Cardiovascular Stress Response While Gaming and Behavioral and Psychometric Assessments of Gamers and Non-Gamers

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CARDIOVASCULAR STRESS RESPONSE WHILE GAMING AND BEHAVIORAL AND PSYCHOMETRIC ASSESSMENTS OF GAMERS AND NON-GAMERS

A dissertation submitted to the Kent State University College of Education, Health, and Human Services in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Bryan T. Dowdell

May 2020

A dissertation written by

Bryan T. Dowdell

B.S., University of Akron, 2013

M.S., University of Akron, 2015

Ph.D., Kent State University, 2020

Approved by

______, Co-director, Doctoral Dissertation Committee Angela L. Ridgel

______, Co-director, Doctoral Dissertation Committee Jacob E. Barkley

______, Member, Doctoral Dissertation Committee Andrew Lepp

Accepted by

______, Director, School of Health Sciences Ellen L. Glickman

______, Dean, College of Education, Health and Human James C. Hannon Services

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DOWDELL, BRYAN T., Ph.D., May 2020 Education, Health, and Human Services

CARDIOVASCULAR STRESS RESPONSE WHILE GAMING AND BEHAVIORAL AND PSYCHOMETRIC ASSESSMENTS OF GAMERS AND NON-GAMERS (123 pp.)

Co-Directors of Dissertation: Angela L. Ridgel, Ph.D. Jacob E. Barkley, Ph.D.

The purpose of this study was to further understand the gamer population by assessing behavioral and psychometric variables of gamers (i.e., those that play video games and self-identify as gamers) compared to non-gamers along with determining the cardiovascular stress response and perceived stress of college-aged adults while playing an esports style video game (Fortnite) for 60 minutes against other people (competitive) and against a computer avatar (non-competitive). Through a survey of 306 college students, we found that gamers, relative to non-gamers, have decreased physical activity and grade point average (GPA) and increased sedentary behavior. Furthermore, we also found that competitive gamers who compete for club, junior varsity, or varsity esports teams are gaming much more than gamers who are not on a team and non-gamers alike.

We also found significant increases in heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) while playing Fortnite compared to resting values. We observed a greater HR, SBP, and DBP response while gaming in a competitive condition compared to a non-competitive condition. Furthermore, gamers had greater

cardiovascular stress reactivity compared to non-gamers. We also reported an increase in state anxiety after the competitive gaming condition. Ultimately, the stress response we have observed while gaming might have adverse effects on health, thus we should further explore this increasingly popular entertainment medium.

ACKNOWLEDGMENTS

I would like to acknowledge the following individuals who were integral to the successful completion of this dissertation. First, I want to thank Dr. Angela Ridgel for her guidance and mentorship from when I first started here at Kent State University all the way through my dissertation. Without her faith in me, I would have never had the opportunity to be here today. Second, I would like to thank Dr. Jacob Barkley for co- advising, overseeing, and dedicating much of his own time towards my dissertation process for the past year. I would also like to thank Dr. Andrew Lepp for also serving on my committee and offering valuable insight and feedback in addition to Dr. Ellen

Glickman for opening the door to this exciting new research avenue. In addition, I would like to thank Maria Hawkins and the rest of the esports members for their gracious donation of gaming computers for our lab. Lastly, I would like to thank my family, friends (you know who you are), along with everyone who has lived with me in The

Beach House during my time here at Kent State University: Tyler Singer, Eliott Arroyo,

Cody Dulaney, and Joe Laudato for their support and patience.

TABLE OF CONTENTS

Page ACKNOWLEDGMENTS ...... iv

LIST OF FIGURES ...... vii

LIST OF TABLES ...... viii

CHAPTER I. INTRODUCTION ...... 1

II. REVIEW OF LITERATURE ...... 5 Challenge Hypothesis ...... 9 Cardiovascular Response to Gaming ...... 10 Cardiovascular Stress-reactivity ...... 12 SAM and HPA Axes ...... 14 Cortisol Response to Stress ...... 15 Cross-stressor Adaptations ...... 18 Need for Research ...... 19 Purpose and Hypothesis ...... 20

III. METHODOLOGY ...... 23 Aim 1 ...... 23 Participants ...... 23 Methods ...... 23 Survey Instruments ...... 24 Statistical Analysis ...... 26 Aim 2 ...... 26 Participants ...... 27 General Protocol ...... 28 VO2max Protocol ...... 30 Gaming Protocol ...... 30 Variables ...... 31 Statistical Analyses ...... 33

IV. BEHAVIORAL AND PSYCHOMETRIC ASSESSMENT OF GAMERS AND NON-GAMERS ...... 34 Introduction ...... 34 Methods ...... 39 The Survey Instrument ...... 40 Statistical Analysis ...... 41 Results ...... 42 Discussion ...... 47

V. CARDIOVASCULAR STRESS RESPONSE WHILE PLAYING FORTNITE IN GAMERS AND NON-GAMERS ...... 53 Introduction ...... 53 Methods ...... 59 Participants ...... 60 Protocol ...... 61 VO2max Protocol ...... 63 Body Composition ...... 63 Gaming Protocol ...... 63 Hemodynamic Measurements (Heart rate and blood pressure) ...... 64 Survey Responses ...... 65 Statistical Analyses ...... 66 Results ...... 66 Systolic Blood Pressure ...... 66 Diastolic Blood Pressure ...... 69 Heart Rate ...... 72 Body composition, Fitness, and Anxiety...... 75 Discussion ...... 76

VI. SUMMARY ...... 83

APPENDICES ...... 85 APPENDIX A. VIDEO GAME SURVEY ...... 86 APPENDIX B. INITIAL VISIT QUESTIONNAIRE ...... 98 APPENDIX C. EXPERIMENTAL VISIT QUESTIONNAIRE ...... 103

REFERENCES ...... 105

LIST OF FIGURES

Figure Page

1. Study design over three visits...... 27

2. Experimental visits ...... 30

3. Gaming minutes per week ...... 45

4. Study design over three visits...... 59

5. Experimental visits ...... 62

6. SBP means and SEM at baseline, during gaming (averaged over 60 minutes), and at the end of recovery ...... 68

7. Peak SBP means and SEM at baseline, during gaming (Peak SBP), and at the end of recovery ...... 69

8. DBP means and SEM at baseline, during gaming (averaged over 60 minutes), and at the end of recovery ...... 71

9. Peak DBP means and SEM at baseline, during gaming (Peak DBP), and at the end of recovery ...... 72

10. HR (bpm) means and SEM during baseline, gaming, and recovery ...... 74

11. Peak HR (bpm) means and SEM during baseline, gaming, and recovery ...... 75

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LIST OF TABLES

Table Page

1. Mean and Standard Deviations of Variables Between Gamers and Non-Gamers ...... 43

2. Pearson’s Correlation Coefficients (R) and Significance (P) Between Variables ...... 46

3. Mean and Standard Deviations of Variables ...... 47

4. Blood Pressure Means and Standard Deviations ...... 67

5. Heart Rate Means and Standard Deviations...... 74

6. Means and Standard Deviations for Age, VO2max, and Body Fat Percentage ...... 76

7. Means and standard deviations for trait and state anxiety ...... 76

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CHAPTER I

INTRODUCTION

Competitive video gaming (i.e., electronic sports or esports), video games in which you compete against other human players, has rooted itself into today’s culture especially amongst the younger demographic. While esports have been around for decades, the emergence of streaming services such as YouTube and Twitch in which gamers will stream their game play for an audience to access along with the popularity of certain games (e.g., Fortnite) have placed esports and the personalities associated with it into today’s pop culture. Market researcher, Newzoo, predicts that the global spending on video games in 2019 will total $137.9 billion (2018). Epic Games, the creator of the popular esports game, Fortnite, has reported there were over 250 million total players as of March 2019 (Iqbal, 2019). The rise of esports is growing at a rapid rate with global revenues increasing 38% compared to 2017 (Newzoo, 2018). Colleges and universities across the world are even starting programs dedicated specifically towards esports and some are even offering scholarships and constructing multimillion-dollar arenas for intercollegiate esports tournaments. Because of this popularity, ease of access, monetary prizes, and exposure through streaming services and social media, playing competitive video games might be especially enticing for adolescents and young adults.

Playing competitive video games is a sedentary activity in nature, and survey data shows that adolescents and young adults who play video games allocate prolonged periods for play lasting two to three hours without breaks (Brooks, Chester, Smeeton, &

Spencer, 2016; Yap & Paul, 2017). While the debate of whether esports should be

considered a “sport” is warranted, the competitive aspect of playing these games cannot be ignored. The International Olympic Committee (IOC) is even considering the inclusion of esports in future Olympics (“Paris 2024 Olympics: Esports ‘in talks’ to be included,” 2018). The competitive nature of these newly emerging esport games such as

Rocket League, League of Legends, Apex: Legends, or Fortnite which can pit a player against up to 99 other players in the same match all while sitting in the comfort of your own couch poses potential physiological and psychological stressors not yet studied.

Specifically, the stressors that could be associated with competitive gaming especially during consistent, prolonged periods of play could lead to chronic stress and the associated increased risk of cardiovascular disease (CVD) such as atherosclerosis as well as mood disorders such as depression (Hemingway & Marmot, 1999; Rozanski,

Blumenthal, Davidson, Saab, & Kubzansky, 2005). Additionally, prolonged video gaming sessions could likely lead to increased sedentary behavior which also increases risk of CVD, metabolic disorders, obesity and ultimately mortality through decreased energy expenditure, increased energy intake, and dyslipidemia (Hamilton, Hamilton, &

Zderic, 2007; Owen, Healy, Matthews, & Dunstan, 2010). However, neither the behavioral and psychometric characteristics of gamers nor the potential cardiovascular stress reactivity associated with esports-style gaming have been well studied.

The first aim of this study was to better understand behavioral and psychometric variables of the gamer population compared to non-gamers through self-reported surveys.

As discussed earlier, competitive gaming has become very popular in the younger demographic in the United States however, the behavioral and psychological

characteristics associated with esports-style gaming are not yet understood. Therefore, developing a further understanding of health-related characteristics (e.g., physical activity, sedentary behavior, anxiety) of this demographic is warranted. We hypothesized that gamers would be less physically active and more sedentary than non-gamers. In addition, due to the competitive nature of these games, we hypothesized that gamers would have higher trait anxiety than non-gamers.

The second aim of this study was to determine the cardiovascular response, or stress reactivity, while participating in competitive gaming in young adult gamers and non-gamers. In addition, we assessed body composition and physical fitness of both gamers and non-gamers as potential moderators and/or mediators of stress reactivity during competitive gaming. We likened competitive gaming to stress tasks involving executive function and social-evaluative threats, thus based off previous literature which has examined non-gaming psychological stressors, we hypothesized that competitive, esports-style gaming would similarly induce cardiovascular stress reactivity (Angelika

Buske-Kirschbaum, Geiben, Höllig, Morschhäuser, & Hellhammer, 2002; Childs, Vicini,

& De Wit, 2006; Dedovic et al., 2005; Jones et al., 2011; Kudielka, Buske-Kirschbaum,

Hellhammer, & Kirschbaum, 2004). To evaluate competitive gaming, we had gamers and non-gamers play two different modes, or conditions, of Fortnite: a single-player mode and a “Battle Royale” mode in which the participant was pitted against 99 other humans. We hypothesized that gamers and non-gamers will exhibit an increase in salivary cortisol, heart rate (HR), and systolic blood pressure (SBP) in both conditions: playing versus the computer and playing versus other humans. However, we also

hypothesized that participants would exhibit increased stress reactivity while participating in the competitive “Battle Royale” mode against humans versus the single- player mode because of the social-evaluative threats of the “Battle Royale” mode. In addition, due to the sedentary nature of playing competitive video games, we hypothesized that gamers would have reduced physical fitness and lean muscle mass compared to non-gamers. Lastly, we hypothesized that those who were more aerobically fit (regardless of gaming status) would have an attenuated stress response compared to those who were less aerobically fit.

CHAPTER II

REVIEW OF LITERATURE

Competitive gaming or electronic sports (henceforth esports), is a rapidly growing entertainment medium that many adolescents, teens, and young adults are actively participating or engaging in (Newzoo, 2018). As of 2018, there are 150 million

Americans who play video games, and the video game industry which is one of the fastest growing economic sectors in the U.S, has generated $43.4 billion in revenue in

2018 alone. (Entertainment Software Association, 2019). While not all of video games are competitive in nature, many adolescents and young adults are regularly engaging in free-to-play competitive games such as Fortnite and Apex Legends. Competitive gaming encompasses a wide variety of genres of video games that pit you against other human players via the internet, notably first-person shooters, third-person shooters, and multiplayer online battle arena.

First-person shooters such as Call of Duty and Overwatch consists of controlling one’s character through the perspective of the player-character’s eyes. Third-person shooters such as Fortnite consists of controlling the on-screen character from an external perspective, usually from over the player-character’s shoulder (“Know Your Genres:

Third-Person Shooters,” 2015). Lastly, Massive Online Battle Arena’s such as League of

Legends and Defense of the Ancients are real-time strategy games in which two teams, typically consisting of five players each, compete against each other with each player controlling one character or “hero” (Mora-Cantallops & Sicilia, 2018).

With the advent and success of 2017’s third-person shooter, Fortnite’s Battle

Royale, a free-to-play massive multiplayer competitive game which pits you against 100 other human competitors, the amount of online streaming on social media streaming networks such as YouTube and Twitch dedicated to gaming has increased dramatically

(Newzoo, 2018). It is predicted that in 2019, there will be an estimated 743 million gaming viewers worldwide (SuperData Research, 2016). Esports tournaments, which pits teams or individual gamers against one another for cash prizes either in venues or from one’s own home, are rapidly growing due to the ease of streaming and quick rise of participation from adolescents and young adults (Newzoo, 2018). Epic Games, the creator of Fortnite, has already pledged to dedicate $100 million in prize money and plans on launching the first Fortnite World Cup in 2019 (The Fortnite Team, 2018). Epic

Games reportedly grossed a $3 billion profit in 2018, which compares similarly to

Amazon’s $3 billion profit in 2017 (Russell, 2018). In addition, many other competitive video games such as League of Legends, Overwatch, Call of Duty, and Rocket League have also garnered worldwide audiences. In 2014, there were 67 million League of

Legends players monthly (R. Li, 2017). This game also had an annual revenue of around

$1 billion making it the most popular global esport during at that time (R. Li, 2017).

Given the widespread popularity of these games it is not surprising that competitive gaming has reached the university level. There is a rapidly expanding list of universities around the United States which have started scholarship-based esports programs. As of 2018, there are over 90 officially-recognized collegiate, varsity esports programs that offer scholarships across the United States, and this number is growing

(National Association of Collegiate Esports [NACE], n.d.). In addition to Kent State

University recently starting its scholarship-based program, other Ohio state universities have started esports programs. These schools include the University of Akron and the

Ohio State University which recently unveiled its multi-faceted esports program including a state-of-the-art arena for student use and esports competitions. The market for esports is thus growing and colleges and universities are increasingly investing in this activity.

As the outlook for competitive gaming looks bright, the accompanying increase in competitive gamers warrants investigation into this specific population. Currently, there is a paucity of research on this population despite this rise in popularity and participation in competitive gaming. If we consider competitive gaming as a potentially different modality than casual gaming, we can start to differentiate the potential social, psychological, and physiological effects upon this distinct group. Competitive gaming requires good communication skills in addition to a variety of other neurological processes. For example, many games include or require fast information processing and the need to make quick fine tune motor responses under severe time constraints, a high degree of working memory, planning, and goal setting (there are many items to keep track of simultaneously with many possible goal states at any given time along with many motor plans that require rapid and precise execution), rapid switching of attention from aimed targets to a more distributed field of view and vice versa, and a high degree of clutter and distraction in that many items of interest are dispersed among many non-target items (Bediou et al., 2018). In fact, Kowal et.al. (2018) recently showed that college-aged

action video gamers (multiplayer first-person shooter games or multiplayer online battle arena games) displayed a different cognitive profile compared to non-gamers. Video game players had significantly improved measures of executive function (e.g., faster

Stroop and trail-making test times) with no differences in error rates compared to non- gamers.

With competitive gaming cementing its place in today’s modern culture, it is important to understand the potential physiological effects associated with it. Prolonged gaming sessions are becoming very common especially amongst adolescents and young adults. Survey research has indicated that among those that consider themselves “video gamers,” most play video games consistently for prolonged sessions at least two to three hours in length without rest or breaks (Brooks et al., 2016; Yap & Paul, 2017). While the formal labelling of video game addiction termed “gaming disorder” is still up for debate amongst the World Health Organization (WHO), prolonged video game sessions pose potential physical and mental health risks (van Rooij et al., 2018), yet there is very limited research on this new entertainment medium. Specifically, the psychological stress that could be associated with competitive gaming especially during consistent, prolonged periods of time could lead to chronic stress and the associated increased risk of cardiovascular disease (CVD) such as atherosclerosis and mood disorders such as depression (Hemingway & Marmot, 1999; Rozanski et al., 2005). It is suggested that the pro-inflammatory and pro-thrombotic factors associated with stress promotes development of atherosclerosis (Hamer, 2006a). Additionally, prolonged video game sessions could lead to increased sedentary behavior which also increases risk of CVD,

metabolic disorders, obesity and ultimately mortality through decreased energy expenditure, increased energy intake, and dyslipidemia (Hamilton et al., 2007; Owen et al., 2010). Although, the exact physiological mechanisms and stimuli behind sedentary behavior driving these risks is still relatively unknown.

Challenge Hypothesis

The nature of the opponent can dictate the psychological and physiological responses when playing video games. Ravaja et al. (2006) show that physiological arousal (cardiac interbeat intervals or heart rate) increases against a human opponent rather than a computer while playing a portable gaming system (Game Boy Advance) and further increases when playing a friend. Furthermore, men have shown changes in various hormone levels in response to competition against other males (Geniole et. al.,

2017). Known as the “Challenge Hypothesis,” men’s testosterone tends to acutely increase when competing against other males in both athletic and non-athletic settings

(Wingfield et. al., 1990). Mazur et. al. (1992) investigated the acute testosterone response during chess competition and found that while both competitors increased acute testosterone, winners demonstrated a more pronounced increase compared to losers suggesting that winning induced greater changes in testosterone levels compared to losing. Steiner et. al., (2010) investigated acute testosterone response during a lab-setting poker match of 25-30 minutes and found that both winner and losers significantly increased acute testosterone levels by roughly 10%. Recently, Gray et al., (2018) investigated men’s hormone response to playing one match of League of Legends, a PC game. The participants, who were esport club members, competed against both computer

and human opponents. While salivary aldosterone levels decreased significantly against both opponents, they did not find any changes in testosterone, cortisol, dehydroepiandrosterone (DHEA), and androstenedione levels. Interestingly, testosterone,

DHEA, and androstenedione increases were positively correlated with play duration.

These preliminary results could be attributed to the fact that the participants competed against familiar competitors (in-group) rather than out-group competitors who the participants did not personally know. In addition, the short duration of play may have contributed to lack of a difference from baseline and when playing against a computer versus playing against a human. Perhaps longer duration of play would illicit a greater response. As of now, the study by Gray et al. (2018) is the only research we are aware of to investigate physiological responses to a current competitive, esports-style video games and this work is limited only to hormonal responses.

Cardiovascular Response to Gaming

There is currently little to no research on the cardiovascular responses of current competitive games. “Exergaming,” referring to using video games with motion controls and requires physical activity (e.g., Nintendo Wii), has been studied extensively in the past decade and has shown increases in physical activity and the corresponding increase in heart rate (HR) and blood pressure (BP) (Sween et al., 2014). However, the cardiovascular response to current esports-style competitive gaming which can be thought of as “sedentary” since it does not require any physical activity except for using a controller or mouse and keyboard, has not been researched. However, there have been studies in the past that have examined the acute physiological response to single-player,

sedentary video games that are now over 22 years old. Borusiak et al. (2008) investigated the cardiovascular response in male adolescents (11-15 years old) while playing Need for Speed 2 on the PlayStation 2 for two 12-minute long gaming sessions.

Results indicated a significant increase (p<.005) from baseline in average heart rate (HR)

(+13.1 bpm), systolic blood pressure (SBP) (+20.8 mmHg), and diastolic blood pressure

(DBP) (+12.1 mmHg) during the gaming sessions. Segal and Dietz (1991) investigated the cardiovascular response in 32 young males and females (16-25 years old) while playing Ms. Pac-Man for 30 minutes. Results indicated a significant increase from a standing baseline in HR, SBP, and DBP. It should be noted however that participants were standing while playing Ms. Pac-Man. Lastly, Wang and Perry (2006) investigated the cardiovascular and metabolic response in young males (7-10 years old) while playing

Tekken 3 on the original PlayStation for 15 minutes. Results indicated a significant increase from baseline in average HR (18.8%, p<.001), SBP (22.3%, p<.001), and DBP

(5.8%, p=.006). In addition, respiratory rate, oxygen consumption, and energy expenditure all increased significantly (p<.001) from baseline during the gaming session.

However, the magnitude of change in oxygen consumption was lower than what the threshold for moderate-intensity physical activity (achieving a metabolic equivalent

(MET) of 3 METs), thus the stress-induced increases in cardiovascular activity was disproportionate to metabolic needs. In other words, HR and BP increased to a greater extent than did oxygen consumption. This increased cardiovascular work without a concomitant increase in metabolic cost is akin to a psychological stress response and over time can lead to cardiovascular detriments. It is important to note that these studies did

not investigate a competitive aspect in that these games were single-player against computer avatars and not against other humans.

Cardiovascular Stress-reactivity

The direct competition in addition to the high cognitive demand induced by these competitive esports games may cause psychological or even physiological stress during gameplay. By definition, cardiovascular stress reactivity is the hemodynamic response to an acute psychological stressor (Manuck, Kasprowicz, & Muldoon, 1990; Treiber et al.,

2003). Standardized stress tests such as the Trier Social Stress Test (TSST; Kirschbaum,

Pirke, & Hellhammer, 1993) and the Montreal Imaging Stress Task (MIST; Dedovic et al., 2005) are established protocols in stress research in which they combine a cognitive task such as mental arithmetic with social pressure components. For example, the MIST has participants complete on a computer screen mental arithmetic tasks that are manipulated to be just beyond the individual’s mental capacity. In addition, the average times along with expected times are shown to induce social pressures to perform well.

These batteries, which are around 5 to 15 minutes in nature, have shown to induce cardiovascular stress response (Angelika Buske-Kirschbaum et al., 2002; Childs et al.,

2006; Dedovic et al., 2005; Jones et al., 2011; Kudielka et al., 2004). Particularly, heart rate and blood pressure increase significantly during these batteries. Furthermore, with the addition of social-evaluative threats such as performance ratings or another participant in the same room (which introduces competition), the responses have been shown to be exacerbated significantly (Childs et al., 2006; Dickerson & Kemeny, 2004).

While some studies did not report gender or age differences as it pertained to stress reactivity, others did (Jones et al., 2011; Kudielka et al., 2004). Jones et al. (2011) reported significantly higher HR and BP responses during the MIST in men compared to women. It was also reported that older participants had a greater BP response during the

MIST compared to younger individuals due to decreased arterial compliance.

Conversely, Kudielka et al. (2004) reported a significantly increased HR stress response during the TSST in females compared to males. Allen, Stoney, Owens, and Matthews

(1993) reported similar cardiovascular responses to the TSST and concluded that females were more “cardiac” reactors while men were more “vascular” reactors to the stress tasks.

However, the equivocal results from these studies make it difficult to determine the underlying mechanism of any potential gender or age differences to stress. The underlying mechanism could be task dependent (speech verse arithmetic tasks), anatomical in that gender differences exist in heart size, or through alterations in sympathetic and parasympathetic balance of the cardiovascular system.

Concisely, research shows that these stress tasks which require executive functioning in addition to social components can induce significant cardiovascular stress reactivity. Precisely, HR and BP are shown to increase significantly during these tasks.

While there is virtually no research examining competitive gaming, participation in this activity can be likened to these psychosocial stress tasks as it incorporates social- evaluative threats (competition against other humans) along with the complex cognitive processes required to perform well in the game. Therefore, it is reasonable to hypothesize

that esports-style gaming creates an environment which may elicit cardiovascular stress reactivity.

SAM and HPA Axes

During situations perceived as being acutely stressful, the 2 main pathways activated are the sympatho-adreno-medullary (SAM) axis and the hypthothalamus- pituitary-adreno (HPA) axis. Both axes are activated by the hypothalamus secreting corticotrophin-releasing hormone (CRH), which stimulates the pituitary gland to release adrenocorticotropic hormone (ACTH). In the more rapidly acting of these pathways, the

SAM axis, ACTH stimulates the adrenal medulla to peripherally release catecholamines, epinephrine and norepinephrine. In the HPA axis, ACTH stimulates the adrenal cortex to release cortisol (Chrousos & Gold, 1992).

Neuroimaging studies have produced equivocal results while undergoing the

MIST. Participants undergoing the MIST have shown decreased activation of the hippocampus, amygdala, ventral striatum, hypothalamus, dorsal and ventral prefrontal cortex, oribitofrontal cortices, temporal poles and anterior and posterior cingulate cortices

(Dedovic et al., 2009; Pruessner et al., 2008). In addition, hippocampus and amygdala activations have been found to be negatively correlated with stress-induced cortisol increases (Lederbogen et al., 2011; Pruessner et al., 2008). However, others have reported increased activation of the hypothalamus while undergoing the MIST (Lederbogen et al.,

2011). These equivocal results highlight the underlying complexity of investigating neural components of stress and the gap in understanding the neural mechanisms of stress-induced changes in the brain.

Cortisol Response to Stress

Cortisol, a glucocorticoid steroid hormone often referred to as the “stress hormone,” has multiple pathways and effects. Stress induced from psychosocial tasks and exercise causes activation of the HPA axis which causes an increase in cortisol secretion from the adrenal cortex (al’Absi & Arnett, 2000; Kirschbaum & Hellhammer, 1994;

Sapolsky, Krey, & McEwen, 1986). Cortisol inhibits GLUT4 translocation which results in a decrease in glucose tissue uptake to preserve blood glucose levels in case of stressful situations (Coderre, Srivastava, & Chiasson, 1991). In addition, cortisol mediates muscle protein catabolism through proteolysis and fat oxidation through lipolysis in order to stimulate gluconeogenesis and store glycogen (Brillon, Zheng, Campbell, & Matthews,

1995). Acute increases in cortisol in response to the TSST elicits an anti-inflammatory response and is positively correlated with increases in granulocytes and natural killer cells and negatively correlated with B cells (A. Buske-Kirschbaum, Kern, Ebrecht, &

Hellhammer, 2007). The cortisol response to stressors usually consists of three phases: basal activity, stress reactivity phase, and the stress recovery phase (McEwen, 1998).

Basal activity is unstimulated, non-stressed hypthothalamus-pituitary-adreno axis activity. The stress reactivity phase defines the cortisol increase from baseline in response to a stressor. Lastly, the stress recovery phase defines the cortisol return to baseline levels.

Cortisol response has been studied abundantly; particularly in response to a psychosocial stressor, it has been shown that plasma, serum, and salivary cortisol will increase in healthy adults (Het, Rohleder, Schoofs, Kirschbaum, & Wolf, 2009; C

Kirschbaum, Kudielka, Gaab, Schommer, & Hellhammer, 1999; Clemens Kirschbaum &

Hellhammer, 1994; Petrowski, Herold, Joraschky, Wittchen, & Kirschbaum, 2010;

Rohleder, Schommer, Hellhammer, Engel, & Kirschbaum, 2001). Equivocal evidence has reported that salivary cortisol peaks five minutes after cessation of the stress task

(Childs et al., 2006), 10-15 minutes after (Het et al., 2009; Petrowski et al., 2010), and even 30 minutes after (Rohleder et al., 2001).Of particular note, Het et al. (2009) investigated the response difference between a TSST and “placebo” TSST which did not include any uncontrollable or social-evaluative threats that the normal TSST entails. This study underwent two separate experiments with one experiment investigating a between- subjects design and the other investigating a within-subjects design. Results for both experiments showed that the TSST induced a significant cortisol response as previously described (increase in cortisol after stressor with a peak 15 minutes after completion of

TSST), while the “placebo” TSST did not induce a response suggesting that unpredictability and social-evaluative threats during a normal TSST induce a greater cortisol stress response.

Interestingly, Zorrilla et al. (1995) showed that subjects with affectively stable personalities and low trait anxiety had higher basal levels of cortisol compared with those who had less stable personalities and high trait anxiety. The authors hypothesize that individuals with stable personalities and higher basal cortisol levels would result in reduced stress response which would attenuate them from potential mood-disrupting effects of stress-induced cortisol increases. Schlotz et al. (2011) found that self-reported perceived stress during and after the TSST was significantly positively correlated with

salivary cortisol response in healthy young males. Lastly, Petrowski, Wintermann, and

Siepmann (2012) demonstrated that the cortisol response to a stress task (TSST) can be attenuated through repeated exposure. Schommer, Hellhammer, and Kirschbaum (2003) also demonstrated that salivary cortisol response was attenuated during each subsequent visit of TSST (four weeks between each visit) in 65 healthy adults. Takahashi et al. (

2005) demonstrated that higher trait anxiety in young adult males was significantly correlated with basal cortisol concentrations. However, they also showed that trait anxiety was not significantly correlated with stress-induced cortisol elevation.

Fitness level can attenuate the cortisol response (Traustadóttir, Bosch, & Matt,

2005). Traustadóttir and colleagues showed that physically more active women had a lower cortisol response to the Matt Stress Reactivity Protocol (MSRP) compared to sedentary women. In addition, older women had a higher cortisol reponse compared to younger women, especially in the sedentary category. Luger and colleagues (1987) provide evidence that trained individuals have a reduced cortisol response to exercise compared to untrained individuals in addition to trained individuals having an elevated basal cortisol concentrations. This is further evidenced by de Diego Acosta et al. ( 2001) in which it was found that young, sedentary men had lower basal cortisol concentrations compared to age-matched endurance-trained men. Contrastingly, Hackney, Fahrner, and

Gulledge ( 1998) show that there is no significant difference in basal cortisol concentration between endurance trained men and sedentary age-matched men.

Individual variability may have led to these equivocal results, however, fitness level should be considered when assessing cortisol response. Perhaps gamers who are more

physically fit would have an attenuated cortisol response to gaming compared to gamers who are less fit.

Cross-stressor Adaptations

The cross-stressor adaptation hypothesis states that exercise training will induce an adaptation in the stress-response system not only to exercise, but also to psychological stressors (Hamer, 2006; Sothmann et al., 1996). Specifically, physically fit aerobically- trained individual’s cardiovascular stress reactivity is blunted during a psychological stressor compared to unfit individuals (Brooke & Long, 1987). Crews and Landers (1987) meta-analysis of 34 studies concluded that aerobic fitness had a significant reduction in stress reactivity. While Jackson and Dishman’s (2006) meta-analysis of 73 studies concluded that while fitness was actually related to slightly increased stress reactivity, fitness was related to better psychological stress recovery. These equivocal results are most likely due to the inconsistencies in methodologies, stressors, and not controlling for acute exercise bouts that participants may have partaken in outside of the studies.

Acute exercise is considered an important modulator of the stress response

(Hamer, Taylor, & Steptoe, 2006). Specifically, the window following exercise is dominated by an increase in parasympathetic outflow which can attenuate the cardiovascular stress reactivity response. This may potentially relate to esports gaming in that acute exercise bouts might impact proceeding gaming sessions by attenuating this stress response. While exercise may have a protective effect against stress reactivity in esports gaming and this notion warrants future examination, the impact of physiologic stress response during esports gaming should first be established.

Need for Research

There is currently a lack of research examining the physiologic responses to competitive esports gaming. We believe, through initial exploratory testing, that heart rate and blood pressure will be significantly elevated during competitive esports-style game play. For this preliminary work we examined a single college-aged male who self- identified as a “gamer” (i.e., someone who regularly plays console or PC video games) during Fortnite play in a laboratory environment for 60 minutes. Heart rate was monitored throughout this session via a chest strap (Polar Electro Oy, Kempele, Finland).

For the entirety of the 60-minute game play session, heart rate was elevated from a baseline of 68 beats per minute (bpm) with peaks up to 150 bpm. This is potentially worrisome as along with the sedentary nature of playing these competitive games, the potential imbalance between the cardiovascular response and the relative absence of metabolic and peripheral demand could, over time, increase the risk for adverse health risks not yet identified in this rapidly growing demographic (i.e., competitive gamers).

Participation in competitive gaming is already at an all-time high. The popularity of games such as Fortnite and Apex Legends has impacted the young demographic as previously described. If competitive gaming induces physiological stress and corresponding cardiovascular reactivity over prolonged periods of time, the gamer demographic may be subject to chronic stress which can lead to many health detriments both psychologically and physiologically (McEwen, 2007). Chronic stress leads to increased risk of psychiatric disorders as well as increased risk of cardiovascular disease

(McEwen, 2007). Depression can develop through chronic stimulation of the HPA axis

induced by stressful events over time (Checkley, 1992; Juruena, Cleare, & Young, 2018).

Chronic stress can also impair vagal tone which results in increased resting HR and BP which ultimately leads to increased risk for CVD (Porges, 1995). Prolonged cortisol activation through increased HPA axis stimulation is also associated with immune system suppression and damage to hippocampal neurons (McEwen, 2007).

Fitness level can perhaps influence stress response during a competitive gaming session due to the cross-stressor adaptation hypothesis explained previously. Specifically, physically fit individuals may have an attenuated stress response during gaming. More so, it would be interesting to see if a proposed stress response would lead to an increase or decrease in actual gaming performance. This serves as another question that could be addressed in future studies once gaming performance measures can be defined. However, along with being physically fit, a decreased stress response during a sedentary activity could decrease the adverse health risks associated with high stress such as decreased risk of CVD or psychiatric disorders.

Purpose and Hypothesis

There were two primary aims of this study. The first aim of this study was to better understand behavioral and psychometric variables of the gamer population compared to non-gamers. As discussed earlier, competitive gaming has rooted itself in the younger demographic in the United States and little is known about the psychometric and behavioral characteristics of this population relative to their non-gamer peers. Thus, a greater understanding of this population is needed. Through a survey, we assessed and compared behavioral (e.g., gaming behavior, physical and sedentary activity) and anxiety

characteristics in self-identified college-aged gamers to a sample of college aged non- gamers. Due to the sedentary nature of playing video games, we hypothesized that gamers would be less physically active and more sedentary than non-gamers. In addition, due to the competitive nature of these games, we hypothesized that gamers would exhibit greater trait anxiety than non-gamers.

The second aim was to determine the cardiovascular response, or stress reactivity, while participating in competitive gaming in young adult gamers and non-gamers. In addition, we assessed body composition and physical fitness of both gamers and non- gamers through anthropometric measurements and a cardiorespiratory fitness (VO2 max) test. Stress reactivity while gaming was assessed by collecting salivary cortisol and recording HR, SBP, and DBP while gaming in two conditions: during a competitive multiplayer game against humans and during the same game against computer avatars.

We likened competitive gaming to stress tasks involving executive function and social- evaluative threats, thus based off previous literature, we hypothesized a stress response while gaming. We hypothesized that gamers and non-gamers would exhibit an increase in salivary cortisol, HR, and SBP in both conditions, and more specifically, we would see increased stress reactivity while participating in competitive gaming against humans compared to the same game against computer avatars because of the absence of a competitive drive. In addition, due to the sedentary nature of playing competitive video games, we hypothesized that gamers would have reduced aerobic fitness and body composition compared to non-gamers. Lastly, we hypothesized that those who are more

aerobically fit (regardless of gaming status) would have an attenuated stress response compared to those who were less physically fit.

CHAPTER III. METHODOLOGY

Aim 1

The first aim of this study assessed behavioral (e.g., physical activity, sedentary behavior) and psychometric properties (e.g., anxiety) through an anonymous survey.

Surveys were distributed throughout the campus at Kent State University in Kent, Ohio.

Participants

In order to qualify to take the survey, the individual must have been a student at

Kent State University and be between the ages of 18-25 years old. Similar to studies conducted by Lepp et al. (2014; 2013) in which they investigated the relationship between physical activity, sedentary behavior, cell phone use, and anxiety, there was a sample size of 306 university students.

Methods

Students were randomly approached (one in every four passing students) in various locations throughout campus by the research team. Those who qualified and consented to the anonymous survey completed a nine page self-report questionnaire

(Appendix A) that collected basic demographic information, video game playing characteristics, cellphone use, physical activity and sedentary behavior using the

International Physical Activity Questionnaire (IPAQ) (Booth, 2000), competitive anxiety using the Sport Competition Anxiety Test (SCAT) (Martens, Burton, & Vealey, 1990), and trait anxiety using the Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer,

1988).

Survey Instruments

General Questionnaire. The first part of the survey consisted of basic demographic information including age, gender, height, weight, grade level, and grade point average (GPA). In addition, the following self-report questions were asked to determine video game behavior and identity:

1. Do you consider yourself a “gamer?” yes or no

2. How many hours do you spend playing video games per week?

3. Do you generally feel stressed when playing a multiplayer game against other

humans? yes or no

4. Do you generally feel stressed when playing a casual single-player game? yes

or no

International Physical Activity Questionnaire (IPAQ). Participants reported their average daily physical activity (light (walking), moderate, and vigorous intensity) and daily sitting (sedentary) activity from the previous week through the validated IPAQ.

Previous research has established significant test-retest reliability (rs=.8) and construct validity (median rho = .3) for physical activity assessment through the IPAQ (Craig et al.,

2003). In addition, assessment of sedentary behavior through the IPAQ has been validated with significant test-retest reliability (r > .75) and construct validity (r > .3)

(Rosenberg, Bull, Marshall, Sallis, & Bauman, 2006). The IPAQ consisted of questions structured as so:

1. During the last 7 days, on how many days did you do vigorous physical

activities like heavy lifting, digging, aerobics, or fast bicycling?

2. How much time did you usually spend doing vigorous physical activities on

one of those days?

Weekly physical activity was calculated using the following equation: weekly physical activity score = (8 METs × Vigorous) + (4 METs × Moderate) + (3.3

METs × Light). Weekly sedentary behavior was calculated using the following equation: weekly sedentary behavior = (minutes of sitting per week day × 5) + (minutes of sitting per weekend day × 2).

Competitive Sport Anxiety Test (SCAT). The SCAT is a validated instrument with significant test-retest reliability and concurrent and construct validity that measures competitive anxiety during a sporting event (Martens et al., 1990). The instructions were modified however by replacing the word “sport” with “competitive multiplayer game” in order to assess anxiety while playing a competitive multiplayer video game. Each question was a three-point Likert scale anchored by “Rarely”, “Sometimes,” and “Often.”

An example of one of the 15 total questions is as followed:

1. Competing against others is socially enjoyable.

Responses were then summed and totaled for a final score. Questions 1, 4, 7, 10, and 13 were not scored to reduce the likelihood of an internal response-set bias. A score of less than 17 indicates a low level of anxiety, 17 to 24 an average level of anxiety, and more than 24 a high level of anxiety.

Beck Anxiety Inventory (BAI). The BAI is a validated state anxiety instrument that measures how bothered someone is by 21 common symptoms of anxiety with a four- point Likert scale anchored by “Not at all (0) to “Severely – it bothered me a lot (3)”

(Beck et al., 1988). Examples from the list include “numbness or tingling,” “heart pounding/ racing,” and “unable to relax.” The responses were summed and totaled for a score. A score of 0-21 indicates low anxiety, 22-35 indicates moderate anxiety, and more than 35 indicates very high anxiety. Previous research demonstrates the BAI has strong internal consistency (Coefficient Alpha = .92) and good test–retest reliability (r = .75;

(Beck et al., 1988). In addition, the BAI has proven to be a valid measure of anxiety with undergraduate students (Creamer, Foran, & Bell, 1995).

Statistical Analysis

All analyses were performed using SPSS for Windows (version 26.0, SPSS Inc,

Evanston, IL). Preliminary analyses were performed to investigate if violations of the assumptions of normality occurred using the Shapiro-Wilk test, linearity using lack of fit tests, and homoscedasticity using residual scatterplots. A two group (gender, gaming identity) ANOVA was used to compare outcome variables. Outcome variables included age, GPA, moderate and vigorous physical activity, sitting behavior, video game behavior (hours played and general stress), and anxiety. Correlational analysis will be performed between video game hours played and physical activity, sitting behavior, and anxiety. Multiple regressions models were run assessing gaming behavior as the criterion variable with the following predictor variables: physical activity, sitting behavior, anxiety, gender, age, and GPA.

Aim 2

This was a two group (gamers, non-gamers) by two gaming condition

(competitive gaming versus humans, non-competitive gaming versus computer avatars)

design with gaming conditions serving as within-subjects independent variables as seen in Table 1. Participants completed three visits and each visit occurred on separate days as seen in Figure 1. All data was collected at the Exercise Science laboratories at Kent State

University in Kent, Ohio.

Figure 1. Study design over three visits. Visits two and three consisted of the two gaming conditions (competitive or non-competitive) which were randomized.

Participants

A total of 16 young adults were recruited to participate in this phase of the study.

We recruited gamers (n = eight) and non-gamers (n = eight) alike. Our definition of

“gamer” entailed playing video games for at least 10 hours a week and self-identifying as a “gamer.” This study was the first to investigate cardiovascular response to current competitive gaming versus a non-competitive game in both gamers and non-gamers. If we liken competitive gaming to previous research investigating stress reactivity during tasks with social pressures and unpredictability such as the TSST, then we could extrapolate an appropriate sample size using G*Power analysis We chose to use Kudielka et al. (2004) due to similarities in design and analysis. Specifically, they investigated HR

and BP response to the TSST in young adults using a desired statistical power of 0.8 and an alpha of 0.05 and observed an effect size of 1.68 for HR reactivity during the TSST.

Given these values, a minimum sample size of six participants were needed to achieve significant differences in reactivity in a condition where subjects underwent a psychological stressor (TSST) versus a control condition. Also, Borusiak et al. (2008), who investigated the cardiovascular response in 17 male adolescents while playing Need for Speed 2, is the most recent study investigating cardiovascular response while gaming and produced an effect size of 1.59 for all changes in HR, SBP, and DBP during game play. This effect size would require a sample size of six to achieve a statistical power 0.8 given an a-priori alpha of 0.05. Using these estimated sample sizes, our proposed sample of 16 participants (eight per group) was more than adequate to assess potential stress reactivity. This larger sample size allowed for possible participant attrition and methodological differences in our proposed study and these previous investigations.

General Protocol

Participants visited for a total of three visits as illustrated in Figure 1. The first visit consisted of completion of the informed consent, a brief questionnaire, familiarization with Fortnite (if needed), body composition assessments, and a VO2 max test to assess cardiorespiratory fitness. Familiarization included an explanation of gameplay and an allotted time of 10 minutes to play the game and become familiar with the controls. The next two visits were randomized with at least 24 hours between each visit. Visits two and three took place at the same time of day to account for cortisol diurnal patterns. Participants were asked to refrain from caffeine, alcohol, and strenuous

exercise up to 12 hours before each experimental visit. In addition, the participant was asked to refrain from eating at least one hour before each experimental visit. The two experimental visits consisted of seated, quiet rest (baseline) for 15 minutes followed by

60 minutes of gaming with one visit having the gaming condition against humans

(competitive) and the other visit having the gaming condition against the computer (non- competitive).

The design for visits two and three is illustrated in Figure 2. Participants first answered a brief state anxiety survey (BAI) and gave a salivary cortisol sample (PRE) followed by 15 minutes of quiet resting (sitting) to record baseline values of HR and BP

(averaged over the last minute). After baseline, the participant played Fortnite for 60 minutes on a PC with the controller of his or her choice (Xbox One, PS4, or mouse and keyboard) against humans in the multiplayer mode (competitive) or against the computer in the single-player mode (non-competitive). Gaming condition order was randomized.

HR was continuously recorded during the entire duration while BP was recorded at the end of baseline, every three minutes while gaming, and at the end of recovery.

Immediately after the cessation of the gaming session, a salivary cortisol sample (IP) was taken. The participant then rested quietly for 30 more minutes with HR and BP averaged and recorded during the last minute. A last salivary cortisol sample was taken at the end

(30 minutes) of this recovery period (30P).

Baseline Gaming Recovery BP BP BP

15 minutes baseline 30 minutes recovery 60 minutes gaming against humans or computer

Figure 2. Experimental visits. During visits two and three, participants rested for 15 minutes for baseline, gamed for 60 minutes in one of two randomized conditions (competitive or non-competitive), and then rested for an additional 30 minutes for recovery. In addition to HR (continuous) and BP being monitored at baseline, during gaming, and during recovery, salivary cortisol was collected at three different time points: before baseline (PRE), immediately after gaming (IP), and 30 minutes after gaming (30P).

VO2max Protocol

VO2 max was assessed during the initial visit one on a cycle ergometer

(VELOTRON, QUARQ, Spearfish, SD, USA) using a metabolic cart (TrueOne 2400,

Parvo Medics, Sandy, Utah). A graded protocol similar to American College of Sports

Medicine (ACSM) guidelines was utilized. The first four stages of the protocol increased in 50W increments starting from 50W. These stages last three minutes. At 12 minutes, the stages increased by 30W every two minutes until volitional fatigue.

Gaming Protocol

Participants played Fortnite on PC using a controller of their choice (Xbox One,

PS4, mouse and keyboard). If the participant never played the game before, the rules were explained during the initial visit along with a brief period in the single player mode, termed Save the World, to allow them to get accustomed to playing the game. For the competitive condition (against humans), the participant played in the “solo” lobby against

99 other human participants. For the non-competitive condition (against the computer), the participant played the “Save the World” mode which consists of similar gameplay to the competitive condition except the player was defending themselves from hordes of computer-controlled avatars. The participant played for 60 minutes in each condition.

Heart rate was monitored continuously while automated BP measurements were taken every three minutes. Once the 60 minutes were complete, the monitor the participant was playing on was turned off, and the post gaming recovery period began in which the participant sat quietly for 30 minutes.

Variables

The following variables were collected.

Hemodynamic Measurements (Heart rate and blood pressure). Heart rate was continuously monitored with a Polar Vantage watch and chest strap (Polar Electro

Oy, Kempele, Finland) during each of the two experimental visits. Heart rate was averaged during the baseline period (BASEHR); the entirety of the 60 minute gaming period (GAMEHR); and the post-gaming recovery period (POSTHR).

Blood pressure (SBP and DBP) was monitored using a Cardiodynamics® (San

Diego, CA, USA) BioZ Dx cardiograph ICG machine with a non-invasive, automated oscillometric cuff two to three centimeters above the antecubital fold during each of the two experimental visits. Blood pressure was taken during the last minute of baseline

(BASESBP, BASEDBP); every three minutes for the entirety of the 60-minute gaming period (GAMESBP, GAMEDBP), and during the last minute of the post-gaming

recovery period (POSTSBP, POSTDBP). The average blood pressure was calculated for the entire 60-minute gaming period.

While heart rate and blood pressure do not fully describe hemodynamic response.

Measurements of cardiac output, vascular resistance, arterial compliance, and ventricular function would offer a more complete look at hemodynamics, however due to the nature of participants needing full control over their arms and hands while gaming, these measures would not be feasible for this study.

Salivary Cortisol. Salivary cortisol is shown to be highly correlated with serum cortisol, specifically, serum and salivary cortisol profiles and concentrations are synchronous over a 24 hour period (Dorn, Lucke, Loucks, & Berga, 2007). We collected saliva to assess salivary cortisol through passive drooling into polypropylene tubes and storing at -80°C. Analysis of salivary cortisol was performed using enzyme immunoassays (Salimetrics, State College, PA, USA). Saliva samples were collected throughout three different time points during each of the two experimental visits: immediately at the beginning of baseline (CPRE), immediately after the gaming condition (IP), and at the cessation of the 30-minute post-gaming recovery period (30P).

Cortisol usually spikes 50-80% half an hour after waking up, called the “cortisol awakening response,” and then rapidly decreases until around early afternoon where it declines slowly until bedtime. (Fries, Dettenborn, & Kirschbaum, 2009). Because of this diurnal pattern, all experimental visits are to take place during the same time of day, preferably during the afternoon when cortisol concentrations are lower to allow for more sensitivity to change.

Body Composition. During the first initial visit, participant’s body composition was assessed using a seven-site skinfold protocol in which the skin and subcutaneous fat was measured to the nearest millimeter in seven different sites on the participant’s right side (tricep, chest, midaxillary, subscapular, suprailiac, abdominal, and thigh utilizing skinfold calipers (Slim Guide, Creative Health Products, Plymouth, MI). Body fat percentage was calculated utilizing previously established equations (Heyward, 1998).

Survey Responses. During the first initial visit, a brief questionnaire (Appendix

B) assessing basic demographics, trait anxiety (BAI), caffeine consumption, and video game preferences and habits (single-player or multiplayer preference and video game hours played a week) was administered. During visits two and three before baseline, participants were given a slightly modified questionnaire with protocol adherence and

Beck’s Anxiety Inventory to assess state anxiety (Appendix C). While trait anxiety can be assessed with this instrument by initially asking how the participant feels during the past month, state anxiety can also be assessed with this instrument by initially asking how the participant feels during that same day in that moment of time (Beck et al., 1988).

Statistical Analyses

To answer our two main questions of this study, a two group (gamers, non- gamers) by two condition (competitive and non-competitive) by three time(before, during gaming, and after) analysis of variance (ANOVA) with repeated measures on condition and time was used to test for any significant interactions or main effects for HR, SBP,

DBP, and cortisol using SPSS (IBM, Version 26). Any significant main or interaction effects were further analyzed with post-hoc mean comparisons (e.g., t-tests). In addition,

we analyzed aerobic fitness (VO2 max), body composition (body fat percentage), and state and trait anxiety scores as possible covariates.

CHAPTER IV

BEHAVIORAL AND PSYCHOMETRIC ASSESSMENT OF GAMERS AND

NON-GAMERS

Introduction

Competitive video gaming (i.e., electronic sports, henceforth, esports) has rooted itself into modern culture especially among the younger demographic, specifically adolescents, teens, and young adults. While esports have been around for decades, the emergence of internet-based streaming services such as YouTube and Twitch in which gamers can stream their game play for an audience to access along with the popularity of certain games (e.g., Fortnite) have placed esports and the personalities associated with it into the current pop culture (Newzoo, 2018). Market research estimates global spending on video games for 2019 was $137.9 billion (Newzoo, 2018). Epic Games has reported there were over 250 million total players of their popular online, competitive game,

Fortnite, as of March 2019 (Iqbal, 2019). This popularity of esports has been growing at a rapid rate with current global revenues increasing 38% compared to 2017 (Newzoo,

2018). Colleges and universities across the world are even starting programs dedicated specifically towards esports and some are even offering scholarships and constructing multimillion-dollar arenas for intercollegiate esports tournaments (National Association of Collegiate Esports [NACE], n.d.). Because of this popularity, ease of access, monetary

prizes, and exposure through streaming services and social media, playing competitive video games might be especially enticing for adolescents and young adults.

While the popularity of esports gaming continues to grow, there is a lack of clarity in defining characteristics of those who regularly play these types of games (i.e., gamers). Better understanding this population is of potential importance as the most recent International Classification of Diseases, 11th edition, includes “Gaming Disorder” as a new disorder characterized by “impaired control over gaming, increasing priority given to gaming over other activities.” (World Health Organization, 2017). Additionally, section III of the most recent Diagnostic and Statistical Manual of Mental Disorders-5

(DSM-5) includes “Internet Video-Game Disorder” (Internet Gaming Disorder, IGD) among a new list of disorders that require additional research (American Psychiatric

Association, 2013). The DSM-5 characterizes IGD as “persistent and recurrent use of the

Internet to engage in games, often with other players, leading to impairment or clinically significant distress” (p. 795). The DSM-5 also uses nine diagnostic criteria ranging from addiction, withdrawal, and habits to diagnose the disorder. However, González-Bueso et al. (2018) argue in their review, these new classifications lack clarity and can easily be interpreted as too broad. For example, almost all major marketed video games require internet connection regardless of genre. In addition, some video games can be played cooperatively with other players, competitively against other humans, or even both. It is also unclear how mobile (cell-phone) games fit in the criteria. Resultingly, this topic has sparked a debate which requires further research to help clarify (Rumpf et al., 2018; van

Rooij et al., 2018). Thus, there is a need to further examine the behaviors and

characteristics of modern gamers. It does seem clear that gaming behavior can potentially be problematic and as esports popularity increases, the prevalence of these new disorders could follow suit. However, it is not yet clear how best to define these behaviors and disorders. Thus, it is important to further study the behaviors and characteristics of gamers to better understand this growing population.

The increasing popularity of esports does raise some specific concerns. Excessive, prolonged periods of game play could lead to increased sedentary behavior and reduced physical activity. Increased sitting and reduced physical activity are independent risk factors for developing cardiovascular disease, metabolic disorders, obesity and ultimately greater mortality risk through decreased energy expenditure, increased energy intake, and dyslipidemia (Hamilton et al., 2007; Owen et al., 2010). However, given the rising popularity of competitive video game play, there is a relative paucity of studies which have sought to compare physical activity and sedentary behaviors in gamers versus non- gamers.

While limited and equivocal, there is some extant literature examining the relationships between gaming, physical activity and sedentary behavior. Numerous studies have reported significant associations between greater screen time and decreased physical activity and greater sedentary behavior; this evidence is typically not limited to gaming but rather electronic screen time in general (Barkley, Lepp, & Salehi-Esfahani,

2016; Lepp et al., 2013; Owen et al., 2010). Specifically focused upon gaming, Arnaez et al. (2018) surveyed individuals of all ages at a gaming convention and found that gamers who reported game play for over three hours a day were less likely to perform moderate

to vigorous physical activity. Survey data from Ballard et al. (2009) support these findings as frequency of exercise was negatively correlated with the length of one gaming session in college-aged males. Contrastingly, Männikkö et al (2015) reported that problematic game behavior scored by a Game Addiction Scale was not associated with decreased physical activity in Finnish adolescents and young adults. However, through multiple regression analysis, this same study found that partaking in high level of moderate-to-vigorous physical activity was associated with lower problematic gaming symptoms. This means that those who participated in greater levels of moderate to vigorous physical activity displayed less problematic gaming symptoms. Taken together it appears that there is equivocal evidence of the relationship between physical activity, sedentary behavior and gaming which may be due to the methodological differences across these studies. These limited and equivocal findings highlight the need for more research on this topic.

In addition to the possibility that video game play may interfere with physical activity, allocating excessive amounts of time to gaming might also interfere with students’ academic responsibilities. Anand (2007) reported that video game play was negatively correlated with grade point average (GPA) in young adults. Additionally,

Weaver et al. (2013) showed that “high usage” college-student, video game players had significantly lower GPA compared to “low usage” gamers. This suggests that more time spent playing video games might equate to less time allocated for academic performance.

However, these studies did not discern between gamer types. With the emergence of esports programs across the country, it would be interesting to see if there are

discrepancies with those who play competitively (i.e. for clubs, teams, and programs) and those who do not.

With the increasing popularity of competitive games, gamers who play competitively or participate in esports events might have different behaviors of gamers who do not play competitively, thus the aforementioned relationships between gaming and behavioral outcomes/personal characteristics might be augmented. The above studies do not discern between these types of gaming preference (i.e. competitive gamers compared to non-competitive gamers). In addition, if video game play is a sedentary activity, previous research has shown that decreased physical activity and increased sedentary behavior is associated with symptoms of anxiety (Ströhle, 2009). It is possible that not only would gamers report greater anxiety than non-gamers but competitive gamers could have particularly high anxiety compared to non-competitive players due to the competitive nature of playing or augmented physical activity and sedentary behavior.

To our knowledge, no study has investigated the relationship between physical activity, academic performance, and anxiety and gaming behaviors in college-aged male and females, as males are predominantly represented in the literature. The primary aim of this study was to better understand behavioral, physical, academic, and psychometric characteristics of college students who self-report as individuals who regularly participate in video game play (i.e., gamers) versus those that do not (i.e., non-gamers). Variables of interest included: the amount of weekly video game play, physical activity, sedentary behavior, anxiety, GPA, and body mass index (BMI). As a secondary aim, a subset of gamers who were categorized as competitive were compared to participants who self-

identified as gamers but were not competitive. Competitive gamers were defined as any participant who was a member of an esports team (either University or club). We feel that developing a further understanding of health-related characteristics (e.g., physical activity, sedentary behavior, anxiety) of gamers who play competitively is warranted.

Competitive gamers likely might participate in tournaments, attend team-based events, or practice and play more than gamers who do not play competitively. As such, any unfavorable associations between gaming and the variables of interest may be exacerbated withing these competitive gamers. Based on prior evidence assessing the relationships between video game play, physical activity, sedentary behavior and academic performance, we hypothesized that self-identified gamers would be less physically active, more sedentary, and have a lower GPA than non-gamers. In addition, due to the competitive nature of marketed video games today, we hypothesized that gamers would have higher trait anxiety than non-gamers. We also hypothesized that a subset of highly competitive gamers (i.e., those on an esports team) would be less physically active, more sedentary, have a lower GPA and report greater anxiety than fellow gamers who did not identify themselves as competitive (i.e., recreational gamers who are not on an esports team).

Methods

Systematic random sampling was used to administer an anonymous, self-report, paper and pencil survey to 18-25 year old undergraduate and graduate students at a large, public university in the Midwestern United States. Research personnel were situated at various high foot-traffic areas across campus and asked each fourth person that walked

past if they wished to complete a brief survey. A sample of 306 (21.0 ± 1.8 years old, n =

185 females) college students completed the survey. Those who agreed to participate in the anonymous survey and were eligible completed a nine page self-report questionnaire measured basic demographic information, video game playing characteristics, cellphone use, physical activity and sedentary behavior using the International Physical Activity

Questionnaire (IPAQ) (Booth, 2000), and trait anxiety using the Beck Anxiety Inventory

(BAI; Beck, Epstein, Brown, & Steer, 1988)..

The Survey Instrument

The first page of the survey was a consent statement. After reading the consent, if participants continued with the survey, consent was implied. Participants were not asked to provide written consent as the survey was anonymous. The survey was fixed choice and was self-administered. Once completed, participants returned the survey to research personnel. The survey took approximately 10 minutes to complete and was approved by the university Institutional Review Board. In part one of the survey, participants provided basic demographic information including age, gender, height, weight, grade level, and grade point average (GPA). In addition, the following self-report questions were asked to determine video game behavior and identity:

1. Do you consider yourself a “gamer?” yes or no

2. How many hours do you spend playing video games per week?

3. Do you play video games competitively for Kent State? (club, JV, varsity)

Participants then reported their average daily physical activity (light (walking), moderate, and vigorous intensity) and daily sitting (sedentary) activity from the previous week

through the validated International Physical Activity Questionnaire (IPAQ) (Craig et al.,

2003; Rosenberg et al., 2006). Walking, moderate, and vigorous physical activity was reported as total minutes per week. Total weekly physical activity was calculated using the following equation: weekly physical activity score = (8 METs × Vigorous) + (4

METs × Moderate) + (3.3 METs × Light). Weekly sedentary behavior was calculated using the following equation: weekly sedentary behavior = (minutes of sitting per week day × 5) + (minutes of sitting per weekend day × 2). Thus weekly sedentary behavior was reported as minutes sitting per week.

Lastly, participants reported trait anxiety using the validated Beck Anxiety

Inventory (BAI), a four-point Likert scale of 21 common symptoms of anxiety (Beck et al., 1988; Creamer et al., 1995).

Statistical Analysis

All analyses were performed using SPSS for Windows (version 26.0, SPSS Inc,

Evanston, IL). To assess our hypotheses, a two sex (male, female) by two group (gamers, non-gamers) multivariate analysis of variance (MANOVA) was used to compare the following outcome variables: age, GPA, physical activity, sedentary behavior, video game behavior (minutes played per week), anxiety, and BMI. Pearson’s Correlation analyses were then performed between video game minutes played per week and the following variables: physical activity, sitting behavior, GPA, BMI, and anxiety. In an effort to identify the strongest predictors of gaming behavior, a stepwise regression was then performed assessing the following model:

Gaming minutes = physical activity (walking, moderate, vigorous and total) +

sedentary behavior + anxiety + age + BMI + GPA.

Lastly, to assess our hypothesis relating to the subset of gamers who played competitively for the university esports team, we ran secondary analyses to determine if there were any differences between the competitive and non-competitive gamers via independent samples t tests for the following variables: gaming minutes, physical activity, sedentary behavior, GPA, BMI, and anxiety.

Results

There was a significant gender by gaming identity interaction (F = 4.717, p =

0.031) for total gaming minutes per week (Figure 1). The interaction was due to a greater difference in gaming minutes for male gamers compared to male non-gamers (980 ± 625 minutes per week male gamers n = 61, 156 ± 153 minutes per week male non-gamers n

= 60) relative to the difference in gaming minutes for female gamers compared to female non-gamers (661 ± 477 minutes per week female gamers n = 25, 35 ± 95 minutes per week female non-gamers n = 160). There were no other significant interactions (F ≤

3.165, p ≥ 0.076). There were main effects of gender as, relative to males, females reported lower gaming minutes (F = 27.024, p < 0.001, 591 ± 642 minutes males, 122 ±

290 minutes females), BMI (F = 7.181, p < 0.01, 25.7 ± 4.8 males, 24.3 ± 4.6 females), greater anxiety (F = 15.199, p < 0.001, 11.28 ± 10.50 BAI males, 17.48 ± 13.38 BAI females), and GPA (F = 4.421, p = 0.036, 3.24 ± 0.40 males, 3.40 ± 0.43 females). The means and main effects of gaming identity are reported in Table 1. Gamers, relative to

non-gamers, reported significantly (F ≥ 4.7, p ≤ 0.03) greater gaming minutes and sedentary behavior as well as lower, walking, vigorous and total physical activity.

Table 1 Mean and Standard Deviations of Variables Between Gamers and Non-gamers Gamer Variable Mean SD n identity Gamer 887 601 86 Gaming minutes (per week)* Non-gamer 68.0 126 220 Total 298 498 306 Gamer 283.5 407.0 86 Moderate Physical Activity Non-gamer 310.8 361.1 220 (minutes per week) Total 303.2 374.1 306 Gamer 220.2 288.3 86 Vigorous Physical Activity* Non-gamer 293.9 325.6 220 (minutes per week) Total 273.2 316.8 306 Gamer 4909 4123 86 MET-mins per week* Non-gamer 6827 5311 220 Total 6288 5073 306 Gamer 4299 1888 86 Sedentary Behavior (minutes Non-gamer 3305 1571 220 sitting per week)* Total 3585 1722 306 Gamer 609.9 670.3 86 Walking (minutes per Non-gamer 979.5 1020.0 220 week)* Total 875.6 948.7 306 Gamer 13.7 12.2 86 Beck Anxiety Inventory Non-gamer 15.4 12.8 220 (total score) Total 14.9 12.6 306 Gamer 21.1 1.7 86 Age (years) Non-gamer 21.0 1.9 220 Total 21.0 1.8 306 Gamer 3.22 0.42 86 GPA Non-gamer 3.38 0.42 220 Total 3.34 0.43 306 BMI Gamer 25.3 5.7 86

Non-gamer 24.7 4.3 220 Total 24.9 4.7 306 *Significant main effect (F ≥ 4.7, p ≤ 0.03) of gamer identity group (gamers, non-gamers)

Correlation analyses are reported in Table 2. Video game minutes was significantly and positively correlated to sedentary behavior (r = 0.179* p = 0.001) and significantly and negatively correlated to GPA (r = 0.30 p < 0.001). In other words, high gaming minutes was predictive of high sitting and a lower GPA. Multiple stepwise regression revealed three significant (p ≤ 0.001) predictor variables associated with gaming minutes per week: sedentary behavior (ΔR2 = 0.096, ΔF = 32.1), walking activity

(ΔR2=0.033, ΔF=11.364), and GPA (ΔR2 = 0.019, ΔF =6.567). Within the final model, sedentary behavior (β = 0.309) was positively associated with gaming minutes and both walking (β = -0.182) and GPA (β= -0.136) were negatively associated with gaming minutes. In other words, according to the regression, those reporting greater gaming minutes per week sat more, walked less and had lower GPAs than students who reported lower weekly gaming minutes. There were no other significant predictors in the regression model.

1200

1000 Gamers Non-gamers

800

600

400 Gaming Gaming (min/week)

200

0 Males Females

Figure 3. Gaming minutes per week. There was a significant interaction between gender and gaming identity (F=4.717, p=0.031) for total gaming minutes per week. There was a greater increase in gaming minutes for male gamers compared to male non-gamers relative to the increase in gaming minutes for female gamers compared to female non- gamers.

Table 2 Pearson’s Correlation Coefficients (R) and Significance (P) Between Variables

Gaming GPA Anxiety MET Vigorous Moderate Walking Sedentary BMI Minutes mins/week intensity intensity activity behavior activity activity

r = 1 r = -0.179* r = 0.047 r = -0.100 r = -0.071 r = -0.039 r = -.101 r = 0.301* r = 0.077 Gaming p = 0.001* p = 0.398 p = 0.069 p = 0.199 p = 0.481 p = 0.066 p < 0.001* p = 0.160 Minutes

r = 1 r = 0.099 r = -0.003 r = 0.023 r = -0.032 r = 0.005 r = -0.041 r = -0.156 p =0.080 p = 0.953 p = 0.676 p = 0.564 p = 0.924 p = 0.462 p < 0.01 GPA

r = 1 r = 0.028 r = -0.012 r = -0.053 r = 0.082 r = 0.072 r = 0.036 p = 0.622 p = 0.824 p = 0.345 p = 0.140 p = 0.201 p = 0.525 Anxiety

r = 1 r = 0.725* r = 0.698* r = 0.732* r = -0.044 r = 0.026 MET p < 0.001* p < 0.001* p < 0.001* p = 0.426 p = 0.633 mins/week

r = 1 r = 0.505* r = 0.131* r = -0.145* r = -0.026 Vigorous p < 0.001* p = 0.018* p < 0.01* p = 0.642 intensity activity

r = 1 r = 0.257* r = -0.130* r = -0.008 Moderate p < 0.001* p = 0.017* p = 0.878 intensity activity

r = 1 r = 0.116* r = 0.062 Walking p = 0.034* p = 0.259 activity

r = 1 r = 0.198* Sedentary p < 0.001* behavior

Independent samples T-tests between competitive (n = 17) gamers who were affiliated with the university’s club, junior varsity, or varsity esports teams and non- competitive gamers (n = 83) showed that competitive gamers had significantly more (t =

4.012, p < 0.001) gaming minutes than non-competitive gamers (1444 ± 573 minutes per week competitive gamers, 820 ± 585 minutes per week non-competitive gamers). In

addition, BMI was shown to be significantly greater (t = 4.816, p = 0.048) in competitive gamers (27.8 ± 8.4 BMI competitive gamers, 24.7 ± 4.9 BMI non-competitive gamers).

No other differences were observed as reported in Table 3 (p ≥ 0.079).

Table 3 Mean and Standard Deviations of Variables Variable Group Mean SD n Gaming Minutes (minutes Competitive 1444 573 17 per week)* Non-competitive 821 585 83 Competitive 496 660 15 Walking (minutes per week) Non-competitive 673 721 83 Moderate Physical Activity Competitive 116 146 17 (minutes per week) Non-competitive 295 409 83 Vigorous Physical Activity Competitive 129 183 17 (minutes per week) Non-competitive 230 291 82 Competitive 3284 2571 15 MET-mins per week Non-competitive 5269 4172 82 Sedentary Behavior (minutes Competitive 4860 1644 15 sitting per week) Non-competitive 4140 1833 83 Beck Anxiety Inventory Competitive 17.4 14.2 14 (total score) Non-competitive 13.2 11.9 79 Competitive 27.8 8.38 17 BMI* Non-competitive 24.7 4.90 81 Competitive 3.12 0.49 17 GPA Non-competitive 3.22 0.41 81 Mean and standard deviations of dependent variables between gamers who were affiliated with the university’s club, junior varsity, or varsity competitive esports teams (competitive) and those who were not affiliated (non-competitive). *Significant difference (t ≥ 4.816, p ≤ 0.048)

Discussion

This is the first study to our knowledge to investigate gaming behavior (gaming minutes per week and competitive nature), physical activity, academic performance, and anxiety in college-aged males and females. Our hypothesis that gamers would have increased trait anxiety was not supported. There were no observed relationships or differences between trait anxiety and any other variable other than the fact that females displayed higher anxiety than males. As expected, gamers reported significantly more minutes per week of game play than non-gamers and male gamers played significantly more minutes per week than female gamers. Our hypothesis that gamers would be less physically active and more sedentary was supported according to our results.

Specifically, gamers reported significantly less walking, vigorous physical activity, MET- minutes per week, and significantly more sedentary activity per week compared to non- gamers. In addition, gaming minutes were significantly and positively associated with sedentary activity in both the correlation and regression analyses. While this is not surprising because gaming is typically a sedentary activity to begin with, it is concerning as excessive sedentary behavior can have negative health implications. It is known that sedentary behavior increases risk of CVD, metabolic disorders, obesity and ultimately mortality through decreased energy expenditure, increased energy intake, and dyslipidemia (Hamilton et al., 2007; Owen et al., 2010). In addition, as a separate risk factor, a decrease in physical activity is associated with an increase in CVD disease, colon cancer, stroke, and body fat percentage, among other adverse health effects (Lee,

Folsom, & Blair, 2003; J. Li & Siegrist, 2012; Wolin, Yan, Colditz, & Lee, 2009).

While previous literature has focused on male gamers specifically, the few studies that have included both genders have demonstrated that males play video games more frequently and for longer durations compared to females in adolescence and college-aged individuals (Padilla-Walker, Nelson, Carroll, & Jensen, 2010; Roberts & Foehr, 2004).

Padilla-Walker et al. (2010) found that college-aged males had significantly higher frequency of video game use (times per week) compared to females. Roberts and Foehr’s

(2004) book reported that adolescent males had significantly more playing time than females. Melkevik et al. (2010) meta-analysis found that boys engaged in significantly more screen-related sedentary behavior than females. This study further supports these claims as college-aged males played video games 319 minutes per week more than college-aged females. It should be noted that there is still a disproportionate number of male gamers (61) to female gamers (25) which may have impacted the results.

Interestingly, gaming minutes was significantly negatively correlated with GPA.

While there was no significant main effect of gaming identity on GPA, the F statistic was trending towards significance. These findings coincide with the findings from Anand

(2007) and Weaver et al. (2013) who demonstrated that video game usage was negatively associated with GPA. As video game popularity and culture continues to grow in adolescents and young adults across the world, this is potentially worrisome especially since the emergence of online streaming and the popularity of games such as Fortnite might be changing the landscape of video game use going forward. Free to play games such as Fortnite and its prevalence in the youth’s culture along with the increasing number of online streaming subscriptions to Twitch, Mixer, and their corresponding

celebrities (termed “influencers”) are making it evident that youth are invested in gaming culture (R. Li, 2017; Newzoo, 2018; SuperData Research, 2016). If video games are more prevalent amongst the youth and influences academics such as having a negative relationship with GPA, then further research should investigate.

Within the subset of gamers, competitive gamers who were affiliated with the university’s club, junior varsity, or varsity esports teams reported 76% more gaming minutes per week than non-competitive gamers. In addition, the competitive gamers had a 3.1 higher BMI compared to non-competitive gamers. While these magnitudes of differences are surprising, it is important to note that presently only 17 participants identified themselves as competitive gamers for the university. With such a small sample size, it might be inappropriate to draw any conclusions for the general competitive gamer population. However, this result does suggest the possibility that competitive gamers many be different than other, non-competitive, gamers. Further research examining these competitive gamers is therefore warranted.

It should be noted that this study has limitations. The self-report nature of the survey might lend itself to under or over estimations in some results, thus caution must be taken interpreting these data. Future research utilizing objective measures is warranted.

This is also a non-experimental study; therefore, we cannot draw any conclusions about causality. For example, while it is possible that allocating large amounts of time to gaming does increase sedentary behavior, it is also possible that individuals who are highly sedentary are drawn to video games but would be sedentary even if the games were not available. In addition, this research was conducted at a single, large, public

university in the American Midwest. Results may be different in other locations and among different populations. Our study could have also benefited from a larger sample size including a larger number of participants from the competitive gaming population.

However, this population is likely relatively small. In order to better understand this population, examining multiple sites (e.g., several universities with esports teams) may be necessary.

In conclusion, our data suggest that gamers, relative to non-gamers, have decreased physical activity and GPA while having increased sedentary behavior. While causality cannot be inferred, we suggest that the drastic increase in gaming minutes per week compared to non-gamers is allocating less time to being physically active and engaging in academic activities. Of particular note, the drastic increase of gaming minutes in competitive gamers compared to non-competitive gamers is interesting in addition to competitive gamers having increased BMI compared to non-competitive gamers. If competitive gamers are gaming much more than gamers and non-gamers alike, then the relationships we observed and potential health and psychosocial risks might be more exaggerated in this population. In the future, especially as schools across the nation adopt esports-focused programs, further investigation on gaming behavior and the resulting health and academic implications is warranted. Longitudinal studies should investigate video game use and other health factors such as physical activity or psychosocial factors such as mental health or academic performance. As gaming culture continues to evolve, it is important to understand the gamer demographic. While taking

caution of the potential risk factors involved, the many benefits and opportunities associated with gaming can positively impact the future generation.

CHAPTER V

CARDIOVASCULAR STRESS RESPONSE WHILE PLAYING FORTNITE IN

GAMERS AND NON-GAMERS

Introduction

Competitive multiplayer video gaming (i.e., electronic sports or esports), gaming in which you compete against other human players is a highly popular activity in young adults and teens. For example, Epic Games, the creator of the popular esports game,

Fortnite, has reported there were over 250 million total players as of March 2019 (Iqbal,

2019). In addition, the amount of online streaming on social media streaming networks such as YouTube and Twitch dedicated to competitive gaming has also increased dramatically (Newzoo, 2018). It is predicted that in 2019, there will be an estimated 743 million gaming viewers worldwide (SuperData Research, 2016). Esports tournaments, which pit teams or individual gamers against one another for cash prizes either in specifically-designed gaming venues or from the comfort of one’s own home, are also rapidly growing in popularity due to the ease of streaming and quick rise of participation from adolescents and young adults (Newzoo, 2018). Epic Games, the creator of Fortnite, has already pledged to dedicate $100 million in prize money and launched the first

Fortnite World Cup in 2019 (The Fortnite Team, 2018). Epic Games reportedly grossed a

$3 billion profit in 2018, which compares similarly to Amazon’s $3 billion profit in 2017

(Russell, 2018). Many other competitive video games such as League of Legends,

Overwatch, Call of Duty, and Rocket League have also garnered worldwide audiences. In

2014, there were 67 million League of Legends players monthly (R. Li, 2017). This game

also had an annual revenue of around $1 billion making it the most popular global esport during at that time (R. Li, 2017).

Despite the growing popularity of competitive gaming, there is a paucity of research examining the behaviors of gamers and the effects of gaming. If we consider competitive multiplayer gaming as a potentially different modality than casual gaming, we can start to differentiate the potential social, psychological, and physiological effects of this distinct activity. Competitive multiplayer gaming requires good communication skills in addition to a variety of other neurological processes. For example, many games include or require fast information processing and the need to make quick fine tune motor responses under severe time constraints, a high degree of working memory, planning, and goal setting (there are many items to keep track of simultaneously along with many motor tasks that require rapid and precise execution), rapid switching of attention from aimed targets to a more distributed field of view and vice versa, and a high degree of clutter and distraction in that many items of interest are dispersed among many non-target items

(Bediou et al., 2018). In addition, the competitive nature of these games may induce psychological and physiological effects. Men have shown changes in various hormone levels in response to competition against other males (Geniole et. al., 2017). Known as the “Challenge Hypothesis,” men’s testosterone tends to acutely increase when competing against other males in both athletic and non-athletic settings (Wingfield et. al.,

1990).

The social and competitive nature of many of today’s most popular games such as

Fortnite, League of Legends, and Call of Duty in addition to the high cognitive demand

induced by these competitive games may cause psychological or even physiological stress which deserves to be investigated. If we liken competitive multiplayer gaming to previous research investigating stress reactivity during tasks with social pressures and unpredictability, we can establish a foundation for this research. By definition, cardiovascular stress reactivity is the hemodynamic response to an acute psychological stressor (Manuck et al., 1990; Treiber et al., 2003). Standardized stress tests such as the

Trier Social Stress Test (TSST; Kirschbaum, Pirke, & Hellhammer, 1993) and the

Montreal Imaging Stress Task (MIST; Dedovic et al., 2005) are established protocols in stress research in which they combine a cognitive task such as mental arithmetic with social pressure components. For example, the MIST has participants complete on a computer screen mental arithmetic tasks that are manipulated to be just beyond the individual’s mental capacity. In addition, the average times along with expected times are shown to induce social pressures to perform well. These batteries, which are around 5 to

15 minutes in nature, have shown to induce cardiovascular stress response (Angelika

Buske-Kirschbaum et al., 2002; Childs et al., 2006; Dedovic et al., 2005; Jones et al.,

2011; Kudielka et al., 2004). Particularly, significant increases in heart rate and blood pressure have been reported during these batteries. Furthermore, with the addition of social-evaluative threats such as performance ratings or another participant in the same room (which introduces competition), the responses have been shown to be exacerbated significantly (Childs et al., 2006; Dickerson & Kemeny, 2004). Cardiovascular stress reactivity has emerged as an independent risk factor for cardiovascular disease (CVD); specifically, stress reactivity from psychosocial stress in youth has been shown to be

linked to subclinical CVD risks particularly through associations with carotid artery intima-media thickness (Lambiase, Dorn, & Roemmich, 2013; Roemmich et al., 2011).

Psychosocial stress has also been shown to account for 30% of the population’s attributable risk of acute myocardial infarction (Yusuf et al., 2004). Competitive gaming exposes the gamer to many of the same potential stressors (e.g., cognitive tasks, social evaluation) contained within these aforementioned batteries. Given this possibility and their popularity, understanding the stress response to competitive gaming is warranted.

While there is a significant amount of research examining the cardiovascular response to playing physically interactive video games (“exergaming”)(Sween et al.,

2014), many of which require physical activity to play, there is comparatively less work done on traditional video games and no work we are aware of examining competitive esports type games outside a recently published study conducted by Gray et al. (2018).

Gray et al. (2018) investigated men’s hormone response to playing one match of League of Legends, a computer game, against teammates or computer bots. Results from that study were equivocal: salivary aldosterone levels decreased significantly against both opponents and they did not find any changes in testosterone, cortisol, dehydroepiandrosterone (DHEA), and androstenedione levels. Interestingly, testosterone,

DHEA, and androstenedione concentrations were positively correlated with play duration. In other words, longer play was predictive of greater concentrations of these hormones. There have only been a handful of studies that have investigated heart rate

(HR) and blood pressure (BP) response while playing traditional sedentary video games

(Borusiak et al., 2008; Segal & Dietz, 1991; Wang & Perry, 2006). While these studies

did indeed show statistically significant increases in HR and BP in children, adolescents, and young adults while playing, there were limitations. Borusiak et al. (2008) showed significant increases in HR (+13.1 bpm), systolic blood pressure (SBP) (+20.8 mmHg), and diastolic blood pressure (DBP) (+12.1 mmHg) while playing Need for Speed 2 on the

PlayStation 2. However, the gaming sessions were only 12 minutes in length and the adolescents were connected to 3-lead ECG electrodes and had a metabolic mask on. The researchers acknowledged that the cardiac reactivity observed might be attributed to the laboratory setting and equipment attached to them. Segal and Dietz (1991) showed significant increases in HR, SBP, and DBP in young males and females while playing the arcade game, Ms. Pac Man. However, the participants were standing while playing which may have led to increased cardiac reactivity. Wang and Perry (2006) showed significant increases in HR (18.8%), SBP (22.3%), and DBP (5.8%) in seven to ten-year-old males during 15 minutes of playing Tekken 3 on the PlayStation 1. Again, these participants had metabolic masks fitted and 12-lead ECG electrodes attached to them while playing. It is important to note that these studies did not investigate modern, competitive esports- style games. Instead the extant literature examined games which were single player against computer avatars and not against other humans.

In addition, fitness, body composition, and trait anxiety may influence stress reactivity while gaming. For example, physically fit, aerobically-trained individual’s cardiovascular stress reactivity is blunted during a psychological stressor compared to unfit individuals (Brooke & Long, 1987). Crews and Landers (1987) meta-analysis of 34 studies concluded that aerobic fitness had a significant reduction in stress reactivity.

While Jackson and Dishman’s (2006) meta-analysis of 73 studies concluded that while fitness was actually related to slightly increased stress reactivity, fitness was related to better psychological stress recovery. These equivocal results are most likely due to the inconsistencies in methodologies, stressors, and not controlling for acute exercise bouts that participants may have partaken in outside of the studies. Lastly, state and trait anxiety has been found to be correlated with stress reactivity: higher stress responses were associated with higher state and trait anxiety scores (Takahashi et al., 2005).

The purpose of this study was to determine the cardiovascular response, or stress reactivity, while participating in competitive, esports-style gaming in young adults who identify as gamers versus a group of non-gamers. In addition, we assessed body composition, aerobic fitness (VO2max), and anxiety of both gamers and non-gamers as potential moderators and/or mediators of stress reactivity during competitive gaming. We liken competitive gaming to stress tasks involving executive function and social- evaluative threats such as the TSST, thus based off previous literature which has examined non-gaming psychological stressors, we hypothesized that competitive, esports-style gaming will similarly induce cardiovascular stress reactivity (Angelika

Buske-Kirschbaum et al., 2002; Childs et al., 2006; Dedovic et al., 2005; Jones et al.,

2011; Kudielka et al., 2004). To evaluate competitive gaming, we had gamers and non- gamers play two different modes, or conditions, of Fortnite for 60 minutes: a single- player mode against computer avatars and the flagship multiplayer “Battle Royale” mode in which the participant is pitted against 99 other humans. We hypothesized that gamers and non-gamers would exhibit an increase in heart rate (HR) and systolic blood pressure

(SBP) in both conditions: playing versus the computer and playing versus other humans.

However, we also hypothesized that participants would exhibit increased stress reactivity while participating in the competitive “Battle Royale” mode against humans versus the single-player mode because of the social-evaluative threats of the “Battle Royale” mode.

In addition, due to the sedentary nature of playing competitive video games, we hypothesized that gamers would have reduced aerobic fitness and increased body fat percentage compared to non-gamers. Lastly, we hypothesized that those who are more aerobically fit (i.e., greater VO2 max) would have an attenuated stress response compared to those who were less aerobically fit.

Methods

This was a two group (gamers, non-gamers) by two gaming condition

(competitive gaming versus humans, non-competitive gaming versus computer avatars) design with gaming conditions serving as within-subjects independent variables.

Participants completed three visits on separate days as seen in Figure 4. All procedures were approved by the Institutional Review Board at Kent State University in Kent, Ohio.

Figure 4. Study design over three visits. Visits two and three consisted of the two gaming conditions (competitive or non-competitive) which were randomized.

Participants

A total of 16 young adult males (23.6 ± 2.0 years old) were recruited to participate in this phase of the study. We recruited gamers (n = eight) and non-gamers (n

= eight) alike. Our definition of “gamer” entailed playing video games for at least 10 hours a week, being familiar with Fortnite, and self-identifying as a “gamer.” We likened competitive gaming to previous research investigating stress reactivity during tasks with social pressures and unpredictability such as the TSST, then we extrapolated an appropriate sample size using G*Power analysis. We chose to use Kudielka et al. (2004) due to similarities in design and analysis. Specifically, they investigated HR and BP response to the TSST in young adults using a desired statistical power of 0.8 and an alpha of 0.05 and observed an effect size of 1.68 for HR reactivity during the TSST. Given these values a minimum sample size of six participants would be needed to achieve significant differences in reactivity in a condition with a condition where subjects underwent a psychological stressor (TSST) versus a control condition. Also, Borusiak et al. (2008), who investigated the cardiovascular response in 17 male adolescents while playing a single-player video game (Need for Speed 2), was the most recent study investigating cardiovascular response while gaming and produced an effect size of 1.59 for all changes in HR, SBP, and DBP during game play. This effect size would similarly require a sample size of six to achieve a statistical power 0.8 given an a-priori alpha of

0.05. Using these estimated sample sizes, our current sample of 16 participants (eight per group) was deemed to be adequate to assess potential stress reactivity. This larger sample

size allowed for possible participant attrition and methodological differences in our proposed study and these previous investigations.

Protocol

Participants came in for a total of three visits as illustrated in Figure 4. The first visit consisted of completion of the informed consent, a brief questionnaire, familiarization with the two gamming conditions (if needed), body composition assessments, and a VO2 max test to assess cardiorespiratory fitness. Familiarization included an explanation of Fortnite gameplay and an allotted time of 10 minutes to play the game in the competitive and non-competitive conditions and become familiar with the controls. The next two visits were randomized with at least 24 hours between each visit.

Visits two and three took place at the same time of day to account for any diurnal patterns. Participants were asked to refrain from caffeine, alcohol, and strenuous exercise up to 12 hours before each experimental visit. In addition, the participant was asked to refrain from eating at least one hour before each experimental visit. The two experimental visits consisted of seated, quiet, rest (baseline) for 15 minutes followed by 60 minutes of gaming and 30 minutes of quiet recovery with one visit having the gaming condition against humans (competitive) and the other visit having the gaming condition against the computer (non-competitive). Participants were left alone during baseline, gaming, and recovery; however, research personnel had to go into the gaming room when blood pressure measurement had to be read every three minutes. This was to mitigate any potential external or social stress as to avoid “white coat syndrome.”

The design for visits two and three is illustrated in Figure 5. Participants first answered a brief state anxiety survey (BAI) followed by 15 minutes of quiet resting

(sitting) to record baseline values of HR and BP. After baseline, the participant played

Fortnite for 60 minutes on PC using a controller of his choice (Xbox, PS4, or mouse and keyboard) against humans in the multiplayer mode (competitive) or against the computer in the single-player mode (non-competitive). Gaming condition order was randomized.

HR was continuously recorded during game play while BP was recorded every three minutes. Immediately after the cessation of the gaming session, the participant answered a brief state anxiety survey. The participant then rested quietly for 30 more minutes with

HR being monitored continuously and BP taken during the last minute of the recovery period. To minimize any external stress or add any potential influence, the protocol took place in a laboratory setting while all research personnel remained outside the room where the participant played the games except when it was time to record the automated blood pressure reading every three minutes. Participants were also monitored through closed circuit television outside the gaming room throughout each protocol.

Baseline Gaming Recovery BP BP BP

15 minutes baseline 30 minutes recovery 60 minutes gaming against humans or computer

Figure 5. Experimental visits. During visits two and three, participants rested for 15 minutes for baseline, played for 60 minutes in one of two randomized conditions (competitive or non-competitive), and then rested for an additional 30 minutes for recovery. HR was measured continuously throughout the entire protocol and BP was measured during the last minute of baseline, during the entire duration of gaming, and during the last minute of recovery.

VO2max Protocol

VO2 max was assessed during the initial visit one on a cycle ergometer

(VELOTRON, QUARQ, Spearfish, SD, USA) using a metabolic cart (TrueOne 2400,

Parvo Medics, Sandy, Utah). A graded protocol similar to American College of Sports

Medicine (ACSM) guidelines will be utilized. The first four stages of the protocol increased in 50W increments starting from 50W. These stages lasted three minutes. At

12 minutes, the stages increased by 30W every two minutes until volitional fatigue.

Body Composition

During the first initial visit, participant’s body composition was assessed using a seven-site skinfold protocol in which the skin and subcutaneous fat was measured to the nearest millimeter in seven different sites on the participant’s right side (tricep, chest, midaxillary, subscapular, suprailiac, abdominal, and thigh utilizing skinfold calipers

(Slim Guide, Creative Health Products, Plymouth, MI). Body fat percentage was calculated utilizing previously established Jackson and Pollock equations (Heyward,

1998).

Gaming Protocol

Participants played Fortnite on PC using a controller of their choice (Xbox One,

PS4, mouse and keyboard). If the participant has not played the game before, the rules were explained during the initial visit along with a brief period in the single player mode, termed “Save the World,” to allow them to get accustomed to playing the game. For the competitive condition (against humans), the participant played “Battle Royale” mode in

the “solo” lobby against 99 other human participants. A solo lobby indicates all players in the lobby are not on a team and everyone competes against each other in a “free-for-all.”

Playing in a solo lobby allowed us to control for potential external influences such as playing and communicating with teammates. The goal of Battle Royale is to be the last team or player (in this case, an individual player) standing. Once a player is eliminated, the match is over for the player. If the participant was eliminated, he was instructed to start another match immediately. For the non-competitive condition (against the computer), the participant played the “Save the World” mode which consists of similar gameplay to the competitive condition except the player completes objectives and defends themselves from hordes of computer-controlled avatars. The participant played this mode in a private lobby to ensure that the participant was playing alone. For both conditions, the participant was given the choice to play with the lab’s Fortnite account or his own account. The participant played for 60 minutes in each condition. HR was monitored continuously while automated BP measurements were taken every three minutes. The participant played by resting their arms on the table in front of them as to maintain consistent blood pressure measurements. Once the 60 minutes were complete, the monitor the participant was playing on was turned off, and the post gaming recovery period began in which the participant sat quietly for 30 minutes.

Hemodynamic Measurements (Heart rate and blood pressure)

Heart rate was continuously monitored (second-by-second bpm) with a Polar

Vantage watch and chest strap (Polar Electro Oy, Kempele, Finland) throughout each of the two experimental visits. Average heart rate was recorded during the 15-minute

baseline period; the entirety of the 60-minute gaming period; and the last 15-minutes of the post-gaming recovery period. In addition, peak heart rate was also recorded during baseline, gaming, and recovery during each gaming condition.

Systolic (SBP) and diastolic blood pressure (DBP) was monitored using a

Cardiodynamics® (San Diego, CA, USA) BioZ Dx cardiograph ICG machine with a non-invasive, automated oscillometric cuff two to three centimeters above the antecubital fold during each of the two experimental visits. When taking the BP measurements during baseline and recovery, the participant had their arms resting on the table in front of them. Blood pressure was taken during the last minute of baseline; the average of every three minutes for the entirety of the 60-minute gaming period, and during the last minute of the post-gaming recovery period. Peak BP was also recorded during each gaming condition for additional analysis.

Survey Responses

During the first initial visit, a brief questionnaire (Appendix B) assessing basic demographics, trait anxiety (BAI), and video game preferences and habits (single-player or multiplayer preference and video game hours played a week) was administered for entry criteria. During visits two and three before baseline, participants were given a slightly modified questionnaire with caffeine, alcohol, and strenuous activity avoidance confirmation and state anxiety (Appendix C). While trait anxiety can be assessed with this instrument by initially asking how the participant feels during the past month, state anxiety can also be assessed with this instrument by initially asking how the participant

feels during that same day in that moment of time (Beck et al., 1988). State anxiety was assessed at baseline and immediately after gaming.

Statistical Analyses

To answer our two main questions of this study, a two group (gamers, non- gamers) by two condition (competitive and non-competitive) by three time (baseline, during gaming, and recovery) analysis of variance (ANOVA) with repeated measures on condition and time was used to test for any significant interactions or main effects for

HR, SBP, and DBP using SPSS (IBM, Version 25). We analyzed both the average HR and BP in addition to the peak HR and BP values during baseline, gaming, and recovery.

A two group by two condition by two time (pre-gaming and post-gaming) ANOVA with repeated measures on condition and time was used to test for any significant interactions or main effects for state anxiety. Any significant main or interaction effects were further analyzed with post-hoc mean comparisons (e.g., t-tests). In addition, we analyzed fitness

(VO2 max), body composition (body fat percentage), and state and trait anxiety scores as possible covariates. However, because these variables were not different between groups they were not included in the final analyses as presented below.

Results

Results are individually reported in the following sections.

Systolic Blood Pressure

As seen in Figure 6, repeated measures ANOVA for SBP resulted in a significant condition by time interaction, F(1,28) = 8.73, p ≤ 0.001, and condition by group interaction, F(1,14) = 12.40, p = 0.003. Post-hoc t-tests reveal that the condition by time

interaction can be explained by the competitive condition having a significantly higher increase in SBP from baseline during gaming compared to the non-competitive condition

(t = 3.36, p = 0.004) in addition to the non-competitive condition not seeing any change in SBP from gaming to recovery (t = 0.105, p = 0.918). The condition by group interaction could be explained by gamers having a more pronounced increase in SBP (t =

3.71, p ≤ 0.008) from the non-competitive to the competitive condition compared to non- gamers. (t = 1.91, p = 0.098). In addition, there was a significant main effect of condition,

(F(1,14) = 7.56, p = 0.016) with increased SBP during the competitive condition compared to the non-competitive condition. Lastly, there was a significant main effect of time (F(2,28) = 17.43, p ≤ 0.001) with SBP increasing during gaming compared to baseline and recovery. In addition, SBP at recovery was higher than baseline (t = 3.53, p

= 0.003). Means and standard deviations are reported in Table 4. There were no additional significant interactions or main effects (F ≤ 1.61, p ≥ 0.22). Peak SBP during gaming is illustrated in Figure 7 which further exaggerates the same interactions and effects (F ≥ 9.94, p ≤ 0.007) as the average SBP during gaming.

Table 4 Blood Pressure Means and Standard Deviations Systolic Blood Pressure Diastolic Blood Pressure Group Condition Baseline Gaming Recovery Baseline Gaming Recovery COMP 104.6 ± 6.5 123.9 ± 13.5 110.0 ± 11.1 65.8 ± 8.4 76.3 ± 9.2 67.8 ± 7.9 Gamer NC 104.8 ± 9.7 109.7 ± 10.8 107.4 ± 11.1 62.8 ± 9.1 67.7 ± 6.3 67.3 ± 11.9 Non- COMP 103.9 ± 3.7 115.9 ± 10.3 106.4 ± 6.8 68.3 ± 4.1 71.9 ± 7.7 67.4 ± 3.5 gamer NC 104.1 ± 4.3 111.1 ± 5.7 113.0 ± 7.8 67.8 ± 4.1 70.6 ± 5.8 69.6 ± 7.8 COMP = Competitive. NC = Non-competitive

Figure 6. SBP means and SEM at baseline, during gaming (averaged over 60 minutes), and at the end of recovery. There was a significant condition by time interaction, F(1,28) = 8.73, p ≤ 0.001, and condition by group interaction, F(1,14) = 12.40, p = 0.003. In addition, there were significant main effects for time (F(2,28) = 17.43, p ≤ 0.001) and condition (F(1,14) = 7.56, p = 0.016). *denotes significant differences (p < 0.05).

Figure 7. Peak SBP means and SEM at baseline, during gaming (Peak SBP), and at the end of recovery. *denotes significant differences (p < 0.05). Peak SBP further exaggerates the interactions and main effects of average SBP as seen in Figure 6.

Diastolic Blood Pressure

As seen in Figure 8, repeated measures ANOVA for DBP resulted in a significant condition by time interaction, F(1,28) = 3.55, p = 0.042. Post hoc analysis revealed that regardless of group, relative to the non-competitive condition, the competitive condition

resulted in a greater diastolic blood pressure during gaming compared to baseline (t =

3.67, p = 0.002) and recovery (t = 3.70, p = 0.002). There was a significant main effect of time, F(2,28) = 10.27, p ≤ 0.001 with DBP increasing during gaming compared to baseline and recovery. Means and standard deviations are reported in Table 1. There were no additional significant interactions or main effects (F ≤ 2.24, p ≥ 0.16). Peak DBP during gaming is illustrated in Figure 9 which again further exaggerates the same interactions and effects (F ≥ 3.85, p ≤ 0.033) as the average DBP while also revealing a group by time interaction, F(2,28) = 5.93, p = 0.007. This can be explained by gamers having a higher increase in peak DBP from baseline to gaming compared to non-gamers

(t = 2.38, p = 0.032).

Figure 8. DBP means and SEM at baseline, during gaming (averaged over 60 minutes), and at the end of recovery. *denotes significant differences (p < 0.05). There was a significant condition by time interaction, F(1,28) = 3.55, p = 0.042. In addition, there was a significant main effect of time, F(2,28) = 10.27, p ≤ 0.001.

Figure 9. Peak DBP means and SEM at baseline, during gaming (Peak DBP), and at the end of recovery. *denotes significant differences (p < 0.05). Peak DBP further exaggerates the same interactions and effects as the average DBP while also revealing a group by time interaction, F(2,28) = 5.93, p = 0.007 in which gamers had a higher increase in peak DBP during gaming compared to baseline and recovery.

Heart Rate

As seen in Figure 10, repeated measures ANOVA for average HR resulted in significant two-way interactions for condition by time, F(2,26) = 8.71, p ≤ 0.001, and

group by time F(2,26) = 3.49, p = 0.046. The condition by time interaction can be explained by a significant decrease in HR after gaming during recovery (t = 2.82, p =

0.002) in the competitive condition while there was no significant difference across these time points in the non-competitive condition (t = 1.06, p = 0.31). The group by time interaction can be explained by average HR significantly decreasing in non-gamers after gaming during recovery in both conditions (t ≤ 2.82, p ≤ 0.03) while gamers showed no significant change after gaming during recovery in either condition (t ≤ 1.93, p ≤ 0.09).

Means and standard deviations are reported in Table 5. There were no additional significant interactions or main effects (F ≤ 3.09, p ≥ 0.063). Peak HR during baseline, gaming, and recovery is illustrated in Figure 11 which further exaggerates the condition by time interaction and negates the group by time interaction seen with average HR as the differences between groups across both conditions and time points are not significant.

However, peak HR introduces main effects of condition, F(1,13) = 5.30, p =0.038, and time, F(2,26) = 20.69, p ≤ 0.001). The main effect of condition is explained by peak HR being significantly higher during the competitive condition compared to the non- competitive condition while gaming (t = 4.227, p ≤ 0.001) regardless of group. The main effect of time is explained by the significant increase in peak HR during gaming compared to baseline and recovery.

Table 5 Heart Rate Means and Standard Deviations Average Heart Rate (bpm) Group Condition Baseline Gaming Recovery Competitive 65.0 ± 2.6 70.0 ± 6.8 67.6 ± 5.8 Gamer Non-competitive 66.3 ± 4.9 64.7 ± 4.5 65.2 ± 3.9 Competitive 69.2 ± 12.4 68.5 ± 8.2 62.2 ± 6.9 Non-gamer Non-competitive 68.2 ± 11.0 65.4 ± 6.4 62.6 ± 6.0

Figure 10. HR (bpm) means and SEM during baseline, gaming, and recovery. *denotes significant differences (p < 0.05). There was a significant condition by time interaction, F(2,26) = 8.71, p ≤ 0.001, and group by time interaction F(2,26) = 3.49, p = 0.046.

Figure 11. Peak HR (bpm) means and SEM during baseline, gaming, and recovery. *denotes significant differences (p < 0.05). Peak HR further exaggerates the condition by time interaction and negates the group by time interaction seen with average HR as the differences between groups across conditions and time points are not significant. Peak HR also introduces main effects of condition, F(1,13) = 5.30, p =0.038, and time, F(2,26) = 20.69, p ≤ 0.001).

Body composition, Fitness, and Anxiety

Means and standard deviations of body fat percentage and VO2max are reported in Table 6. There were no significant differences between body composition and VO2max

between groups, thus they could not be classified as possible covariates. Repeated measures ANOVA for Beck’s state anxiety inventory (BAI) resulted in a main effect of time, F(1,14) = 8.14, p = 0.013. There were significant increases in state anxiety after competitive gaming (0.44 ± 0.96 BAI pre, 1.75 ± 1.81 BAI post, t = 3.24, p = 0.006).

Means and standard deviations of anxiety are reported in Table 7. There were no other significant interactions or main effects (F ≤ 4.43, p ≥ 0.054).

Table 6 Means and Standard Deviations for Age, VO2max, and Body Fat Percentage

VO2max Body Fat Group n Age (years) (ml/kg/min) (%) Gamer 8 22.6 ± 2.2 38.6 ± 5.6 11.9 ± 5.0 Non-gamer 8 24.5 ± 1.8 37.0 ± 6.2 17.7 ± 5.9

There were no significant differences between age, VO2max, and body fat percentage.

Table 7 Means and standard deviations for trait and state anxiety Trait Anxiety Beck's State Anxiety Score Group (score) Condition Pre Post Competitive 0.63 ± 1.19 1.75 ± 1.49 Gamer 1.75 ± 2.38 Non-competitive 0.50 ± 0.54 0.62 ± 0.74 Competitive 0.25 ± 0.71 1.75 ± 2.19 Non-gamer 3.63 ± 5.26 Non-competitive 0.88 ± 1.25 1.5 ± 1.77 There was a significant increase in state anxiety regardless of group (p = 0.006).

Discussion

To our knowledge, this is the first study to assess cardiovascular reactivity and state anxiety while playing a competitive multiplayer video game (Fortnite). We have demonstrated that gaming, particularly when playing against other people (competitive condition), does indeed illicit a significant cardiovascular stress response in both gamers and non-gamers supporting our initial hypothesis. Notably, both average and peak SBP

were significantly elevated during game play in both gamers and non-gamers. The competitive game mode elicited an increased SBP response while gaming compared to the non-competitive condition and gamers exhibited greater increases in SBP during the competitive gaming condition.

DBP also increased significantly during gaming from baseline, more so in the competitive condition, relative to the non-competitive condition and this effect was greater in gamers versus non-gamers. Peak HR increased significantly while gaming in both gamers and non-gamers. For gamers, average HR also increased from baseline while gaming to a greater extent during the competitive condition; average heart rate then decreased back to baseline levels. Non-gamers saw a decrease from baseline in average

HR while gaming which further decreased during recovery. Concisely, while gaming induced significant cardiovascular stress reactivity in both groups, gamers experienced greater cardiovascular stress reactivity compared to non-gamers and these differences were even more pronounced while playing in the competitive condition. This further supports our hypothesis that gamers would have greater cardiac stress reactivity compared to non-gamers. While it seems as though non-competitive gaming still elicits a cardiac stress response, competitive gaming elicited a cardiac stress response that was even greater than non-competitive gaming. We also reported an increase in state anxiety after competitive gaming through an increase in state BAI scores. This furthers the stress response seen during competitive gaming and agrees with previous research that saw increases in state anxiety during stress tasks (Takahashi et al., 2005).

While previous research has investigated HR and BP response while playing video games, these studies only utilized single player games that were not played against other players or included “exergaming” games, video games that were not sedentary in nature (Borusiak et al., 2008; Segal & Dietz, 1991; Sween et al., 2014; Wang & Perry,

2006). A novel aspect of this study was the comparison between non-competitive gaming and competitive gaming along with using one of the most popular, unstudied esports games, Fortnite. With the popularity and emergence of competitive gaming and esports games such as Fortnite in youth, it is imperative to understand the impact of these competitive games. In addition, we had our participants play for 60 minutes, which was significantly longer than the play sessions of the above studies in which 12, 15, and 30- minute sessions were used. Longer video game play sessions are becoming more common amongst the youth (Brooks et al., 2016; Yap & Paul, 2017), thus mimicking a longer play session in our study may better reflect how these games are actually played.

Our study also tried to minimize as much environmental stress as possible. A potential limitation to the prior studies examining stress reactivity during video game play was the potential external influence of being attached to ECG leads, wearing a metabolic mask, and having personnel in the same room on stress reactivity. Our study tried to minimize any environmental stress by leaving them alone during the protocol along while not having any distracting attachments or social pressures. We strived to reflect as accurately as possible a naturalistic setting to allow the participants feel as comfortable as possible.

This may have been reflected in the non-competitive condition, in which average DBP and HR did not change significantly during gaming from baseline or recovery amongst

all participants. More so, average HR decreased significantly after baseline while gaming and during recovery in the non-competitive condition in non-gamers. This lends to the absence of environmental distractions and illustrates that non-gamers seemingly become more relaxed as they play non-competitively.

Concisely, playing a competitive esports-style game (Fortnite) induced cardiovascular stress reactivity and this was especially true for participants who self- identified as gamers. Cardiovascular stress reactivity has emerged as an independent risk factor for CVD; specifically, stress reactivity from psychosocial stress in youth has been shown to be linked to subclinical CVD risks particularly through associations with carotid artery intima-media thickness (Lambiase et al., 2013; Roemmich et al., 2011).

Psychosocial stress has also been shown to account for 30% of the population’s attributable risk of acute myocardial infarction (Yusuf et al., 2004). While the mechanism behind stress reactivity’s association with increased risk of CVD are not well understood, one proposed mechanism is that repeated exposure to transient increases in BP and resultant shear stress over time may cause damage to the arterial system which can lead to atherosclerosis (Vale, 2005). Compounded with the fact that Fortnite along with all other esports games are sedentary in nature, this is potentially worrisome. Sedentary behavior alone is a risk factor for cardiovascular disease (CVD), metabolic disorders, obesity and ultimately mortality through decreased energy expenditure, increased energy intake, and dyslipidemia (Hamilton et al., 2007; Owen et al., 2010). Thus, gamers may be at a higher risk of developing CVD and other comorbidities. We demonstrated almost a

20 mm Hg increase in average SBP for the entire 60-minute duration of competitive

gaming compared to resting baseline with peak SBP values increasing even more so. We also demonstrated significant increases in DBP and HR. With the popularity of Fortnite and other esports games amongst the youth along with the fact that prolonged gaming sessions are becoming more common (Brooks et al., 2016; Yap & Paul, 2017), if gamers are undergoing consistent prolonged transient increases in SBP, DBP, and HR on a daily basis, then potential adverse health risks may develop. This warrants future research.

In addition, we hypothesized that gamers would have decreased aerobic fitness and increased body fat percentage. The data does not support this hypothesis as there were no statistically significant difference between gamers and non-gamers. Because there was no difference between groups, we did not utilize VO2max and body fat percentage as potential covariates. However, it is interesting to see no differences between gamers and non-gamers in these categories. Because gaming is a sedentary activity, those who actively partake in gaming (gamers) would be expected to have decreased aerobic fitness and body composition.

While this study was novel in nature, there were still limitations. Other video games might induce different stress responses. Fortnite’s gameplay can have the player not engage against other players for minutes at a time, thus the “action” can be sporadic in nature. A different video game that has a quicker pace of action might induce an even greater stress response. In addition, our protocol did not allow the participant to play on a team or with friends. Introducing a team aspect or pressure of winning might have induced an increased stress response from the social-evaluative influence. We propose the competitive aspect of gameplay (as evident in the competitive condition) is driving

the stress response. However, the underlying mechanism could be task dependent (type of game being played, playing with friends or alone, tournaments, etc.) or perhaps through alterations in sympathetic and parasympathetic balance of the cardiovascular system.

Another limitation was our small sample size in addition to the broad definition of what constitutes a “gamer” a “non-gamer.” We defined gamers as anyone who self- identifies as a gamer along with playing at least 10 hours of video games per week. The gamers and non-gamers alike might have had a broad range of video game experience.

Some gamers and non-gamers had more Fortnite experience compared to other non- gamers which potentially influenced the results. In the future, if strictly Fortnite gamers who play professionally or for a club or school team are recruited, then perhaps we might see a different stress response. In addition, while we tried to mitigate environmental influence on stress as much as possible, we cannot ignore that a laboratory setting could have potential influence on our measured responses. Lastly, while we utilized an automated oscillometric blood pressure method which took blood measurements at specific time points, continuous monitoring of blood pressure through Finapres methodology would offer a more complete look at the stress response. Due to the nature of participants needing full control over their arms and hands while gaming, these measures were not feasible for our study however.

In conclusion, an esports style game, Fortnite, induced a cardiovascular stress response while gaming. Fortnite has been one of the most popular video games amongst our youth, and as gaming culture continues to evolve, it is important to understand the physiological and psychological impact of playing competitive games. The stress

response we have observed while gaming might have adverse effects on health.

Nevertheless, there are many benefits and opportunities associated with gaming for the youth. Schools across the country are adopting esports programs, offering new social avenues for adolescents and young adults, and some universities are even providing scholarships and career opportunities. However, we should take caution as gaming continues to grow. If there are adverse health risks, we should further investigate to potentially counteract these risks.

CHAPTER VI

SUMMARY

Competitive gaming (i.e. esports) is an extremely popular medium of entertainment due to its accessibility and prevalence amongst the youth. Many schools and universities across the United States are incorporating varsity teams and even offering gaming minors and majors as part of their curriculum. However, there is a lack of research investigating competitive gaming and the behaviors and possible effects associated with participation in this activity. The purpose of this study was twofold. The first aim was to further understand the gamer population by assessing behavioral and psychometric variables of gamers (i.e., those that play video games and self-identify as gamers) compared to non-gamers. The second aim was to determine the cardiovascular stress reactivity and perceived stress of college-aged adults who either did or did not identify as gamers while playing an esports style video game (Fortnite) for 60 minutes against other people (competitive) and against a computer avatar (non-competitive).

We reported that gamers, relative to non-gamers, have decreased physical activity and grade point average (GPA) and increased sedentary behavior. Furthermore, we also found that competitive gamers who compete for club, junior varsity, or varsity esports teams are gaming much more than gamers who are not on a team and non-gamers alike which implies that these relationships and potential health and psychosocial risks might be more exaggerated in this population. With regards to the cardiovascular stress response while gaming, we also reported significant increases in HR, SBP, DBP while gaming compared to resting values. We saw a greater HR, SBP, and DBP response while

gaming in a competitive condition compared to a non-competitive condition.

Furthermore, gamers had greater cardiovascular stress reactivity compared to non- gamers. We also reported an increase in state anxiety after the competitive gaming condition.

To our knowledge, this was the first study to assess the behavioral characteristics, psychometrics, and academic performance of gamers and competitive gamers and this was also the first study to investigate the cardiovascular stress response to competitive gaming, specifically Fortnite. It is important to understand the physiological and psychological impact of playing competitive games as the popularity of this activity continues to expand. We feel our results provide support for future research to explore these potential physiological or psychological effects. Ultimately, the stress response we have observed while gaming might have adverse effects on health, thus we should take caution as competitive gaming continues to evolve and become more prevalent amongst the youth.

APPENDICES

APPENDIX A

VIDEO GAME SURVEY

Appendix A

Video Game Survey

Informed Consent for Stress, Anxiety, and Physical Activity in Gamers and Non-gamers

Before taking part in this survey, please read the consent form below.

You are being invited to participate in a research study. This study examines lifestyle and gaming characteristics of young adults at Kent State University. The survey includes questions on game time, physical activity, mood, and cell phone use. The principal investigator for this study is Dr. Jacob Barkley (KSU) while the co-investigators are Bryan Dowdell and Peter Gates (KSU). The information provided by students such as yourself is indispensable, and we appreciate your help.

This consent form will provide you with information on the research project, what you will do as a participant, and any associated risks and benefits of the research. Your participation is voluntary. Participation or non-participation will not affect your status at the university in any way. Please read this information carefully.

Procedures The study involves completion of a paper survey. The survey will take about 5-10 minutes to complete. Please know that all responses are treated as confidential, and in no case will responses from individual participants be identified. Rather, all data will be pooled and results published in aggregate form.

Voluntary Participation Taking part in this research study is entirely up to you. You may choose not to participate or you may discontinue your participation in the survey at any time without penalty.

Criteria for inclusion/exclusion To be included in this study, you must be at least 18-25 years old and an undergraduate at Kent State University

Benefits This research will not benefit you directly. However, your participation in this study will help us to better understand gaming.

Privacy and Confidentiality The data that are collected will be confidential and will be stored securely. Your study-related information will be kept confidential within the limits of the law. All records will be kept in a secure location, and only the researchers will have access to the data. Identifiers will be removed prior to any data analysis conducted by the researchers. Individuals who participate in this study will not be identified in any publication or presentation of research results.

Risks and Discomforts No deception is involved, and the study involves no more than minimal risk to participants (that is, the level of risk encountered in daily life).

Contact Information If you have any questions or concerns about this research, you may contact Dr. Jacob Barkley at [email protected] or 330-672-0209, or Bryan Dowdell at [email protected] or 440-315-1211. This project has been approved by the Kent State University Institutional Review Board. If you have any questions about your rights as a research participant, you may call the KSU IRB at 330-672-2704 or contact the office via email at [email protected].

If you are an undergraduate at Kent State University between the ages of 18-25 years of age, understand the statements above, and freely consent to participate in the study, you may continue to the survey.

KENT STATE UNIVERSITY College of Education Health and Human Services

Instructions: Please answer each question as accurately and honestly as possible. There are no right or wrong answers, rather, the answers depend on your own life experience and perceptions.

Part I: Demographics and General Questions

The following contains items pertaining to your demographic and background information. For each item, please respond with the appropriate information.

1. What is your sex (Check appropriate response)? Male Female

2. What is your age? ______

3. What is your height? ______inches

4. What is your weight? ______lbs.

5. Do you play console / PC video games regularly? (Yes/No) ______

6. What is your school status? (Circle specific grade) o Undergraduate (Freshman – Sophomore – Junior – Senior) o Graduate (MS or PhD)

7. How many credit hours are you registered for this current semester? ______

8. What is your current, cumulative Kent State University grade point average (GPA)?

Part II: Technology Use Survey

The following contains items pertaining to your general and specific cell phone and video game habits and behaviors. For each item, please respond with the appropriate information.

1. Do you consider yourself a “gamer?” (Yes/No) ______

2. How many hours do you spend playing video games per week? ______

3. Do you play competitively for any Kent State University esports teams (junior varsity, varsity, club)? (Yes/No) ______

Skip to Question #8 if you are not affiliated with an esports team or do not play competitively.

4. Which of these teams are you on: junior varsity, varsity, or club? ______

5. What video game do you competitively play for Kent State University? ______

6. How long have you been playing this game competitively for? (months) ______

7. How many hours do you spend playing video games other than your competitive game of choice per week? ______

8. Do your generally feel stressed when playing a multiplayer game against other humans? (Y / N)

9. Do you generally feel stressed when playing a casual single-player game? (Y / N)

______

1. As accurately as possible, please estimate the total amount of time you spend using your cell phone each day. Please consider all uses except listening to music. For example: consider calling, texting, email, social networking (e.g., Facebook, Twitter, Snapchat, Instagram, Pintrest, etc.), sending photos, gaming, surfing the internet, watching videos, and all other uses driven by “apps” and software.

Hours: ______Minutes: ______

2. As accurately as possible, please estimate a) the total number of text messages that you send and receive each day and b) the total number of calls you make and receive each day.

a) Number of texts sent: ______Number of texts received: ______

b) Number of calls made: ______Number of calls received: ______

3. Think about the total amount of time that you spend using your cell phone each day. Please indicate what percentage of that time the phone is used for the following purposes:

______LEISURE ______WORK

______SCHOOL Total = 100%

4. When you are using your cell phone you are most likely to be (circle one):

• Sitting or lying down

• Standing

• Walking or moving

INSTRUCTIONS: In the space provided, please provide a score for each item with a value of 1 through 7 which best describes you: 1 = very untrue of me, 2 = untrue of me, 3 = somewhat untrue of me, 4 = neutral, 5 = somewhat true of me, 6 = true of me, 7 = very true of me

1. I have used my mobile phone to make myself feel better when I was feeling down

2. When out of range for some time, I become preoccupied with the thought of missing a message/text or call

3. If I don’t have a mobile phone, my friends would find it hard to get in touch with me

4. I feel anxious if I have not checked for messages or switched on my mobile phone for some time

5. My friends and family complain about my use of the mobile phone

6. I find myself engaged on the mobile phone for longer periods of time than intended

7. I am often late for appointments (e.g., work, class) because I’m engaged on the mobile phone when I shouldn’t be

8. I find it difficult to switch off my mobile phone

9. I have been told that I spend too much time on my mobile phone

10. I have received mobile phone bills I could not afford to pay

Part III. Physical activity survey The following questions ask about your leisure time physical activity. For each item, please respond with the appropriate information.

We are interested in finding out about the kinds of physical activities that people do as part of their everyday lives. The questions will ask you about the time you spent being physically active in the last 7 days. Please answer each question even if you do not consider yourself to be an active person. Please think about the activities you do at work, as part of your house and yard work, to get from place to place, and in your spare time for recreation, exercise or sport.

Think about all the vigorous activities that you did in the last 7 days. Vigorous physical activities refer to activities that take hard physical effort and make you breathe much harder than normal. Think only about those physical activities that you did for at least 10 minutes at a time.

1. During the last 7 days, on how many days did you do vigorous physical activities like heavy lifting, digging, aerobics, or fast bicycling?

_____ days per week 2. How much time did you usually spend doing vigorous physical activities on one of those days?