAL theory of motor learning (Optimizing Performance Through

Intrinsic Motivation and Attention for Learning [OPTIMAL]) (Wulf & Lewthwaite, 2016),

identifies three factors that contribute to enhancing learning and performance. These factors are:

settings that promote enhanced expectancies (EE) for performance, autonomy support (AS), and

an external focus (EF) of attention (Wulf & Lewthwaite, 2016). These variables facilitate the

efficiency of translating intended movement goals into action (termed goal-action coupling)

(Wulf & Lewthwaite, 2016). Immediately after exercise, there is a recovery period which results

in an increase in oxygen consumption that is referred to as excess post-exercise oxygen

consumption (EPOC) (Gaesser et al., 1984). To the best of our knowledge, no literature exists

that examines exercise performance and the physiological responses of excess post-exercise

oxygen consumption (EPOC) where all three variables of the OPTIMAL theory are implemented

consecutively during exercise.

Studies demonstrating the concept of EE include research by Rosenqvist and Skans

(2015), which described the notion that when learners’ expectancies of their performance are

positive, this can increase their likelihood of more success, which results in improved

performance and learning. For example, this concept of expectancies is associated with the

beliefs of positive outcome expectations that include placebos and extrinsic rewards (Bandura,

1977; Wager & Atlas, 2015; Fiorillo, Newsome, & Schultz, 2008). There have been numerous

studies that have determined the effectiveness of enhancing leaners’ expectancies (for reviews,

see Wulf, 2007b; Wulf & Lewthwaite, 2016). These investigations specifically look into the

2

effects of the type of feedback, which in this case is positive feedback. Lines of research

regarding positive feedback include self-modeling and social-comparative feedback. Self-

modeling is referred to as edited video feedback about a learners’ best performance. Since

confidence has been identified to have a direct effect on performance, the term self-efficacy is

used when an individual’s belief regarding his or her ability to produce a desired result (Moritz,

Feltz, Fahrbach, & Mack, 2000; Rosenqvist & Skans, 2015). Studies have also shown that by

providing (false) positive feedback to participants, suggesting that they did better than average

compared to when negative feedback or even no feedback (control conditions), improved

performance and learning of the group that received positive feedback following practice trials

(Chiviacowsky & Wulf, 2007; Chiviacowsky, Wulf, Wally, & Borges, 2009; Saemi, Porter,

Ghotbi-Varzaneh, Zarghami, & Maleki, 2012; Saemi, Wulf, Varzaneh, & Zarghami, 2011).

Second, AS refers to allowing individuals to have a sense of control over their practice

conditions or environment. This is particularly important because it allows individuals to have a

sense of autonomy over their actions, which is associated with motivation and performance

(Cordova & Lepper, 1996; Deci & Ryan, 2008; Tafarodi, Milne, & Smith, 1999). Situations that

present an opportunity for choice affect human motivation because it allows the learner to

exercise control over their environment. In addition, literature with respect to motor learning and

control has demonstrated that by giving learners control over certain aspects of the practice

conditions, providing incidental choices, and the type of instructional language, has been found

to influence motor learning. For example, learning was improved when individuals were given

control over when feedback was administered with numerous movement tasks, including

throwing tasks (Janelle, Barba, Frehlich, Tennant, & Cauraugh, 1997) or the accuracy of the

throws (Chiviacowsky, Wulf, Laroque de Medeiros, Kaefer, & Tani, 2008). Autonomy-

3

supportive language has also demonstrated to influence motor learning. For instance, instructions

that gave a learner a sense of choice rather than instructions that were worded in a controlling

way led to superior learning (Hooyman, Wulf, & Lewthwaite, 2014). Studies have also

demonstrated that by providing learners with choices that are unrelated to an actual performance

task revealed a positive effect on learning (Lewthwaite, Chiviacowsky, & Wulf, 2015).

Therefore, by allowing participants to choose the order in which they perform their exercises

may have an additional benefit on learning and performance, as they could be more willing to

perform the exercises for longer periods of time (Wulf, Freitas, & Tandy 2014). Consistently,

research studies have demonstrated learning enhancements when an individuals need for

autonomy is supported. This suggests that allowing a learner to have an opportunity to be

involved and collaborate in the learning process facilitates effective retention.

Last, another important contributor to motor learning is a persons’ attentional focus. This

factor involves the focus of attention that learners should target to enhance performance and

learning. There have been numerous studies revealing that by utilizing an EF of attention on an

intended movement effect, rather than an internal focus of one’s body parts or movements

enhances performance and learning (for reviews, see Lewthwaite & Wulf, 2017; Wulf &

Lewthwaite, 2016; Wulf, 2013). When performers reduce their focus on themselves and rather

direct their attention to the task goal, this results in a more efficient performance by facilitating

fluidity in movement outcomes. For instance, studies done by Chiviacowsky, Wulf, and Wally,

(2010), Wulf and Dufek (2009), Jackson and Holmes (2011), demonstrated enhanced

performance through an external focus involving maximum force production and balance. In

addition, less effort is required when enhanced outcomes are achieved, as findings determined a

reduction in oxygen consumption, heart rate, and muscular activity (Stoate, Wulf & Lewthwaite,

4

2012; Iwatsuki, Wulf, & Navalta, 2018; Montes, Wulf, & Navalta, 2018). Wulf (2007a, 2013),

suggesting that, “The necessity of focusing on the intended movement effect to achieve

successful outcomes reflects a fundamental movement principle.” Therefore, reliable evidence

confirms that an external focus of attention produces enhanced performance benefits and

learning to various tasks, regardless of one’s skill level, age, or individual differences (Wulf

2007a, 2013).

Clearly there is a large body of research to support the impacts involved with

implementing positive motivational and attentional focus factors on the outcomes of motor

performance and learning. For example, a schematic of the OPTIMAL theory illustrates that

through goal-action coupling, intended movement goals are translated into actions. This in turn

reinforces performance and learning through the effects of motivation and attention (see Figure

1) (Wulf & Lewthwaite, 2016). The factors of OPTIMAL theory influence energy expenditure in

a number of ways. For instance, Montes and colleagues (2018) found that providing social-

comparative feedback improved running performance in trained runners. Social-comparative

feedback is a useful approach for evaluating one’s own competence. In the study by Montes et al.

(2018), participants in the enhanced expectancy group revealed enhanced maximal oxygen

consumption compared to the control group. In another recent study, Iwatsuki, Wulf, and

Navalta (2018), provided participants with an opportunity to choose images to view during their

submaximal run. Their findings revealed that enhanced movement efficiency was achieved in

regard to decreased oxygen consumption in comparison to the control group who had no choice.

Additionally, greater movement speed, endurance, and movement efficiency were reflected with

an external focus. For example, faster sprint times and quicker running pace for an agility task

were observed when individuals focused their attention externally, rather than internally (Porter

5

et al., 2010; Ile et al., 2013). As discussed above, several studies have supported the variables in

the OPTIMAL theory and its influence on energy expenditure. Therefore, it is valuable to further

investigate excess post-exercise oxygen consumption (EPOC) when all three variables are

applied.

Figure 1. Schematic of the OPTIMAL theory

There is an elevated oxygen cost associated with exercise, and continued measurement

after an exercise bout is termed EPOC (Gaesser et al., 1984). EPOC is thought to represent a

recovery measure and is reported in both duration (the length of time required to return to resting

values) and magnitude (the accumulated oxygen cost while returning to baseline). EPOC can be

affected by a number of factors, including the intensity of activity (Knuttgen, 1962 & 1970;

Sedlock et al., 1989), exercise duration (Knuttgen, 1970), and modality (LaForgia et al., 2006;

Lyons et al., 2007). If exercise intensity is held constant, the EPOC response increases linearly as

exercise duration is lengthened (Knuttgen, 1962; LaForgia et al., 2006). Recent research has also

6

shown that both the EPOC duration and magnitude increase as simulated altitude increases

(Navalta, Tanner, & Bodell, 2018). Since there are several lines of evidence that support the

OPTIMAL variables and their influence on oxygen consumption, we believe this will also have a

direct influence on EPOC response in regard to oxygen cost associated with exercise. Therefore,

since findings reveal that OPTIMAL variables enhance movement efficiency and reduced

oxygen consumption, we believe that EE, AS, and EF will influence EPOC response, energy

expenditure, and duration of exercise carried out at the same absolute workload. To our

knowledge, no studies exist with participants having exercised under these conditions. As

oxygen availability is altered with increasing levels of elevation, we speculate that the EPOC

response will also be modified.

STATEMENT OF PURPOSE

The purpose of the present study is to examine whether implementing EE, AS, and EF

during a cycle exercise bout at a simulated altitude of 21,001 feet elevation will have an effect on

exercise performance and EPOC response. Our primary aim is to observe if these three factors

will influence performance measures and the EPOC response relative to a control condition.

STATEMENT OF HYPOTHESES

We hypothesize that administering EE, AS, and EF will result in a longer duration of

cycle exercise (performance outcome) at a simulated altitude of 21,001 feet, relative to a control

condition. We also hypothesize that, when all three factors are applied during exercise, both the

EPOC duration and magnitude response will be modified in regard to exercise duration carried

out at the same absolute workload.

7

CHAPTER 2

LITERATURE REVIEW

Attentional and motivational factors on motor performance and learning have shown to

enhance exercise performance and movement efficiency. In the OPTIMAL theory of motor

learning, there are three factors that contribute to enhanced learning and performance. These

factors include settings that promote: enhanced expectancies (EE) for performance, support a

learners’ need for autonomy (AS), and maintain an external focus (EF) of attention (Wulf &

Lewthwaite, 2016). These three variables facilitate motor learning and performance through the

coupling of goals to intended actions. Excess post-exercise oxygen consumption is oxygen

consumption (VO2) that remains elevated above baseline values following exercise. As oxygen

remains elevated, the increased metabolism cost continues to be a factor in the thermic effect of

activity (LaForgia et al., 2006). Therefore, this literature review will give an overview of the

research with respect to the three variables associated with the OPTIMAL theory of motor

learning and their influence on energy expenditure and exercise performance. In addition, studies

involving the physiological adaptations of EPOC and exercise performance of simulated altitude

training will be reviewed.

Enhanced Expectancies

The concept of enhancing expectancies refers to positive predictions of what is to occur.

Numerous studies have demonstrated the effectiveness of enhancing learners’ performance

expectancies in practice conditions (for reviews, see Wulf, 2007b; Wulf & Lewthwaite, 2016).

These investigations include the effects regarding the motivational components of certain types

of feedback which include providing positive feedback and social-comparative feedback, as well

8

as self-modeling. Specifically, a study by Stoate et al. (2012) revealed that providing experienced

runners with favorable feedback in relation to their movement efficiency, resulted in reduced

oxygen consumption values, increasing running efficiency at 75% of their VO2max, and reduced

perceptions of effort. This study aimed to examine improvements in performance rather than

learning. In another study, Montes et al. (2018) revealed that maximum aerobic capacity was

enhanced when trained runners were provided with social-comparative feedback on their

VO2max test and led to believe that their values were above the average of their peers. Thus,

enhancing their performance expectancies increased maximal oxygen consumption, indicating a

greater physical working capacity relative to their own previous values, and relative to a control

group. Therefore, their results also indicate that a maximum aerobic capacity was not achieved

on their initial VO2max test.

Providing learners with normative feedback, such as the average performance scores of

other learners, is a potent basis for evaluating one’s own competence. This type of feedback has

been demonstrated to increase perceived competence by alleviating doubts or concerns about

one’s performance abilities (Avila et al., 2012). This type of feedback increased motivation and

satisfaction to learn and has demonstrated a positive effect (Wulf et al., 2013; Stoate et al.,

2012). In comparison, control conditions and negative feedback hinder learning due to the fact

that learners tend to focus on self-related concerns and cognitions. For instance, participants

learning a balance task reacted to negative normative feedback by getting defensive and

behaving in a self-enhancing manner to compensate for the perceived threat (Wulf, Lewthwaite,

& Hooyman, 2013).

9

Interestingly, even video feedback displaying a learner’s best performance compared to

their average performance or no video feedback resulted in heightened intrinsic motivation and

overall satisfaction with their performance (Clark & Ste-Marie, 2007). This form of feedback is

called self-modeling. Essentially, learners who watched edited videos of their best swimming

performance demonstrated enhanced learning in comparison to the control group. This study

further substantiates the consistent findings in regard to performance benefits associated with

enhanced learning and the positive motivational effects of positive feedback. For example,

instilling the idea that a learner is likely to perform optimally under pressure, can result in an

increased ability to execute task performance (McKay, Lewthwaite, & Wulf, 2012). Studies done

by Witt et al. (2012), Chauvel et al. (2015), Marchant et al. (2018), suggest that increased

confidence of an individuals’ performance capabilities enhances motor performance. Ultimately,

by enhancing the expectancies of future performance, attention is directed to the actual task goal

and reduces the self-related thoughts that may hinder performance (Wulf & Lewthwaite, 2016).

As reviewed above, findings indicate that enhanced expectancies on performance outcomes

facilitate learning.

Autonomy

As defined previously, autonomy support refers to allowing performers to have a sense of

control over their practice conditions or environment. Autonomy support is a crucial factor in the

OPTIMAL theory of motor learning (Wulf & Lewthwaite, 2016). Studies observing practice

conditions that enhance motor skill and learning include: allowing individuals to have control

over their practice conditions, instructional language, and incidental choices (for reviews, see

10

Wulf, 2007b; Wulf & Lewthwaite, 2016). These proposed variables and their effects on motor

performance and learning will be described in greater detail in upcoming review.

There have been numerous studies demonstrating learning enhancements when

individuals have the freedom to control aspects of their practice conditions (for review, see Wulf

& Lewthwaite, 2016). Learning advantages with self-controlled feedback have been

demonstrated with various movement tasks, such as movement accuracy and throwing tasks. For

example, in one study, participants practiced a ski-simulator task and had, or did not have, the

opportunity to decide on which trials to use poles that were placed on the floor to help them

maintain their balance (Wulf & Toole, 1999). The results of this study revealed that participants

in the self-controlled group did better in terms of learning compared to their yoked counterparts

when a retention test without the poles was used to measure learning.

An incidental choice can be described as having little to no relevance to a motor task

performance. Yet there is evidence supporting facilitated learning and motor performance when

incidental choices in practice conditions are implemented. For instance, a recent study by

Iwatsuki, Navalta, and Wulf (2018) investigated the effect of autonomy support on movement

efficiency. Participants ran at a submaximal intensity (65% of their oxygen uptake) for a duration

of 20 minutes. Prior to the running performance, participants in the choice group were given an

opportunity to choose photos to view during their run, including the order in which they would

be shown. Thus, participants in the yoked control group viewed the same photos in the same

order of their counterparts. The results of this study demonstrated that by providing participants

with a choice, movement efficiency is enhanced. Reduced oxygen consumption and heart rate

11

were also reported in the choice condition compared to the control at an equivalent exercise

workload.

Since autonomy is defined as the need to actively participate in determining one’s control

over the environment, this variable contributes to goal-action coupling, where goals are

effectively coupled with intended actions (Wulf & Lewthwaite, 2016). The study by Iwatsuki et

al. (2018), reveals that movement efficiency can be enhanced by conditions in which learners’

autonomy is supported, even when the choices are incidental. Additionally, studies have also

been shown to reduce VO2 consumption in trained runners during a submaximal run and increase

aerobic capacity (Stoate et al., 2012; Montes et al., 2018). Therefore, it is clear that autonomy

support has an extensive impact on motor learning in various ways. For example, studies have

demonstrated that when learners have a sense of a more active involvement in performance and

learning, this fosters effective information processing and motivational factors that influence task

interest when learners are given a choice (Lewthwaite et al., 2015; Chiviacowsky et al., 2012).

External Focus

Numerous studies have demonstrated that feedback and instructions that direct learner’s

attention externally (movement effect) rather than internally (actual movement) facilitate

performance and learning (for review, see Wulf & Lewthwaite, 2016). Therefore, it is essential

to address the literature on the effects of an external focus of attention for motor learning.

Adopting an external focus of attention during performance triggers automatic control processes

that are associated with efficient and effective movement (Wulf, 2013). For instance, instructions

that directs the focus externally, rather than internally, produce enhanced learning and

performance. Various measures of movement efficiency and effectiveness have been examined

12

to provide evidence that having an external focus of attention effectively speeds the learning

process in order to achieve a higher skill level sooner (Wulf, 2007a).

In one study, balance learning was found to be enhanced when learners’ attention was

directed to focus on the markers on a balance platform compared to their feet movement

(Chiviacowsky et al., 2010; McNevin et al., 2003; Shea & Wulf, 1999; Jackson & Holmes,

2011). In other studies, movement accuracy was enhanced when participants focused on the

outcome of their movements (e.g., ball trajectory, club motion) rather than on themselves (e.g.,

arms, wrists) (Wulf & Su, 2007; Bell & Hardy, 2009). More direct measures of attentional focus’

effects on efficiency are oxygen consumption, muscular activity (EMG), and heart rate. For

example, a study by Schücker et al. (2009) examined the effect of attentional focus on running

economy. The purpose of this study was to determine differences in physiological response, if

any, during endurance exercise. They reported that the external focus group generated the lowest

oxygen consumption relative to an internal focus (running movement) condition and relative to

an internal focus (breathing condition) in trained runners during their 30-minute submaximal run.

Results revealed that running economy was increased in terms of reduced oxygen consumption

in the external focus condition. This study is in line with motor control research demonstrating

the influence on endurance exercise with regards to the physiological response of oxygen

consumption when an external focus is applied.

For instance, numerous studies have reported faster sprint starts and running times for an

agility task when athletes adopted an external focus compared with an internal focus (Porter et

al., 2010; Ille et al., 2013). When performers reduce their focus on themselves and rather direct

their attention to the task goal, this results in a more efficient performance by facilitating

13

automaticity in movement outcomes. Therefore, an external focus of attention is believed to be

an essential contributor to effective goal-action coupling. Accordingly, by producing successful

performance outcomes, effective movement is executed with ease, which might translate to

enhanced expectancies for future performance (Pascua et al., 2015; Wulf et al., 2014).

OPTIMAL theory of motor learning

The OPTIMAL theory builds on the assumption that motivational and attentional factors

contribute to learning by strengthening the coupling of goals to intended actions (Wulf &

Lewthwaite, 2016). Motivation is the driving force for human behavior and perceptions of

autonomy (Radel, Sarrazin, & Pelletier, 2009). For example, when an individual is motivated,

they will act upon their belief in producing a positive outcome. Therefore, it is not a surprise that

learners are better able to facilitate motor learning through EE of practice conditions. According

to Wulf and Lewthwaite (2016), OPTIMAL theory explains that promoting learners’

expectations for future performance will result in successful movement outcomes. As previously

described, expected success or reward triggers a dopaminergic response associated with

motivational influences on motor activity. Thus, these responses prime the neural connectivity

involved in executing motor tasks (Wise, 2004).

As discussed previously, AS refers to allowing individuals to have a sense of control over

their practice conditions or environment. Practice conditions in which learners receive autonomy

support, such as an opportunity of choice (self-controlled feedback), result in enhanced learning

compared to conditions that are more controlling. Situations where learners sense a lack of

autonomy elicits the stress hormone cortisol to be released. This has a direct effect on the brain’s

reward network, which might contribute to degraded learning under those conditions (Hooyman,

14

Wulf, & Lewthwaite, 2014). In summary, the study by Hooyman et al. (2014) found that under

autonomy-supportive environments, greater intrinsic motivation, effective motor learning, and

enhanced information processing occurred relative to a yoked control group. Last, adopting an

EF of attention allows learners to focus on the task goal in order to optimize performance. In

fact, focusing on the task goal (external focus) promotes a direct advantage to movement

effectiveness and efficiency (Wulf & Lewthwaite, 2016; Iwatsuki et al., 2018).

The evidence that prompted the development of the OPTIMAL theory has valuable

implications for practical settings. These include settings in which motor skills can be taught or

learned, whether in a rehabilitation environment, or with sports and hobbies. Furthermore, there

is evidence that combinations of the OPTIMAL theory factors (EE, AS, and EF) on motor skill

learning and performance have enhanced effects, relative to implementing only one factor

(Marchant et al., 2018; Pascua et al., 2015; Wulf et al., 2017; Abdollahipour et al., 2017). To

date, there are currently two studies that have examined the effects of applying the three factors

of OPTIMAL theory consecutively in a single study trial. The study done by Chua, Wulf, and

Lewthwaite (2018) investigated the immediate effects on vertical jump tasks when EE, AS, and

EF were implemented consecutively rather than simultaneously. Their results revealed that all

three conditions enhanced performance with each addition of a variable relative to the control

group. Recently, Wulf and colleagues (2017) demonstrated that by implementing all three factors

in acquisition enhanced learning to a greater extent, relative to combinations of two factors.

Thus, their findings revealed that by combining all three key factors of the OPTIMAL theory

learning and performance were enhanced.

15

Excess post-exercise oxygen consumption (EPOC)

EPOC is defined as the oxygen uptake VO2 that remains above the baseline values

following exercise performance (Gaessar & Brooks, 1984). Immediately after exercise, there is a

recovery period which results in an increase in oxygen consumption that is rereferred to as

excess post-exercise oxygen consumption (EPOC). According to Sedlock et al. (1989) and Short

and Sedlock (1997), there is a correlation between the duration and magnitude of EPOC with

regard to increasing the intensity of an exercise bout. Therefore, when the intensity of exercise is

held constant, this results in a prolonged duration to the EPOC response (Knuttgen, 1970;

LaForgia et al., 2006).

During exercise, there is an increased energy need in order to support oxygen uptake.

Once exercise is terminated, VO2 levels may still be heightened over baseline values for a

period of time. When exercise duration increases, so does the EPOC response (Knuttgen, 1970;

LaForgia et al., 2006). EPOC can be affected by a number of factors, including the intensity of

activity (Knuttgen, 1962 & 1970; Sedlock et al., 1989) exercise duration (Knuttgen, 1970), and

modality (LaForgia et al., 2006; Lyons et al., 2007). If exercise intensity is held constant, the

EPOC response increases linearly as exercise duration is lengthened (Knuttgen, 1962; LaForgia

et al., 2006). Therefore, studies examining post-exercise energy expenditure have proposed that

when physical activity of high intensity increases, this correlates to an increase EPOC compared

to shorter intensities of physical activity with the equivalent workload. In addition, there is a

larger energy cost that is associated with high intensity exercise on metabolic stress, resulting in

a longer duration of return back to homeostasis (Thorton et al., 2002; LaForgia et al., 2006; Short

et al., 1997).

16

There is a growing body of literature examining the EPOC response of various exercise

duration and modalities. Such studies involve resistance training (Farinatti et al., 2016; Thorton

et al., 2002), treadmill running (Cunha et al., 2016), and cycling (Sedlock et al., 1989; Short et

al., 1997). The results of these studies reveal that there appears to be a larger EPOC response

associated with the engagement of a greater amount of muscle mass across modalities (Cunha et

al., 2016), (Sedlock et al., 1989; Short et al., 1997). For example, a study done by Chad and

Wenger (1985) examined the influence that exercise duration has on EPOC response of cycle

exercise at 70% of VO2max. They looked at exercise durations lasting 30, 45 and 60 minutes and

found that as the duration of exercise was increased, EPOC was also increased by 2.3- and 5.3-

fold when exercise duration was lengthened from the respective times of 30 minutes to 45 and 60

minutes. The growing body of literature on the interaction between the duration and intensity of

exercise is still not fully understood, hence it is difficult to distinguish the direct effects of each

of these factors separately. However, since there is a sequential relationship correlated with the

intensity, magnitude, and duration of exercise, EPOC becomes measurable, indicating that they

work collectively rather than in addition.

For example, a study by Sedlock et al. (1992), used a limited range of exercise conditions

to examine the effects on the duration of EPOC with respect to exercise intensity. In one

sequence of experiments, duration of exercise was kept constant, whereas the intensity was

varied between 50% versus 75% of their VO2max. In another series, they did the opposite, so

intensity was then kept constant at 50% of their VO2max, but the duration was varied from 30

minutes versus 60 minutes of exercise. Therefore, since an arduous exercise load was not

performed, the duration and magnitude of EPOC did not produce substantial effects. Their

results revealed that the duration and magnitude of EPOC were both influenced by the intensity

17

of exercise, whereas the duration of exercise only had an effect on EPOC duration. Furthermore,

literature in this line of study has suggested that EPOC increases when higher exercise intensity

is produced in comparison to shorter exercise bouts in terms of equivalent workload.

Simulated Altitude

Simulated altitude training is a relatively new approach and certainly an advancement in

the field of exercise science. Numerous studies have been conducted to mimic similar effects

with respect to altitude training (Chapman et al., 1998; Biggs et al., 2017; Porcari et al., 2016;

Sinex & Chapman 2015). The purpose of simulating altitude is to replicate high-altitude

conditions, or rather, reduce the amount of partial pressure of oxygen that an individual can

consume (Orhan et al., 2010). Arrival at altitude has the effect of decreasing maximal oxygen

uptake and this corresponds with the decrease of the partial pressure of oxygen and lower blood

oxygenation levels (Calbet et al., 2009; Naeije R., 2010). With the introduction of exercise at

higher altitude, any given absolute workload represents a greater intensity when compared to sea

level due to the associated decrease in aerobic capacity (i.e., the workload is carried out closer to

the VO2peak) (Naeije R., 2010). Early studies have reported basal metabolic rate to increase upon

initial exposure to altitude and then to decrease with acclimation (Burrus et al., 1974; Klausen et

al., 1968). However, other investigations have reported no change in resting metabolic rate with

high altitude exposure (Armellini et al., 1997; Reeves et al., 1967). Taken together, it is unclear

what effect acute altitude exposure will have on the EPOC response.

Simulated altitude research has been shown to enhance exercise performance and sport

preparation as well as rehabilitation strategies of elite endurance athletes (Gore et al., 2007).

Essentially, the body utilizes various systems: cardiovascular, pulmonary, skeletal muscle, and

18

endocrine in order to respond to altitude training under normobaric (simulated) and hypobaric

hypoxia (actual) conditions in an effort to provide sufficient oxygen to these various systems

(Wilber et al., 2004). The physiological adaptations correlated with simulated and actual altitude

training has been shown to improve athletic performance in high-intensity and endurance sports,

as well as military and tactical environments. For example, the physiological responses to

exercise at altitude include metabolic adjustments that are required in order to provide adequate

oxygen to tissues in response to imposed hypoxic stress (Mazzeo et al., 2008). For instance, a

study done in 2011 by Czuba et al., examined the effectiveness of intermittent hypoxic training

(IHT) at a 95% workload of lactate threshold on oxygen uptake and endurance performance in

well-trained male cyclists. Their results revealed increases in VO2max, quicker time trial

performance, and increased power output in trained cyclists. Three weeks of hypoxic training

produced effective training techniques for enhancing endurance performance and oxygen

consumption at sea level when intermittent hypoxic training at lactate threshold intensity was

added (Czuba et al., 2011).

Similarly, in 1999 Rodriguez and colleagues compared trained vs untrained individuals

over a period of nine days, in which a simulated altitude was administered for 3-5 hours per day

at 13,000- 18,000 ft. Results showed substantial improvements in lactate threshold and maximal

pulmonary ventilation. Similarly, studies by Katayama et al., (2003), Roels et al., (2005), also

found that IHT resulted in an increase in VO2max despite no changes in hematological

parameters. Essentially, IHT might have the capability to match the intensity of training and

actual competition, resulting in enhanced performance as opposed to continuous hypoxic

exposure (Roels et al., 2005). Even though some studies might yield what is considered minimal

19

improvements in training, those small improvements can translate significantly in athletic

performance.

Recent renewed attention on the interaction between physiological exercise responses and

altitude increases has specific applications. The only literature to date that examines the EPOC

response with normobaric hypoxia was completed by Navalta et al. (2018). They observed the

EPOC response to increasing levels of simulated altitude following a bout of cycle ergometry.

Their results revealed an increase in duration and magnitude of EPOC with simulated altitude

increases at exercise carried out at the same absolute workload. The evidence on the EPOC

response to altitude training is relevant due to the fact that there is an elevated caloric

expenditure in which carbohydrates are the primary source of energy in association with altitude

exposure (Brooks et al., 1991). Therefore, additional studies that mimic similar exercise

intensities are necessary in order to clarify whether or not EPOC is affected differently in regard

to various aerobic exercise modes. To further support the OPTIMAL theory of motor learning on

exercise performance, the purpose of the present study is to examine whether implementing EE,

AS, and EF during a cycle exercise bout at a simulated altitude enhances exercise performance

and adaptations of EPOC.

20

CHAPTER 3

METHODOLOGY

Participants

To estimate a required sample size, power analysis software, G*Power 3.1, was used.

Based on an estimated large effect size (f = 5.7) with the α-level set at .05 and the power value

set at .80, the sample size of 15 participants was needed to detect an effect (Iwatsuki, Navalta, &

Wulf, 2018). Potential participants were screened using the ACSM Health Risk Questionnaire,

and those who are low risk (no signs/symptoms of pre-existing or diagnosed cardiovascular

disease, pulmonary, and/or metabolic disease; and no greater than one cardiovascular disease risk

factor) were provided with informed consent documents that was approved by the institutional

review board (protocol #1356612-4). A total of sixteen individuals were included in this study

(female N = 8, male N = 8) with similarities in age for both male and female participants (24.6 ±

5.1). Men were taller than the women on average (women = 164.5 ± 4.2 cm, men = 173.3 ± 8.4,

P = 0.05) and had significantly greater body mass (women = 60.7 ± 7.6 kg, men = 81.8 ± 11.5, P

= 0.01). Two participants were classified as outliers in this study as their cycle exercise duration

exceeded 40 minutes, which is considered prolonged exercise. Due to prolonged exercise their

EPOC response was increased substantially. These two outliers were excluded from data

analysis.

Table one lists descriptive characteristics of the combined total of male and female participants

as well as descriptive deviation between male and female participants.

21

Data are presented in means and standard deviations (±).

Equipment

On both visits, participants performed cycle exercise on a Wattbike Pro (Nottingham,

UK), which is a stationary bike ergometer that features a duel air and magnetic system. This

ergometer provides an output on real-time LED display and is capable of providing medium to

high resistance up to approximately 3760 watts. Simulated altitude was administered with the

Everest Summit II generator (Hypoxico Inc., New York, NY) fitted with a high-altitude adapter

capable of simulating altitudes up to 6401 m (21,001 ft). This hypoxicator reduces the fraction of

oxygen by absorbing oxygen availability from room air; this air was delivered to participants

through a HEPA filter, a series of plastic hosing and rebreathing bags, and was fitted to the face

with a neoprene mask. Oxygen consumption prior and following exercise bout was obtained

using the Provo Medics metabolic system (Parvo Medics, Sandy, UT). This metabolic analysis

system features oxygen and carbon dioxide analyzers, a pneumotach breath volume measurement

system, and sample pump and flow controller. Participants were connected to the metabolic

system through plastic tubing and a two-way breathing valve with mouthpiece and headgear.

Procedures

Individuals who participated in this study reported to the UNLV Exercise Physiology

Laboratory for testing on two separate occasions. All testing was carried out in the morning

starting between 06:00 and 08:00 following a usual nigh of sleep, with each visit being separated

22

72 hours apart for their specified time slot. Participants were informed to report to each testing

session well rested and hydrated following an overnight fast. A repeated measures research

design, with all individuals completing baseline oxygen consumption measures, a cycle to

fatigue protocol under simulated altitude of 21,001 feet elevation (the approximate elevation of

Denali Peak, Alaska), and an EPOC session each visit. This is similar to a previous investigation

completed in our laboratory (Navalta et al., 2018).

Prior to each exercise test, individuals were outfitted with a heart rate monitoring device

(Polar Electro Bethpage, NY, USA) and sat quietly to perform a 15-min baseline oxygen

measurement by breathing into the laboratory metabolic analysis system. Participants were then

allowed a proper warm up before exercise based on a self-selected workload. Seat height was

modified so that the participants legs were close to full extension during each pedal stroke.

Following their warm up, a fingertip pulse oximeter was used to monitor their initial blood

oxygen saturation levels and throughout the duration of exercise. Once connected to the altitude

simulator (Hypoxico Inc., New York, NY) through a neoprene mask, participants then performed

cycle exercise at a constant workload of 100 W. They did so by looking on the LED display

provided by the cycle ergometer monitor to match the required workload, while breathing air

from the altitude simulator set at 9.1% fraction of oxygen. Exercise was terminated when the

intensity of cycling could not be maintained at a workload above 80 W for greater than 10

seconds (Camic et al., 2009), or when the participant gestured to terminate exercise on their own.

Upon completion of exhaustive exercise, final blood oxygen saturation values were recorded,

and participants were reconnected to the metabolic analysis system and instructed to sit quietly

until oxygen values have returned to baseline.

23

Testing sessions were completed in a counterbalanced order and carried out in an

“optimized” and control condition that was separated 72 hours apart for their specified time slot.

The “optimized” condition utilized the components of OPTIMAL theory (autonomy support

(AS), enhanced expectancy (EE), and external focus (EF). Specifically, EE was delivered in the

form of a statement, that was made by the investigator’s assistant thirty seconds into the cycle

exercise stating to the investigator that: “His/her speed is above average at this time,” AS: prior

to cycle exercise, participants in the optimized condition were given the opportunity to choose

from 5 of 10 photos shown to them on a computer screen as well as the order in which they

chose to view these photos during exercise, and EF: one minute into cycle exercise, the research

assistant instructed the participant to focus on “driving the pedals toward the ground.” Following

the one-minute mark, this statement was given throughout the entire exercise duration, when

participants dropped below a workload of 100 W. The control condition utilized a yoked

research design. Respectively, the same participants received identical treatment that was given

in their optimized condition, through “neutral” statements. The neutral statement for EE was

made by the investigator’s assistant thirty seconds into the cycle exercise stating to the

participant that: “We will be looking at your average speed during exercise.” For AS:

participants were yoked to their counterparts in the optimized condition for the order and images

shown during cycle exercise duration, and EF: one minute into cycle exercise, the research

assistant instructed the participant to “continue pedaling normally.” Following the one-minute

mark, this statement was given throughout the entire exercise duration, when participants

dropped below a workload of 100 W.

Following their second visit, participants were informed on the true nature of this present

study, provided with explanation (e.g. O2 consumption and resting metabolic rate), and thanked

24

for their time. Performance measures included: mean heart rate, mean power output, total

duration of cycle exercise under simulated altitude, and EPOC response. Average heart rate was

obtained through the heart rate monitoring device throughout the duration of cycle exercise and

blood oxygen saturation was monitored by a fingertip pulse oximeter. EPOC duration was

determined by taking a running 5-min average of the participants oxygen values returned to or

below baseline values (EPOC duration). EPOC magnitude was determined by summing the net

oxygen consumption for each minute during the EPOC period (Navalta et al., 2018).

Statistical Analysis

All data are recorded as mean ± standard deviations. Duration of cycle exercise bout, and

EPOC data were each analyzed using paired t-tests. Statistical analysis were performed with the

criterion for p < .05 for identifying statistically significant results.

25

CHAPTER 4

RESULTS

Eighteen participants, 9 male and 9 females completed the protocol. One male and one female

were classified as outliers in this study, making the total count to 16 individuals who were

included in data analysis. Mean watts were significantly higher in the optimized condition during

hypoxic exercise compared to the control condition (mean watts optimized = 126.4 ± 25.7,

control = 112.7 ± 20, p = 0.019, see Table 2. Blood oxygen saturations (SPO2) and heart rate

(HR) showed no significant difference between the two conditions (see table 2).

Data are presented in mean and standard deviation (±), with significance* accepted at p < 0.05.

Blood oxygen saturation (SPO2) presented in percentage. Heart rate (HR) in beats per minute.

Participants were able to cycle longer in the optimized condition relative to the control (p = .03),

with exercise carried out at the same absolute workload (see Figure 2).

26

Figure 2. Performance outcomes in the optimized and control conditions

Fig. 2. Cycle exercise duration (performance outcome) at a constant workload of 100 W under

simulated altitude of 6401 m (21,001 ft) to fatigue, during the optimized and control conditions.

Data indicate means and standard error (bars). The main effect on cycle exercise performance

between both conditions was significant, p = .03.

EPOC duration in participants who performed cycling exercise at 100 W under simulated

altitude of 6401 m (21,001 ft) to fatigue following optimized and control conditions resulted in

no statistically significant difference, (p = 0.14, see Figure 3).

Figure 3. Excess post-exercise oxygen consumption duration

Fig. 3. Excess post-exercise oxygen consumption duration in participants (N = 16) who

performed cycling at 100 W under simulated altitude of 6401 m (21,001 ft) to fatigue, following

optimized and control conditions. Data indicate means and standard error (bars).

11.1

9.3

0

2

4

6

8

10

12

14

16

Condition

CY

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(M

IN)

Cycle Exercise Performance

Optimized Control

*

27.5

33.2

0

5

10

15

20

25

30

35

40

Condition

TIM

E (

MIN

)

EPOC Duration

Optimized EPOC Control EPOC

27

EPOC magnitude also resulted in no statistically significant difference, following optimized and

control conditions (p = 0.38, see Figure 4).

Figure 4. Excess post-exercise oxygen consumption magnitude

Fig. 4. Excess post-exercise oxygen consumption magnitude in participants (N = 16) who

performed cycling at 100 W under simulated altitude of 6401 m (21,001 ft) to fatigue, following

optimized and control conditions. Data indicate means and standard error (bars).

In addition, there was no significant difference between visits for cycle exercise duration (p =

0.30) and resting metabolic rate (p = 0.26). Figures 5 and 6 represent visit 1 and visit 2

comparisons of exercise performance and resting metabolic rate.

Figure 5. Exercise performance comparison between visits

Fig. 5. Cycle exercise duration comparison from visit 1 to visit 2. There was no significant

difference between visits, p = 0.30. Data indicate means and standard error (bars).

140.7156.4

0

20

40

60

80

100

120

140

160

180

Condition

TO

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ET

(M

L O

2)

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Optimized EPOC Control EPOC

0

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VisitsCY

CL

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ISE

DU

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(MIN

)

Exercise Performance Comparison

Visit 1 Visit 2

28

Figure 6. RMR comparison between visits

Fig. 6. Resting oxygen measurements was obtained prior to each exercise test. There were no

significant differences in RMR between visits, p = 0.26. Data indicate means and standard error

(bars).

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Visits

ML

O2

Resting Metabolic Rate Comparison

Visit 1 Visit 2

29

CHAPTER 5

DISCUSSION

The purpose of this study was to investigate whether implementing EE, AS, and EF

during a cycle exercise bout at a simulated altitude of 21,001 feet elevation would have an effect

on exercise performance and EPOC response. The primary aim was to observe if these three

factors would influence performance measures and the EPOC response in comparison to a

control condition. We hypothesized that administering EE, AS, and EF consecutively would

result in a longer duration of cycle exercise (performance outcome) at a simulated altitude of

21,001 feet, relative to a control condition. We also hypothesized that when all three variables of

the OPTIMAL theory were applied consecutively during exercise, both the EPOC duration and

magnitude response would be modified in regard to exercise duration carried out at the same

absolute workload.

Our first hypothesis was confirmed, as the main effect on cycle exercise performance

between both conditions was significant. Participants were able to cycle on average two minutes

longer in the optimized condition relative to the control, with exercise carried out at the same

absolute workload. For our second hypothesis, EPOC duration and magnitude in both conditions

were not statistically significant despite longer cycle exercise duration in the optimized

condition. The results in our investigation of the EPOC duration and magnitude response are

intriguing due to the fact that previous studies revealed that when exercise duration increases, so

does the EPOC response (Knuttgen, 1970; LaForgia et al., 2006). For example, a study done by

Chad and Wenger (1985) examined the influence that exercise duration has on EPOC response

of cycle exercise at 70% of VO2max. They looked at exercise durations lasting 30, 45 and 60

30

minutes and found that as the duration of exercise was increased from those respective times,

EPOC was also increased to 2.3- and 5.3-fold.

In terms of exercise performance, the present outcomes are in line with previous studies

demonstrating that exercise performance is enhanced when implementing one of these variables.

For example, Montes et al. (2018) found that maximum aerobic capacity (VO2max) was

enhanced when trained runners were provided with social-comparative feedback on their

VO2max test, and led to believe that their values were above the average of their peers. Thus,

enhancing their performance expectancies increased maximal oxygen consumption, indicating a

greater physical working capacity relative to their own previous values, and relative to a control

group. In another recent study, Iwatsuki et al. (2018), provided participants with an opportunity

to choose images to view during their submaximal run. Their findings revealed that enhanced

movement efficiency was achieved in regard to decreased oxygen consumption in comparison to

the control group who had no choice. Similarly, Schücker et al. (2009) reported that an external

focus group generated the lowest oxygen consumption relative to an internal focus (running

movement) condition and relative to an internal focus (breathing condition) in trained runners

during their 30-minute submaximal run. Finally, in terms of oxygen utilization, a previous study

provided evidence that oxygen consumption was reduced during treadmill running at

submaximal intensity under enhanced expectancy conditions (Stoate et al., 2012). Thus, each

factor (EE, AS, EF) individually has been shown to enhance exercise performance. The present

study identified benefits in cycle exercise performance during simulated altitude in the optimized

condition relative to the control when all three variables of the OPTIMAL theory were applied

during exercise.

31

In addition, our results are consistent with and expand previous findings showing that

combinations of all three OPTIMAL variables (EE, AS, or EF) can result in greater benefits than

any of these factors alone (Abdollahipour et al., 2017; Marchant et al., 2018; Pascua et al., 2015;

Wulf et al., 2014, 2015, 2017). However, there are certain differences between previous studies

and this current one. For instance, to the best of our knowledge, this is the first study to

investigate aerobic exercise performance as well as the exercise recovery with EPOC response

where all three variables in OPTIMAL theory are applied consecutively during exercise. The

results of this study revealed that cycle exercise performance was enhanced in the optimized

condition relative to the control condition: participants were able to cycle longer with exercise

carried out at the same absolute workload. In addition, EPOC duration and magnitude produced

no significant difference between conditions. Therefore, despite longer exercise duration in the

optimized condition relative to the control, participants were able to return to their baseline

oxygen levels after similar duration of recovery time. From a physiological standpoint, it appears

that these three variables in part or all together, may influence oxygen delivery and utilization

following exercise. While further investigation is imperative, it is tempting to suspect that the

subtle benefits of oxygen recovery observed in the optimized condition could be responsible with

regard to EPOC response. Thus, further investigation is necessary to examine the physiological

parameters of other exercise intensities to asses if similar results are produced.

There were some limitations to the current investigation, the first in regard to the external

cue for cycle exercise performance. In both conditions, participants were able to view their cycle

time on the Wattbike display. Although results showed no statistically significant difference

when comparing visit one to visit two exercise performance, we cannot generalize these findings

for all participants involved. Additionally, following cycle exercise bout, participants were

32

allowed a proper cool down as a precautionary measure. Thus, we cannot account for an accurate

EPOC measure due to the time from when exercise was terminated to participants being

reconnected to the metabolic analysis system. Therefore, it’s essential to control for these

confounding variables in order to support internal validity. Based on the findings from our study,

it would be interesting to further explore other exercise intensities to asses if similar EPOC

response and exercise performance outcomes are produced. Future studies should also control for

confounding variables that may influence exercise performance in order to support internal

validity. In our case, the LED display would need to be flipped so that participants are not able to

view their cycle time and instead match their exercise workload to a metronome.

This study is applicable for potential benefits of oxygen delivery and utilization following

exercise, when all three factors of the OPTIMAL theory are applied consecutively during

exercise. Coaches, trainers, instructors, and clinicians may consider the appropriate implications

of these variables in their respected settings for enhancing exercise performance and recovery.

We view these findings as another piece of evidence to support the OPTIMAL theory factors

demonstrating increased movement efficiency and oxygen utilization (Stoate et al., 2012; Montes

et al., 2018; Iwatsuki et al., 2018; Schücker et al., 2009). Therefore, the present findings provide

preliminary evidence for further exploration on the possible benefits of the OPTIMAL theory

variables with exercise recovery (EPOC response) and exercise performance.

33

APPENDIX A

IRB Approval

34

APPPENDIX B

Informed Consent

INFORMED CONSENT

Department of Kinesiology of Nutrition Sciences

TITLE OF STUDY: Excess Post Oxygen Consumption to Exercise at Simulated Altitudes

INVESTIGATOR(S): Dr. James Navalta, Cierra Ugale, Nate Gentry, Cody Kwong, Jeff

Montes, and Nathaniel Bodell

For questions or concerns about the study, you may contact Dr. Navalta at (702) 895-2344.

For questions regarding the rights of research subjects, any complaints or comments regarding

the manner in which the study is being conducted, contact the UNLV Office of Research

Integrity – Human Subjects at 702-895-2794, toll free at 877-895-2794 or via email at

[email protected].

_______

Purpose of the Study

You are invited to participate in a research study. The purpose is to test oxygen recovery after

exercise at a simulated altitude of up to 21,001 feet elevation (the approximate elevation at the

peak of Denali, AK). The purpose of this study is to examine how individuals recover from

physical exertion at higher attitudes and examine performance adaptations under such conditions.

Participants

You are being asked to participate in the study because you fit the following criteria: between the

ages of 18-44 years and classified as “low” risk for cardiovascular disease. After filling out the

Health Risk Questionnaire it is possible that you will not be allowed to participate further in the

study. You will be excluded from the study if you are categorized as “moderate” risk, have an

implantable device (such as a Pacemaker), or have orthopedic, cardiovascular, respiratory, or

metabolic conditions.

Procedures

If you volunteer to participate in this study, you will be asked to come to the Exercise Physiology

Laboratory on two different occasions to cycle on a stationary bike at a simulated altitude of

21,001 feet elevation (the approximate elevation at the peak of Denali, AK). At the beginning of

each visit, you will be outfitted with a heart rate monitoring device and asked to sit quietly for

35

15-min while breathing into a mouthpiece connected to a system that will measure your oxygen.

You will then perform a cycle exercise at the simulated altitude of up to 21,001 feet to

exhaustion (maximal effort), while breathing air from an altitude simulator. We anticipate the

duration of exercise to last no longer than 10 minutes depending on whether or not you are

conditioned to altitude training. Once you are done, you will be reconnected to the equipment

that measures oxygen and sit quietly until your values have returned to resting values.

Benefits of Participation

You will be able to receive free information regarding your measurements from each testing

session. It is hoped that the results of this study will provide information about how individuals

recover from physical exertion at higher altitudes.

Risks of Participation

There are some risks associated with exercising at simulated altitude. The amount of oxygen

delivered each breath may decrease, as well as the amount of oxygen that enters your lungs,

however as you will be exercising at a relatively low intensity these changes are not expected to

be noticeable. Additionally, all you need to do is remove the mask if you are not adapting well to

the simulated altitude. The American College of Sports Medicine has stated that the risk of death

during or immediately after a maximal exertion test is less than or equal to 0.01%, while the risk

of an acute myocardial infarction is less than or equal to 0.04%. Data from these surveys

included a wide variety of healthy AND diseased individuals. Since you are an apparently

healthy adult between the ages of 18 - 44 years and are considered “low-risk” according to the

American College of Sports Medicine guidelines, no medical supervision is necessary during the

exercise test. There may be discomforts associated with the test. Muscle soreness, nausea,

breathlessness, dizziness, and lightheadedness may occur. Muscle soreness may ensue 24-48

hours later. The tests will be stopped any time you are not adapting well to the activity or when

any major discomfort arises.

Cost /Compensation

There will not be financial cost to you to participate in this study. The study will take

approximately 2 hours of your time over the course of the two visits; however, there is no

compensation for your time.

Confidentiality

All information gathered in this study will be kept as confidential as possible. No reference will

be made in written or oral materials that could link you to this study. All records will be stored

in a locked facility at UNLV for 3 years after completion of the study. After the storage time,

any identifiable information will be destroyed. Unidentifiable data will be stored in locked

storage indefinitely.

Voluntary Participation

Your participation in this study is voluntary. You may refuse to participate in this study or in any

part of this study. You may withdraw at any time without prejudice to your relations with

UNLV. You are encouraged to ask questions about this study at the beginning or any time during

the research study.

36

Participant Consent:

I have read the above information and agree to participate in this study. I have been able to ask

questions about the research study. I am at least 18 years of age. A copy of this form has been

given to me.

Signature of Participant Date

Participant Name (Please Print)

1356612-4, Expiration Date: 02/20/2022

37

APPENDIX C

ACSM Health Risk Questionnaire

AHA/ACSM Health Risk Questionnaire Assess your health status by marking all true statements

______________________________________________________________________

History You have had: ____ a heart attack ____ heart surgery ____ cardiac catheterization ____ coronary angioplasty (PTCA) ____ pacemaker/implantable cardiac ____ defibrillator/rhythm disturbance ____ heart valve disease ____ heart failure ____ heart transplantation ____ congenital heart disease

Symptoms ____ You experience chest discomfort with exertion ____ You experience unreasonable breathlessness ____ You experience dizziness, fainting, or blackouts ____ You take heart medications

Other health issues ____ You have diabetes ____ You have asthma or other lung disease ____ You have burning or cramping sensation in your lower legs when walking short distances ____ You have musculoskeletal problems that limit your physical activity ____ You take prescription medication(s) ____ You are pregnant

______________________________________________________________________

Cardiovascular risk factors

____ You smoke, or quit smoking within the previous 6 months ____ Your blood pressure is >140/90 mm Hg ____ You take blood pressure medication ____ Your blood cholesterol level is >200 mg/dL ____ You have a close blood relative who had a heart attack or heart surgery before age 55 (father or brother) or age 65 (mother or sister) ____ You are physically inactive (i.e., you get <30 minutes of physical activity on at least 3 days per week) ____ You are > 20 pounds overweight

______________________________________________________________________

____ None of the above

38

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and autonomy support have additive benefits for motor performance in children.

Psychology of Sport and Exercis, 32, 17-24.

An, J., Wulf, G., & Kim, S. (2013). Increased carry distance and X-factor stretch in golf through

an external focus of attention. Journal of Motor Learning and Development, 1, 2-11.

Armellini, F., Zamboni, M., Robbi, R., Todesco, T., Bissoli L., et al. (1997). The effects of high

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CURRICULUM VITAE

CIERRA BRITTANY UGALE

CURRICULUM VITAE

[email protected]

EDUCATION

M.S., Exercise Physiology 2017 – Present

University of Nevada, Las Vegas

B.S., Kinesiological Sciences 2015 – 2016

University of Nevada, Las Vegas

B.S., Community Health Sciences 2010 – 2013

University of Nevada, Reno

TEACHING EXPERIENCE

Group Skills Fitness Instructor, Life Time Athletic 2016 – Present

Lead, instruct, and motivate groups in exercise activities for

adolescents and educate healthy lifestyle behaviors.

Teaching Assistant, Mountain View Montessori School 2013 – 2014

Reinforce lessons by teachers and reviewed material with students.

Snowboard Instructor, Sky Tavern Ski Resort 2012 – 2014

Taught proper snowboarding techniques appropriate to the ability

level of individuals being instructed.

Teaching Assistant 2012 – 2014

Assist faculty with instructing CHS 271 lab course, exams, record

keeping, and other miscellaneous projects.

RELATED EMPLOYMENT EXPERIENCE

Assistant Strength Coach (Alpha Strong), Life Time Athletic 2019 – Present

Life Time Athletic Running Coordinator, Life Time Athletic 2016 – Present

Physical Therapy Tech, Desert Valley Therapy 2015 – 2016

Rehabilitation Technician (OPT & IPT), Renown Health 2013 – 2015

Teaching Assistant, Mountain View Montessori School 2013 – 2014

Physical Therapy Tech, Wildcreek Physical Therapy Inc. 2011 – 2013

SERVICE

Secretary Officer, Kappa Iota Nu 2015

University of Nevada, Las Vegas

Snowboard Instructor, Sky Tavern 2012

CASI level 1 & 2 snowboard instructor certification

Treasurer Board Member, Nevada Physical Therapy Club 2011

University of Nevada, Reno

Large Group Coordinator, Intervarsity Christian Fellowship 2010

University of Nevada, Reno

45

COMMUNITY SERVICE

Night to Shine 2018 – Present

Prom night experience, serving individuals with special needs, age

16 and older.

Young Adult Ministry Small Group Leader 2016 – Present

The Crossing Church

Project 150 2016 – Present

Non-profit charitable organization that meets the needs of

homeless and disadvantaged high school students in Southern

Nevada.

Casa De Luz 2016– Present

Community resource center that provides services to neighborhood

families in the Las Vegas Community.

Spread the Word Nevada 2016 – Present

Children’s literacy non-profit corporation dedicated to donating

books to children within Nevada’s at-risk and low income

communities.

PROJECTS

Senior Internship Project 2016

Designed and implemented the effects on employer-based exercise

programs on healthcare costs at MountainView Hospital.

Sports Nutrition Performance Clinic 2014

Integrated nutrition assessments to build the knowledge of

individuals, and meet the needs and goals of student athletes.

Health and Wellness Program 2013

Provided health and wellness evaluations for fitness and physical

therapy services, combined with treatment plans with a registered

dietician.

RESEARCH EXPERIENCE

Research Assistant 2017

Running Economy, Exercise Physiology Laboratory, UNLV

Developed protocol and collected data.

AWARDS

Governor Guinn Millennium Scholarship 2008 – 2012

$10,000 award for undergraduate coursework

CERTIFICATIONS

Certified Strength and Conditioning Specialist (CSCS) 2019

First AID/CPR and AED certification (renewed for the fourth time) 2019

Signs and Symptoms of Illness/Blood Borne Pathogens 2016

CASI level 1 & 2 Snowboard Instructor Certification 2012

46

PROFESSIONAL MEMBERSHIPS

National Strength and Conditioning Association 2019

American College of Sports Medicine 2019

PROFESSIONAL REFERENCES

James W. Navalta, Ph.D., FACSM

Associate Professor, M.S. Graduate Coordinator, Department of Integrated Health Sciences

University of Nevada, Las Vegas

4505 S. Maryland Pkwy, Las Vegas, NV 89154

Phone: 702-895-2344

[email protected]

Ceferino Vilafuerte, PT, DPT

Far West Division Director of Rehabilitation Operations

Healthcare Corporations of America

2360 Corporate Circle #225, Henderson, NV 89074

Phone: 702-498-4497

[email protected]

Christopher Mitchell

General Manager

Life Time Athletic - Summerlin

10721 West Charleston, Las Vegas, NV 89135

Phone: 702-228-2611

[email protected]